Browse Items (16488 total)
Sort by:
-
Quantum-inspired algorithms for cognitive computing: Enhancing cloud-based problem-solving
The convergence of quantum-inspired algorithms and cloud-based frameworks represents a transformative shift in computational capabilities tailored to the human-centric goals of Industry 5.0. Unlike Industry 4.0, which focused on automation and digitization, Industry 5.0 emphasizes intelligent systems that complement human decision-making. Quantum-inspired algorithms, derived from the principles of superposition and entanglement, offer superior capabilities in optimization and pattern recognition without requiring quantum hardware. When integrated with scalable and distributed cloud computing infrastructures, these algorithms enable high-performance cognitive computing, tackling previously intractable problems across domains. This chapter explores the theoretical foundation and practical implementation of such systems, including quantum-inspired neural networks (QiNNs), quantum-inspired immune algorithms (QiIAs), and quantum-inspired particle swarm optimization (QPSO). These models exhibit enhanced accuracy and efficiency in applications like pattern recognition, anomaly detection, and multiobjective optimization. Real-world case studies in finance, cybersecurity, healthcare, and smart grid management highlight their impact on risk modeling, threat mitigation, and decision support systems. The chapter further proposes a cloud integration framework, addressing challenges in scalability, performance, and security. Implementation strategies and architectural designs are discussed with a focus on dynamic resource management, real-time analytics, and secure deployment. The synthesis of these technologies marks a significant advancement toward achieving adaptive, intelligent, and secure computational ecosystems, aligned with the values and vision of Industry 5.0. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
Next-gen cloud intelligence: Cognitive computing for a sustainable digital future
Cognitive cloud computing is an innovative paradigm that links intelligent systems with cloud infrastructure having distributed and scalable capabilities. Next-generation cloud intelligence is explored with regard to the foundational principles, enabling technologies, and emerging applications. It focuses on cognition cloud systems to maximize energy efficiency, improve predictive analytics, and encourage human-centric computing for various domains such as education, healthcare, finance, and environmental management, including sustainability. Some of the enabler technologies for AI are natural language processing, computer vision, and speech recognition, and the advancement of AI integration, neuromorphic computing, and quantum-enhanced models. The coming of bloc, edge, and the cognitive security mechanisms make it the basis for an invulnerable, transparent, and context-aware system. The chapter also explores green data centers, energy-efficient algorithms, and circular economy principles. The ethical development of cognitive cloud systems is critically analyzed, and data privacy, algorithmic bias, explainability, and regulatory frameworks are considered as challenges for their ethical development. In general, the contribution of this work is to provide a complete framework for the analysis of the impact that cognitive computing can have to make over cloud-based ecosystems as intelligent, adaptable, and eco-friendly platforms through the means of digital infrastructure and AI-driven services. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
A sustainable approach to cloud computing: A comprehensive analysis of load balancing techniques in cognitive environments
Cloud computing is one of the rising eras in big-scale computing, where information is processed in big information centers. One of the demanding situations with that is to accomplish load balancing of all the nodes. In addition, it's essential for proper utilization of assets and division of the work. This indicates that effective allocation and usage of the computing resources among clients in sharing mode leads to satisfactory users as well as good utilization of resources. Balancing the load in huge data-centric systems and networks is required to achieve the stated goal. The load balancing problem is considered as an optimization problem, and the rules for a load provide the number of compensations (scalability enabling, bottleneck avoidance) and resource consumption. A number of proposed algorithms exist for the load balancing problem in the cloud environment. Efforts have been made in this chapter to examine and review a few of the weight balancing algorithms in cloud computing. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
Enhancing operational efficiency in cloud computing through computational intelligence for environmental sustainability
In the era of generative artificial intelligence technologies, an increasing number of organizations are adopting solutions such as ChatGPT, DeepSeek, Gemini, and Bard, and there has been a significant increase in computationally intensive operations that impact data center operations, as these require a high CPU- and GPU-based infrastructure and a large amount of electricity to run. In this data-driven environment, there is a need to look for and identify a holistic solution to address the concern of managing cloud operations with sustainable practices that can help not only reduce the carbon footprint but also be environment and pocket friendly. Organizations have started adopting smart intelligent technologies and sensors that track and help minimize and prescribe solutions to address the high usage of cloud resources. In this study, we have tried to check the impact of the role of such technologies in optimizing energy consumption, reducing the carbon footprint, and enabling eco-friendly, sustainable practices within cloud operations. While going through the study, you will find that the adoption of new technological solutions that leverage sensors and artificial intelligence-based smart technologies has a profound impact on long-term energy efficiency gains. The study highlights the importance of leveraging computational intelligence-based solutions to drive long-term environmental benefits, while also emphasizing the role of sustainable solutions, such as renewable energy integration and collaborative partnerships, in achieving the desired outcomes. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
A comparative study of machine learning: Models for web tracker detection
Web trackers, used by websites, collect user data and monitor online activity, often with or without explicit consent. With concerns for online privacy, there is a growing need to detect these web trackers. This study evaluates several machine learning (ML) techniques for detecting web trackers, focusing on evaluating their performance from the key metrics such as accuracy, precision, and recall. We analyzed supervised methods, such as random forest, support vector machines (SVM), neural networks, gradient boosting, and unsupervised methods, including DBSCAN and isolation forest. Models were trained on a comprehensive dataset extracted from URLs with feature engineering, and data preprocessing techniques were applied to enhance model performance and detect both known and unknown trackers and normal traffic. Our results indicate that supervised models outperform unsupervised methods, demonstrating their superior ability in distinguishing web trackers from normal traffic. This work highlights the effectiveness of ML-based tracker detection and outlines opportunities for improving privacy protection through adaptive supervised learning methods. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
AI-driven defense mechanisms for Sybil and DDoS attacks in cloud networks
With massive DDoS attacks and Sybil attacks targeting national digital frameworks, financial institutions, and vital infrastructures, India is seeing an unparalleled increase in cyber threats. These attacks reveal significant vulnerabilities in national cybersecurity by jeopardizing system availability and integrity. Sybil attacks use numerous falsified identities to get unauthorized control over trust-based systems, while DDoS attacks flood networks with illegal traffic, making services inaccessible. This study investigates advanced machine learning (ML)-based identification and prevention strategies, including support vector machine (SVM), random forest (RF), decision tree (DT), and logistic regression (LR). To identify attack patterns, the methodology entails gathering actual network traffic data, preprocessing it to extract key information, and then using these models. To identify the best strategy, a comparison study is carried out depending on various parameters such as accuracy, precision, recall, and computing efficiency. The research suggests that random forest outperforms other ML algorithms in detecting Sybil attacks and DDoS attacks, achieving the maximum stability and accuracy. Nevertheless, the classification method is improved by merging decision trees and logistic regression, which further increases detection accuracy. In order to actively fight changing cyber threats, our findings highlight how important it is to include machine learning-driven security frameworks into India's cybersecurity infrastructure. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
Eco-friendly operations in Reality: Analysis of key factors of sustainability performance in manufacturing companies
Sustainable product design (SPD) focuses on eco-friendly products. The energy efficiency (EE) optimizes energy use in buildings, transportation, and industry. The policies that reduce the consumption at the macro-level rebound effects are debated. The evidence leans toward waste management (WM) needing strategies like recycling and source reduction in developing countries. This study examines the sustainable product design, energy efficiency, and waste management on overall sustainability performance in manufacturing companies. The study used correlation analysis, descriptive statistics, and multiple linear regression to quantify the significance of these factors. Results show that all three variables significantly contribute to sustainability performance and support the hypotheses. SPD, EE, and WM positively impact overall sustainability performance, and evidence leans toward strong relationships among variables, but multicollinearity could complicate findings. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
Navigating the ethical landscape of artificial intelligence: Challenges, frameworks, and responsible deployment
In artificial intelligence (AI), machine learning (ML) has become a game-changing concept that allows systems to learn from experience and get better without explicit programming. This chapter explores the main ideas, techniques, and applications of ML, offering a succinct introduction to the field. The first step in the process is to gain a basic understanding of supervised learning, which is the process by which algorithms learn to make predictions or judgements from labelled training data. Next, we introduce unsupervised learning, which emphasizes finding patterns in unlabelled data and frequently results in interesting findings and clustering. To emphasize the importance of reinforcement learning in decision-making processes, the paradigm is presented where agents learn by interacting with an environment and receiving feedback. Ideas related to ML, such as feature engineering, model assessment, and the balance between variance and bias, are discussed. The significance of quality data in ML applications is emphasized, along with the impact of data pretreatment on model performance. It also clarifies how neural networks, a branch of ML, simulate the workings of the human brain. The ability of deep learning, a branch of ML that makes use of multi-layered neural networks, to handle challenging tasks such as speech and picture recognition is being investigated. In order to emphasize the necessity of responsible ML model deployment and usage, practical factors are emphasized, including the significance of ethical considerations and responsible AI. The final section of the chapter offers a preview of MLs future, discussing issues and trends that practitioners and researchers should be aware of. This chapter essentially functions as a thorough introduction to ML principles, providing an overview of the wide range of ML approaches, applications, and ethical issues that support the technologys transformative potential across a range of industries. 2025 selection and editorial matter, G. Sucharitha, Anjanna Matta, M. Srinivas and Sachi Nandan Mohanty; individual chapters, the contributors. -
The Adoption of AR and VR Emergency Room Procedures
[No abstract available] -
The Future of Insurance: How AI is Shaping the Sector
The combination of AI and insurance was once an idyllic idea, but it has now become an imperative, transforming how insurers analyze, manage, and mitigate risks. AI benefits the insurance sector in a variety of ways. It aids in accelerated underwriting, hazard assessment, more equitable pricing, and tailored policies. AI helps to speed up claim processing and detect fraudulent activity. Furthermore, AI enables the performance of operations by means of lowering administrative overhead via automation. The purpose of the study is to examine the current and potential AI applications in the insurance sector and also to analyse the impact of AI in the insurance sector. The study employed a descriptive research design and the data has been collected through secondary sources like journals, books, reports, websites, etc. The study found that AI has enabled insurers to streamline processes, enhance efficiency, and offer extra customized danger evaluation for its clients. This has led to a seamless and handy revel in for policyholders, in addition to extra correct underwriting and pricing for insurers. Additionally, AI has facilitated the automation of claims processing, taking into account quicker and extra correct claims settlements. However, the implementation of AI within the coverage enterprise also presents demanding issues that need to be solved. 2026 Alka Agnihotri, Anuja Pandey and Balamurugan Balusamy. -
Sustainable Synergy: Exploring the Relationship Between Environmental Marketing and Green Entrepreneurship for Business
Environmental marketing and green entrepreneurship have emerged as vital approaches for businesses to integrate sustainability principles into their operations. This study explores the relationship between environmental marketing and green entrepreneurship as a sustainable approach for businesses. The methodology adopted for this study involves an exploratory literature review, which includes a review of scholarly articles on environmental marketing and green entrepreneurship. The sample description included a wide range of literature from diverse sources, covering various aspects of environmental marketing and green entrepreneurship. Literature was analyzed using qualitative thematic analysis, which involves identifying recurring themes, patterns, and insights from the literature. The results reveal that environmental marketing and green entrepreneurship are closely interconnected with the themes that emerged. The discussion revolves around the conclusions drawn from the results, including the implications for businesses, consumers, and society, and the potential benefits of integrating environmental marketing and green entrepreneurship as a sustainable approach. The study provides insights for businesses, policymakers, and other stakeholders on the importance of environmental marketing and green entrepreneurship in driving sustainable business practices and fostering environmental sustainability. 2025 by Apple Academic Press, Inc. -
Influence of Digital Voice Assistance on Consumer Purchase Intention
The focus of this research is to analyze how digital voice assistants (DVAs) influence the buying decisions of consumers when it comes to online shopping in the Delhi-NCR region. The study involves surveying 225 online shoppers using a purposive sampling method to collect data on their perceptions and experiences with DVAs in online shopping. To study consumer purchase intention, we conducted a data analysis that involved a structural equation model, which focused on the perceived ease of use and perceived usefulness of DVAs. The paper highlights the crucial role of DVAs in shaping consumer purchase intention and influencing purchase decisions in the context of e-commerce/online shopping. The study has significant implications for e-commerce companies and marketers. To promote their adoption, they should think about integrating DVAs into their online purchasing platforms, grow DVA trust, and explain their advantages. Additionally, businesses ought to spend money creating more complex DVAs that can offer tailored advice and recommendations based on client preferences and previous purchases. 2025 by Apple Academic Press, Inc. -
Does Customer Co-Creation Influence Customer Loyalty? A Special Reference to Online Video Games
The research investigates the factors influencing consumer loyalty through co-creation in online video games in Delhi National Capital Region (Delhi- NCR). The study focuses on four independent variables, social motivation, personal motivation, utilitarian motivation, and hedonic motivation, to examine customer engagement through value co-creation, which is mediated through attitudinal and behavioral loyalty. The study collected 200 respondent data through an online survey administered to online video game players in the Delhi-NCR region and analyzed the data using the Statistical Package for the Social Sciences (SPSS) software. The study has several implications for online video game companies operating in the Delhi-NCR region to improve their co-creation strategies and enhance customer loyalty through value co-creation. The study backs to the body of knowledge on co-creation, customer engagement, and loyalty in the online video game industry. However, the stud has a limitation which include the sample size as the data has been collected only from Delhi NCR. 2025 by Apple Academic Press, Inc. -
Decoding sustainability: A machine learning-based analysis of socioeconomic drivers in global sustainable developmental goals progress
Sustainability, a concept that gained prominence with the Brundtland Report in 1987, is defined as a development approach that addresses present needs without jeopardizing the ability of future generations to meet theirs. Over the years, sustainability has evolved beyond its initial environmental focus, now encompassing economic, social, and political dimensionsmaking it an essential pillar of modern development initiatives. To drive global sustainable development forward, the United Nations adopted the 2030 Agenda, featuring 17 Sustainable Development Goals (SDGs). These goals aim to resolve some of the most pressing challenges faced by humanity, including poverty eradication, climate action, gender equality, and economic growth. The SDG Index, which evaluates a countrys progress toward these goals, helps measure and compare performance across nations. The Intersection of Socioeconomic Factors and SDG Progress is significant for the growth of a country. A countrys Gross Domestic Product (GDP) has often been seen as a key economic indicator, reflecting its ability to invest in sustainable initiatives. However, sustainability is not solely dependent on financial resourcessocial factors play a critical role. To assess the connection between well-being and sustainability, researchers often analyze the Happiness Index alongside SDG scores. Countries demonstrating both high happiness levels and strong sustainability scores provide valuable insights into the relationship between social welfare and global progress. Furthermore, machine learning (ML) techniques have emerged as powerful tools in sustainability research. By analyzing vast datasets, AI-driven approaches can predict trends, optimize resources, and enhance policy implementationaccelerating progress toward a sustainable future. The Evolving Landscape of Sustainability and Its Global Impact is realized using statistical and ML approaches in this study. Rethinking Strategies for a Sustainable Tomorrow is very important in 2025 as we are approaching 2030 very fast. Understanding the underlying factors influencing SDG scores allows nations to refine their approaches to sustainability. By tailoring action plans based on socioeconomic conditions, governments can improve their policies, ensuring both environmental stewardship and enhanced quality of life for their citizens. As global challenges evolve, interdisciplinary approachesspanning technology, economics, and social scienceswill continue to shape sustainability efforts, fostering a future where development aligns seamlessly with environmental and societal well-being. 2026 selection and editorial matter, Siddhartha Bhattacharyya, Jan Plato, Soumyadip Dhar, Naba Kumar Mondal, Ivan Zelinka, Jyoti Sekhar Banerjee and Abhijit Das; individual chapters, the contributors. -
Healthcare and wearables in smart cyber-physical systems
[No abstract available]