For some applications, precision is crucial, yet for others, neither exact computation nor precise outcomes are necessary. For instance, a photo taken with an Visually, an 8 MP camera and a 10 MP camera are comparable. In fields like computer science speech recognition, graphics, searching, machine learning, and physical simulation, Perfect solutions may not be possible. The Approximate Computing (AxC) paradigm is gaining traction as a novel architecture for delivering better energy efficiency a

In today's world, video surveillance systems generate a massive influx of data, often overwhelming human operators. This deluge of information makes it challenging to effectively monitor and analyze events in real-time, hindering proactive intervention and efficient post- event investigation. Artificial intelligence (AI) offers a powerful paradigm shift in addressing these limitations. By endowing surveillance systems with the ability to automatically perceive, reason, and learn from visual data, AI promises to unlock unprecedented levels of efficiency, accuracy, and actionable insights. This exploration delves into the crucial need for an efficient mechanism to harness the transformative potential of AI within video surveillance. Efficiency, in this context, encompasses several key aspects: optimized resource utilization (computation, storage, bandwidth), rapid and accurate processing of video streams, scalability to handle large deployments, and seamless integration with existing infrastructure. Without a robust and efficient underlying mechanism, the promise of AI-powered surveillance risks being bottlenecked by practical limitations. The subsequent discussion will highlight the challenges and opportunities in building such an efficient mechanism, exploring various AI techniques like deep learning, computer vision algorithms, and edge computing strategies. It will also touch upon the importance of data management, model optimization, and system architecture in achieving truly efficient and impactful AI-driven video surveillance applications. The goal is to pave the way for surveillance systems that are not merely passive recording devices but intelligent sentinels capable of proactively safeguarding our communities and assets.

Automating the manual method of finding indexed backlinks of websites can significantly reduce the cost and time associated with performance improvement. Automating the process of finding indexed backlinks offers several benefits. It reduces the time required to perform backlink analysis, lowers operational costs, and enhances accuracy. The automation of backlink analysis involves various technologies, including web crawlers, data mining algorithms, and machine learning models. Web crawlers systematically browse the web to find backlinks, while data mining algorithms analyze the collected data to assess the quality and relevance of each backlink. Machine learning techniques further refine this process by identifying patterns and predicting the potential impact of backlinks on website performance. With faster and more accurate backlink analysis, businesses can make informed decisions that enhance website performance. Automation tools provide detailed insights into the quality and impact of backlinks, allowing for precise adjustments to SEO strategies. This leads to improved search engine rankings, increased organic traffic, and better overall performance metrics.

Changes and continuous progress in logistics and productive systems make the realization of improvements in decision making necessary. Simulation is a good support tool for this type of decisions because it allows reproducing processes virtually to study their behavior, to analyze the impact of possible changes or to compare different design alternatives without the high cost of scale experiments. Although process simulation is usually focused on industrial processes, over the last two decades,

The notoriety of versatile applications has soar over the most recent couple of many years. Cell phone reception is currently dominating that of workstations and PCs. This is on the grounds that cell phones have a wide assortment of utilizations that permit the client to get done with jobs from any area utilizing a little, versatile gadget. There are a few disadvantages to this, like the requirement for cross-stage application similarity. The present engineers are basically worried about making

The recent change in administrative procedures and instructional strategies in professional institutions is created by tools of enterprise resource planning (ERP). In this research, statistical modeling, hypothesis testing, and correlation analysis are utilized in order to understand the trends, barriers, and impacts of ERP adoption in creative teaching and learning in professional education. While student service adoption grew from 30% to 65% and ERP adoption from 40% to 75%, throughout the same period, it was 2020 to 2023. The effect of ERP on helping to increase the instructional effectiveness has been proven (p < 0.05) by means of the hypothesis testing with a 40% increase in instructional effectiveness. The regression study results indicate a correlation of R² = 0.78 of the ERP adoption with administrative efficiency. However, two main obstacles are very high implementation costs (45%) and lack of skilled workers (35%). The strategic proposal to adopt cloud ERP, AI adaptive learning, ERP subsidies by the government, and training initiatives by the faculty. This work enhances the digital transformation of higher education by supplying data that allows for the best ERP implementation as regards professional institutions in India.

Recommender systems represent a high economic, social, and technological impact at international level due to the most relevant technological companies have been used them as their main services considering that user experience and companies sales have been improved. For this reason, these systems are a principal research area, and the companies optimize their algorithms with hybrid approaches that combine two or more recommendation strategies. A systematic literature review on the hybrid approa

Machine learning has two phases: training and testing. In the training phase, a set of examples (i.e., data with their corresponding labels) are available. With a given machine learning algorithm, the example data are used to train a model (i.e., tune its parameters) so that it can identify the relationship between input data and the labels. In the testing phase, input data without labels go through the same methodology as the training phase for preprocessing, feature extraction, and feature red

Information and Communication Technologies (ICT) have revolutionized numerous sectors, including education, healthcare, agriculture, manufacturing, and business. However, the adoption of these technologies is not uniform across industries, with various barriers preventing full utilization. This paper explores the barriers that hinder ICT adoption and identifies the opportunities that exist in overcoming these challenges. By examining various sectors, this paper aims to provide insights into how different industries can leverage ICT to improve efficiency, productivity, and innovation. The study also addresses the importance of policy frameworks, infrastructure investments, and public-private partnerships in overcoming the barriers to ICT adoption. It suggests strategies to enhance digital literacy, cybersecurity, and access to affordable technology to ensure that ICT benefits are accessible to all. By providing a holistic view of both the challenges and opportunities of ICT, the research aims to contribute to the development of inclusive, sustainable digital strategies for governments, businesses, and educational institutions. The findings underscore the critical role of ICT in fostering global connectivity, supporting economic resilience, and promoting social well-being in an increasingly digital world. This study explores the barriers and opportunities associated with the adoption of Information and Communication Technology (ICT) across various sectors, including education, business, healthcare, and government. As ICT continues to transform industries globally, understanding the challenges to its widespread implementation is crucial. The research identifies common barriers such as infrastructure limitations, digital illiteracy, high costs of technology, and resistance to change. These challenges can hinder the effective use of ICT, particularly in developing countries and traditional sectors. However, the study also highlights significant opportunities created by ICT, such as enhanced productivity, improved access to services, and global connectivity. In education, ICT fosters collaborative learning and broader access to information, while in business, it supports innovation, efficiency, and global market reach. In healthcare, ICT enables telemedicine and better patient care management, and in government, it enhances transparency and service delivery through e-governance. By examining these barriers and opportunities, this research aims to provide recomme

In the modern era of hybrid warfare, governments have switched to the unorthodox use of information warfare to impose their national will on an adversary with apparent anonymity and without breaking international laws on other nations' sovereignty.To wage the fight in infospace, the Fifth Dimension of Warfare, information security must be a crucial component of national security.Electronic warfare, psychological operations, cyberwarfare, military deception, and operational security are all possi

From the internet, news stories, schematics in papers, and advertisements, we are exposed to a substantial amount of pictures every day. Visitors are tasked with deciphering the meaning of these sights. Despite the fact that most images are devoid of text descriptions, people can nonetheless make meaning of them. It is necessary that the system be able to interpret some form of image caption if automated captions are required by the public. There are several reasons why image captioning is essen

In the last decade, several research efforts have been directed to integrating performance analysis in the software development process. Traditional software development methods focus on software correctness, introducing performance issues later in the development process. This approach is not adequate since performance problems may be so severe that they may require considerable changes in the design, for example at the software architecture level, or even worse in the requirements analysis. Several approaches have been proposed to address early software performance analysis. Although some of them have been successfully applied, there is still a gap to be filled in order to see performance analysis integrated in ordinary software development. We present a comprehensive review of the recent developments of software performance research and point out the most promising research directions in the field.

Thousands use websites at some point of time for other. Cloud has limitation in maintaining load obtained from all demands at time any point of time. It results in destroy of the entire network. It is the process in which computing resources and workloads are distributed to more than one server. Workload is divided between two or more servers, hard drives, system interface and other computing resources resulting in good use and system response time. A huge traffic web site requires a high powerf

Enterprise Resource Planning (ERP) is of paramount importance in Industry 4.0 since Micro, Small, and Medium-Sized Enterprises (MSMEs) depend on this tool to simplify their operations, make better decisions, and gain a competitive advantage. This research uses statistical modeling, regression analysis, and correlation analysis to identify the trends, difficulties, and advantages of ERP implementation in Indian MSMEs from 2020 to 2023. According to the findings, ERP adoption grew almost threefold from 25 percent to 55 percent in 2023, and the greatest (40 percent) increase (2021) was due to the pandemic’s digital transformation, which led to higher adoption of ERP. The correlation study states that ERP installation has a good influence on supply chain management (r = 0.987), cost reduction (r = 0.975), and operational efficiency (r = 0.989). Through the process of hypothesis testing (p = 0.047), it is confirmed that after 2022, the substantial shift in ERP adoption trends takes place. While there are still some issues, and one of them is the high implementation cost, the shortage of skilled people, and integration issues. According to multiple regression analysis (R² = 0.967), the two biggest factors for the success of ERP implementation are the workforce skills and the budgetary limitations. To speed up adoption of MSMEs, it recommends cloud-based solutions, as well as cybersecurity and government ERP infrastructure to aid the government, SMEs, and MSMEs. The findings make contributions to the growing body of research on ERP tactics for deployment in MSMEs in Industry 4.0.

The study investigates the state of Indian politics during the partition, with a focus on the role of key political figures, the impact of political parties, and the communal violence that ensued. The methodology involves a systematic review approach as well as first-hand accounts. The study sheds light on the complex interplay of political forces and communal tensions during this tumultuous period. The key conclusion drawn from this study is that the actions of political leaders and parties hea

This qualitative content analysis decodes the complex interplay of verbal and non-verbal cues in Mohsin Hamid's Exit West. Focused on characters Saeed and Nadia, the study reveals nuanced power dynamics and challenges to traditional gendered communication norms. Examining instances of conformity and resistance to gendered communication ideals, the analysis sheds light on emotional intelligence and societal negotiation. The study not only enhances understanding of character dynamics but contribut

This paper includes the detail study of many techniques that have been found useful and important during the time of data cleaning process as well as consolidating the data quality in databases. For Instance, existing classification of dirty data types from the literature will be reviewed to present the multiple dirty data types observed in different data sources. Data cleaning methods, Data quality, data quality dimensions are reviewed in this chapter. They provide the foundation of development of the proposed data cleaning framework.

We are living in an era characterized by rapid technological advancement. The time when most activities were performed manually has passed, as technology now plays a significant role in everyday life. One of the most influential technologies shaping modern society is Artificial Intelligence (AI). AI has seamlessly integrated into daily activities and is continuously transforming the way people work and interact, often without users realizing its presence. Its applications are evident in-home automation systems, self-driving vehicles, smartphone applications, wearable devices, and many other areas. Wherever it is applied, AI brings significant transformation, making it one of the most progressive technologies witnessed in the contemporary world. Similarly, the e-commerce industry has revolutionized the way business is conducted in India. India has emerged as one of the fastest-growing e-commerce markets globally and is expected to expand at an even faster pace in the coming years. The integration of AI into e-commerce has further accelerated this transformation. AI now plays a crucial role in the e-commerce sector, marking a major technological shift over the past decade. E-commerce platforms increasingly rely on AI to manage large volumes of customer data, interact with consumers through chatbots, and enhance product search, sorting, and recommendation processes. AI enables efficient data capture, processing, and analysis at scale, offering greater accuracy and operational efficiency. Moreover, e-commerce companies are leveraging AI to develop customer-centric search systems, retarget potential buyers, optimize sales processes, enable voice-based search, enhance personalized recommendations, and detect and manage fake reviews. The proposed paper aims to examine the application of AI in the e-commerce industry and analyze its impact on e-commerce portals.

The exponential rise in social media usage in India has been accompanied by a significant increase in cybercrime incidents. This study undertakes a comparative analysis of cybercrime cases reported on major social media platforms—Facebook, Instagram, Twitter (X), and YouTube—between 2019 and 2023. Drawing on data from government records, platform transparency reports, and expert interviews, the study categorizes prevalent cybercrimes such as identity theft, cyberbullying, hate speech, financial fraud, and obscene content dissemination. It further examines the technical vulnerabilities exploited and assesses the adequacy and enforcement of existing legal frameworks, including provisions under the Information Technology Act, 2000 and the Indian Penal Code. The results reveal a steady rise in cybercrimes, platform-specific patterns of abuse, and gaps in law enforcement and digital policy. The study concludes with recommendations for legal reform, platform accountability, and enhanced digital literacy to effectively address the growing threat of cybercrime in India.

As artificial intelligence (AI) and machine learning (ML) technologies become increasingly integrated into society, ethical issues surrounding these technologies have gained widespread attention. Key ethical concerns include biases in AI models, fairness in decision-making, and the transparency of AI systems. AI and ML algorithms have demonstrated remarkable capabilities in automating complex tasks, ranging from decision-making in hiring processes to providing healthcare diagnoses. However, these advancements also present challenges in terms of fairness, accountability, and trust. Bias in AI models, often inherited from historical data, can lead to discriminatory outcomes, while lack of transparency can hinder users' ability to understand and trust the decisions made by these systems. This paper explores these critical concerns and examines their implications for AI adoption across multiple sectors, such as healthcare, finance, criminal justice, and hiring. It discusses how biased datasets, flawed algorithmic assumptions, and opaque models contribute to unfair outcomes, particularly for marginalized communities. The paper presents strategies to mitigate bias, promote fairness, and enhance transparency in AI systems, such as diversifying training datasets, implementing fairness-aware algorithms, and adopting explainable AI (XAI) methods to improve interpretability. Additionally, it highlights the role of governance and regulation in addressing ethical challenges and ensuring that AI technologies are deployed responsibly. By addressing these challenges, we can ensure that AI systems are developed in ways that are ethically sound, socially responsible, and beneficial to all members of society. Ethical AI holds the potential to drive innovation while safeguarding against the perpetuation of societal inequalities. Ultimately, the goal is to create AI systems that are equitable, trustworthy, and able to positively impact various facets of life, fostering a future where technology serves humanity in an ethical and responsible manner.

Nurses are on the frontline of patient interaction in any clinic or hospital. Being emotionally intelligent means understanding ourselves, handling our emotions in a mature way, understanding others and helping them to handle their emotions. Life skills training helps to build confidence in communication, cooperative and collaborative skills, problem solving, socializing and recognize the impact of their actions. Methods: One Group Pre-test Post-test Research design was adopted in this study. Th

This research walk-through the various methods used by exclusive and upscaled organizations that minimized its marketing Champaign and budgeting. Generally, posh products and service are not marketed through Integrated channels that are commonly available. As these companies differentiate themselves through unique innovative features they opt for specific single streamed path for marketing that directly reach-out its targeted customer group than spending on popularity promotions. Posh and exclus

Cyber Crime is technology based crime committed by technocrats. This paper deals with Variants of cyber crime held in Chhattisgarh between 2005to 2019. Under this, the Age wise Clustering of arrested people has been displayed on basis of cybercrime in Chhattisgarh .data mining DBSCAN and Hierarchical algorithm is used for clustering. A DBSCAN algorithm is based on this intuitive notion of “clusters”. The key idea is that for each point of a cluster, the neighborhood of a given radius has to

The growing need for image annotation research stems from the growing necessity to manage large collections of images. An effective annotation and retrieval system is urgently needed because of the enormous volume of picture data available on the internet and the affordability of inexpensive digital cameras. Image annotation and retrieval systems have traditionally depended on keyword annotations. These approaches begin with photos that have been manually tagged with textual tags. The semantics

With the rise of collaborative robots, the need for safe, reliable, and efficient physical human–robot interaction (pHRI) has grown. High-performance pHRI requires robust and stable controllers suitable for multiple degrees of freedom (DoF) and highly nonlinear robots. In this article, we describe a cascade-loop pHRI controller, which relies on human force and pose measurements and can adapt to varying robot dynamics online. It can also adapt to different users and simplifies the interaction by

India has one of the greatest and speediest creating masses of Internet users in the world, which is evaluated to be around 190 million as of June 2014 and growing rapidly. India at this point has the third greatest Internet people on earth today, after China with 620 million and the US with 275 million. The advancement in the Internet base in India is at present surprising. It required quite a while from the associate of the Internet with show up at 100 million users. The second 100 million wil

Banks are playing a very important role in providing financial assistance for the various business sectors and agricultural sectors. But from the last few years banking institutions financial health is not in good condition. Major public banks are going through a liquidity crisis. The large volume of NPAs is negatively impacting the growth of the Indian economy. The 'District co-operative banks bad loan ratio hinting towards 12.6 per cent. The bad-debt ratio of commercial banks stood at 7.5 per

Inventory management is generally recognized to be of sufficient importance to warrant the appointment of a person to carry specific responsibility for it .The study investigated the relationship between company performance and inventory management. The researcher used inventory days as a dependent variable and gross profit and net profit as an independent variable. The inventory connotes the value of raw materials, consumables, spares, work –in– progress, finished goods and scrap in which a com

Feature extraction is essential in bioinformatics because it transforms genome sequences into the feature vectors required for data mining activities such as classification and clustering. The data mining activities enable us to classify or cluster the newly sequenced genome to the known families. Nowadays, a variety of feature extraction strategies are available for genome data. Nevertheless, several existing algorithms do not extract context-sensitive key properties, also some approaches extra

In the computing scene, cyber security is going through tremendous changes in technology and its operations of late, and data science is driving the change. Eliminating security occasion models or snippets of information from cyber security data and building seeing data-driven model, is the best way to deal with make a security system modernized and shrewd. To understand and investigate the real ponders with data, different canny philosophies, machine learning procedures, cycles and systems are

The Increasing Volume, Variety, And Velocity of Data Generated in Today”s Digital Age Pose Significant Challenges for Data Scientists in Extracting Valuable Insights. Data Preprocessing, A Crucial Step in The Data Science Workflow, Involves Cleaning, Transforming, And Integrating Raw Data to Improve Its Quality and Usability for Analysis. The Role of a Database Management System (DBMS) In Supporting Data Science Workflows, Particularly Data Preprocessing, Is the Focus of This Research Paper. Thi

The concept of green branding is increasingly becoming a useful instrument toward the establishment of sustainable identities by businesses seeking to capture the expanding electric vehicle (EV) market. This study provides a comparison of green branding of two Indian giants Tata Motors and Ola Electric. The paper discusses the nature by which these companies communicate sustainability, involve consumers and distinguish themselves as eco-friendly companies. The paper determines which green efforts are effective and which are not and also what the consumer perception is towards each brand using descriptive statistics as well as hypothesis testing using primary survey data. The results indicate the major distinctions between tactics and emphasize the potential role of branding in gaining consumer confidence and loyalty in Indian EV market.

The industrial sector values innovations and adaptation greatly. This progress ought to result in the use of new technologies for sustainable manufacturing. Smart manufacturing calls for a global perspective on the technology being used to in order to be environmentally friendly. In this context, Thanks to the continuous research in the field of artificial intelligence, AI-based approaches, such as machine learning, are currently established in the industry to achieve sustainable production (AI)

Machine translation refers to the sub-field of computational linguistics that investigates the use of software to translate text or speech from one language to another. On a basic level, MT performs mechanical substitution of words in one language for words in another, but that alone rarely produces a good translation because recognition of whole phrases and their closest counterparts in the target language is needed. Not all words in one language have equivalent words in another language, and m

Cloud computing is the upcoming environment in the use of computer technology. Numerous users are keen to put their information in cloud; since balancing load in cloud a risky. Load balancing resource allocation plays a vital role. This chapter deals with the research motivation, the objectives and the thesis organization.

Artificial Intelligence (AI) is transforming labor markets globally by redefining skill requirements, occupational structures, and employability standards. In India, where demographic advantage intersects with rapid digital expansion, AI adoption is reshaping workforce readiness across sectors such as information technology, banking, healthcare, manufacturing, and education. According to the India Skills Report (2026), national employability has risen to 56.35%, with more than 40% of the IT and gig workforce using AI tools, reflecting accelerated digital integration. Government initiatives including FutureSkills PRIME, Skill India Digital, and reforms under the National Education Policy (2020) aim to institutionalize AI literacy from school to professional levels. However, disparities in AI adoption, infrastructure gaps, and skill mismatches persist. This study analyzes secondary data, policy frameworks, and Indian case examples to evaluate AI’s role in future job readiness. It concludes that systematic reskilling, industry–academia collaboration, and inclusive AI governance are essential to harness India’s demographic dividend in the AI-driven global economy.

Mammography, which exposes patients to a low dose of radiation, is often regarded as the simplest way for identifying breast cancer in its initial stages. However, there are certain risks associated with the procedure. It helps radio-graphic breast cancer examination detect any growth or lump in the early stages, even before it becomes obvious to the doctor or the woman herself, and that these rays are not dangerous if used at yearly intervals, as recommended by the National Guidelines for early

By clustering the data, people can obtain the data distribution, observe the character of each cluster, and make further study on particular clusters. In addition, cluster analysis usually acts as the preprocessing of other data mining operations. Therefore, cluster analysis has become a very active research topic in data mining. Data mining is a new technology, developing with database and artificial intelligence. It is a processing procedure of extracting credible, novel, effective and underst

Websites are essentially client/server applications - with web servers and browser clients. Consideration should be given to the interactions between html pages, TCP/IP communications, internet connections, firewalls, applications that run in web pages (such as applets, JavaScript, plug-in applications) and applications that run on the server side (such as CGI scripts, database interfaces, logging applications, dynamic page generators, asp, etc.). Additionally, there are a wide variety of server

Efficiently executing image categorization with high spatial quality imagery from remote sensing can bring great benefits to scene classification. Effective feature representation is critical in the development of high-performance scene categorization techniques because sensor data processing is tough. Remote sensing as well as deep learning abilities have made it easier to extract spatiotemporal information for classification. Furthermore, other scientific disciplines, together with remote sensing, have made significant advances in image categorization by convolutional neural networks (CNNs), and transfer learning is being combined. Image categorization in this article was performed to enrich the accuracy of Scene classification using Transfer learning utilizing pre-trained Alex Net, and Visual Geometry Group (VGG) networks and compared with feature extraction methods. First, features were retrieved from the pre-trained network's second fully-connected layer and employed in SVM classification. Second, substituting the last layers of pre-trained networks with the notion of transfer learning was used to categorize new datasets. It is executed on the UCM Dataset as well as the SIRI-WHU Dataset. The proposed methodologies produced improved accuracy of 95% for UCM, 93% for SIRI-WHU Datasets. The categorization of scene images into a distinct set of meaningful groups based on the image contents is important in the analysis of imaging sensor, aerial and satellite images because of its importance in an extensive range of applications, significant various approaches for remote sensing data scene classification have been developed throughout the last few decades. The capacity to handle large dimensionality data and perform well with limited training samples, as well as high accuracy, drew a lot of attention with the introduction of SVM machine learning. We can use a pretrained network as a commencement for learning a new deep learning task by replacing the pretrained network's fully connected layer and classification layer.

Artificial Intelligence (AI) is fundamentally transforming global workforce landscapes, creating both disruption and unprecedented opportunities. This research examines AI's dual role as driver of workforce transformation and solution for preparing workers to thrive in AI-enabled economies. Analysis reveals that nearly half of workers' core skills will be disrupted by 2027, yet AI-powered personalized learning can accelerate reskilling at unprecedented scale[1][2]. We examine critical skills for 2026—including AI literacy, data analytics, and adaptive learning—while exploring how organizations can leverage AI-driven learning systems, microlearning platforms, and intelligent skill gap analysis[3][4]. Policy frameworks from India's AI Talent Mission and World Economic Forum initiatives provide implementation roadmaps[5][6]. Achieving workforce readiness requires coordinated investment in AI-powered education infrastructure, continuous learning ecosystems, equitable access, and collaborative partnerships. Strategic integration of AI into skill development, while maintaining human-centered approaches, enables adaptive, resilient workforces prepared for future digital economies.

The integration of artificial intelligence (AI) into the automotive industry has revolutionized the way we drive and interact with vehicles. This paper explores the ethical implications and responsible use of AI in automotive technology. It delves into the multifaceted challenges and opportunities that arise as AI becomes increasingly intertwined with vehicles, emphasizing the need for a comprehensive ethical framework The potential job displacement due to autonomous vehicles and the ethical con

The rapid evolution of generative Artificial Intelligence has significantly transformed pedagogical practices, particularly in lesson planning and instructional strategy design. This paper explores the integration of generative AI tools such as large language models and AI-driven content generation platforms into contemporary teaching frameworks. The study examines how generative AI supports educators in designing structured lesson plans, creating differentiated instructional materials, developing formative assessments, and enhancing student engagement through personalized learning pathways. The research paper analyzes opportunities including time efficiency, curriculum alignment, adaptive content generation, multilingual support, and data-informed instructional decisions. It further discusses pedagogical transformation from teacher-centered approaches to AI-assisted collaborative learning environments. However, the integration of generative AI also presents challenges such as ethical concerns, data privacy risks, over-dependence on automation, and the need for teacher digital competence. Using conceptual analysis and emerging classroom case insights, the study proposes a balanced AI-integrated lesson planning framework that maintains teacher agency while leveraging AI capabilities. The paper concludes by outlining future pathways, including AI literacy training & strategies for educators, policy development, responsible AI governance in education, and hybrid human-AI pedagogical models. The findings suggest that generative AI, when used responsibly and strategically, can enhance instructional design, foster creativity, and contribute to more inclusive and adaptive educational ecosystems.

The advent of quantum computing offers transformative potential in drug discovery, promising to revolutionize the pharmaceutical industry by drastically enhancing the accuracy and efficiency of molecular simulations. Traditional computational methods often struggle to model complex molecular systems due to the quantum nature of chemical interactions, limiting the scope of drug discovery. Quantum computing, with its ability to process and simulate quantum states, can overcome these barriers, enabling more precise predictions of molecular behavior, protein folding, and drug- target interactions. This paper explores the role of quantum computing in accelerating drug discovery, from predicting the electronic structures of molecules to optimizing molecular interactions for improved binding affinities. By leveraging quantum algorithms such as the Variational Quantum Eigen-solver (VQE) and Quantum Phase Estimation (QPE), researchers can simulate chemical reactions and molecular properties that were previously intractable using classical computers. Additionally, quantum- enhanced machine learning algorithms are paving the way for the identification of novel compounds and personalized therapies tailored to individual molecular profiles. Despite current limitations in quantum hardware, hybrid classical-quantum approaches are already demonstrating promise in lead discovery, protein structure prediction, and reducing drug resistance. As quantum technologies continue to evolve, they hold the potential to significantly reduce drug development timelines, lower costs, and enable more effective precision medicine. This new era, fueled by quantum computing, is poised to deliver breakthroughs in treatments for complex diseases, leading to a paradigm shift in how medicines are discovered and developed.

The categorization of scene images into a distinct set of meaningful groups based on the image contents is important in the analysis of imaging sensor, aerial and satellite images because of its importance in an extensive range of applications, significant various approaches for remote sensing data scene classification have been developed throughout the last few decades. The capacity to handle large dimensionality data and perform well with limited training samples, as well as high accuracy, dre

Cloud computing is the upcoming environment in the use of computer technology. Numerous users are keen to put their information in cloud; since balancing load in cloud a risky. Load balancing resource allocation plays a vital role. This chapter deals with the research motivation, the objectives and the thesis organization.

Asthma prevalence is currently rising sharply as a result of population growth, rising environmental pollutants, and changing lifestyles. As a result, the goal of this study was to locate Tehran, Iran's asthmatic areas by taking both environmental and geographic aspects into account. Data from 872 locations in children who have asthma and 13 ecologic factors that affect illnesses (distance to play areas and roadways, weather patterns, itemperature, ihumidity, iairipressure, iwindispeed, particul

In this present article we have developed an optimal payment policy for deteriorating items. The nature of deterioration rate is non-instantaneous and preservation technology is used to control the deterioration. Demand rate depends on stock level and selling price as hybrid type function. Shortages are permitted and partially backlogged with constant rate. A trade credit policy is considered to establish an optimal payment policy. As global changes cannot be ignored hence effect of inflation is

The efficiency of a database is a critical component in determining how well it can be used. In order to ensure that requests are carried out in a timely manner, distributed real-time database systems, also known as DRTDBS, need to be developed on all levels of database architecture. The number of missed deadlines should be kept to a minimum as much as possible, since this is the major performance goal of DRTDBS. Because of the difficult nature of this task, conventional methods are insufficient

Recommendation systems have become an integral part of many online platforms, aiding users in finding relevant and personalized content. These systems rely heavily on user preference data to generate accurate and effective recommendations. However, handling and processing vast amounts of user data pose significant challenges, necessitating the use of a robust and efficient database management system (DBMS). This research paper investigates the critical role of DBMS in supporting recommendation s

The field of database management systems (DBMS) plays a crucial role in modern information systems. The process of system analysis and design is a fundamental aspect of developing effective and efficient DBMS solutions. This research paper aims to study and analyze the process of system analysis and design as applied to DBMS, with a focus on understanding its key components, methodologies, and best practices. The paper begins by providing an overview of DBMS and its significance in contemporary

This research paper aims to provide a comprehensive analysis of the global market for advanced drill data management solutions and presents a detailed forecast for its growth in the coming years. The increasing demand for efficient drilling operations and the rising adoption of digital technologies in the oil and gas industry have led to the emergence of advanced drill data management solutions. The paper begins with an overview of the market, highlighting the key drivers, challenges, and trends

Plant tissue culture is a crucial technology that facilitates the growth, preservation, and improvement of therapeutic plants. In order to create genetically homogeneous plants, this approach entails cultivating plant cells, tissues, or organs in a regulated setting that is free of pollutants. Since its invention more than a century ago, tissue culture has undergone substantial development, leading to discoveries like totipotency and improvements in nutrient-rich medium. The method makes it easi

Many educators have had the experience of not being able to reach some students until presenting the information in a completely different way or providing new options for student expression. Perhaps it was a student who struggled with writing until the teacher provided the option to create a graphic story, which blossomed into a beautiful and complex narrative. Or maybe it was a student who just couldn't seem to grasp fractions, until he created them by separating oranges into slices. Because of these kinds of experiences, the theory of multiple intelligences resonates with many educators. It supports what we all know to be true: A one-size-fits-all approach to education will invariably leave some students behind. However, the theory is also often misunderstood, which can lead to it being used interchangeably with learning styles or applying it in ways that can limit student potential. While the theory of multiple intelligences is a powerful way to think about learning, it’s also important to understand the research that supports it.

Artificial Intelligence (AI) is increasingly reshaping educational practices and redefining instructional roles within academic libraries. This paper examines the integration of AI in information literacy instruction, a core responsibility of libraries in supporting critical thinking, ethical information use, and lifelong learning. As digital information environments grow more complex, AI-driven tools are being adopted to enhance the effectiveness, accessibility, and personalization of information literacy education. The paper explores the use of AI technologies such as conversational chatbots, intelligent tutoring systems, adaptive learning platforms, and recommender systems in delivering information literacy instruction. These tools enable personalized learning pathways, real-time instructional support, and continuous learner engagement beyond traditional classroom settings. AI-based systems also assist librarians in curriculum integration, assessment of learning outcomes, and identification of students’ information-seeking challenges. Despite these opportunities, the adoption of AI in library instruction raises important challenges related to data privacy, algorithmic bias, transparency, and the potential over-reliance on automated guidance. The evolving pedagogical role of librarians and the need for AI-related competencies are highlighted as critical considerations. Adopting a conceptual and analytical approach, this paper emphasizes the importance of responsible and ethical AI integration and positions academic libraries as key contributors to information-literate and digitally competent learners in AI-driven educational environments.

A novel convolutional neural network named JM-Net [67] is designed that has fewer parameters since different size of convolution kernels are applied in same layer unlike fully convolutional layer. Deep color models [68] of CNN are developed by exploring different color spaces and their combination and all of them are fused to investigate the importance of color within the deep learning framework for aerial scene classification. This proves its’ effectiveness by improving the classification performance compared to using only the RGB image as input to the network as a general practice. Two novel deep architectures, texture coded two-stream deep architecture and saliency coded two-stream deep architecture which are based on the idea of feature-level fusion are proposed in a work to further improve the classification accuracy. Remote sensing image scene classification with deep learning (DL) is a rapidly growing field that has gained significant attention in the past few years. While previ

The Java Enterprise Edition (Java EE) has provided the industry with a standard suite of APIs and services for developing server - side applications in Java. As Java EE applications increase in size and complexity the constraints imposed by the existing component model restrict utility. The Servlet is the server side web programming language Listener is a set of classes. It is used for server side Application.There is some Listeners ServletRequestListener, HttpSessionListener, HttpSessionBinding

Artificial Intelligence (AI) is transforming education by enhancing teaching effectiveness, student engagement, and learning outcomes. In health sciences education, including physiotherapy and rehabilitation sciences, AI-enabled tools support lesson planning, personalized learning, assessment, and skill development. This paper explores the ethical, safe, and fair use of AI in teaching and learning processes. It highlights how AI tools assist educators in developing interactive content, generating case-based learning scenarios, and providing timely feedback to students. The integration of AI also supports competency-based education aligned with NEP 2020 by promoting critical thinking, clinical reasoning, and digital literacy. Additionally, AI-driven systems can help identify learning gaps and support student well-being through adaptive learning strategies. Despite these benefits, responsible use, data privacy, and academic integrity must be ensured. The paper concludes that ethical integration of AI can enhance educational quality while preparing students for future healthcare practice.

E-governance means that force that is behind digital transformation, such as digital inclusion, transparent governance, and effective service delivery. This research more specifically studies the effect of Common Services Centers (CSCs), internet penetration, and a digital literacy campaign in the establishment of e-governance in India. Using hypothesis testing and growth rate models, the study looks at how the infrastructure of e-governance and the trend of digital adoption are related statistically. The r = 0.991, p < 0.001 means that the study results indicate that having CSCs implemented is highly positively correlated. The research on the forecast showed that digital literacy started at 27.23% annually from 2015 to 2020, while internet use scaled 15.43% from 2015 to 2023. Though growth rates diminished in recent years, this proves that the digital inclusion measures succeeded. The report suggests that there are investments that need to become more digitally literate, inclusivity of AI in governance, cybersecurity standards that can be improved, and expansion of the CSC (country sufficient condition) coverage. With these, India will be able to fully reap the potential of Society 5.0 and make India a digital society based on people making inclusion and technological empowerment.

The rise of artificial intelligence (AI) and big data analytics has transformed the economic and social landscape, giving rise to the phenomenon of surveillance capitalism. In this system, personal data is harvested, analyzed, and monetized, often without users’ explicit consent. This paper explores the mechanisms through which AI facilitates data exploitation, the societal and ethical implications of surveillance capitalism, and potential strategies for mitigating its adverse effects. By synthesizing literature from technology studies, economics, and ethics, the paper provides a comprehensive understanding of how AI-driven surveillance capitalism reshapes privacy, autonomy, and power relations in the digital age.

This paper addresses critical challenges in current blockchain-enabled Internet of Things (IoT) architectures, identifying key issues that warrant architectural modifications. The proposed solution, termed the "Blockchain-Enabled Edge-Fog and Cloud-Based Architecture for IoTs," is introduced in subsequent sections to overcome these challenges. The Issues section highlights various problems within existing architectures, setting the stage for the innovative solution presented in this work. It del

The advancement in the field of AIA that will improve image retrieval performance. The suggested approach comprises three basic stages: segmentation, feature extraction and annotation. Shape images are segmented using edge detection techniques, whereas colour images are segmented using thresholding, region-based, and clustering-based techniques. K-means clustering on the brightness level of an image results in clusters creating image regions. Feature extraction is the process of transforming hig

Over the past few years, Artificial Intelligence (AI) has made deep inroads into education, and one of its most hopeful applications is in the area of student mental health. This is especially relevant in India, where millions of students struggle silently with academic pressure, anxiety, and depression, often without any meaningful support. Indian universities and colleges are grappling with a rising tide of psychological distress among students, made worse by social stigma, a shortage of trained counsellors, and uneven access to mental health services. This paper looks at how AI tools—such as chatbots, sentiment analysis systems, predictive analytics, and wellness apps—can offer practical, scalable help to students who need it. Drawing on published research, real-world case studies, and policy discussions, the study explores how AI-based interventions have been used across Indian institutions to catch early signs of distress, offer timely emotional support, and connect students with professional help. It also takes a critical look at serious concerns around privacy, bias in algorithms, unequal digital access, and the need for strong ethical guidelines. The overall finding is that AI holds real promise for narrowing the mental health gap in Indian higher education—but only if these tools are designed with cultural awareness, built for multiple languages, and governed responsibly.

This Research explores the many facets of the Internet of Things (IoT), looking into its various protocols, uses, and the crucial security issue. In this investigation, we examine the field of Internet of Things protocols, from well-known standards to cutting-edge innovations, emphasizing their features and applicability for diverse uses. In addition, we examine the wide range of IoT applications in industries including smart cities, transportation, and healthcare, explaining both their opportun

Numerous real-time applications have a need for data services in decentralised settings. However, being able to provide such data services is difficult owing to the lengthy delays that are associated with distant data accessing and the severe time constraints that are associated with real-time transactions. When the amount of time required to compute the transactions in a set is more than the amount of time available on the processor, overload occurs. There have been many methods proposed to dea

Open source software has revolutionized the technology landscape by enabling collaborative development and sharing of software designs. This research paper aims to provide an overview and critical analysis of various open source software designs across different domains. The paper explores the design principles, development processes, and community engagement within these projects. Through an extensive review of open source software projects, this research aims to shed light on the strengths, we

Over the years, our dependence on technology has risen tremendously. Many scenarios need our use of information technology (IT), though. Since our dependence on technology has resulted in a major shift in how we interact with the rest of the world, Systems that were formerly entirely autonomous are now designed to work in concert with one another as well as human users. A vast range of applications and devices are being networked all over the place and for many different reasons. Analytical mod

Volume alludes to tremendous measure of created and put away data not in Terabytes but rather Zettabytes or Yottabytes. The "size" shows value of data and its capability to be considered as "MIMO LTE based antenna" or not. The volume was identified with the size of data. At present data was in pettabytes and in not so distant future it will be of zettabytes. Consistently, in the computerized universe, we make around 2.5 Exabyte's of data. Each time we click on mouse, each telephone call we make,

The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy

AI has had a substantial influence on image processing, allowing cutting-edge methods and uses. The foundations of image processing are covered in this chapter, along with representation, formats, enhancement methods, and filtering. It digs into methods for machine learning, neural networks, optimization strategies, digital watermarking, picture security, cloud computing, image augmentation, and data pretreatment methods. The impact of cloud computing on platforms, performance, privacy, and secu

The use of Artificial Intelligence (AI) in academic research is rapidly transforming the landscape of knowledge generation, data analysis, and scholarly communication in India and across the globe. AI-powered tools assist researchers in conducting systematic literature reviews, managing large datasets, performing advanced statistical analyses, and generating predictive models with improved speed and accuracy. Machine learning algorithms and natural language processing technologies enable the identification of patterns, trends, and relationships that may not be easily detected through traditional methods. AI enhances research productivity by automating repetitive tasks such as data coding and citation management. Despite these advantages, the integration of AI in research raises important concerns related to data privacy, algorithmic bias, transparency, authorship, and academic integrity. Responsible use and clear ethical guidelines are essential to maintain credibility and accountability in scholarly work. This paper provides a comprehensive review of AI's role in academic research with a particular focus on the Indian higher education context, aligned with the National Education Policy (NEP) 2020.

E-commerce markets are growing at noticeable rates. The online market is expected to grow by 56% in 2015–2020.E-commerce allows customers to overcome geographical barriers and allows them to purchase products anytime and from anywhere. Retailing in India is one of the pillars of its economy and accounts for about 10 percent of its GDP. The Indian retail market is estimated to be worth $1.3 trillion as of 2022 and estimated to reach $2 Tn by 2032. India is one of the fastest growing retail market

Smart Cities, powered by the Internet of Things (IoT), are rapidly transforming urban living by seamlessly integrating technology into daily life. However, the exponential growth in interconnected devices and systems also brings forth concerns related to security, privacy, and trustworthiness. Addressing these concerns, this paper introduces an innovative Blockchain Model for Trust Management (BMTM) tailored for the IoT ecosystem in Smart Cities. The decentralized nature of blockchain technolog

Remote Sensing (RS) imageclassification has recently attracted great attention for its application in different tasks, including environmental monitoring, battlefield surveillance, and geospatial object detection. The best practices for these tasks often involve transfer learning from pre-trained Convolutional NeuralNetworks (CNNs). AcommonapproachintheliteratureisemployingCNNsforfeatureextraction, and subsequently train classifiers exploiting such features. In this paper, we propose the adoption of transfer learning by fine-tuning pre-trained CNNs for end-to-end aerial image classification. Our approach performs feature extraction from the fine-tuned neural networks and remote sensing image classification with a Support Vector Machine (SVM) model with linear and Radial Basis Function (RBF) kernels. To tune the learning rate hyperparameter, we employ a linear decay learning rate scheduler as well as cyclical learning rates. Moreover, in order to mitigate the overfitting problem of pre

In today's increasingly interconnected and digitalized world, cybersecurity has emerged as a fundamental pillar of institutional resilience. Institutions in a variety of sectors—educational, governmental, healthcare, and corporate—are facing unprecedented cyber dangers, including data breaches, ransomware attacks, insider threats, and phishing scams. While developments in cybersecurity technology have resulted in increasingly sophisticated defense mechanisms, institutional leadership continues to play an important role in determining the effectiveness and sustainability of cyber measures. Leadership is more than just a support job; it is a strategic enabler that influences policy direction, resource priority, and the overall company culture towards security. This research investigates the critical role that institutional leadership plays in the effective implementation of cybersecurity policies. It contends that leadership involvement is critical at all stages of cybersecurity planning and implementation, from developing a strategic vision and risk management framework to fostering a culture of cybersecurity knowledge and accountability. The study uses a multidisciplinary approach that includes an in-depth review of academic literature, analysis of institutional case studies, and an examination of relevant cybersecurity policy frameworks to identify the mechanisms by which leadership actions (or inactions) directly impact cybersecurity outcomes. Key findings show that institutions with proactive leadership have much higher cybersecurity preparation, policy compliance, and incident response capabilities. Institutions that lack leadership participation frequently face fragmented policies, insufficient financing, limited staff training, and poor coordination between IT departments and executive teams. Leadership's capacity to communicate the importance of cybersecurity, establish quantifiable targets, and incorporate security into institutional strategy has been demonstrated to be crucial in overcoming these implementation problems. The research presents the Leadership-Centric Cybersecurity Implementation (LCCI) Model as a strategic framework for institutional leaders. This approach identifies five key areas of leadership influence: strategic visioning, policy integration, resource allocation, cultural embedding, and governance oversight. The concept highlights that cybersecurity should not be viewed only as a technological issue, but rather as an organizatio

Due to the fact that OSS is having free of cost access to the tools and technology, teaching and learning through OSS can be done in a wonderful manner. The gap of digital divide can be filled with the novel idea of OSS. Reliability measurement is a prime concern in OSS as it is being updated by many developers constantly. Research is being carried to develop SRGMs for OSS in order to check its reliability under different environmental conditions. Reliability of Mozilla Firefox, Apache, Genome e

With the rapid advancement of digital image editing tools, manipulating images has become easier than ever. Among various tampering techniques, image splicing—where segments from one or more images are combined into a single composite—remains a prominent method of forgery. The implications of such forgeries are significant, particularly in sensitive domains like journalism, forensics, and social media, where image authenticity is paramount. The growing accessibility of image editing tools has made image splicing a prevalent method of forgery, posing challenges for detection.Passive image forgery detection has gained significant attention in recent years due to the rapid advancements in digital image editing tools. Among various forgery techniques, image splicing remains a common method for tampering. Detecting image splicing presents substantial challenges. This paper proposes a novel algorithm combining deep learning and wavelet transform for spliced image detection. A Convolutional Neural Network (CNN) is utilized for automatic feature extraction, followed by Haar Wavelet Transform (HWT). Support Vector Machine (SVM) is then employed for classification. Additionally, experiments replace HWT with Discrete Cosine Transform (DCT), followed by Principal Component Analysis (PCA). The algorithm is evaluated on public datasets (CASIA v1.0 and CASIA v2.0) and demonstrates high accuracy with a compact feature vector. Results confirm the effectiveness of the proposed approach in detecting spliced images with improved performance.

Brain tumor detection using magnetic resonance imaging (MRI) faces critical challenges due to the presence of noise, intensity inhomogeneity, and complex anatomical structures. This research proposes an intelligent pre-processing algorithm based on a hybrid approach combining anisotropic diffusion filtering, adaptive histogram equalization, and wavelet thresholding to reduce noise and enhance image quality. The algorithm also integrates a convolutional neural network (CNN)-based denoiser trained on a dataset of brain MRIs to further refine the images. This pre-processing pipeline significantly enhances segmentation and classification accuracy in downstream tasks using U-Net and ResNet architectures. Experimental results demonstrate improvements in peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Dice coefficient, thereby establishing the efficacy of the proposed method in clinical and research settings.

Big data actually implies an assortment of very large datasets which cannot be processed easily by implementing traditional computing methods. Big data is not merely a data, it has rather transformed into a comprehensive topic, which encompasses number of tools, procedures and frameworks. In general, big data is a datasets that could not be observed, attained, managed, and administered with hold-style IT and software/hardware components within a bearable period. Big Data technologies pronounce a

Using this analysis one can generate a new sequence of random but related states which look similar to the original. This Markov process is stochastic in nature which has the property that the probability of transition from a given state to any future state depends only on the present state and not on the manner in which it was reached. The simulator is developed in this chapter to compute n-step e steady state stationary transition probabilities for various state of the software under maintena

The term "Internet of Things" (IoT) refers to a network of devices, both large and small, that are linked together and made available online. Because IoT devices may increase efficiency, accuracy, and financial advantage while decreasing the need for human intervention, they provide the greatest degree of adaptability and convenience in our day-to-day operations. The burden of safety, confidentiality, and communication There are also emerging issues in the Internet of Things. Several privacy-pre

The widespread use of IoT in society has created a broad target for malicious actors to exploit. This concise paper introduces IoT and its security issues, covering its ecosystem and protocols. The paper then delves into the vulnerabilities present in IoT systems. Its intended audience is those with existing network knowledge but who are new to IoT. The study concludes that the shortage of IoT standards and limitations in device resources are critical issues. Opportunities exist for research int

Alzheimer's disease (AD) results in memory loss and impairment, which can give rise to additional symptoms. It has a significant impact on the lives of patients and unfortunately, there is no cure. However, early detection of AD can be beneficial in initiating appropriate treatment to prevent additional brain damage. In recent years, researchers have utilized machine learning techniques to classify AD. These methods involve using manually prepared features and a classifier with a complex archite

The large and interconnected nature of IoT devices has made maintaining strong security a serious problem as the Internet of Things (IoT) has proliferated. This research investigates how Artificial Neural Networks (ANNs), a kind of machine learning, might improve Intrusion Prevention Systems (IPS) designed for Internet of Things contexts. Conventional IPS techniques are unable to provide sufficient security for Internet of Things devices due to their limitations, including limited battery life a

In the past 35 years numerous software reliability growth models (SRGMs) have been proposed under diverse testing and debugging (T&D) environments and applied successfully in many real life software projects but no SRGM can claim to be the best in general as the physical interpretation of the T&D is not general. Unified modeling approach proves to be very successful in this regard and provides an excellent platform for obtaining several existing SRGM following single methodology. It forms the ma

In this research, we present a method for analysing packet data networks using the OPNET simulation tool. The goal is to educate users about packet-level networks, such as the Fibre Distributed Data Interface and Network Intrusion Simulation. The FDDI protocol is investigated by switching between two networks with different settings. High utilisation on the FDDI LAN reduces the cost of transporting data, and geographically dispersing servers and workstations in different buildings to make use of

Data mining, an urging requirement in the current era and whose scope of research is expected to be for upcoming decades. Among the well versed techniques of data mining association rule mining plays a prodigious role. This technique emphasizes on curious association, correlations, frequent patterns etc. from the given data sources to be mined. The primary task of association mining resides in uncovering the frequent patterns and exploring the association rules. Multiple variation of association

Earth observation is a collection of information about the earths surface whether it be physical, chemical and biological systems using earth observation satellite or earth remote sensing satellite or directly captured from aircrafts. With the increasing volume of high-resolution remote-sensing images due to development of such earth observation technologies, there necessitate automated systems for analyzing as well as classification of these images for various applications like land mapping, ve

In the era of digital transformation, data integrity, security, and transparency have become crucial components in various sectors, ranging from finance to healthcare. Blockchain technology, initially introduced as the foundation for cryptocurrencies like Bitcoin, has evolved beyond its original use case to address broader challenges in data management. This paper explores the role of blockchain in revolutionizing data integrity by providing secure, transparent, and tamper-proof systems for recording and verifying transactions. By decentralizing control and ensuring immutable records, blockchain offers promising solutions to long-standing issues related to data security, trust, and transparency. This paper discusses the underlying principles of blockchain technology, its impact on different industries, and the challenges and future directions for its integration into data security frameworks.

This Research highlight the pivotal role of machine and deep learning in fortifying the security and privacy of Internet of Things (IoT) systems. As IoT devices proliferate across various domains, ensuring robust security measures becomes increasingly critical. Machine and deep learning techniques offer sophisticated solutions to tackle the evolving challenges in this domain. This abstract delves into the application of these technologies in enhancing the security and privacy of IoT ecosystems,

The increasing use of the Internet of Things (IoT) technology has raised concerns about the security and privacy of the devices and data involved. IoT devices are highly connected, and their security risks and vulnerabilities can have significant impacts on the entire network. Therefore, there is a need for a systematic framework to assess and manage IoT security risks. This research paper proposes a framework for IoT security risk assessment and management that aims to identify potential risks

Stroke is a leading cause of long-term disability, often resulting in motor impairments that require intensive rehabilitation. Traditional stroke rehabilitation approaches have shown promising results, but advancements in technology have paved the way for innovative interventions. Brain-computer interfaces (BCIs) have emerged as a potential tool for enhancing stroke rehabilitation outcomes by directly connecting the brain with external devices or computer systems. This paper provides a comprehen

The integration of financial management and Human Resource Management has emerged as a critical pathway for business growth and tactical decision-making. Driven by rapid development in Artificial Intelligence (AI), Organizations can significantly reduce operating cost/expenses and increase cost effectiveness through automated processes in all the HR functions as Talent acquisition, Training & Development, performance management, etc. This study investigates the relationship between financial implications of Artificial Intelligence in Human Resource Management. This systematic review explores the impact of AI technology in the transformation of HRM into strategic role in cost efficiency, budget allocation in operation and decision-making. It will also focus on the economies resulting from data analysis and decision-making, as well as problems encountered in this technology. Research results have shown that AI maximizes efficiency in HR activities by streamlining repetitive tasks, thereby reducing operational costs substantially. Predictive models have also allowed technology driven decision-making. Consequently, there has been effective synchronization of human capital with financial outcomes. Nonetheless, the sustainability of AI strategy in human capital development has posed some risks that need to be effectively mitigated. In the summing up, integration needs to be balanced, so that long-term organizational viability is assured as well as the ability to bargain the best way through the intricacies of the contemporary digital economy.

The exponential growth of data necessitates robust and scalable processing techniques, propelling cloud-based big data frameworks like Apache Hadoop, Apache Spark, and Apache Flink into prominence. This study presents a comparative analysis of these three leading frameworks, evaluating their performance, resource utilization, scalability, and operational costs within real-world hybrid cloud environments (OpenStack and Azure). Utilizing benchmarks such as WordCount, TeraSort, K-means, and various classification workloads, our methodology involved systematic experimentation and analysis. Results indicate a consistent performance ranking by execution time: Flink outperformed Spark, which in turn surpassed Hadoop. Resource utilization varied significantly, with Hadoop exhibiting the highest inter-node data transfer and Spark the least. All frameworks demonstrated superior horizontal scalability compared to vertical scaling. Cost analysis revealed Spark as the most economical solution, while Hadoop proved to be the costliest. Specific benchmark insights highlighted Spark's approximate 5x faster performance for classification tasks over Hadoop, although its efficiency reduced with increasing dataset size. The discussion emphasizes that the preferred tool depends on the specific use case: Hadoop for cost-driven large-batch jobs, Spark for memory-bound iterative analytics, and Flink for low-latency stream processing. Spark also offers a more mature ecosystem, while Flink provides generally faster failover through its checkpointing mechanism. The study concludes that while all frameworks have their merits, horizontal scaling is crucial for cloud deployments, and Spark currently offers the best overall value for hybrid environments.

Association policy Mining identifies intriguing relationships or correlations between data pieces. With vast volumes of data being generated and kept on a constant basis, several companies are increasingly interested in mining association rules from their databases. Because rule is one of most expressive & human readable representations of information, association rule mining is a critical task in datamining. Usually association rule mining techniques generate too many rules and it reduces the efficiency of the process. Further it complicates the pruning process also. As a result, it is required to identify a limited group of fundamental rules that allow for prediction without creating the whole set of rules. This paper proposes an efficient theoretical model of the method for creating crucial rules without producing the whole set of rules. Because the algorithm creates just the necessary rules, the rule set produced is less in size. The time necessary to generate the rules is likewise

"This paper provides a comprehensive analysis of CNN's (Convolutional Neural Network) potential in the classroom and its implications for the future of education. The purpose of this research is to investigate how CNNs may be used in the classroom, specifically for tasks like deep learning ; speech and picture recognition, NLP, and more. Improved student engagement, individualised instruction, and higher achievement are just some of the many acknowledged benefits of incorporating CNNs into the c

Cyberattack is knowing victimization of device, tech-dependent networks and companies. Cyberattacks use malicious code to modify statistics, common sense, or device code, resulting in consequences because of which records can be compromised and can end result to cybercrimes, consisting of records and identity theft. Cyberattack is find of knowing hobby — maybe over prolonged period of time — to modify, interrupt, betray, shame, or demolish adversary facts or computer device or networks and/or ap

Artificial Intelligence (AI) is increasingly influencing educational practices worldwide, leading institutions to reconsider how teachers are prepared and supported in their professional roles. Traditional professional development models, often limited to periodic workshops and general digital training, do not adequately address the demands of AI-enabled classrooms. This paper examines how AI can enhance teacher professional development by enabling personalized learning pathways, strengthening instructional design, and supporting data-informed decision-making. It also analyses key ethical concerns associated with AI integration, including data privacy, algorithmic bias, teacher autonomy, and equitable access to technology. Drawing on international perspectives, particularly UNESCO’s ethical AI framework and India’s National Education Policy 2020, the study proposes a human-centered approach to integrating AI in teacher education. The paper argues that AI can support meaningful professional growth when implemented responsibly, with strong ethical safeguards and sustained institutional commitment.

Artificial Intelligence (AI) has reshaped art, music, and literature, sparking a new era of creative possibilities and collaborative potential, where algorithms generate novel sounds, styles, and narratives. This paper delves into the methodologies and applications of advanced AI tools like DALL·E 3, Musicfy.lol, and ChatGPT, which are transforming creative processes by generating digital art, composing music, and crafting literary works. These technologies employ machine learning algorithms, neural networks, and vast datasets to emulate and expand upon human imagination, offering tools for democratizing creative expression and enhancing productivity. However, their rise brings challenges such as ethical dilemmas, questions of originality, biases in training data, and concerns over intellectual property and employment displacement. By inspecting the interplay between humankind ingenuity and machine capabilities, this study highlights the evolving role of AI in shaping the future of creativity and provides insights into fostering a balanced coexistence between human and AI-driven artistic endeavours. Finally, this paper also explores the historical evolution of AI in creative fields, from early experiments in computer-generated art and music to the sophisticated tools of today. By examining real-world case studies, such as the AI-generated portrait "Edmond de Belamy" and the use of AI in film scoring, this study highlights both the transformative potential and the limitations of AI in art, music, and literature. Furthermore, it addresses the ethical implications of AI-generated content, including issues of bias, authenticity, and the future of human creativity in an increasingly automated world.

The software Developer build the large scale Application using these component. J2EE plateform consist of set of Service Programming Interface(API and Protocals) that provide the functionality for developing multitiered web based application. JSP are used to create and genrated the dynamicaly Web Pages which based on SOAP,XML,HTML etc .The Web Based Application Architecture is Different type such as One Tier, Two Tier , Three Tier etc. The three Tier Architecure Application is based on Three

The purpose of this study is to investigate the interaction between human and robot in our society. Human robot interaction is a multidisciplinary area of research interaction. It is a multidisciplinary research comprising of the technological abilities of robots. The design of robot embodiment and geographical studies and lab based research who are human participants with robots in a controlled environment. Therefore the discipline involved with human robot interaction or computer science are H

Although research into gesture recognition has been ongoing for decades, it remains difficult. It's not an easy problem to solve because of the complicated nature of the setting, the camera, and the lighting. Therefore, an efficient and reliable method for gesture recognition using RGB video is presented in this paper. To begin, we use their hair and eye colour to determine if they have skin. The next step is to get the hand's outline and segment it. We understand the gesture now. The experiment

Cloud computing is the upcoming environment in the use of computer technology. Numerous users are keen to put their information in cloud; since balancing load in cloud a risky. Load balancing resource allocation plays a vital role. This chapter deals with the research motivation, the objectives and the thesis organization.

Image forgery detection plays a critical role in maintaining the authenticity and integrity of digital content. Traditional forgery detection algorithms often face limitations in terms of time efficiency and detection accuracy. Emerging methods leveraging deep neural networks have shown promise in addressing these challenges. In this study, we propose a hybrid approach combining deep learning (DL) and machine learning (ML) techniques for passive image forgery detection. The DL component classifies images into forged and non-forged categories, while the ML-based color illumination model effectively localizes the forged regions. This hybrid methodology enhances both detection accuracy and interpretability. The performance of the proposed approach is rigorously evaluated against widely-used public datasets, including CASIA1.0, CASIA2.0, BSDS300, DVMM, and the CMFD image manipulation dataset. Our results demonstrate superior accuracy, achieving 99% on CASIA1.0, 98% on CASIA2.0, 98% on BSDS300, 97% on DVMM, and 99% on the CMFD dataset. Additionally, the computational efficiency of the approach outperforms traditional methods, making it suitable for real-time and large-scale applications. This hybrid framework is designed to address various forms of image manipulations, including splicing, copy-move forgeries, and region duplication, thereby providing a robust solution for digital forensic investigations. Future work will explore integrating advanced preprocessing techniques and leveraging multimodal datasets to further enhance robustness and applicability.

Websites are essentially client/server applications - with web servers and browser clients. Consideration should be given to the interactions between html pages, TCP/IP communications, internet connections, firewalls, applications that run in web pages (such as applets, JavaScript, plug-in applications) and applications that run on the server side (such as CGI scripts, database interfaces, logging applications, dynamic page generators, asp, etc.). Additionally, there are a wide variety of server

The rapid development of Artificial Intelligence (AI) is reshaping education, presenting both opportunities and challenges for teachers. AI tools personalizing teaching, analyze student performance, streamline assessment, making instruction more effective and engaging. Data-driven approaches help teachers better understand students’ individual needs. However, significant challenges remain, including a lack of technical training, limited resources, digital inequality, data privacy, and ethical issues. Striking a balance between technological innovation and human sensitivity is crucial to avoid overreliance on technology. This research paper examines these opportunities and challenges through teacher experiences and educational data, emphasizing the importance of teacher training, responsible AI use and, teacher-centered integration. The study argues that AI should complement, not replace teachers, ultimately enhancing teaching effectiveness.

Artificial Intelligence (AI) is emerging as a transformative force in higher education governance. This study investigates how AI technologies influence financial and administrative decision-making processes and how such integration impacts institutional governance. Through a mixed-method research design involving surveys, interviews, and case studies from multiple institutions, the study examines AI’s role in enhancing transparency, efficiency, accountability, and strategic planning. Findings indicate that AI significantly improves financial forecasting, resource allocation, administrative workflows, and compliance monitoring, thereby strengthening governance structures. However, challenges such as ethical concerns, data privacy, skill gaps, and policy limitations are identified. The research proposes a governance framework that integrates AI capabilities with institutional leadership practices to support evidence-based decision making and sustainable institutional growth.

Artificial Intelligence (AI) systems, increasingly integrated into critical sectors such as healthcare, finance, and criminal justice, have demonstrated significant potential to enhance decision-making processes. However, the pervasive issue of bias within these systems poses substantial risks, including the perpetuation of societal inequalities and the erosion of public trust. This paper provides a comprehensive analysis of the origins of AI bias, its multifaceted impacts, and a review of current mitigation strategies. Through a synthesis of existing literature and case studies, we aim to offer a nuanced understanding of AI bias and propose pathways toward more equitable AI systems.

In the fast-paced world of insurance, where technology plays an increasingly vital role in managing policies, processing claims, and engaging with customers, the importance of strong cybersecurity measures has never been more pronounced. This research delves into the pivotal role cybersecurity plays in the realm of digital insurance. It delves into the vulnerabilities that come hand in hand with online platforms and explores how cyber threats can impact both insurers and their clientele. The study sheds light on the rising instances of cyberattacks, from data breaches to ransomware attacks, which jeopardize sensitive customer data and erode trust in digital insurance solutions. Moreover, it delves into effective cybersecurity practices such as encryption, multi-factor authentication, and continuous monitoring to safeguard data integrity and privacy. By examining recent case studies and industry analyses, this research underscores the significance of cultivating a culture of cybersecuri

MIMO LTE based antenna was being produced by everything around us consistently. Each computerized procedure and web based life trade delivers the MIMO LTE based antenna. It was transmitted by frameworks, sensors and cell phones. Regularly around 2.5 quintillions of data can be made. The 2019 IDC Digital Universe study composes that around 130 exabytes of data were made and put away in 2015. With the quick increment in this, it developed to 1227 exabytes in 2020 and was anticipated to develop at

Electroencephalography is a widely used clinical and research method to record and monitor the brain’s electrical activity – the electroencephalogram (EEG). Machine learning algorithms have been developed to extract information from the EEG to help in the diagnosis of several disorders (e.g., epilepsy, Alzheimer’s disease, and schizophrenia) and to identify various brain states. Despite the elegant and generally easy-to-use nature of machine learning algorithms in neuroscience, they can produce

Biometric authentication has gained widespread adoption in security-critical applications due to its ability to provide reliable and unique identification. Among various biometric modalities, fingerprint recognition remains one of the most popular and effective techniques. However, individual fingerprint features alone may not always provide sufficient accuracy, especially in noisy or less-than-ideal conditions. To address this limitation, multimodal biometric systems, which combine multiple biometric traits, offer enhanced performance in terms of accuracy, security, and robustness. This research proposes a novel multimodal biometric system that leverages fingerprint features alongside other biometric modalities, such as facial recognition or iris scans, to improve identification and verification accuracy. The system utilizes advanced feature extraction techniques, including minutiae matching, texture-based methods, and deep learning-based fusion approaches, to effectively combine and

The recent advancement in internet 2.0 creates a scope to connect people worldwide using society 2.0 and web 2.0 technologies. This new era allows the consumer to directly connect with other individuals, business corporations, and the government. People are open to sharing opinions, views, and ideas on any topic in different formats out loud. This creates the opportunity to make the “Big Social Data” handy by implementing machine learning approaches and social data analytics. This study offers a

This research paper aims to provide a comprehensive review of the effects of social networking websites on students. With the increasing popularity and widespread use of platforms such as Facebook, Instagram, Twitter, and Snapchat, it is crucial to understand the impact they have on the academic, social, and psychological well-being of students. By examining a wide range of studies conducted on this topic, this paper discusses the positive and negative effects of social networking websites, expl

An optimization method known as the genetic algorithm is used to handle nonlinear or non-differentiable optimization problems. It looks for a global minimum using ideas from evolutionary biology. It avoids local optima and looks for global fitness since it is the resilient solution when measured against fitness standards. However, there are still some instances and circumstances where solutions are offered based on local optimal values, which completely undermines the use of GAs for optimization

The convergence of artificial intelligence (AI) and library science has catalyzed the emergence of a new paradigm: the smart library—a dynamic, data-driven ecosystem capable of adapting to learners' individual needs in real time. This article examines how AI technologies, including machine learning, natural language processing (NLP), intelligent recommendation systems, chatbot-assisted reference services, and adaptive learning platforms, are being integrated into academic and public library infrastructures to support personalized learning. Drawing on peer-reviewed literature, institutional case studies, and emerging theoretical frameworks, the paper argues that smart libraries represent a fundamental transformation in how knowledge is curated, accessed, and pedagogically leveraged within smart education environments. The study explores the technical architecture of AI-enabled library systems, their pedagogical implications, ethical considerations surrounding data privacy and algorithmic bias, and the challenges of implementation across diverse institutional contexts. Findings suggest that while AI-enhanced libraries significantly improve learning outcomes, engagement, and resource discovery efficiency, their success depends on equitable design principles, robust digital infrastructure, and ongoing collaboration between library professionals, educators, and technology developers. The article contributes an integrated conceptual model—the Smart Library Learning Ecosystem (SLLE)—as a framework for understanding and guiding the development of next-generation library services in the context of smart education.

With the exponential growth of connected devices and sophisticated cyber threats, traditional rule-based firewalls are becoming increasingly inadequate in providing real-time and adaptive security. This paper explores the design, implementation, and performance of AI-powered smart firewalls, which utilize machine learning (ML), deep learning (DL), and anomaly detection techniques to protect endpoints and networks. Smart firewalls can dynamically learn threat patterns, adapt policies in real-time, and detect zero-day attacks with minimal human intervention. This research proposes a hybrid firewall model incorporating both supervised and unsupervised learning to create an intelligent threat prevention system. Experimental results demonstrate high detection accuracy, low false positive rates, and real-time performance efficiency.

Computing resources such as networks, servers, storage, services, applications, and software are pooled and made available to multiple users over the Internet in a cloud computing setup. In today's era of information technology, we have complete access to data on all major developments in the relevant disciplines. Various small and large scale (manufacturing, automation, television, and constructions industries), Geographical Information System (GIS), Military intelligence fusion (MIS), business

Sentiment analysis utilises natural language processing (NLP) techniques to create software that interprets text similarly to human comprehension. Sentiment analysis is a crucial business intelligence tool that allows organisations to improve products and services by analysing digital text to determine the author's viewpoint on a subject. This information is employed to augment customer service and elevate brand reputation. Naive Bayes, Support Vector Machines, and Logistic Regression are commonly utilised in sentiment analysis to predict sentiment in Amazon reviews by analysing textual features and training the model on labelled data. These models aid organisations in understanding customer satisfaction, enabling data-driven decision-making, and leveraging the extensive collection of Amazon reviews to foster corporate growth and success. The primary objective of this study was to conduct a thorough examination of the sentiments expressed in Amazon reviews for a variety of product cate

Cybercrime refers to illegal activities carried out using computers and the Internet. These crimes can range from stealing personal information and financial fraud to hacking and spreading malicious software. Cybercrime poses significant risks to individuals, businesses, and governments worldwide. Human beings, endowed with unique cognitive abilities, have transformed the world through innovation and adaptation. From basic survival needs to the complexities of modern society, our intellectual pr

This research paper explores the use of social networking sites in recent times, highlighting the significant shifts, trends, and impacts on individuals, communities, and society as a whole. It delves into the evolving nature of social networking platforms, the role they play in shaping interpersonal relationships, communication patterns, and the overall social fabric. The paper discusses both the positive and negative implications of social networking sites and offers insights into their potent

In fact, e-governance can be seen as a broader concept that identifies and evaluates the impacts that ICT and artificial intelligence technologies on the practice and administration of government, on relations between civil servants and society in general, and on interactions with elected representatives or external stakeholders including non-governmental organizations or private institutions. Several studies have examined how e-governance is radically transforming public sector organizations an

Image mining means to separate affiliations and models which are not unequivocally managed there of mind from crude data images. Image mining is a particularly arranged system considering data mining, man-made thinking, reenacted information, image recuperation, image making due, PC vision and edifying grouping, etc. Image mining's capacity of finding solid image plans opens different assessment fields to new edges. Mining huge arrangement of images, and joined data mining of colossal combinatio

It is understood that there are various models that achieve feeble programming quality. It is basic to invite the focal drivers of low quality in deals to have the choice to successfully improve your techniques in mentioning to improve the possibility of your thing. Fortunately, what's more locate that improving quality improves time-to-market of the most basic features. To recognize mutilations can be: bugs, joins that weren't referenced, features that were referenced currently are of immateria

Cloud computing has revolutionized the way applications are developed, deployed, and accessed. Cloud-based applications offer numerous benefits such as scalability, cost-effectiveness, and easy maintenance. However, ensuring the quality of these applications is a complex and challenging task due to the dynamic nature of cloud environments. In recent years, artificial learning techniques have emerged as a promising approach to address the quality challenges associated with cloud-based application

This study investigates the application of Digital Image Processing (DIP) techniques for early cancer nodule detection in scanned medical images. By comparing and optimizing various algorithms, we aim to enhance the accuracy and efficiency of identifying cancerous nodules at their nascent stages. The process encompasses image acquisition, preprocessing, segmentation, feature extraction, classification, and post-processing. We explore different approaches within each stage, such as noise reductio

Thousands use websites at some point of time for other. Cloud has limitation in maintaining load obtained from all demands at time any point of time. It results in destroy of the entire network. It is the process in which computing resources and workloads are distributed to more than one server. Workload is divided between two or more servers, hard drives, system interface and other computing resources resulting in good use and system response time. A huge traffic web site requires a high powerf

For a number of decades, the conventional approach to medical picture analysis for cancer relied on human-defined characteristics as its foundation. In many cases, low-level image qualities like as intensity, contrast, and a limited number of texture metrics served as a source of motivation for the development of these features. It was challenging to capture the high-level, complex patterns that a skilled radiologist uses to determine whether or not cancer is present in a patient 1. In the subcl

Electronic governance, commonly referred to as ―e-governance‖, has emerged as a critical aspect of modern-day governance. It involves the use of digital technologies to deliver efficient, transparent, and citizen-centric government services and processes. In India, ―e-governance‖ has been a significant policy objective for several years, with the government launching various initiatives to improve citizen participation and reduce corruption. However, the implementation of ―e-governance‖ in India

The proliferation of food delivery platforms has intensified the need for sophisticated customer relationship management strategies. This research presents a comprehensive analysis of customer behavior and segmentation for Zomato, utilizing a synthetic dataset and the scalable data analytics capabilities of Google Cloud Platform (GCP). A realistically modeled dataset of 1,014 customers was first generated to simulate key behavioral attributes and then processed through a modern cloud-native pipeline involving BigQuery and BigQuery ML. After rigorous exploratory data analysis (EDA), an unsupervised K-Means clustering model was deployed to segment the customer base based on three key behavioral features: total orders, average rating given, and customer tenure. The analysis identified four distinct customer segments, revealing significant behavioral patterns not immediately apparent from the existing loyalty tier structure. These segments range from highly-satisfied new users to long-tenured, highly-active but critically-rating customers. The end-to-end process—from synthetic data generation to strategic insight—demonstrates a reproducible framework for customer analytics and provides actionable, data-driven recommendations for targeted marketing and customer retention.

Cybercrime is any criminal activity that involves computers and networks. It's a broad term that covers a wide range of illegal activities, from stealing personal information to disrupting critical infrastructure. Cybercrime refers to illegal activities carried out using computers and the Internet. These crimes can range from stealing personal information and financial fraud to hacking and spreading malicious software. Cybercrime poses significant risks to individuals, businesses, and government

Fiber distributed data networks have gained significant prominence in recent years due to their ability to provide high-speed and reliable connectivity. However, as the demand for data continues to grow exponentially, it becomes crucial to optimize the performance of these networks to ensure efficient data transmission and meet user expectations. This research paper presents a comprehensive review of performance optimization techniques employed in fiber distributed data networks. It explores var

Web-based systems are playing an important role in modern computer savvy society today. Because of the pervasive nature and the massive user population, various existing software engineering approaches need to be adopted for web engineering and web quality assurance (WebQA) is the application area which deals with analysis, testing, quality/reliability improvement for web-based applications. Davila-Nicanor et al. [DAV2005] focused on the development of a methodology for the evaluation and analy

Enterprise networks in the country of cybersecurity are subject to increasingly complex attacks, which calls for strong defenses. By adding randomness into security operations, probabilistic protocols provide a fresh strategy that hinders attackers' capacity to anticipate and take advantage of weaknesses. The purpose of this comparative study is to investigate how probabilistic protocols might improve network resilience against various cyber dangers, such as advanced persistent threats (APTs), p

The proliferation of Internet of Things (IoT) devices has revolutionized various industries, enabling seamless connectivity and smart automation. However, this interconnectedness also poses significant security risks, as IoT networks become prime targets for malicious actors. Traditional security approaches fall short in effectively identifying and mitigating IoT vulnerabilities due to the dynamic nature of IoT environments. In this research paper, we propose an innovative approach, Adversarial-

The growing skills gap between higher education outcomes and labour market demands has intensified the need for innovative approaches to prepare students for future employment. Artificial Intelligence (AI) has emerged as a transformative tool in enhancing skill development and improving job readiness through personalized learning pathways, adaptive training systems, virtual simulations, and data-driven career guidance. This paper examines key AI applications that support both technical and soft skill development in higher education and explores their role in aligning academic learning with evolving workforce requirements. Drawing on case examples from Indian universities and leading international institutions, the study highlights effective AI-driven practices that enhance employability and workforce preparedness. The paper further discusses how successful global models can be adapted within the Indian higher education context to address large-scale skill gaps, promote inclusive learning, and strengthen industry–academia linkages. By synthesizing current applications and practical experiences, this study underscores the potential of AI to transform higher education into a more responsive and future-oriented system capable of producing skilled, job-ready graduates.

This study examines the effectiveness of Database Management Systems (DBMS) in enhancing administrative efficiency and instructional quality in primary schools within Dhule district. With growing challenges such as manual record-keeping, administrative workload, and limited access to real-time data, DBMS provides a digital solution that streamlines student records, academic performance tracking, staff management, and reporting. The system enables data-driven teaching, facilitates communication among teachers, parents, and administrators, and supports informed decision-making for policy and resource allocation. Despite barriers such as inadequate infrastructure, limited digital literacy, and budget constraints, DBMS significantly improves school management, data security, and transparency. The findings suggest that with adequate training, infrastructure support, and wider adoption of cloud-based and AI-enabled systems, DBMS can play a transformative role in strengthening primary education administration in Dhule and similar regions.

Privacy concerns in the realm of social media have become a pressing issue in the digital age. The exponential growth of social networking platforms has given rise to a host of privacy-related challenges that demand careful examination. This research paper delves into the multifaceted landscape of privacy concerns in social media, encompassing issues related to data security, information sharing, user awareness, and regulatory frameworks. By analyzing the intricacies of privacy concerns in socia

Mobile phone in hands give you more options of shopping than the mobile phone lying in pocket. Consumers want to take the advantage of advanced technologies of their smart phones for revolutionizing their buying process (Bandara et al., 2020; Wagner et al., 2020).Augmented reality, Virtual reality, and Artificial intelligence are some of the advanced technologies of the smart phones whichgive evolution to the new platform for shopping, called m-commerce (Blaise et al., 2018). Globally, m-commerc

Artificial Intelligence (AI) has vast potential in marketing. It aids in proliferating information and data sources, improving software's data management capabilities, and designing intricate and advanced algorithms. AI is changing the way brands and users interact with one another. The application of this technology is highly dependent on the nature of the website and the type of business. Marketers can now focus more on the customer and meet their needs in real time. By using AI, they can qui

This research investigates the implications of smart additive manufacturing (SAM), which is also known as smart three-dimensional (3D) printing, for industrial production logistics and inventory management as well as for digital supply chains (DSCs) and digital value chains (DVCs). The research is conducted within the context of Industry 4.0. The fourth industrial revolution is frequently referred to as "Industry 4.0." This transformation has given rise to the development of new digital technolo

Advancements in Artificial Intelligence (AI) are reshaping inclusive and special education by providing personalized, adaptive, and scalable solutions for diverse learning needs. AI-driven technologies such as speech-to-text systems, language processing tools, computer vision, and predictive analytics support learners with disabilities by enhancing accessibility, engagement, and individualized instruction. This paper examines how AI tools assist in identifying learning challenges, customizing educational content, supporting communication for students with hearing or visual impairments, and offering data-driven insights for educators and specialists. It also discusses teacher preparation, ethical considerations, and the potential digital divide among learners. Findings suggest that, when carefully implemented with inclusive design principles and stakeholder collaboration, AI-enhanced practices can improve learning outcomes, promote autonomy, and foster equitable education opportunities. The paper concludes with recommendations for policy development, teacher training, and future research to harness AI benefits while safeguarding ethical and accessibility standards in inclusive education.

Stroke [CVA] is the sudden loss of neurological function caused by an interruption of blood flow to the brain. Large numbers of people who survive a stroke are left with permanent impairment of arm and hand function, even after completion of conventional rehabilitation programs. The standard neuro physiological facilitation technique use for hemiplegicupperlimb have notbeenconfirmedto promote functional recovery of hemiplegic limb. This promotethat more research needs to be conducted for same. C

Considering the use of web stages in various fields, remote systems with high data rates and amazing redirection limits were very famous. These requirements were reliably incompatible with single-input and single-output (SISO) antennas. Therefore, multi-input and multiple-output (MIMO) printed antennas, another type of antenna setup, had emerged as a sensible competitor for quick correspondence upgrades. In such schemes, sending and receiving data were energetically supervised using a coplanar o

Open Source Software (OSS) has gained significant prominence in the software industry, with its collaborative and transparent development model. This research paper aims to provide a comprehensive analysis of the approaches employed in developing open-source software. The study investigates key aspects such as community collaboration, version control, licensing, and quality assurance methodologies. By understanding these approaches, software developers and researchers can gain valuable insights

Artificial Intelligence (AI) is reshaping global workforce dynamics by redefining skill requirements and transforming professional training systems. As automation, machine learning, and data-driven technologies become integral to industries, AI-enabled platforms are playing a critical role in developing future-ready competencies. This paper explores how AI supports skill development through personalized learning pathways, intelligent tutoring systems, virtual simulations, and predictive analytics. It highlights the importance of digital literacy, critical thinking, creativity, and socio-emotional skills in preparing individuals for emerging job markets. The study also examines global initiatives promoting AI-based workforce training and lifelong learning. While AI enhances accessibility and efficiency in professional development, concerns regarding job displacement, digital inequality, and ethical implications require strategic policy responses. The paper concludes that AI, when integrated responsibly with education and vocational systems, can significantly strengthen employability, adaptability, and sustainable economic growth in the rapidly evolving digital era.

Fog Computing has marked an onset of appealing technology pertinent in the domain of Information Technology. Smart homes are equipped with many smart devices interacting with each other and hence generating tremendous amount of data. The increasing use of these smart homes increases thenetwork usage, energy consumption. Although there are many smart devices in smart home which generate data but CCTV is one of the main devices that contributes to a large data volume and transmission rate. The images and video streams data were sent to cloud but there is always the issue of storage and processing the data on cloud. Fog computing comes up as a solution is to improve mobility, security, and on-demand while addressing current Cloud computing problems like energy consumption, latency, and network bandwidth usage. The objective of this paper is to introduce the fog layer in smart home model which will analyse whole CCTV data here and only filtered data will be sent to cloud. This strategy lowers energy consumption, latency, and network bandwidth usage. Using the iFogSim2 network simulator, the FBSHM model shows efficiency in response time and optimization of energy use, latency, and network bandwidth usage.

The ethical Dimensions of artificial intelligence (AI) involve crucial matters including impartiality, equity, and responsibility, all of which are essential for guaranteeing the responsible application of AI. Unfair outcomes and the reinforcement of pre-existing prejudices might result from biased data, discriminating algorithms, or systemic imbalances in AI systems. Using inclusive design principles to promote fairness, regular algorithmic audits, and the use of representative and diverse data

Mobile Ad Hoc Networks ((MANETs)) address a critical shift from conventional organization designs, basically because of their decentralized nature and dynamic geographies. Not at all like customary organizations that depend on fixed framework, for example, base stations or switch-es, MANETs work with no concentrated control. All things being equal, hubs inside the organ-ization are answerable for both steering and handing-off information, shaping impermanent, on-the-fly correspondence ways through multi-jump transmissions. As nodes move or join or leave the network, each MANET node acts as both a host and a router, adapting dynamically to changes in the network's topology. Versatile Impromptu Organizations (MANETs) address a decentralized remote organization structure where hubs speak with one another without de-pending on a previous framework. The powerful geography of MANETs, joined with their de-pendence on multi-jump directing, acquaints a few difficulties related with data trade, steering effectiveness, and correspondence unwavering quality. This paper examines the routing proto-cols, data dissemination strategies, network scalability, security concerns, and communication reliability of MANET information corresponding mechanisms. Reproduction results are intro-duced to assess the exhibition of various steering conventions under different portability exam-ples and traffic conditions, alongside ideas for upgrading data trade in MANET conditions. This examination intends to add to the continuous advancement of MANET correspondence methodologies by offering bits of knowledge into how data correspondence can be enhanced to fulfill the needs of progressively intricate and asset compelled networks.

The internet has made a significant impact on all parts of modern lifestyle and businesses and people have become susceptible to various cyber attacks. Every year, cybercrime rates are increasing and causing loss of privacy, reputational and financial damage, and intellectual property violations. The INDIA has become a key target for those criminals because of high levels of tourism and economic activity, rise of oil and gas sector, and uptake of technology. A lot of changes have been made to th

Using this analysis one can generate a new sequence of random but related states which look similar to the original. This Markov process is stochastic in nature which has the property that the probability of transition from a given state to any future state depends only on the present state and not on the manner in which it was reached. The simulator is developed in this chapter to compute n-step e steady state stationary transition probabilities for various state of the software under maintena

The “Aspect Based Sentiment Analysis” (ABSA) task is a classic Sentiment Analysis problem that focuses on understanding people’s opinion on aspect level. This main task is sub-divided into multiple works including the extraction of aspect term, recognition of aspect category, recognition of sentiment word and classification of emotions (positive, negative, conflict, neutral) for aspect term and also for aspect category. Here we propose the system for recognizing aspects and analyzing the sentimen

In the rapidly evolving era of the Internet of Things (IoT), data security and computational efficiency have become crucial factors, especially within healthcare applications where continuous monitoring and wireless data transmission are routine. Traditional cryptographic techniques such as AES and RSA, while robust, impose significant computational and energy burdens on resource-limited IoT devices. The objective of this paper is to design real-time efficient method for providing data protection in IoT-based smart diabetic monitoring system. The proposed Enhanced Lightweight Cryptographic Algorithm (ELCA) employs an optimized ARX (Addition–Rotation–XOR) structure that supports fast operations, strong diffusion and strong resistance to timing and side-channel attacks. The model further incorporates advanced key management and nonce-tracking mechanisms to ensure non-repetition, data integrity and end-to-end authentication. Experimental results revealed that the proposed algorithm exhibited superior scalability and stable performance across different data sizes, confirming its suitability for resource-constrained IoT environments.

This paper presents an in-depth exploration of the application of Support Vector Machines (SVM) for detecting security threats in Wireless Sensor Networks (WSNs). With the increasing use of WSNs across various domains, ensuring their security is of paramount importance. This research investigates the effectiveness of SVM algorithms in identifying various attacks such as Denial-of-Service (DoS), Sybil attacks, Blackhole attacks, and Wormhole attacks. The study compares SVM with other machine learning models and demonstrates its superior performance in terms of accuracy, precision, and recall. The proposed model is evaluated using real-time datasets and simulated environments, providing comprehensive insights into the applicability of SVM for WSN security.

The advent of web-based social networks has revolutionized communication, collaboration, and content sharing, enabling unprecedented global connectivity. However, this evolution has also exposed users to a myriad of cybersecurity threats, including identity theft, phishing attacks, cyberbullying, misinformation, and large-scale data breaches. These risks compromise user privacy, trust, and platform integrity. This research provides a comprehensive analysis of current threats targeting social networks and evaluates state-of-the-art security measures to counteract them. It further explores emerging technologies such as artificial intelligence, blockchain, and advanced encryption techniques as robust solutions to enhance security and user confidence. By proposing strategic frameworks and policies, this study aims to foster safer online interactions and minimize digital vulnerabilities.

This paper introduces a lightweight security framework for human tracking enabled by the Internet of Things (IoT), specifically addressing scenarios related to pandemics such as COVID-19. The paper identifies key challenges associated with IoT-based human tracking, focusing on enterprise, cloud computing, and superlative layers. The proposed framework paradigm is constructed to mitigate these challenges and enhance the overall security of the system. The subsequent sections detail the steps invo

Artificial Intelligence (AI) has emerged as one of the most influential technologies of the twenty-first century, significantly transforming education, employment, and workforce development. As industries increasingly adopt automation and intelligent systems, the demand for new skills and competencies continues to rise. This research paper examines the role of AI in skill development and its importance in preparing individuals for future job readiness. The study discusses how AI supports personalized learning, lifelong skill acquisition, and career adaptability while also addressing challenges such as job displacement, ethical concerns, and the digital divide. The paper emphasizes the need for balanced human–AI collaboration, inclusive education policies, and continuous up skilling to ensure sustainable employment in a rapidly changing world.

This study explores the challenges and scope of Information and Communication Technology (ICT) in contemporary society, with a focus on its adoption, impact, and potential in various sectors across the globe. As ICT continues to evolve at a rapid pace, its integration into everyday life and across industries has transformed economies, governance, education, healthcare, and business operations. However, while the benefits of ICT are far-reaching, numerous challenges remain that hinder its universal adoption and equitable distribution, particularly in developing regions and among marginalized populations. This research investigates these obstacles, including issues related to digital divide, accessibility, digital literacy, cybersecurity, affordability, and infrastructure inadequacies, which prevent full utilization of ICT's capabilities. The vast scope of ICT, emphasizing its potential in driving economic development, social inclusion, and technological innovation. Key sectors such as education, healthcare, business, governance, and environmental sustainability are explored to assess the transformative impact of ICT and its role in addressing global challenges like climate change, poverty, and inequality. Moreover, the research investigates how emerging technologies like artificial intelligence (AI), big data, cloud computing, and the Internet of Things (IoT) are reshaping industries and offering new opportunities for growth, efficiency, and sustainable development.

The combination of blockchain, AI, and IoT is transforming industries. Blockchain provides safe, transparent data management, AI enables intelligent analysis of IoT data, and IoT generates important real-time insights. This integration is leading to breakthroughs in supply chain management, healthcare, smart cities, and more, boosting efficiency, security, and decision-making processes. This study investigates the convergence of Blockchain, Artificial Intelligence (AI), and the Internet of Thing

Masterminding is significant, in any case learning is head. You need to push structures, for example, iterative improvement, that help packs find what accessories truly require and make up for lost time with that data. It's in addition basic for a social affair to routinely mull over what they're doing and a brief timeframe later act to improve their procedure. It's not basic to begin programming improvement by portraying a hard and fast affirmation, and when in doubt that shows up, clearly, to

Due to its ability to enable the on-demand outsourcing of cloud resources on a pay-per-use basis, cloud computing (CC) has advanced the IT sector. It gives both small and large enterprises the chance to grow, innovate, and develop new values. Users can access cloud-based services from any point in the world and use the applications and data that are managed and administered by others. The rapidly expanding needs of the current resource-demanding business and IT sectors have prompted awareness of

The concept of Artificial Intelligence (AI) is fast disrupting assessment practices in education by changing the traditional approaches to measuring students to dynamic, data-driven evaluation ecosystems. In this paper, the author will discuss the role of AI-based evaluation and personal feedback systems in determining the future of assessment by improving accuracy, relevance, and engagement of learners. Standardized test and manually graded systems have been traditionally criticised as being too inflexible, slow to provide feedback, and failing to represent the complex learning patterns of individual students. On the contrary, using machine learning, natural language processing, and predictive analytics, AI systems provide real-time feedback, ongoing assessment, and personalized learning journeys. The ability of AI to extract a large amount of data during educational interactions can enable educators to learn more about the strengths of the learners, their misconceptions, and the emotional interactions they have. However, amid the benefits, which are strong, such as scalability, efficiency and personalization, there are obstacles such as ethical issues of data privacy, algorithmic fairness and the probability that the human judgment in education will be eroded. To develop an equilibrium model that ensures that AI-supported assessment practices do not marginalize human educators, this paper takes a multidisciplinary approach by integrating the pedagogical theory with technological innovation. This study envisions an assessment ecosystem that is both adaptive, equitable, learner-centered and ethically sound through an analysis of the present AI assessment tools, pedagogical implications, case studies, and future trends of AI assessment. Finally, assessment in the future will be in balancing the intelligent systems with human wisdom to increase meaningful feedback and deeper learning among every learner.

Cloud computing is the upcoming environment in the use of computer technology. Numerous users are keen to put their information in cloud; since balancing load in cloud a risky. Load balancing resource allocation plays a vital role. This chapter deals with the research motivation, the objectives and the thesis organization.

In the latter few decades of the 20th century, the east-west battle came to an end and some type of collaboration amongst once-rivals emerged. This collaboration was made possible by governments' acceptance of regional and international institutions as effective conflict-resolution tools. However, there are ongoing hazards and threats, both old and new. Age-old conflicts of a national, ethnic, religious, and cultural nature that suppressed throughout the second cold war have begun to reemerge wi

This study deals with a comparative analysis of algorithms for solving transcendental equations. It includes algorithms for RF-EXP, RF-Halley, Arc-sine, Tanh and RF-ArcTanh methods to check the accuracy, number of iterations, and errors for the solution of the trigonometric, logarithmic, and exponential equations. Several numerical examples are presented to illustrate the algorithms’ efficacy which are programmed in MATLAB. The findings indicate that Arc-sine and RF-ArcTanh, RF-Halley, and Tanh

This paper presents a multiband MIMO antenna based on meander lines with an L-shaped metallic strip. A monopole antenna with many short-circuited transmission line sections acts as an inductor, altering the impedance characteristics of the antenna; this arrangement is referred to as a meander line. 69% of the antenna size was reduced with the use of a line slot DGS (Defective Ground Structure) to reduce the mutual interaction between the antenna components and the insertion of two U slots on the

The rapid integration of Artificial Intelligence (AI) in education has transformed teaching, learning, assessment, and administration. However, its widespread adoption raises significant ethical, safety, and fairness concerns. This paper examines the principles guiding the responsible use of AI in educational settings, focusing on data privacy, transparency, accountability, inclusivity, and bias mitigation. It explores international policy frameworks and regulatory guidelines that promote human-centered AI systems while protecting learners’ rights. The study highlights potential risks such as algorithmic discrimination, surveillance concerns, academic integrity issues, and unequal access to digital resources. By emphasizing ethical design, stakeholder participation, and regulatory oversight, the paper argues that AI can enhance educational outcomes without compromising equity and safety. The paper concludes that ensuring ethical, safe, and fair AI implementation requires collaborative efforts among educators, policymakers, technologists, and institutions to create trustworthy and inclusive digital learning environments.

The rapid expansion of Massive Open Online Courses (MOOCs) has revolutionized online education by providing learners worldwide with access to diverse learning opportunities. However, despite their potential, MOOCs often suffer from low completion rates and varying levels of student engagement. This research paper explores the integration of personalized feedback mechanisms and adaptive content delivery in MOOCs as a means to enhance student success. By leveraging data mining techniques, artificial intelligence (AI), and learning analytics, the study aims to provide a comprehensive framework that supports individualized learning experiences. The proposed model focuses on monitoring learner progress, providing timely feedback, and dynamically adapting content to match learners’ needs and abilities. Results from empirical studies suggest that personalized feedback and adaptive content significantly contribute to improved learning outcomes, engagement, and satisfaction among MOOC learners.

In the present time, the World Wide Web has evolved into a distributed data space that contains a few billion pages and 100 million devices. Customers continue to face difficult challenges in their search for the information they want, despite the fact that there is an abundance of data available to them online. As a result of the exponential growth of the Internet, the most important objective for research is to develop a search engine that is both accurate and efficient. Among the many subfiel

Introduction: Visual reaction time is a component of cognitive function. Reaction time is defined as interval of time between presentation of stimulus and appearance of appropriate voluntary response in subject. The measurement of visual reaction time has been used to evaluate Processing speed of central nervous system and coordination between Sensory and motor system. It determines the alertness of a person because how quickly a person responds to stimulus depends on reaction time. Method: a co

A computer has various resources comprising monitor, CPU (Central Processing Unit), networking devices, primary and auxiliary storage devices, printer-cum-scanner, track-pad and many more. In order to manage these resources, an operating system needs a scheduler which can define the pre-arrangement of resources lest some specific situation or a process demands it. Cloud computing systems are on the path of great success economically as they are capable of providing huge amount of different kinds

Data security has become a primary prerequisite and need in day to day life. Today’s most of the systems can be hacked and it generates very high risks to our confidential files inside the systems. There are various security reasons; also we use various methods to save as possible data like forms, text pictures, videos etc. Data security used to provide Cryptography and steganography but both of them has a problem. In Cryptography the main problem is the cipher text looks meaningless, so the attacker will interrupt the transmission or make more careful checks on the data from the sender to the receiver. In Steganography problem the presence of hidden information is revealed or even suspected, the message is become known. By using good security techniques of accession control. We can resolve many security problems. In this paper describes Steganography, Encryption/Decryption Algorithm, Cryptography technique and different algorithm used by these techniques. Steganography based on technical and non-technical steganography and also categorized based on its domain. High security layers have been proposed through three layers to make it difficult to break through the encryption of the input data and confuse steganalysis too. This paper describes comparative study using various algorithms.

The primary objective of this research is to develop and evaluate Artificial Intelligence (AI)-driven tools that enhance learning experiences and educational outcomes across various educational stages. By doing so, this study seeks to contribute to the growing body of knowledge on Artificial Intelligence (AI) in education and provide practical solutions for educators and policymakers striving to improve educational practices. In summary, this research embarks on a comprehensive exploration of Ar

Data Distribution can be obtained by clustering data. In this work we observed the characteristics of selected cluster, and make a further study on particular clusters. Also, cluster analysis generally acts as the preprocessing of other data mining operations. Consequently, cluster analysis has become a very active research topic in data mining. Data mining is a new technology, developing with database as well as artificial intelligence. It is a processing procedure of extracting credible and ef

With the improvement of data innovation the datas are put away in the cloud and it should be ensure the security of data and the executives of the data simultaneously. By these requests the reversible data hiding in encoded images (RDH-EI) draws in an ever increasing number of analysts consideration. Here propose a novel system for RDH-EI dependent on RIT (Reverse image Transformation). Herethe substance of the first image can be tansform to the substance of another image. Then, at that point th

The primary objective of this research is to develop and evaluate Artificial Intelligence (AI)-driven tools that enhance learning experiences and educational outcomes across various educational stages. By doing so, this study seeks to contribute to the growing body of knowledge on Artificial Intelligence (AI) in education and provide practical solutions for educators and policymakers striving to improve educational practices. In summary, this research embarks on a comprehensive exploration of Ar

Recommender systems represent a high economic, social, and technological impact at international level due to the most relevant technological companies have been used them as their main services considering that user experience and companies sales have been improved. For this reason, these systems are a principal research area, and the companies optimize their algorithms with hybrid approaches that combine two or more recommendation strategies. A systematic literature review on the hybrid approa

The Indian defence industry has a long history, although modern weapons are just roughly 200 years old. The oldest known activity7 was the founding of the Gun Carriage Agency by the East India Company close to Calcutta at the start of the eighteenth century. It wasn't a particularly noteworthy development, but it signalled the creation of the Defence Industrial Base and slowly cultivated the country's specific skills, artisanal work practises, and knowledge for producing weapons. Since the milit

Artificial intelligence (AI) hold a significant importance for India across various sectors due to its potential to drive innovation, improve efficiency and address the complex challenges. It is crucial for development of India across various dimensions like economic, social and technological. As India continues to navigate the challenges of the 21" Century, embracing and leveraging Al will be key to unlocking the new opportunities aiming at fostering innovation, and positioning India as a global leader in rapidly evolving landscape of technology & Al. The National Policy of Education (NEP), 2020 has acknowledged the significance and role of disruptive technologies in shaping the landscape of education. In such a technology-driven knowledge environment, it becomes imperative to acquaint oneself with different disruptive technologies like "Artificial Intelligence (AI), Block Chain Technology, Machine Learning and Data Science etc. "The policy emphasizes that India must take a leading role in preparing its professionals in areas like AI. The rapid advancement of artificial intelligence (AI) has permeated various facets of our lives, including education. In the realm of teacher professional development, AI presents both opportunities and challenges. This article delves into the role of AI in supporting teacher training and development programs, exploring how educators can harness AI to enhance teaching practices, improve student outcomes, and optimize administrative processes. As artificial intelligence becomes more common in our daily lives, its effect on education calls for both enthusiasm and caution.Supporters believe that AI offers great opportunities for personalized learning, making administrative tasks easier and bringing new ways to teach. However, there are still worries about privacy, fairness and the possibility of replacing traditional teaching jobs. A study by the Digital Education Council found that 86% of students admit to using AI in their studies. To further that, 24% use it daily and 54% use it weekly.

This research develops EmoNet, an emotion identification system using Deep Neural Networks, to accurately detect various facial emotions. EmoNet, with 40 layers, outperforms conventional models, achieving an 8% accuracy improvement on FER2013 and a 0.2% improvement on JAFFE, and can classify 3589 images into seven categories in under 2.77 seconds. The model handles variations in facial size, illumination, and angles, utilizing residual layers to enhance classification and engagement detection. T

Clustering is a Machine Learning technique that involves the grouping of data points. Given a set of data points, we can use a clustering algorithm to classify each data point into a specific group. In theory, data points that are in the same group should have similar properties and/or features, while data points in different groups should have highly dissimilar properties and/or features. Clustering is a method of unsupervised learning and is a common technique for statistical data analysis us

The aim of this research is to demonstrate the optimal strategy of inventory system for perishable items with hybrid type demand under the effect of inflation partial backlogging and preservation technology with a certain ratio. Preservation technology (PT) used to control the deterioration. In this discipline, the concept of advance payment policy with discount facility is includes. This inventory is modeled mathematically by governing differential equations, and solving all these equation, in

Computer vision has a tough time detecting human facial emotions. Emotion may now be discerned from a video or photograph using computer vision and machine learning. In this research, we will describe a technique for identifying facial emotion that makes use of the Haar-Cascade Classifier and convolutional neural networks. Seven distinct facial expressions were employed in the experiment based on data from FER2013, the facial expression recognition dataset. The epoch variety improves the CNN mod

Artificial Intelligence (AI) is increasingly shaping contemporary education systems, with recent reports indicating that over 60-70% of higher education institutions globally have adopted or are piloting AI enabled tools for teaching, learning or administration. The global AI in education market are projected to surpass 20-25 billion U.S. dollars by 20-30, reflecting rapid growth in adaptive learning platforms, automated assessment systems, and intelligent tutoring applications. Evidence suggests that AI supported personalized learning can improve student engagement by 30-40% and reduce teacher’s administrative workload by nearly 20-30%, enabling educators to focus more on student centered instruction and mentoring. AI driven assistive technologies also enhance accessibility for learners with disabilities and support inclusive education in remote and underserved areas. However, significant challenges reflected. Nearly 35-40% of educational institutions in developing regions report inadequate digital infrastructure, while concerns related to data privacy, algorithmic bias, and ethical governance continue to affect trust in AI adoption. Studies indicate that more than 50% of teachers feel insufficiently trained to effectively integrate AI tools into pedagogy, highlighting the need for professional development and digital literacy initiatives. Future pathways emphasize evidence-based policy making, investment in infrastructure, teacher capacity building and development of ethical AI frameworks to ensure responsible and equitable implementation. By combining technological innovation with human centered educational values, AI has the potential to transform learning ecosystems, enhance academic performance, and prepare learners for the demands of an increasingly digital and knowledge-driven society.

The integration of Artificial Intelligence (AI) in educational environments has revolutionized the way academic engagement is analyzed and predicted. However, the "black-box" nature of many AI models limits their interpretability, raising concerns about trust, fairness, and accountability. This research proposes an Explainable AI (XAI) framework for predicting student engagement in online and offline learning environments. By leveraging state-of-the-art deep learning models and incorporating explainability techniques such as SHAP (Shapley Additive Explanations), the study aims to enhance the transparency and usability of engagement prediction systems. The paper evaluates the proposed framework through extensive experimentation and a case study in a hybrid learning setup.

The evolution of e-governance from its initial stages to E-Governance 2.0 marks a significant transformation in the way public administration systems function. E-Governance 2.0 leverages cloud computing technologies to offer more interactive, efficient, and citizen-centered services. This paper explores the integration of cloud computing into the realm of public administration, emphasizing its potential to enhance service delivery, improve data management, and foster citizen engagement. By examining key aspects such as scalability, cost-effectiveness, and the promotion of transparent governance, the paper argues that cloud computing provides an ideal platform for modernizing governmental operations. The paper also discusses the challenges and risks associated with cloud adoption, including data privacy concerns and infrastructure limitations. Through case studies and real-world examples, the research highlights how cloud technologies have been successfully implemented in various global contexts, offering lessons for future public sector innovation. Ultimately, the paper concludes that the convergence of cloud computing and E-Governance 2.0 holds the potential to reshape the landscape of public administration, making it more responsive, accessible, and accountable to citizens.

Indicators of climate change such as greenhouse gas concentrations, sea level rise, ocean heat, and ocean acidification each have new benchmarks set for the year 2011. These new benchmarks were created in the year 2011. This is yet another strong indicator that human activities are causing changes on a global scale on land, in the oceans, and in the atmosphere. These changes will have large and long-lasting effects, and this is yet another strong signal that these changes are being generated. If

Artificial Intelligence (AI) is no longer a distant technological concept; it has become an integral part of contemporary educational systems worldwide. From adaptive learning platforms and automated grading tools to intelligent tutoring systems and predictive analytics, AI is reshaping the way knowledge is delivered, consumed, and evaluated. These technologies promise more personalized learning experiences, improved efficiency in educational administration, and wider access to knowledge. However, alongside these opportunities, AI introduces a range of ethical, safety, and fairness challenges that cannot be ignored. Issues such as data privacy, algorithmic bias, surveillance, digital inequality, and the erosion of human-centered education demand critical reflection. This research paper examines the ethical, safe, and fair use of AI in education from a human-centered perspective. It argues that while AI can significantly enhance educational outcomes, its use must remain grounded in transparency, accountability, and inclusivity. The study highlights how unequal access to technology may widen educational gaps and how biased algorithms can reinforce existing social inequalities. It further emphasizes the importance of safeguarding student privacy, promoting responsible data governance, and ensuring that educators remain central to the learning process. The paper concludes that ethical AI in education must prioritize human dignity, equity, and social justice, ensuring that technological progress benefits all learners rather than a privileged few.

Qualitative and mixed methods play a prominent role in mental health services research. However, the standards for their use are not always evident, especially for those not trained in such methods. This paper reviews the rationale and common approaches to using qualitative and mixed methods in mental health services and implementation research based on a review of the papers included in this special series along with representative examples from the literature. Qualitative methods are used to

The simulator developed in this chapter has been executed for 100000 number of simulation runs in order to compute system reliability for web-based software. The results obtained for web-based software reliability corresponding to different data sets of values of web page reliability. It is found that the system reliability increases as the web page reliability increases. The simulator described in his chapter will be of great importance to evaluate the reliability of web based software. The tra

In today’s time, AI has become very dominant. AI has established its important place in almost every field-where it is education, agriculture, marketing or any other sector. In teacher education AI plays a very significant role. Artificial Intelligence (AI) is transforming teacher education and professional development by introducing very effective learning systems. AI-enabled platforms provide real-time feedback. This paper explores the role of AI in pre-service and in-service teacher education, highlighting its impact on instructional design, reflective practice, and continuous professional growth. It examines global initiatives such as UNESCO frameworks on AI in education and adaptive learning systems used by platforms like Coursera and Google for Education. The study discusses benefits including personalized training pathways, skill gap analysis, and administrative efficiency, while addressing ethical concerns such as data privacy and algorithmic bias. The paper concludes that AI-enabled systems, when implemented responsibly, can significantly strengthen teacher preparation and lifelong professional development in the evolving digital education ecosystem.

Database management systems (DBMS) are crucial for the progression of information technology, facilitating the storage, retrieval, and manipulation of vast data across several fields. This review article provides a thorough analysis of the history, evolution, current trends, and future challenges in database management. The DBMS curriculum includes various disciplines such as data modeling, implementation, and analysis. This course offers a robust basis for IT students to analyze, design, and implement a database system. Besides fundamental skills, IT students must be proficient in diagnosing database performance issues when the system does not meet end-user expectations. In this regard, database tuning involves several tactics designed to improve database performance and is crucial for providing an effective database curriculum. Nonetheless, these competencies are frequently lacking in database management systems curricula. Furthermore, the study investigates issues pertaining to scalability, security, privacy, and ethical considerations in the era of ubiquitous data. This comprehensive research seeks to furnish scholars, practitioners, and enthusiasts with essential insights into the history, present condition, and future of database administration.

In cloud computing environments based on the Internet of Things, efficient resource allocation strategies are essential for maximizing performance and guaranteeing dependable service delivery. In order to define performance benchmarks and direct resource allocation, this study examines the function of policies and service-level agreements (SLAs) in cloud resource management. Policies and Service Level Agreements (SLAs) guarantee that cloud services follow established guidelines, enabling auto-sc

The goal of the multidisciplinary discipline of natural language processing (NLP), which lies at the interface of artificial intelligence, computer science, psychology, and linguistics, is to improve human-computer interaction by enabling machines to comprehend and process human language. In order to describe and implement language comprehension and production, natural language processing (NLP) merges computer approaches with linguistic characteristics such as phonetics, syntax, semantics, and p

The main aim of this study is to discuss the Role of Machine Learning Techniques on Image Content Management. The thesis is advancement in the field of AIA that will improve image retrieval performance. The suggested approach comprises three basic stages: segmentation, feature extraction and annotation. Shape images are segmented using edge detection techniques, whereas colour images are segmented using thresholding, region-based, and clustering-based techniques. K-means clustering on the bright

कृत्रिम बुद्धिमत्ता (Artificial Intelligence) के तीव्र विकास ने शिक्षा के क्षेत्र में व्यापक परिवर्तन की संभावनाएँ उत्पन्न की हैं। विशेष रूप से जनरेटिव आर्टिफ़िशियल इंटेलिजेंस (Generative AI) ने शिक्षण-अधिगम प्रक्रिया को अधिक प्रभावी, लचीला एवं छात्र-केंद्रित बनाने में महत्वपूर्ण भूमिका निभाई है। वर्तमान शिक्षा प्रणाली में शिक्षकों को पाठ योजना निर्माण, विविध अधिगम स्तरों के अनुरूप शिक्षण रणनीतियाँ विकसित करने, मूल्यांकन प्रक्रिया को सुदृढ़ बनाने तथा समय प्रबंधन जैसी अनेक चुनौतियों का सामना करना पड़ता है। ऐसे में जनरेटिव AI एक सहायक एवं सशक्त उपकरण के रूप में उभरकर सामने आया है। यह शोधपत्र जनरेटिव AI द्वारा सशक्त पाठ योजना एवं शिक्षण रणनीतियों का समग्र विश्लेषण प्रस्तुत करता है। अध्ययन का उद्देश्य यह समझना है कि किस प्रकार AI आधारित उपकरण शिक्षकों को पाठ उद्देश्यों के निर्धारण, शिक्षण सामग्री के सृजन, गतिविधि-आधारित अधिगम, वैयक्तिक शिक्षण तथा सतत मूल्यांकन में सहायता प्रदान करते हैं। जनरेटिव AI शिक्षकों को विभिन्न प्रकार की शिक्षण सामग्री—जैसे कार्यपत्रक, प्रश्नोत्तरी, परियोजना कार्य, केस स्टडी तथा डिजिटल संसाधन—तैयार करने में समर्थ बनाता है, जिससे कक्षा शिक्षण अधिक रोचक और सहभागितापूर्ण हो जाता है। शोधपत्र में यह भी स्पष्ट किया गया है कि जनरेटिव AI शिक्षण रणनीतियों को नवाचारी स्वरूप प्रदान करता है। सहयोगात्मक अधिगम, समस्या-समाधान आधारित शिक्षण, अनुभवात्मक अधिगम तथा मिश्रित शिक्षण (Blended Learning) जैसी विधियों को AI के माध्यम से अधिक प्रभावी ढंग से लागू किया जा सकता है। यह तकनीक विद्यार्थियों की आलोचनात्मक सोच, रचनात्मकता एवं डिजिटल दक्षताओं के विकास में सहायक सिद्ध होती है। राष्ट्रीय शिक्षा नीति 2020 के संदर्भ में जनरेटिव AI का विशेष महत्व है, क्योंकि यह नीति तकनीक-समर्थ, कौशल-आधारित एवं अनुभवात्मक अधिगम पर बल देती है। जनरेटिव AI इन उद्देश्यों की प्राप्ति में एक सशक्त माध्यम बन सकता है, बशर्ते इसका उपयोग संतुलित एवं नैतिक रूप से किया जाए। हालाँकि, जनरेटिव AI के उपयोग से संबंधित कुछ चुनौतियाँ भी सामने आती हैं, जैसे डेटा गोपनीयता, तकनीकी निर्भरता, नैतिकता तथा शिक्षकों में डिजिटल दक्षता का अभाव। शोधपत्र में इन चुनौतियों के समाधान हेतु प्रशिक्षण, नीति-निर्देशों तथा मानव-तकनीक के संतुलित समन्वय पर बल दिया गया है। अतः यह अध्ययन दर्शाता है कि जनरेटिव आर्टिफ़िशियल इंटेलिजेंस शिक्षक सशक्तिकरण का एक प्रभावी साधन है, जो शिक्षण-अधिगम प्रक्रिया को आधुनिक, समावेशी एवं गुणवत्तापूर्ण बना सकता है।

The growing demand for Heterogeneous Ad hoc Networks (HANETs) certainly makes the QoS parameters of paramount importance within the HANET, and consideration for secure routing protocols further grows as interconnected devices within a smart network are put onto the same platform. Different devices related to the Internet of Things and others have aroused important needs at the same time concerning performance and secure measures for the proper and effective transition of data within HANETs. However, existing models are quite inefficient in accomplishing the requirements of multi-facets QoS and secure routing, hence proving to be a failure and vulnerability under various scenarios. Current approaches tend to inadequately deal with dynamic network conditions, inadequately scale up, and inadequately adapt to emerging security threats. However, these shortcomings ease the running of smart network networks smoothly and expose it to potential security risk, which ultimately reduces user trust and utility to smart network technologies. The paper outlines a comprehensive review related models to enhance QOS parameters and secure routing protocols leading to the advancement of HANETS. On one hand, the review will consider such methods in-depth: multipath routing protocols, fuzzy logic-based QoS models, and machine learning-based security protocols. Each method shall be cruised through the operational principles, its effectiveness in the enhancement of network performance, and its capability to reaffirm security. Multipath Routing Protocols: Further explored to see how it can be effective in enhancing reliability, reducing latency through multiple path data delivery; Case in point-AOMDV Models of QoS Using Fuzzy Logic: These are viewed for the nature of their decisions with adaptability that assures that all resources are optimised for creating dynamic changes in the network leading to very high QoS. Additionally, these Security Protocols based on ML are checked for the advanced features about threat detection and mitigation that they own, while using real-time data analysis in pre-emption and neutralization of security threats. Some of the benefits that these models carry along as well include better network reliability, reduced latency level, and more adaptability to changes in network conditions, and security frameworks. Through a careful consideration of such methods, this review draws out potential ways through which the present mismatch experienced between nee

In the age of growing cyber threats and digital vulnerabilities, organizations must adopt proactive strategies to safeguard sensitive data. Ethical hacking—particularly white hat techniques—plays a critical role in identifying security weaknesses before they can be exploited by malicious actors. This paper explores the strategic implementation of white hat hacking as a defense mechanism, reviews the most commonly used tools and techniques, and analyzes the legal, ethical, and organizational considerations surrounding its use. A case-based methodology is used to assess how organizations across various sectors implement ethical hacking as part of their cybersecurity framework.

Natural disasters such as avalanches and landslides pose significant threats to human life, infrastructure, and the environment, especially in mountainous regions. Traditional early warning systems often fail due to harsh terrain, unpredictable weather, and lack of real-time monitoring. This research proposes a Rapid Response Wireless Sensor Network (WSN) Framework for early detection and alert generation of such disasters. The framework incorporates multi-parametric sensing (soil moisture, vibration, pressure, and acoustic signals), real-time data transmission, edge-based processing, and automated alerting systems. Simulations using NS-3 and hardware validation on a scaled terrain model demonstrate that the system can detect disaster-prone triggers with 92% accuracy and deliver alerts within 8 seconds. The proposed model enhances disaster preparedness, ensures fast response, and supports local administration in risk-prone areas.

This paper introduces a Hybrid VGG16 model leveraging transfer learning to improve the accuracy of emotion detection in online learners. By freezing the initial layers of the VGG16 model and adding custom convolutional layers, the model is fine-tuned to effectively detect emotions, significantly outperforming traditional convolutional models in terms of accuracy and other evaluation metrics. The enhanced emotion detection system facilitates personalized learning by providing real-time emotional

Context: This research paper adds to existing literature that it is essential to become aware of the cons and pros of AI, before applying it in any field. Artificial intelligence is used in scientific research, social research and education. Stakeholders are generally unaware of the proper applications and limitations of artificial intelligence. AI has advantages like high accuracy, speed and convenience along with drawbacks like intermixing and fabricated content. Objectives: 1. To study the level of artificial intelligence awareness of stakeholders of higher education institutions of Sirsa District. 2. To study the significance of differences in mean scores of artificial intelligence awareness of stakeholders of higher education institutions of Sirsa District based on demographic variables. Methodology: Descriptive research was used as a survey by investigators through a self-developed Google form. A sample of 131 stakeholders was selected through a purposive sampling from Sirsa District of Haryana state. Both qualitative and quantitative data were used in this study. Statistical techniques- p-p plots, K-S test, Shapiro-wilk test, Percentage analysis, Mean, S.D., t- test and ANOVA one-way were applied in this study to compute results. Results: It was found that 25.95% (N = 34), 45.80% (N = 60) and 28.24% (N = 37) stakeholders of higher education institutions of Sirsa District had respectively a below average, average and above average level of artificial intelligence awareness. Qualitative analysis suggested that stakeholders of higher education institutions of Sirsa District had different levels of artificial intelligence awareness that depended on their personal perceptions. The quantitative analysis suggested that there were no significant differences in the artificial intelligence awareness of stakeholders of higher education institutions of Sirsa District based on demographic variables. These results are useful for higher educational institutions to improve artificial intelligence awareness among stakeholders of their institutions.

The National Education Policy (NEP) 2020 marks a transformative shift in India's educational landscape, emphasizing the integration of technology, particularly Artificial Intelligence (AI), to enhance learning outcomes and bridge educational disparities. This paper explores the opportunities and challenges associated with embedding AI within the NEP 2020 framework, drawing insights from recent literature and studies. While AI offers personalized learning, efficient administrative processes, and improved accessibility, it also presents challenges such as data privacy concerns, infrastructural limitations, and the need for teacher training. A comprehensive understanding of these facets is crucial for the successful implementation of AI in India's education system.

Artificial Intelligence (AI) has revolutionized cybersecurity, enhancing threat detection, fraud prevention, and data protection mechanisms. However, the same technology that fortifies digital security also presents new vulnerabilities and threats. AI-driven cybercrime, such as deepfake frauds, automated hacking, and privacy intrusions, has emerged as a significant challenge. This paper explores the dual role of AI in cybersecurity, analyzing its benefits and potential threats. Furthermore, it evaluates current regulatory frameworks and suggests strategies for mitigating AI-driven cyber risks while maximizing its protective capabilities.

The study investigates the relationship between perceived service quality and customer satisfaction among digital printing businesses in Santiago City. The relationship between the demographic profile and customer satisfaction was also examined. This cross-sectional study is based on primary and secondary data. A sample of 369 respondents who availed of the service at least once from the various digital printing businesses in Santiago City participated in the study. A non-probability sampling te

Antiepileptic drugs (AEDs) play a crucial role in treating epilepsy, with expected variability in their pharmacokinetics across diverse patient groups such as children, the elderly, pregnant individuals, and those undergoing polytherapy with potential drug interactions. Additionally, ensuring patient adherence is essential. Therapeutic drug monitoring (TDM) serves as a valuable tool for maintaining treatment quality. Given the prevalence of pharmacokinetic variability among AEDs, TDM enables a p

The advent of Artificial Intelligence (AI) has significantly transformed the teaching and learning landscape. AI-powered tools and techniques offer personalized learning experiences, automate routine tasks, and provide new opportunities for enhancing educational outcomes. This paper explores the applications, benefits, challenges, and future prospects of AI in education. Drawing upon recent studies and technological advancements, the paper highlights how AI-driven systems can foster more effective, engaging, and inclusive learning environments.

The radical expansion of big data produced by digital, enterprise and sensor based environments has made data exploration projects more complex and expensive to run. Conventional retrieval pipelines that are based on fixed indexing and static caching policies tend not to respond to the changing workload, which leads to poor performance and resource underutilization. This paper introduces a conceptual design of a cost-conscious retrieval pipeline combining an adaptive index selection and workload-conscious caching as part of a single cost-optimization framework. The effectiveness of the proposed design was evaluated based on a simulated experimental set up with 90 different query workloads. The descriptive and percentage-based analysis indicated improvement in query latency, 81.11% had realized a calculational cost reduction, and 63.33% had increased cache efficiency, with 62.22% of the system judged to be effective or highly effective on the whole. The results suggest that cost-conscious optimization implemented in a holistic fashion, both in indexing and caching, can yield improvements in technical performance and cost-efficiency, and that adaptive self-optimizing retrieval architectures have the potential to enable scalable and economically sustainable data exploration on a large scale.

Shrimp farming is a rapidly expanding component of global aquaculture, contributing significantly to food security, rural livelihoods, and export earnings. However, the sector faces persistent challenges, including water quality fluctuations, disease outbreaks, feed inefficiency, environmental degradation, and market volatility. This study examines the transformative role of Artificial Intelligence (AI) in enhancing productivity, profitability, and sustainability in shrimp farming systems. AI-driven technologies such as machine learning, IoT-based sensors, computer vision, and predictive analytics enable real-time monitoring of water parameters, optimized feeding strategies, early disease detection, biomass estimation, and automated farm management. Comparative analysis indicates substantial improvements in production growth and feed conversion efficiency under AI-integrated systems. Furthermore, AI supports climate-resilient aquaculture through adaptive management and resource optimization. Despite its potential, adoption remains constrained by high investment costs, limited technical expertise, and infrastructural gaps. Overall, AI-based shrimp farming represents a significant shift toward precision and sustainable aquaculture development.

The necessity for effective and scalable data retrieval systems to support high-performance analytics has increased due to the big data's explosive expansion from various digital sources. The complexity, volume, and dynamic nature of large-scale data environments are typically too much for traditional data retrieval techniques, which rely on static indexing and rule-based query optimization. The application of artificial intelligence-based intelligent data retrieval procedures to improve big data analytics performance is investigated experimentally in this paper. To provide flexible, context-aware, and effective data access, the suggested framework combines machine learning, deep learning, natural language processing, and reinforcement learning techniques. Metrics including query response time, retrieval accuracy, scalability, and resource consumption are used in comparative analysis to assess system performance. The findings show that by lowering latency, increasing data relevance, and optimizing computational resources, AI-driven retrieval techniques greatly increase analytics efficiency. The study highlights the potential of intelligent data retrieval frameworks as a fundamental element of next-generation big data analytics systems, especially in settings that need high scalability and real-time processing.

The increasing presence of Artificial Intelligence (AI) in higher education has transformed pedagogical practices in language and literature classrooms. While conceptual scholarship highlights its potential for enhancing interpretation and communication, empirical classroom-based perspectives remain limited. This study integrates theoretical analysis with student perception data to evaluate AI’s role in literary learning. A survey of thirty postgraduate literature students was conducted to examine AI’s impact on interpretation, critical thinking, writing development, classroom participation, and ethical awareness. Findings reveal strong student support for AI as a tool for contextual understanding and vocabulary development, alongside concerns regarding overdependence and originality. The study concludes that AI functions most effectively as a cognitive scaffold within human-guided pedagogy rather than as a replacement for traditional literary engagement.

Cloud computing's quick uptake has completely changed how businesses provide services and manage IT resources. This study looks at how cost-effective cloud computing frameworks can improve organizational performance and maximize resource use. The study examines how managerial staff and IT experts see cloud adoption, pricing structures, scalability, and automation systems using a fictitious descriptive and analytical research design. Structured questionnaires are used to supposedly collect data, which are then evaluated using frequency and percentage methods. According to the research, companies that use cost-effective cloud frameworks benefit from more operational flexibility, better resource management, and notable cost savings—all of which add up to superior organizational performance. The paper emphasizes that optimizing these advantages requires smart implementation and efficient cloud governance. The findings highlight the significance of implementing affordable cloud technologies as a tactical instrument for long-term business expansion and a competitive edge.

The advancement of artificial intelligence has brought both opportunities and challenges to the business world, and its potentially disruptive impact has attracted the research interest of management scholars. This exploratory research applied a systematic literature review approach to explore the nexus between AI and competences to help both firms and individuals better address the disruptions from AI. After reviewing relevant publications from the Business Source Complete database for the past decade (2011-2021), we selected 65 debates and issues on AI and perspectives linked with competences. Furthermore, we synthesize two frameworks (RBV framework for firm-level; Key and STEM competences for individual-level) and an overview to gain a holistic understanding of the nexus between AI and competences. We found relatively little empirical evidence in the literature, the implementation of AI was still in its preliminary stages, and the frameworks we aggregated industry and yield richer insights.

Parental engagement plays a vital role in enhancing students’ academic performance, motivation, and overall development. With the increasing shift toward digital and home-based learning, especially after the COVID-19 pandemic, parents are required to take a more active role in their children’s education. Artificial Intelligence (AI) has emerged as a powerful tool to support and strengthen parental involvement in home learning environments. This research paper explores how AI-based educational technologies can increase parental engagement by providing personalized learning support, real-time feedback, learning analytics, and collaborative learning opportunities. The study reviews existing literature on AI-mediated family learning, discusses benefits and challenges, and highlights how AI can foster effective parent-child collaboration. The paper concludes that while AI has significant potential to enhance parental engagement, careful implementation, digital literacy, and ethical considerations are essential for successful outcomes.

As higher education institutions prepare students for the workforce, so the emergence of generative AI has caused a major dilemma. In the Present scenario, development of digital skills must become a normative aim in students, while simultaneously preserving academic integrity and credibility. The challenges, which are facing by them, is not simply a matter of using AI responsibly because of typically of reconciling i.e. preparing students for the future of work and maintaining the traditional role of developing personal academic skills. Because of critical thinking, the ability to acquire knowledge and the capacity to produce original work. Objectives must be typically balanced in Higher education institutions while addressing financial considerations, creating value for students and employers, and meeting accreditation requirements. There are multiple-case study of fifty universities across eight countries examined institutional response to generative AI. As proposed actions varied widely, the content analysis revealed apparent confusion and a lack of established best practices, from complete bans on generated content to the development of custom AI assistants for students and faculty. We concluded that timely innovation will be required for the apparent confusion of higher education institutions and suggest every possible approaches to that. However our results will suggest that their top concern now is the potential for irresponsible use of AI by students to cheat on assessments. In the short term and long term, We, therefore, recommend that, the credibility of awards is urgently safeguarded and argue that this could be achieved by ensuring at least some human-proctored assessments are integrated into courses, e.g., in the form of real-location examinations and viva voces.

Artificial intelligence (AI) and drone technologies are driving a significant shift in agriculture. Precision agriculture has become more popular due to the growing need for food worldwide, the effects of climate change, the reduction of productive land, and the requirement for sustainable farming methods. In agricultural systems, real-time monitoring, data-driven decision-making, and optimal resource management are made possible by AI-powered analytics in conjunction with unmanned aerial vehicles (UAVs), commonly known as drones. By enhancing crop productivity, lowering input costs, and minimising environmental effect, this research paper investigates how Artificial Intelligence (AI) and drone technology might support smart and sustainable agriculture.The study investigates applications include crop health monitoring, yield prediction, soil analysis, precision spraying, irrigation management, insect detection, and climate adaption startegies.The challenges it evaluates include high implementation costs, data privacy concerns, technical skill gaps, legislative barriers, and infrastructure limitations, especially in developing countries. AI-integrated drones greatly increase operational efficiency and sustainability parameters, according to case studies and recent technology developments.

Artificial Intelligence is now a potent instrument for transforming the educational system in our digital world. It has significantly influenced teaching methods, learning processes, and professional development practices. The National Education Policy (NEP) 2020 has substantially promoted the integration of technologies like artificial intelligence in order to ameliorate the norms of school teacher According to NEP 2020, this review article examines the effects of artificial intelligence on teacher training and professional growth. It examines how advancements in virtual classrooms, learning analytics, intelligent tutoring systems, adaptive learning platforms, and AI-based technologies have transformed teaching strategies and educational methodologies. The study also shows how AI facilitates personalized knowledge, nonstop evaluation, and the ongoing professional development of preceptors. The research also examines the major advantages, challenges, and constraints of using artificial intelligence in teacher training schools. Topics covered include data security, digital literacy, ethical concerns, resistance to technological progress, and a lack of sufficient technological infrastructure. The review concludes that there's a large eventuality for artificial intelligence to ameliorate school teacher education and professional development programs. Still, careful drug, respectable training, and ethical execution are still necessary for long- continuing and successful issues.

This study explored how Natural Language Processing (NLP) can be used in predictive analytics to understand consumer behavior and market trends using consumer generated text data on a massive scale through online reviews, comments and customer reactions on social media. The study followed a mixed-methods design, which involved the use of NLP algorithms such as sentiment analysis, topic modelling, opinion mining and word-embedding to predict behavioural predictors such as purchase intention and market sentiment by using machine learning algorithms like Logistic Regression, Support Vector Machine, Random Forest, and Neural Networks. The results showed that NLP was effective in extracting meaningful information out of unstructured text which identified the most important themes in the consumers mind such as product quality, price perception, service experience, brand trust and convenience in delivery. The superior forecasting accuracy was found in advanced predictive models, specifically the Neural Networks and ensemble-based models, and the sentiment scores were found to have a strong positive correlation with the purchase probability. On the whole, the paper has emphasized the importance of NLP-based predictive analytics to boost successfully marketing intelligence and make organizations anticipate consumer trends, guide data-driven decisions, and stem competitive power in the online market.

This paper explores the role of Microsoft Power BI in the field of Business Intelligence (BI). With its integration of advanced data analytics, real-time insights, and ease of use, Power BI has become a transformative tool for businesses seeking to enhance their decision-making processes. The research highlights the features, benefits, challenges, and real-world applications of Power BI in various industries, examining its effectiveness compared to traditional BI tools and its future potential.

This research examines the roles of cloud service providers, businesses, and end users in relation to the adoption, effectiveness, and challenges associated with various data privacy and accountability strategies in cloud computing. The study emphasizes key strategies that demonstrate significant acceptance and effectiveness, such as encryption, multi-factor authentication, and adherence to regulations, based on a structured survey involving 250 participants. However, newer methods, particularly AI-driven privacy tools, encounter considerable challenges due to their technical intricacies. The findings indicate that end users play a vital role in adhering to best practices, while cloud service providers hold the primary responsibility for ensuring security and compliance. Additionally, the research outlines how liability and privacy concerns are distributed among stakeholders. It underscores the importance of robust frameworks and continuous improvements in cloud security and privacy measures.

The digital revolution has fundamentally transformed educational paradigms, creating both unprecedented opportunities and significant challenges. This paper examines the comprehensive impact of e-learning and digital transformation in education, analyzing pedagogical shifts, technological advancements, and socioeconomic implications. Through an extensive literature review, we explore the evolution of digital learning platforms, their comparative effectiveness with traditional methods, and existing adoption barriers. The study highlights key innovations including adaptive learning systems, immersive technologies, and artificial intelligence applications while addressing critical issues of digital equity, data privacy, and pedagogical adaptation. Our findings indicate that while digital education offers substantial potential for personalized learning experiences, its successful implementation requires coordinated efforts in infrastructure development, educator training, and policy reform. The paper concludes with actionable recommendations for developing equitable and effective digital learning ecosystems that can bridge current educational disparities.

Artificial Intelligence (AI) has transitioned from a theoretical concept into a groundbreaking force that is reshaping industries and enhancing human potential. This paper explores AI’s transformative journey, starting from its role in automating repetitive tasks to its present-day capacity for fostering autonomy in complex systems. With advancements in AI, machines can now go beyond executing programmed instructions—they can make decisions, learn from experience, and adapt to new situations, thus broadening their capabilities. The progression from automation to autonomy is not just a technological shift but a societal one, deeply impacting fields such as healthcare, transportation, education, and entertainment. Autonomous systems like self-driving cars and virtual assistants illustrate how AI can minimize human involvement, boost efficiency, and improve accuracy in decision-making. However, this leap forward introduces significant challenges, including ethical concerns, privacy risks, and the demand for well-structured regulations. This paper highlights the technological breakthroughs driving this evolution, including machine learning, deep learning, and natural language processing. These tools empower AI to process enormous amounts of data, identify patterns, and deliver insights that were previously unattainable. Furthermore, the discussion extends to AI’s potential in fostering interdisciplinary solutions and tackling global issues like climate change and pandemic preparedness. Central to this exploration is the need for a balanced approach to innovation and accountability. While AI offers unparalleled opportunities to redefine the future, its development must remain mindful of its societal and ethical impacts. Questions surrounding trust, transparency, and responsibility are critical to ensuring that AI serves humanity’s best interests. In summary, this paper offers a detailed examination of AI’s shift from automation to autonomy, emphasizing its growing influence and the importance of guiding its development responsibly. It sheds light on how AI can create a smarter, more efficient, and ethically sustainable future for all.

Deepfake images have become a serious problem. With tools like GANs and diffusion models now widely available, creating fake but convincing face images is no longer limited to experts. Most detection systems today look at low-level pixel patterns — blurring, noise, color mismatch. But the core problem is: train on one dataset, test on another, and accuracy collapses. The model memorized noise patterns, not manipulation itself. This paper proposes Semantic Feature Fusion (SFF) — a framework that goes beyond pixels and looks at meaning-level cues: facial geometry, identity coherence, scene lighting consistency, and expression analysis. Tested on FaceForensics++, Celeb-DF, DFDC, and WildDeepfake without cross-dataset fine-tuning, our model outperforms pixel-based baselines significantly on unseen datasets.

AI has transformed language learning, literature engagement, and communication skill development in India. Tools like chatbots, virtual tutors, and NLP systems provide personalized feedback and interactive learning, enhancing vocabulary, grammar, and communication. AI enables interactive analysis, content creation, and multilingual access in literature, which deepens critical thinking. Supporting regional languages, AI broadens inclusive education and digital literacy. Adaptive AI learning fosters autonomy, motivation, and creativity. AI enriches language learning, deepens literary understanding, and builds communication skills for diverse contexts.

The rapid advancement of Artificial Intelligence (AI) has revolutionized communication technologies, enabling real-time language translation across diverse cultural and linguistic landscapes. With the integration of Natural Language Processing (NLP), Neural Machine Translation (NMT), and deep learning algorithms, AI-powered translation tools have become essential in breaking language barriers that traditionally hindered global collaboration. This paper examines the technological foundations, real-world applications, societal impacts, challenges, and future directions of real-time AI translation in fostering cross-cultural communication, international business, education, and diplomacy.

Software reliability is a critical aspect of ensuring the safe and efficient operation of various systems, including safety-critical applications like interlocking software in railway control systems. This research paper proposes a novel Software Reliability Prediction Model (SRPM) specifically designed for interlocking software to improve system reliability. The model incorporates various factors such as software complexity, fault density, and operational profile, leveraging historical data and

The rapid advancement of Artificial Intelligence (AI) is transforming the global workforce and redefining the nature of skills required for future employment. Traditional education and training systems are increasingly challenged to keep pace with technological change, automation, and evolving job roles. This paper explores the role of AI in enhancing skill development and preparing learners for future job readiness in a dynamic and technology-driven economy. AI-powered tools such as adaptive learning platforms, intelligent tutoring systems, virtual simulations, and data-driven career guidance are enabling personalized, flexible, and efficient learning experiences. These technologies help learners acquire both technical skills, such as digital literacy and data analysis, as well as essential soft skills, including problem-solving, critical thinking, creativity, and collaboration. Furthermore, AI assists educators and institutions in identifying skill gaps, predicting labor market trends, and aligning curricula with industry needs. The study also highlights the importance of ethical AI use, inclusivity, and human–AI collaboration to ensure equitable access to skill development opportunities. By integrating AI into education and training frameworks, institutions can bridge the gap between academic learning and real-world job requirements. The paper concludes that AI, when strategically implemented, has the potential to empower individuals, enhance employability, and foster lifelong learning, thereby contributing significantly to future job readiness and sustainable workforce development.

The rapid growth of digital commerce has generated massive volumes of structured, semi-structured, and unstructured data from transactions, user interactions, social media, and IoT-enabled devices. Traditional data processing systems are inadequate for handling the scale, velocity, and complexity of such data. Cloud computing has emerged as a scalable, flexible, and cost-effective solution for big data management and predictive analytics in e-commerce environments. This paper presents a comprehensive study of cloud-based architectures for big data processing, explores predictive analytics techniques used in e-commerce, and proposes a layered cloud framework integrating distributed storage, real-time analytics, and machine learning models. The study discusses implementation using platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. Furthermore, a case study inspired by global e-commerce platforms such as Amazon and Alibaba Group is examined to demonstrate scalability, predictive modeling, and operational optimization. The paper concludes with challenges, security considerations, and future research directions in cloud-enabled intelligent e-commerce systems.

Artificial intelligence is changing the face of the financial sector; more precisely, in stock valuation and decision-making. Previously, stock valuation depended on the approach of doing things manually, by intuition, and by complex mathematical models, among others. AI changed the rule of the game. By handling vast amounts of data and identifying intricate patterns, AI has improved the decision-making process of investors, analysts, and financial institutions. This review explores the use of artificial intelligence in stock valuations, particularly in terms of its impacts on price forecasts, risk estimation, and portfolio management.

Video streaming platforms generate massive volumes of user interaction data every minute in the form of views, likes, comments, watch time, and sharing behavior. Predicting video engagement in real time has become essential for recommendation systems, advertisement planning, and content optimization. However, centralized data collection raises privacy concerns and increases computational load. This study proposes a hierarchical federated deep learning framework for real-time video engagement forecasting using large-scale user feedback distributed across multiple edge nodes. The model integrates local sentiment representations, behavioral metrics, and temporal viewing patterns without transferring raw user data to a central server. Experimental analysis shows improved prediction stability, reduced communication overhead, and enhanced generalization across heterogeneous user groups. The proposed approach demonstrates that hierarchical aggregation improves forecasting accuracy while maintaining user privacy.

"Web usage mining" refers to the process of analysing the patterns of user access to web servers. This article is written in response to questions about the limitations of data mining. Our primary focus will be on the many approaches that may be taken to obtain information that is kept on the internet via the use of these technologies. Additionally, we discussed cloud mining, which is a method that involves the utilisation of cloud computing in order to gather information from the internet. The

Artificial Intelligence has moved from the margins to the mainstream of educational discourse, promising personalized learning, efficient assessment, and data-driven decision-making. Yet beneath these promises lies a more fundamental transformation—one that alters how knowledge itself is organized, interpreted, and transmitted. This paper moves beyond instrumental evaluations of AI in education to examine it as a structural phenomenon that reshapes the underlying architecture of learning. Drawing on the linguistic structuralism of Ferdinand de Saussure, the anthropological frameworks of Claude Lévi-Strauss, and the knowledge-power analysis of Michel Foucault, the study investigates how AI systems function as coded meaning-making apparatuses rather than neutral tools. Through this lens, AI emerges as a force that reconfigures educational authority, curriculum design, assessment logic, and student subjectivity. The paper examines both the transformative possibilities—democratized access, adaptive learning pathways, and multimodal knowledge representation—and the structural risks embedded within algorithmic systems, including epistemic standardization, categorical reduction, and the reinforcement of existing inequalities through biased data architectures. It concludes by advocating for reflexive, transparent, and ethically grounded AI systems that preserve interpretive diversity while harnessing computational capabilities. By proposing a structuralist framework for understanding AI-mediated pedagogy, the study highlights how algorithmic systems are reshaping the epistemological foundations of contemporary education.

The study has examined an improved Transmission Interval-based Multipath (TIMIP) protocol geared towards reducing latency in applications focused on real time communication, such as video conferencing and autonomous systems. The improved TIMIP remains fundamentally consistent with the original TIMIP, but has been augmented with adaptive adjustments to the transmission interval, packet scheduling based upon priorities, and intelligent or smart path selection to enhance the ability to tolerate dynamic network conditions and heavy traffic. Simulation results over a wider range of network congestion and node densities tested show that there was approximately a 22-23% reduction in end-to-end latency using the improved TIMIP. Furthermore, the improvements with the latency using the improved TIMIP were achieved without sacrificing throughput or packet delivery ratio, both of which demonstrated moderate improvements while still providing reliable and efficient data transmission. Performance comparisons with other commonly used protocols demonstrated that the improved TIMIP provided the best opportunity to achieve low latency, while maintaining a high level of performance in the network. The application of the improved TIMIP protocol demonstrates a viable, scalable alternative that can be utilized in latency sensitive environments. The results relative to the improved TIMIP protocol suggest that this research represents significant potential for the next generation of communication infrastructures that require fast and reliable multipath data transmission.

Charitable organizations often face issues of transparency, trust, and accountability in the management and distribution of donated funds. Traditional systems lack the capability to provide real-time verification of transactions and ensure that donations reach the intended beneficiaries. This paper proposes a blockchain-based charity tracking system that ensures transparency, traceability, and immutability of all transactions involved in the donation process. By leveraging smart contracts and decentralized ledgers, donors can verify how their contributions are utilized, while NGOs and intermediaries are held accountable through automated and tamper-proof records. The proposed model improves public confidence, minimizes fraud, and promotes efficient utilization of charitable resources.

The increasing integration of Artificial Intelligence (AI) into higher education has created both opportunities and tensions, particularly in disciplines rooted in interpretation, critical reflection, and independent thinking. English studies, traditionally centered on close reading, analytical reasoning, and creative engagement, now encounters AI tools capable of generating essays, summaries, and interpretations. This shift raises important pedagogical questions about authorship, originality, and the very nature of intellectual development in the humanities. This paper adopts an interdisciplinary perspective, bringing together insights from English education and computer science to examine the evolving role of AI in literary pedagogy. It explores how AI tools can support learning processes while also posing significant risks to critical thinking, academic integrity, and the cultivation of a personal voice. From a literary standpoint, concerns arise regarding students’ potential dependence on AI for interpretative work, which can circumvent the essential struggle with a text that leads to genuine understanding. From a computational perspective, understanding the functioning, limitations, and inherent biases of large language models becomes essential for their ethical and effective use. The paper argues that deep collaboration between English and computer education can lead to a balanced pedagogical framework. Such a framework emphasizes AI literacy, critical engagement with AI-generated outputs, and a pedagogy of responsible use. By integrating disciplinary strengths, educators can transform AI from a potential threat to academic rigor into a meaningful tool that enhances reflective learning and prepares students for a complex, technologically mediated world. The study contributes to broader discussions on the future of humanities education and the necessity of interdisciplinary approaches in a technologically driven academic environment.