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Toward Smart 5G and 6G: Standardization of AI-Native Network Architectures and Semantic Communication Protocols
Semantic communication and AI-native design are widely recognized as defining features of 6G, yet existing surveys often treat them conceptually or in isolation. This article provides a standards-oriented perspective that integrates these paradigms and evaluates their implications for architectural design and standardization. We make three concrete contributions: 1) we propose enriched KPI frameworks, security and privacy taxonomies, and interoperability prescriptions that extend beyond current 3GPP, ITU-T, and O-RAN activities; 2) we analyze implementation trade-offs such as computational overhead of semantic encoding and the scalability of federated learning in ultra-dense deployments; and 3) we demonstrate the potential of semantic communication through a UAV case study, highlighting measurable improvements in bandwidth, latency, and coordination efficiency. These contributions distinguish our work from prior surveys by moving beyond high-level vision toward feasibility analysis and concrete standardization pathways, thereby offering actionable insights for the evolution of semantic-aware 6G systems. 2017 IEEE. -
Hybrid EconometricMachine Learning Models for High- Dimensional Data: Robust Approaches to Anomaly Detection and Inference
This chapter, according to the authors, looks at making modeling robust and interpretable when faced with complex, irregular, and contaminated data environments. As empirical research continues to move towards larger datasets having numerous variables and high dependency among variables, along with instances of irregular data, traditional models of analysis can no longer be very effective. The chapter looks at the possibility of having a comprehensive modeling approach by integrating concepts of robustness like bounded sensitivity and stability analysis, along with flexible modeling at the analytical and computational levels. A major focus is on finding influential data points, rare observations, and structural breaks during the modeling process and not after the process is complete. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Reflexive Praxis in University Classrooms in India: A Case Study
This article presents the case study of a university teacher's journey focusing on struggles he faced in the personal and professional space during his teaching career that shaped his pedagogic practices. Bourdieu's structural parameters and Engstr's (1987) theory of expansive learning provided analytical concepts, including reflexivity, to study the pedagogical praxis of this teacher. The analysis of data collected using the biographical narrative interviewing method, classroom observation, and autobiographical writings of the teacher reveals that as he questions his social positioning, academic "field," and intellectual bias, he experiences conflicts and tensions that arise from several disruptions resulting in pain and frustrations at one level and at another level shaping his desire and the ability to engage critically and historically with the processes and outcomes of personal and pedagogic interrogations. He realizes that there is no "A" algorithm for developing reflexivity. It takes a lifetime for a teacher to build a reflexive praxis. 2025 Common Ground Research Networks. All rights reserved. -
Predicting Football Players Market Value via Machine Learning
Football, arguably the most popular sport in the world, has become much more than just a sport, it is a multibillion-dollar industry with its center in Europe. Every year millions of euros are spent in transfer window to buy and sell players and a common theme that has been seen is players not living up to the price the clubs paid for them. This research aims to predict football players market values using machine learning techniques. Departing from traditional methods that broadly categorize players into positions like Goalkeeper, Defender, Midfielder, and Forward, this study provides a more nuanced approach by classifying players into specific roles such as Center-back, Full-back, Defensive Midfielder, Attacking Midfielder, and Winger. By incorporating performance metrics tailored to each position and weighing the performance indicators based on the relevance to that specific position, the research aims to provide a robust method to predict players market value within a negotiation tolerance range. Using data from the past three seasons, including detailed player performance statistics and contractual details, models were developed to assist clubs in making data-driven transfer decisions. Machine learning algorithms, including Random Forest Regressor and Light GBM, were utilized, with RMSE and R2 Score as evaluation metrics. Both algorithms demonstrated robust performance, with some positional models predicting market values within an acceptable error range of 312million, enabling clubs to negotiate transfer fees with greater precision based on empirical evidence of player performance. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Rebel Foods: Pioneer in Indian internet restaurant and cloud kitchen
Learning outcomes The learning outcomes are as follows: to identify the potential for disruptive innovation in the changing dynamics of the online food industry with the emergence of technology; to examine appropriate responses to emerging disruptive (environmental) threats in developing a sustainable food retail business; to evaluate the process of executing change management within the organisation, as per the dynamic external business environment; and to develop marketing and distribution strategies that can be created based on a competitive business environment by applying disruptive technological initiatives. Case overview/synopsis Rebel Foods Private Limited was one of the largest internet restaurant and cloud kitchen companies, founded by Jaydeep Barman and Kallol Banerjee in 2016. Rebel Foods had established its presence in 70 cities owing to its extensive network of 450 cloud kitchens with more than 4, 000 internet restaurants. It was involved in all three steps of the food on demand industry: ordering, distribution and order fulfillment. Faasos (earlier name of Rebel Foods) had invested around four years in the restaurant industry using the brick-and-mortar format, slowly shifting to the online business model. Due to the spread of the COVID-19 virus in March 2020, Rebel Foods closed 70% of its kitchens and began selling do-it-yourself meal kits. After the pandemic, the kitchens at Rebel Foods used cutting-edge technology such as robotics, drum machines, automatic Tawas (pans) and auto fryers. The chefs at Rebel Foods had automated the cooking process using modern technology like computer vision and artificial intelligence. Despite all efforts, the companys operating revenue had dropped by 27.5% during FY21 to reach 405.1 crore INR, down from 558.7 crore INR in FY20. Sales have also been declining in the past few years. Complexity academic level The learners can be early-career entrepreneurs operating in the food industry and/or enrolled in postgraduate or short-term management programs. The case can also be used in Executive MBA courses in Strategic Management, Marketing Management and Entrepreneurship Management. Supplementary material Teaching notes are available for educators only. Subject code CSS 8: Marketing. 2025 Emerald Publishing Limited -
Sustainable Financial Management (SFM) in E-Banking: Strategies and Challenges in Dubai
This paper explores the landscape of sustainable financial management (SFM) in e-banking within Dubai, focusing on the strategies banks implement to promote sustainability and the challenges they encounter. As e-banking continues to expand, sustainability has become crucial for banks aiming to minimise their environmental impact and enhance operational efficiency. The study examines various sustainable strategies, such as the adoption of energy-efficient technologies, the shift towards paperless transactions, and promoting digital customer engagement. Additionally, it considers the regulatory framework and policy support that facilitate sustainable practices in the banking sector, the challenges that banks face, including high implementation costs, technological hurdles, and issues related to customer adoption through case studies. The research illustrates the effectiveness of different approaches and solutions to these challenges. The findings underscore the significance of an integrated strategy aligning economic objectives with environmental sustainability, ultimately benefiting the banking industry and society over time. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Transforming network security through zero trust architecture: Principles, challenges, and future directions
Given the continued expansion of cyber threats; such perimeter-based resistance traditional security strategies have proven to be inadequate. No one is trusted by default, either inside or outside the network, in a Zero Trust architecture. Zero Trust Architecture (ZTA) is a modern security model that demands consistent authentication of users and devices and denies any presupposition of implicit trust. It should also be of strong authorization, network division, and authentication. This article covers the principles, components, pros, and cons along with zero trust implementation strategies and the impact on network security. 2026, Taru Publications. All rights reserved. -
Deep and Hybrid Ensemble Learning Methods for Enhanced Live-Birth Prediction in Fertility Treatments
The prediction of live birth outcomes using Assisted Reproductive Technologies (ART) remains a complex task owing to the high inter-patient variability and non-linear clinical interactions. This study presents a comparative evaluation of hybrid machine-learning models to improve in vitro fertilization (IVF) success prediction using a real-world anonymized dataset of 2,000 ART cases. After pre-processing (including missing value imputation, feature selection via Recursive Feature Elimination with Cross-Validation, and class balancing using SMOTE with k=5), four hybrid models were developed: stacking with XGBoost as the meta-learner, weighted ensemble, autoencoder-based feature fusion, and cascading classifiers. Models were evaluated using accuracy, AUC, precision, recall, and F1-score metrics, and compared against a baseline Random Forest classifier. The stacking model (XGBoost with Random Forest, MLP, and SVM base learners) achieved the best performance, with an accuracy and 0.999 AUC of 0.985. The weighted hybrid ensemble followed an accuracy of 0.953 and AUC of 0.994. The statistical significance of the improvements was confirmed using Wilcoxon Signed-Rank and McNemars tests (p < 0.05). To enhance model transparency, SHapley Additive exPlanations (SHAP) was applied to interpret base model contributions in the stacking architecture. These results support the application of AI-driven hybrid modelling for personalized IVF treatment planning. Future work will focus on prospective validation and clinical decision support system (CDSS) integration to assess deployment feasibility. (2025), (Slovene Society Informatika). All rights reserved. -
Artificial Intelligence and Machine Learning in Advanced Materials Science: A New Era of Innovation
The increase of artificial intelligence (AI) and machine learning (ML) in the discovery, design, and optimization of new materials is causing a rapid acceleration of the field of materials science. This chapter addresses the principles for how artificial intelligence and machine learning can enable the predictive modeling, high-throughput screening, and smartproduction ofpolymers, alloys, ceramics, and nanomaterials. Emphasized are some of the techniques, such as hybrid approaches to artificial intelligence and physics, generative models, and reinforcement learning. Some important problems in the chapter are spoken about: lack of standards, incoherence of data, and unfeasibility of explaining the models. Along with that, this also attempts to investigate how the advantages of upcoming malware such as quantum computing, edge synthetic intelligence, and open information facilities can reinforce the research and innovation in the forthcoming age of materials. 2026 by IGI Global Scientific Publishing. All rights reserved. -
The convergence of IoT, ML, and big data in self-service innovation
Internet of Things, Machine Learning, and Big Data analytics have been bringing a transformation to self-service technologies with automation, personalization, and operations efficiencies. IoT captures the data in real-time by linked sensors; ML will try to find the patterns, providing the output; and Big Data would analyze large datasets to deliver actionable insights. All three together bring smarter and adaptive systems in retail, health care, and rural service industries. The applications include inventory management, personalized healthcare forecasting, and community kiosks. While these innovations are achieved, interoperability, scalability, and high implementation costs in rural areas are some of the persistent challenges. This can be addressed by taking advantage of edge computing, cloud solutions, and the collaboration of stakeholders. Further innovations like 5G and federated learning will shape self-service technologies in the future to deliver efficient, inclusive, and innovative solutions. 2026, IGI Global Scientific Publishing. -
Data-driven education: Leveraging big data, AI, and machine learning for smarter learning environments
Big Data, artificial intelligence (AI), and machine learning are transforming education with self-paced learning, precise insights, and automated decisions. The effects these technologies have on education are transformative, especially when it comes to improving student achievement, advancing administrative operations, and personalizing the learning experience, as stated in this chapter. Big Data collects and analyzes immense volumes of data, while AI powers automation, makes predictions, automates evaluations, and enables the possibility of adaptive learning. Chatbots, recommendation engines, and tutoring systems enhance student-focused digital education. Still, integrating these technologies comes with challenges such as privacy and ethical issues, algorithm discrimination, and data security concerns. Some of the new trends are explainable AI (XAI) for ethical decision-making, blockchain and federated learning for privacy-preserving analytic systems, and verifiable credentials. In addition, XR, AI-based virtual laboratories, and neurosymbolic AI will have great consequences on the future learning environment. AI in education offers scalability and inclusivity but demands ethical regulation, governance, and resource management. This chapter recommends sustained effort in research, policy changes, and ethical integration of AI for the best possible use thereof. The education sector, by incorporating human-centered AI approaches, can create a just, sustainable future of learning that is accessible to all students around the world. 2026 Elsevier Inc. All rights reserved. -
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. -
AI-Driven Consumer Behavior and Decision Making
Artificial Intelligence (AI) has transformed consumer behavior and decision-m aking, impacting purchasing habits, brand relationships, and marketing efforts. This systematic review synthesizes evidence on AI- influenced consumer behavior, analyzing its effects on attitudes, likes, and decision- making. Major areas of study encompass AI- facilitated recommendation systems, personalized engagement marketing, AI in online advertising, and AI- facilitated automation in retail and service sectors. The research presents both the benefits and pitfalls of AI integration, such as better customer experiences, data privacy fears, and information cocoons. AI has revolutionized conventional marketing practices by facilitating hyper-personalization and predictive analyses, engaging customers while incurring ethical issues. Research also highlights the importance of AI in sectors like fashion, entertainment, and business- to- business (B2B) marketing, offering insights into consumer trust and perceptions of privacy. 2026, IGI Global Scientific Publishing. All rights reserved. -
Development of a VR-Based Solid Waste Management Awareness Platform Utilizing YOLOv12 and MSCNN
Waste management is not an issue concerning an individual but a collective responsibility. It refers to our environment. Our project, 'Solid Waste Management,' verifies the efficacy of virtual reality, a novel learning modality for the user, acquiring knowledge of waste segregation and the right way of waste disposal simulation through virtual reality. The implication of virtual reality's addition into the educational system was analyzed through the acceptance of the model and the acquisition of knowledge through the task performed by the user. The simulation and the environment of virtual reality are implemented through the use of Spatial Awareness, Haptic Simulation, Hand Tracking using HCI, and Immersive Learning Environment for a genuine simulation experience for the user. The simulation environment and software were developed using the Unity environment creating a gamified world using a 3D environment and the Blender software used for the development of the 3D models. The simulation environment's implementation into the various HMD devices also used the OpenXR plugin. The simulation is further segmented into two parts: interior and exterior waste management. This novel simulation technique will not only enable the acquisition of the most requisite skills of waste segregation but also the acquisition of environment knowledge and the promotion of people towards sustainable practices. 2026 IEEE. -
The role of digital technologies in open innovation
This chapter explores how digital platforms serve as enablers of open innovation by connecting diverse networks of individuals, companies, and institutions. It examines how platforms like GitHub, Innocentive, and Quirky facilitate collaboration, knowledge exchange, and co-creation between internal and external stakeholders, including startups, big enterprises, independent innovators, and research institutes. The chapter also addresses challenges such as security issues, intellectual property concerns, and management complexities in collaborative processes. It discusses best practices for leveraging digital platforms effectively in open innovation, aiming to foster sustainability, adaptability, and continuous growth in a dynamic market. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Navigating the Complexities of Indoor Air Quality: Critical Analysis of Key Pollutants and Monitoring Sites
Indoor Air Quality (IAQ) plays a crucial role in human health, as individuals spend a significant portion of their time indoors. Many existing studies overlook the compounded effects of common environmental factors like temperature and humidity, as well as the health risks posed by long-term exposure to indoor pollutants. However, this review tries to explore how modern building materials, and indoor activities impact IAQ, with a focus on key pollutants such as ultrafine particulate matter (PM2.5 and smaller), volatile organic compounds ammonia, formaldehyde, chlorine, and radon. The review emphasizes the need for more research on IAQ, especially in developing regions, heritage settlements, and the need for standardized evaluation approaches. Moreover, there is a need of practical and affordable solutions that integrate advanced sensing technologies, real-time data insights, and easy-to-use systems to enable people to aware and take control of their IAQ. This review highlights the importance of more rigorous research, creative strategies, and strong policy support to address these challenges, particularly for communities in developing regions, crowded spaces, and vulnerable populations. The main motive is to enhance understanding of indoor air pollution and promote the development of healthier, more sustainable indoor environments. The objective is to support SDG-3 Good Health and Well Being, SDG-11 Sustainable Cities and Communities, SDG-13 Climate Action. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Exploring Reproductive Autonomy Among Economically Dependent Married Women: A Qualitative Study
This qualitative study explores how economic dependency within a system of patriarchy shapes the experiences of reproductive autonomy among nine married women aged 2050 years (mean age 44.6 years) in India. In-depth interviews were conducted using purposive and snowball sampling. Data were analyzed using Braun and Clarkes reflexive thematic analysis. Eight themes revealed three interconnected layers: structural forces (economic dependency and familial control), negotiated agency (micro-level resistance within patriarchal constraints), and cultural reproduction (culturally embedded gendered norms). Findings connect to feminist standpoints and Marxist feminist theories, demonstrating the need for interventions addressing economic independence within larger structural and cultural transformation. 2026 Society for Menstrual Cycle Research. -
Climate Change, Risk Management, and ESG: An Indian Perspective
Abstract Mr. Kofi Annan, the Secretary General of the United Nations Organization remarked that the world is reaching the tipping point beyond which climate change may become irreversible. If this happens, we risk denying present and future genera-tions the right to a healthy and sustainable planet that the whole of humanity stands to lose (RBI, Reserve Bank of India Publication, 2022). Climate change has negatively impacted humanity. It has become a focal point of discussion amongst researchers, academicians, and policymakers alike. It has become imperative for companies to assess and mitigate climate risks as a part of the sustainability journey. Many investors and lenders are integrating ESG aspects, including climate risk, into their investment decision-making processes. The Securities Exchange Board of India (SEBI) intro-duced Business Responsibility and Sustainability Reporting (BRSR), which is a comprehensive framework that seeks disclosures regarding the ESG performance of the companies (SEBI, 2021). However, one of the glaring issues is that most of the disclosures are voluntary in nature, and there are no penalties prescribed for non-disclosure by listed companies in India. In this chapter, we delve into risks posed by climate change on businesses and understand the significance of including the aspect of climate change in corporate planning and strategies. Another important aspect that has been discussed in the chapter is how ESG can be used to address the risks, issues, and challenges posed as a result of climate change. Further, a comparative approach has been adopted to understand the ESG policy framework in India and some prominent jurisdictions like the US, the UK, China, and Japan. We are of the view that framing policies related to mandatory ESG reporting for listed companies and compliance with ESG norms will be the deciding factor for the existence, future readiness, and sustainability of Indian businesses. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Climate Change, Risk Management, and ESG: An Indian Perspective
Abstract Mr. Kofi Annan, the Secretary General of the United Nations Organization remarked that the world is reaching the tipping point beyond which climate change may become irreversible. If this happens, we risk denying present and future genera-tions the right to a healthy and sustainable planet that the whole of humanity stands to lose (RBI, Reserve Bank of India Publication, 2022). Climate change has negatively impacted humanity. It has become a focal point of discussion amongst researchers, academicians, and policymakers alike. It has become imperative for companies to assess and mitigate climate risks as a part of the sustainability journey. Many investors and lenders are integrating ESG aspects, including climate risk, into their investment decision-making processes. The Securities Exchange Board of India (SEBI) intro-duced Business Responsibility and Sustainability Reporting (BRSR), which is a comprehensive framework that seeks disclosures regarding the ESG performance of the companies (SEBI, 2021). However, one of the glaring issues is that most of the disclosures are voluntary in nature, and there are no penalties prescribed for non-disclosure by listed companies in India. In this chapter, we delve into risks posed by climate change on businesses and understand the significance of including the aspect of climate change in corporate planning and strategies. Another important aspect that has been discussed in the chapter is how ESG can be used to address the risks, issues, and challenges posed as a result of climate change. Further, a comparative approach has been adopted to understand the ESG policy framework in India and some prominent jurisdictions like the US, the UK, China, and Japan. We are of the view that framing policies related to mandatory ESG reporting for listed companies and compliance with ESG norms will be the deciding factor for the existence, future readiness, and sustainability of Indian businesses. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Developing mathematical models to analyze economic growth patterns in emerging market dynamics
A key factor in determining national development and directing successful market strategies is economic growth. Making better judgements in developing countries is facilitated for investors and policymakers by having a better understanding of the main drivers of growth. The purpose of this paper is to use mathematical models to explain how economic growth patterns vary among the major growing nations. It examines the effects of inflation, foreign investment, trade, and current account balances on the GDP growth of five major economies India, China, Russia, Brazil, and South Africa between the year 2005 to 2025. The study presents how these variables connect to growth and vary among nations using techniques like logistic regression, linear regression, and ANOVA. TARU PUBLICATIONS.