The startup ecosystem plays a crucial role in fostering innovation, employment generation, and economic development. In recent years, governments, educational institutions have undertaken several initiatives to promote entrepreneurship among students. In India, various government initiatives such as Startup India, make in India, and Atal Innovation Mission have played a crucial role in promoting entrepreneurial activities among young individuals. Despite these efforts, the level of awareness and perception of the startup ecosystem among students varies significantly. This study aims to examine the level of awareness and perception of students regarding the startup ecosystem and government initiatives for the startups. The findings of the study are expected to provide insights into students’ entrepreneurial mindset, awareness of startup policies, and perceived challenges and opportunities within the startup ecosystem.

Artificial Intelligence (AI) has seen great adoption in marketing and brand communication which in turn has changed how companies interact with consumers. While AI brings to the table personalization, automation, and enhanced customer engagement, it also brings in issues of brand safety and consumer trust. Many consumers are still very much at arm’s length with what is put forth by AI in terms of issues like generation of false content, spread of misinformation, algorithmic bias, exaggerated AI claims (AI washing) and also due to the lack of regulatory structure. This study aims to explore the effect of the use of AI on brand safety and consumer trust from the viewpoint of the target respondents. Primary data were collected using a structured questionnaire and were distributed to respondents that routinely engage with AI-integrated platforms. This study focuses on how the ethical use of AI, along with transparency and responsible communication, affects consumer trust and perceptions of brands. AI reports that we see that while there is a great chance for AI to improve brand value it also puts at risk trust and brand image with poor or unethical use. Transparence, ethical conduct and proper regulation is key in to0ting down brand safety issues and in building up consumer confidence. The study also brings to light the issue of companies’ role in putting in place responsible AI practices which is vital to its long term customer trust and protection of brand reputation in the growing digital space.

The ARIA transformer architecture is revealed as a viable solution to the Common Intent Orchestration (CIO) framework, addressing the stability and causality issues of multi-agent systems. The use of multi-stream sigmoid attention models, while enabling long-context tasks, is often marred by problems such as gradient instability and autoregressive violations. The ARIA model overcomes all 18 critical failures of such approaches through a set of six strategic innovations, including L2 Normalised Sigmoid Attention, Content Relative RoPE, and Lagged State Memory. The use of Entropy Variance Monitors for parallel repair and Precision Anchor Tokens ensures structural stability without incurring computational bloat. Complexity analysis of the model indicates a highly efficient time complexity of O(N^1.2) and a moderate 1.3x KV cache overhead. The ARIA model meets all ten architectural requirements for a reliable agentic orchestration, a major leap in the field of high-stability large-scale language modeling.

Cold start latency is a well‑known bottleneck in serverless computing, often causing noticeable delays when functions are invoked after periods of inactivity. In multi‑cloud environments, differences in architecture, resource management, and workload behaviour introduce additional complexity. An experimental study on predicting cold start events and comparing performance across two widely used serverless platforms: Microsoft Azure Functions and Google Cloud Platform Cloud Functions. Large‑scale execution datasets are collected independently from both platforms and used to train and evaluate the models within their respective cloud environments to detect cold start events based on latency percentiles, request arrival patterns, memory usage, and temporal features to check cross cloud cold start detection the models were trained and tested separately on datasets from Azure-2019 and Google Cloud Functions-cold start dataset. The results show that both the platform Azure and GCP have different features, so in the experiment, both datasets were trained and tested with their respective cloud environment, and the models used were Logistic Regression, XGBoost, and Random Forest classifiers. These models perform accurately in a cloud environment. But cross-cloud detection of cold start using machine learning performs poorly due to different architectures and features. The research suggests that cross-cloud cold start detection can be done by collecting real world dataset. In both the experiment, XGBoost and Random Forest out performs the Logistic Regression. Taken together, the findings emphasize the strength of tree‑based models in capturing complex, non‑linear performance dynamics that linear techniques are unable to represent effectively.

Institutional funding is seen to play an essential role in ensuring the operational effectiveness and sustainability of Non-Governmental Organizations (NGOs). As development organizations, these institutions are largely dependent on institutional funding systems to ensure the successful execution of development activities and sustain themselves. This research aims to identify the role of institutional financial sources, including government institutions, development finance institutions, and donor organizations, in enhancing the operational effectiveness of these organizations. It also explores the various financial assistance schemes, including grants, subsidies, project financing, and technical assistance, and their overall impact on operational effectiveness. This research is based on primary and secondary data, including literature, policy guidelines, and publications from relevant institutions. Based on the research, institutional financial assistance at critical times is seen to enhance accountability, operational effectiveness, and sustainability of the organizations. It also emphasizes the need for better coordination of financial institutions and the adoption of sound financial management practices to ensure greater operational effectiveness.

The quick rise of Generative AI is sparking a rapid rise in hyperscale data centers. The extreme carbon impact associated with these facilities is being studied in great detail, however, very little attention has been given to the extreme amount of freshwater used in evaporative cooling. Current hardware-focused solutions like mechanical liquid cooling can be very expensive and shift a large portion of the carbon footprint toward also generating a large amount of electricity. The Eco-Adaptive Water Brain Framework being discussed in this paper is an innovative software based architecture that will drastically reduce water use by designing solutions at the OS level and at the routing level. This framework will utilize spatio-temporal workload scheduling to continuously evaluate the local server's water stressed condition. The framework will route active urgent queries to energy efficient models Low Water Mode while heavy non urgent training workloads will be queued to run later at night when evaporative cooling requirements are naturally reduced with cooler ambient temperatures.The multi layered algorithmic process used in this new framework provides a hardware agnostic and sustainable method that will greatly reduce the water footprint of generative AI

Skill development and employability skills are the core elements of modern education, as they prepare students to pursue subsequent academic and career opportunities. The current study looks at the employability and skill development of students in the seventh through tenth grades at Vidya Classes in the Thane district. The study was carried out as part of a community engagement initiative in which the researcher planned and led sessions on employability and skill development, with an emphasis on digital literacy, leadership, problem-solving, teamwork, and communication. Simple random sampling and a structured questionnaire were used to gather primary data. The impact of demographic characteristics was investigated using ANOVA analysis. The findings indicate that there is no significant difference between age groups in their fundamental comprehension of skill development (p = 0.8565 > 0.05). However, student problems are considerably influenced by their age and gender (p < 0.001). Although it shows a small motivational influence, the function of the school was not found to have a statistically significant impact on students' interest and involvement (p = 0.056). According to descriptive findings, students have a high level of awareness: 93% have heard of employability skills, and 100% strongly think they comprehend skill development. Even though students understand the value of digital, problem-solving, and leadership skills, many report feeling under pressure to perform well academically and participating in few skill-based activities. The study emphasizes how crucial structured skill development interventions are for raising student engagement and improving the use of practical skills.

Digital Twins (DTs) are transforming sustainable development by creating dynamic virtual replicas of physical systems for real-time monitoring, simulation, and intelligent decision- making. This paper integrates four critical pillars—virtual replication, improved decision- making, energy efficiency, and smart infrastructure—to present a unified framework for DT- enabled sustainability. Through a PRISMA systematic review of 55+ studies (2019–2026) and simulated analytical modeling across 100 smart-city nodes, 100 building instances, and 80 buildings with 20 grid zones, the study demonstrates that DTs improve decision accuracy by up to 85%, achieve 20–50% energy savings through modular frameworks, reduce grid transmission losses by 40%, and increase renewable integration to 68% at autonomous intelligence levels. The proposed hierarchical framework integrating IoT edge devices, AI optimization layers, and cloud simulations enables seamless grid-building coordination. Key gaps include limited interoperability, cybersecurity vulnerabilities, and insufficient empirical data from emerging economies. This research contributes to UN SDGs 7, 11, and 12.

This paper analyses the challenges Artificial Intelligence (AI) is helping solve in the field of xenotransplantation. Xenotransplantation is the field of transplanting animal organs into humans. Due to the global shortage of transplantable human organs, xenotransplantation has the potential to save lives. AI performs many of the complex tasks needed to make xenotransplantation a reality, including gene editing via CRISPR-Cas9, real-time monitoring of transplant rejection, remote biosafety screening, and more. Using a descriptive study and exploratory research strategy, this paper analyses secondary data and professional surveys collected from surgeons and pathologists. This paper also presents the findings from the survey study. From this data, the author developed AI risk management models to minimise hyper-acute rejection, which is a severe form of transplant rejection that occurs immediately after the transplant. AI can improve long-term Graft Re-integration (these are “Graft Survival” and “Graft Function” periods) via virtual cross-matching/new predictive modelling. This report establishes that even though many of the participants have expressed that there are many risks associated with AI, the reality is that xenotransplantation may soon (within 10 years) become a primary method of organ replacement surgery. As a result, the practice of organ transplants will have a vastly different focus.

Traditional leather production causes serious environmental problems, including excessive water consumption, harmful chemical treatments, and greenhouse gas emissions, which has increased the need for safer and more sustainable alternatives. Fungal mycelium is a promising option because it is renewable, grows rapidly, and naturally decomposes in the environment. In this study, bio-leather was produced from fungal mycelium cultivated on lignocellulosic agricultural waste, allowing efficient use of biomass while reducing environmental impact. The fungus was grown under controlled temperature and moisture conditions until a thick and uniform mycelial mat was formed. After the substrate was fully colonized, the lignocellulosic agricultural residue was dried, and treated to improve flexibility. The final material had a leather-like texture and showed sufficient strength for light-use applications. Its chemical composition and structural properties were analysed using Fourier Transform Infrared Spectroscopy (FTIR) and X-Ray Diffraction (XRD). FTIR results showed absorption peaks corresponding to chitin, polysaccharides, and proteins in the mycelium structure, while XRD patterns indicated a partially crystalline arrangement related to chitin fibres that support mechanical strength. Antioxidant activity was evaluated using the phosphomolybdenum assay and demonstrated moderate total antioxidant capacity. Protein analysis confirmed the presence of structural proteins that contribute to firmness and binding within the material. Overall, the developed mycelium bio-leather was lightweight, flexible, and environmentally friendly because it avoided toxic tanning processes. Compared to animal-derived and synthetic leather, this material provides a more sustainable production method by using agricultural residues and requiring less energy. The findings suggest that mycelium-based bio-leather can serve as a practical and eco-friendly alternative for accessories, packaging, and biodegradable products, although further improvements in growth conditions and post-processing methods may enhance durability and support large-scale production.

Cloud computing is now an essential element of the modern digital infrastructure. Various applications, including artificial intelligence, big data analytics, e-governance, and global commerce, are supported by it. Cloud technologies can lead to greater operational efficiency and scalability. However, the rapidly expanding cloud ecosystem has brought about several environmental issues. Most prominently, the main concerns are energy consumption and carbon emissions from large-scale data centres. The shift of cloud computing towards a carbon-neutral platform is going to be supported by law and regulatory intervention, rather than just by technology. This is a result of the influence of environmental legislation, climate policies, and the requirements of compliance with environmental, social, and governance (ESG). The present study utilises an interdisciplinary methodology in which engineering solutions for carbon-neutral cloud infrastructure are integrated with the legal and regulatory frameworks of sustainability. The paper presents an extensive review of the carbon footprint of cloud computing, ways to technically cut down on carbon emissions, legal requirements in the leading jurisdictions, contractual responsibilities of cloud service providers, ESG disclosure requirements, and the legal harm of greenwashing. The author maintains that the production of reliable carbon-neutral cloud systems depends on the harmonious collaboration of engineers, lawyers, and policymakers. The author, therefore, puts forward a compliance-driven roadmap that integrates the element of legal responsibility in the design and operational decision-making of cloud architecture..

The expansion of platform-based informal labor markets across the developing and developed world has produced a class of workers whose contractual precarity is structurally embedded rather than incidental. This paper proposes a graph-theoretic framework to model these labor markets as weighted directed graphs in which nodes represent workers, employers, and intermediary platforms, and edges encode asymmetric relationships of economic dependency, information inequality, and contractual power. We introduce the concept of vulnerability propagation — a process by which adverse shocks such as wage theft, sudden platform deactivation, or unsafe working conditions spread through the network via contagion dynamics analogous to epidemic spreading models. Drawing on structural hole theory, betweenness centrality, and percolation thresholds, we argue that exploitation is not a random occurrence but a topologically predictable event concentrated at specific network positions. The framework offers a principled basis for policy interventions that target the network architecture itself rather than individual workers or firms, and we illustrate its applicability through a case study of platform-mediated delivery labor markets in urban India.

Digital wallets have become an important part of modern payment systems, especially among young adults. A digital wallet is a mobile application that allows users to store money and make payments using smartphones. This study focuses on the adoption of digital wallets and the digital payment behaviour of young adults. Young people prefer digital wallets because they are easy to use, save time, and reduce the need to carry cash. The study examines different factors that influence the use of digital wallets, such as convenience, speed, availability of smartphones, internet access, and attractive offers like cashback and discounts. It also considers security features such as passwords, fingerprint locks, and face recognition, which increase user trust in digital payments. At the same time, the study highlights some challenges, including fear of online fraud, data privacy issues, and technical problems like poor network connectivity. Understanding digital payment behaviour is important for banks, businesses, and service providers to improve digital wallet services. The study shows that young adults are the main users of digital wallets due to their familiarity with technology. Overall, the adoption of digital wallets is increasing, and they are expected to play a major role in future cashless payment systems

This study focuses on deep technology that addresses key healthcare and education issues. In healthcare, there are problems of timely or correct diagnosis of a disease, whereas in education, there is a lack of standardization in pedagogy. Biotechnology and genomics aid in precise molecular diagnosis and treatment in healthcare. Artificial intelligence, machine learning, and robots assist in the development of adaptive learning environments for education. In healthcare, there exists a case study where AI helps diagnose cancer at its initial stages. In turn, patient prognosis improves significantly. Khan Academy is one practice example under artificial intelligence and machine learning, which aids in delivering learning materials across the globe, thereby extending learning beyond geographical and financial barriers. These interventions are aimed at increasing the precision and speed of diagnosis in healthcare, as well as skill-based education. In addition, the applications provide evidence of increased access, convenience, and equity in essential public service provision. The findings also emphasize the need to further invest in technological innovation, thereby advancing health and educational outcomes while encouraging equitable access to empowering technology for diverse populations.

Artificial Intelligence is rapidly transforming contemporary Project Management practices by enabling advanced Decision Support Systems that enhance data-driven managerial processes. This study examines the impact of AI-Based Decision Support Systems on Managerial Effectiveness in project environments, with particular attention to planning, risk forecasting, and Resource Allocation. Drawing on survey-based empirical evidence, the research evaluates how Predictive Analytics and intelligent automation influence decision accuracy, speed, and consistency. The findings indicate that AI-enabled Decision Support Systems significantly improve managerial insight by processing large-scale project data and generating actionable recommendations. However, the study also identifies practical limitations, including overreliance risks and the continuing need for Human-AI collaboration in complex project contexts. Overall, the research demonstrates that Artificial Intelligence functions most effectively as a strategic decision-support partner rather than a managerial replacement, offering practical implications for organizations seeking to strengthen Project Management performance through data-driven innovation and evidence-based managerial practices across dynamic and technology-intensive project environments worldwide today. These managerial insights provide a structured foundation for future empirical investigations and guide practitioners in responsibly integrating AI-driven tools into everyday managerial workflows without undermining professional expertise or contextual decision-making quality within modern project-based organizations operating under conditions of uncertainty and competitive pressure globally today and beyond.

Artificial Intelligence (AI) has rapidly transformed modern society, improving efficiency across various sectors. However, its advancement has also contributed to the emergence of new forms of crime such as deepfake fraud, AI-based phishing, identity theft, and digital exploitation. This research examines AI as a catalyst in enabling sophisticated and large-scale criminal activities, with particular focus on its impact on women and children. The study is based on secondary data collected from research papers, news reports, and credible institutional sources. The research emphasizes the need for increased awareness, stronger cybersecurity measures, and responsible use of AI to ensure digital safety and protect society from emerging technological threats.

The rapid shift toward Artificial Intelligence (AI) in business has created a “trust gap” where autonomous systems manage critical decisions—like hiring and lending—without enough human oversight. Current AI models often operate as “black boxes,” leading to risks like algorithmic bias, lack of transparency, and failure to meet legal regulations. While organizations want the efficiency of AI, they often lack a structured way to manage these ethical risks. This study proposes a Multi-Layer Ethical Governance Framework designed to bridge this gap. Unlike existing methods that handle ethics and technical performance separately, our framework integrates them into one system. It uses five functional layers—Data Governance, Ethical Evaluation, Governance Monitoring, Explainability, and Human Oversight—to ensure AI decisions are fair, understandable, and legally compliant. We tested this approach using a real-world Credit Risk Assessment scenario. The results show that the framework successfully flags hidden biases and provides clear “reason codes” for AI decisions, allowing humans to step in and prevent errors. This research provides a scalable and practical roadmap for businesses to adopt AI responsibly, ensuring that technology remains a tool for progress rather than a source of unfairness

When people travel into remote areas, losing cellular service is common, but in emergencies this lack of connectivity can be life-threatening. This research presents the OffGrid SOS IoT system, an autonomous communication device that operates independently of internet and cellular networks. The system integrates an ESP32 microcontroller with GPS and LoRa modules to transmit distress signals over long distances. Edge AI is embedded directly into the device to process motion data from an IMU and detect hard falls, while also recognizing predefined panic keywords using a built-in microphone. Upon detecting a potential emergency, a countdown timer is triggered to minimize false alarms. If not canceled, the system transmits encrypted GPS coordinates to nearby rescue nodes. Powered by a Li-ion battery, the device ensures privacy, energy efficiency, and reliable communication in zero-network zones.

India, as the world’s largest democracy, relies on a constitutionally governed electoral system to ensure free and fair elections. While Electronic Voting Machines have improved efficiency, concerns regarding bogus voting, duplicate or repeated entries, impersonation, and unauthorized access continue to affect public perception of electoral transparency. Strengthening digital verification mechanisms has become essential to enhance voter trust and system integrity. This study examines a digital-based anti-bogus voting system aimed at improving security and transparency in Indian elections. The system emphasizes structured voter authentication, identity validation, and controlled vote-casting mechanisms to minimize fraudulent practices. By integrating secure digital verification processes within the existing electoral structure, the system supports sustainable electoral governance and reduces opportunities for malpractice. The study further analyses the public perception regarding digital authentication in elections which will highlight the potential of technology- driven solutions in reinforcing democratic trust and institutional credibility.

The analysis of social networks has become an important interdisciplinary re-search area involving mathematics, sociology, and computer science. Graph theo-retic models provide useful tools for representing relationships among individuals, yet classical matrix representations often fail to incorporate variations in influence strength within networks. In this paper, we introduce a matrix-based framework called the Social Influence Spectral Matrix (SISM). The proposed matrix integrates network connectivity, in-fluence weights, and degree-based reinforcement into a unified spectral model. We also introduce a new measure called the Spectral Social Cohesion Index (SSCI) to quantify the strength of community formation in a network. At the present stage, the study should be viewed as a proposed framework accompanied by preliminary theoretical observations. The results presented here provide motivation for further mathematical investigation and empirical validation in the study of influence dynamics in social networks.

The accelerating integration of digital transformation, sustainability imperatives, and innovation- driven economic models has redefined the framework of youth employability and rural development in India. This study examines the impact of government-led skill enhancement initiatives, particularly those implemented under the Public-Private Partnership (PPP) model through Project Implementing Agencies (PIAs), on youth employability and entrepreneurial readiness in Maharashtra. Focusing on Edulight Learning Services Pvt. Ltd. under the Deen Dayal Upadhyaya Grameen Kaushalya Yojana (DDU-GKY) framework, the study examines the impact of digitally integrated training ecosystems on rural youth. It evaluates how such initiatives enhance innovation orientation, sustainability awareness, and startup preparedness. The study adopts a descriptive and analytical mixed-method approach using questionnaires, field visits, and secondary data from published sources to evaluate digital competency, entrepreneurial inclination, sustainability awareness, employment outcomes, and training performance under DDUGKY in Maharashtra. The findings highlight the transformative potential of digital skill ecosystems in strengthening rural employability and entrepreneurial capacity. The study recommends expanding industry partnerships and advanced digital modules to further enhance innovation-driven outcomes and sustainable entrepreneurship among rural trainees.

The world of distributed computing is currently experiencing a significant shift. Traditional methods for trans- mitting signals no longer meet the demands of systems that need to operate at high speeds. This paper introduces the concept of the Algorithmic Cloud-Edge-Quantum (ACEQ), emphasizing the increasingly blurred lines between cloud, edge, and quantum computing as we look ahead to 2026. Instead of proposing an entirely new architecture, the focus lies on key principles that drive faster operations through innovative organization. The ACEQ framework is constructed on a software-defined 9- Layer Quantum-Converged Open Systems Interconnection (OSI) model. This model integrates the Quantum Entanglement-Based Replication Algorithm (QERA) with established frameworks such as FLAMENCO. By treating entanglement as a valuable tool in networking, this approach enhances traditional data- packet transmission methods and improves data synchronization. This research explores the exciting possibilities of distributed computing while challenging existing norms in the field.

This study explores the future of green logistics by analyzing student perspectives on Artificial Intelligence (AI) as a driver of sustainable startup operations. With increasing environmental concerns and rising carbon emissions from global supply chains, startups must integrate innovative and sustainable solutions. AI technologies such as route optimization, demand forecasting, predictive analytics, and automated ESG reporting offer significant potential to enhance operational efficiency while reducing environmental impact. Using a quantitative research design, data were collected from 52 university students through a structured survey and analyzed using Chi-Square and Friedman tests. The findings reveal that students perceive AI as financially viable mainly at later growth stages rather than at inception. Importantly, respondents show stronger trust in a “Digital Synergy” model that combines AI automation with human oversight. Financial constraints emerged as the primary barrier to AI adoption. The study proposes a strategic framework for integrating AI-driven sustainability in startup ecosystems aligned with global ESG standard AI efficiency

This study looks at how Buy Now Pay Later service customers spend money when they are feeling impulsive. It wants to see how users’ moods affect their spending. The study also wants to know if knowing about money can help people make choices when they are feeling happy or sad . 38 people answered a questionnaire that asked them to rate how they feel about spending money. The study found out that users’ emotions have an effect on how they spend money especially when they are stressed or happy. When people know more about money, they can control their impulses better. This means that knowing about money can help people make choices when they are feeling emotional. The study is important because it shows that teaching people about money can help them make good choices when they buy things using Buy Now Pay Later services. Buy Now Pay Later services are affected by how people feel and what they know about money. The study tells us that we should teach people about money so they can make choices when they spend money using Buy Now Pay Later services.

The rapid digitalization of financial markets has significantly increased youth who trade in stock trading and cryptocurrency in India. This study looks at the sociological impacts of this trend focusing on how it changes people's traditional career goals, family dynamics, and mental health. Using a descriptive research design, primary data was collected through a structured questionnaire from a sample of 52 young adults in Maharashtra. The results show a clear change in how people think about money. Many people think trading is good way of make money, but it also increases stress levels and changes value from collectivist to individualism. Chi-Square analysis showed strong links between trading participation and sociological changes in the sample. Due to the limited sample size, these findings cannot be broadly generalized. The study concludes that while digital trading democratizes financial access, it necessitates targeted financial literacy and emotional support frameworks for young investors.

Recent developments in financial markets, including an increase in the complexity of the markets as well as an expansion in the derivatives trading markets (i.e., the movement away from traditional stocks and bonds) has placed additional cognitive burdens on retail investors, and has led to a greater potential for behavioral biases to play an increased role in the decision-making processes of retail investors. This research seeks to better understand how market volatility and financial literacy relate to retail investor behaviour in India, and to provide a technology-enabled decision-support framework (DSF) to allow for more rational investment decision-making. An exploratory quantitative approach was taken based on secondary data sources and simulated investor sentiment analysis. Statistical techniques including correlation, regression and chi-square tests were performed using SPSS and have demonstrated that increasing volatility of the financial markets has a positive relationship with behavioral biases, and that higher levels of financial literacy are associated with lower levels of panic-selling behaviour. Even though access to the financial markets has improved, behavioral biases continue to negatively affect retail investor outcomes. The proposed DSF combines principles of behavioral finance with AI-based analytics to assist investors in filtering out market noise and maintaining disciplined decision-making for longer periods of time in increasingly complex financial environments.

Cloud computing has evolved into a foundational technology underpinning contemporary digital infrastructure, providing scalable, on-demand, and cost-effective computing services. Nevertheless, with the rapid proliferation of Internet of Things (IoT) devices, real-time applications, and data-intensive systems, traditional cloud computing faces significant challenges, including elevated latency, bandwidth constraints, and prolonged response times. Centralized cloud architectures often fail to satisfy the stringent requirements of latency-sensitive workloads. Edge computing has emerged as a complementary paradigm by processing data in proximity to its source, thereby minimizing communication delays and enhancing overall system performance. This paper presents a comprehensive survey on edge computing and its transformative impact on the future of cloud computing. It explores prevailing industry viewpoints, novel architectural frameworks, and recent technological advances in edge–cloud integration. The study evaluates how hybrid edge–cloud architectures improve performance, scalability, and resource utilization. It further examines critical challenges, such as security vulnerabilities, interoperability issues, and dynamic resource management. Based on this analysis, it becomes evident that edge computing will play a pivotal role in shaping next-generation distributed computing systems by enabling faster, more intelligent, and highly efficient data processing.

The rapid growth of AI-driven personalization in Indian e-commerce has fundamentally altered how consumers discover and purchase products online. Platforms such as Amazon, Flipkart, and Myntra increasingly rely on artificial intelligence to deliver targeted recommendations, creating a shifting dynamic between algorithmic influence and consumer decision-making. This study examines consumer susceptibility to AI-driven personalized retailing across four key dimensions: awareness of AI personalization techniques, its influence on buying behaviour the moderating role of data privacy concerns, and the impact of platform trust on long-term engagement and repeat purchase intention. A quantitative, descriptive research approach was adopted, with primary data collected from Indian consumers through a structured online questionnaire. The study finds that AI personalization meaningfully shapes consumer purchasing behaviour, with platform trust emerging as a particularly significant factor. The findings contribute to a growing understanding of how intelligent retail systems influence Indian consumers and carry implications for ethical AI deployment in e-commerce