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Artificial Intelligence in Language Teaching: Exploring Translanguaging, Eco-Linguistics, and Community-Based Learning
This chapter critically examines the role of Artificial Intelligence (AI) in second and foreign language teaching through the lens of eco-linguistics, with a particular focus on translanguaging. It explores three key dimensions of AI's impact: the theory of language, the theory of language learning, and the evolving role of the teacher. Using an argumentative and experimental methodology, the chapter integrates theoretical insights from eco-linguistics and translanguaging to evaluate AI-assisted tools. It highlights the opportunities these tools provide, such as personalized learning and adaptive feedback, while addressing their limitations, including challenges to emotional connection, language standardization, and the diminishing role of human interaction. The chapter proposes a hybrid model that combines AI with community- based language teaching to mitigate these issues, preserving the communication ecology and fostering more holistic language learning practices. 2026, IGI Global Scientific Publishing. -
Strategic retention for sustainable growth: Integrating employee retention with long-term organizational success
The research investigates how employee retention approaches fuel business expansion through their contribution to operational sustainability and market solidity and organizational endurance. Businesses that adopt official retention strategies diminish turnover rates and develop innovative approaches to expand global markets. Businesses which merge HR and leadership insights with data analytics make workforce management match organizational goals thus creating an environment where retention acts as a performance driver. Active retention programs in dynamic business environments produce operational improvements along with enhanced innovation because they empower workers to address business challenges successfully. Through this study researchers demonstrate why organizations need full-scale support measures that include workforce development alongside mentoring services along with workplace flexibility policies for maintaining employee involvement. 2026, IGI Global Scientific Publishing. All rights reserved. -
Technology, Automation, and the Future of Work for an Ageing Workforce
The chapter summarizes the studies on the collective impact of automation, AI, and ageing of work on older adults. It examines the trends in demographics, task change mechanisms, skills obsolescence, augmentation opportunities, human factors and ergonomic design, as well as policy and organizational response. Practice is exemplified by case snapshots in manufacturing, services, and health services. The chapter states that the situation will depend on design, training, and governance: where human-centered implementation and lifelong learning (including inclusivity) lead to the extension of productive working lives; otherwise, threats to equity and employability increase. 2026, IGI Global Scientific Publishing. All rights reserved. -
Policy Landscape and Incentives on AIPowered Green Finance
The application of AI in green finance has enormous potential to tackle climate change, but its effect would be determined by the strength and integrity of governance mechanisms as well as well- designed incentives. Good policy serves to set the bar but also incentives and responsible use in finance in AI applications. In the absence of such governance, exposures including algorithmic discrimination, data mining and unequal access to technology are likely to compound rather than advance social goals. Thus, AI governance has become a priority for governments, financial regulators and multilateral organisations. Policy frameworks achieve two parallel goals: on the one hand they provide boundaries to invest in technology and at the same time these very policy mechanisms direct capital towards environmentally friendly investment through incentives like tax benefits, financial grants, green debts etc. 2026 by IGI Global Scientific Publishing. -
Digital Pathways to Inclusive Health Reform: Addressing Mental Health Inequities Among Informal Workers in India
This chapter examines how digital transformation can address mental health inequities among informal workers in India, focusing on discrimination, labor precarity, and unequal access to care. Using empirical insights from Kerala within broader national and global contexts, it analyzes how psychological distress among native and migrant workers emerges from intersecting socioeconomic, institutional, and digital factors. Drawing on psychology, public health, economics, and digital governance, the chapter proposes a phygital mental health framework integrating physical services with digitally enabled care pathways. Emphasis is placed on ethical leadership, data governance, and economic sustainability, highlighting both opportunities and risks of digital mental health expansion. The chapter demonstrates how compassionate, inclusive, and accountable digital reforms can translate mental health policy into effective instruments for equity, social justice, and sustainable health system transformation. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Sustainable Leadership and Organizational Responsibility
The changing environment of the workplace, which is determined by globalization, digital transformation, climate issues, and hybrid work practices, requires leadership to be more human- centered, responsible towards ethics, and sustainable performance. This chapter examines the notion of sustainable leadership and organizational responsibility and how positive psychology offers an effective system to create successful, robust, and meaningful working environments. The chapter explores how leaders can apply ideas of human flourishing, including strengths, optimism, resilience, and meaning, to the organizational culture, employee engagement, and leadership development. It shows how organizations can get beyond transactional management as it is a solution to creating an inclusive, ethical and high- performing organization and therefore can be used to encourage individual development and organizational success. Empirical studies, new models, and concrete case studies are integrated in order to close the gap between theory and practice and give viable solutions to developing sustainable leadership and responsible organizational practices in a fast- changing world. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Sustainability of Circular Fashion in India
The Indian fashion industry, valued at USD 100 billion, faces pressing sustainability challenges, including resource depletion, labor issues, and excessive textile waste. This study explores the potential of circular fashion in advancing Sustainable Development Goal 12 (SDG 12) by promoting responsible consumption and production. Through consumer surveys, business insights, and interviews with eco- entrepreneurs, the research examines awareness, adoption barriers, and opportunities in circular fashion. Findings reveal growing consumer interest in sustainable apparel, yet concerns about product quality, hygiene, and brand credibility persist. Businesses acknowledge the potential of circular models but struggle with skill shortages, inventory management, and sanitation costs. The study highlights the need for policy interventions, investment in recycling technologies, and consumer education to accelerate circular fashion adoption. By embracing reduce,reuse, and recycle principles, Indias fashion sector can transition towards a more sustainable and resilient future. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Diversity Equity Inclusion Trifecta: Crafting Catchment Belonging Through Employer Branding
This conceptual paper explores the innovative approach of DEI trifecta crafting catchment belonging through branding. The study proposes a novel approach for organizations to tailor their employer branding efforts to the socio-cultural context of their catchment area, enhancing their ability to attract, engage, and retain a diverse workforce. This study draws an extensive review of literature from newspapers, HR Magazines, employer branding, and DEI. Through the synthesis of these concepts, a novel concept of Trifecta is framed. This study unveils the significance of catchment-specific employer branding in acknowledging talents diverse and unique expectations within a local community. It emphasizes that DEI efforts are most impactful when contextualized within the cultural norms and values of the catchment area. However, by integrating these principles into branding strategies, organizations create a stronger sense of belonging and employee engagement. 2025 by IGI Global Scientific Publishing. -
Human capital challenges in sustainability start-ups
Sustainability start-ups face unique human capital challenges (HCCs) like lack of brand awareness, competition, turnover, burnout, limited growth opportunities, resources, and expertise gaps. This mixed-methods research chapter examines strategies to address these challenges, surveying 200 start-ups and interviewing 20 CEOs and HR managers. Findings advocate for investment in brand awareness, unique benefits, positive work environments, professional development, and transparent career paths. Effectiveness varies based on start-up needs, but HCCs significantly impact performance. Prioritizing talent attraction, retention, and development is crucial for sustainability start-ups. 2025 by IGI Global Scientific Publishing. All rights reserved. -
The Role of Digital Media in Shaping Public Opinion and Political Discourse
The impact of digital media on public opinion and political debate is examined in this chapter. The significance of digital media in political communication and its effect on public opinion and political discourse are introduced at the outset of the chapter. It analyses the pros and cons of using social media for political campaigns. Further probes deeper into the proliferation of false information and propaganda in digital media, its effect on public opinion and political discourse, and the measures taken to counter it. It discusses the difficulties and ethical concerns of using digital media to encourage civic participation. This chapter provides a concise overview of the discussion and draws attention to the implications for future study and application. 2026, IGI Global Scientific Publishing. All rights reserved. -
InclusiVision: Exploring Deep Learning Techniques for Enhanced Audio Description Generation
The rise of technology has facilitated access to entertainment media in various formats like audio, images, videos, and memes. This diverse multimedia landscape, however, poses challenges for visually impaired individuals who primarily rely on auditory means and cannot consume the visual content freely available today. InclusiVision addresses this challenge by introducing audio descriptions (AD) generated through advanced technology for images and short videos. These narrated verbal descriptions provide details about visual elements such as people, objects, colors, and settings, making the content more accessible and comprehensible for the visually impaired. To enhance accessibility, InclusiVision offers two essential phases: the Image Description phase, which generates short audio descriptions for images, and the Video Description phase, which employs algorithms to narrate key visual aspects in short videos. Both the image and video captioning generate short captions explaining key points of the visuals. It employs basic encoder-decoder modeling to help achieve the task. Hence, the primary objective of InclusiVision is to promote accessibility and inclusivity in entertainment and educational media by providing contextually relevant audio descriptions. 2026, Bentham Books imprint. -
Navigating the Green Frontier: Unveiling Investor Strategies in Green Bond Market vs. Traditional Markets
Green bonds have emerged as a key instrument in sustainable finance, but their risk-return profile remains poorly understood. This study examines the risk-return profile of green bonds compared to traditional markets over a midterm investment horizon. Daily closing prices of five indices (S&P Green Bond Index, S&P 500 Index, S&P GSCI Gold Index, S&P Global Clean Energy Index, and S&P 500 Carbon Efficient Index) from August 2021 to August 2024 were analysed using descriptive statistics and GARCH (1, 1) models. Green bonds exhibit higher volatility persistence and leverage effects compared to traditional markets. There is a significant positive correlation between the S&P Green Bond Index and the S&P Global Clean Energy Index. The S&P GSCI Gold Index exhibits low volatility and no significant leverage effects. Investors should consider the unique risk-return profile of green bonds when constructing portfolios. Portfolio managers should employ sector-specific risk management strategies. Policymakers should consider the potential benefits of green bonds in promoting sustainable finance and mitigating climate risk. This study contributes to the growing body of research on green finance and sustainable investing, providing valuable insights into the midterm risk-return dynamics of green bonds. 2025, Bentham Books imprint. -
A Computational Data-Granular Model Highlighting the Evolving Fintech Landscape in India
The Fintech sector in India has undergone remarkable development, complementing the significant progress in financial technology designed to simplify financial services and provide innovative solutions. This study aims to discover and analyze two significant knowledge gaps in the Indian Fintech sector. It seeks to identify and examine the evolving patterns in web searches for potential career opportunities in the Fintech sector, providing perspectives into the trendline data from the country. Secondly, the study will assess employment in the Fintech Sector in India, emphasizing Position Titles, the geographical distribution of opportunities, and market trends from 2015 to 2023. Furthermore, it will examine the motivation and strategies essential for supporting and developing the Fintech sector in India. It performs a trend analysis on Fintech, Finance, and Accountancy searches and how they have changed over the years. By addressing these gaps, the research aims to provide valuable insights into the Fintech industry's dynamics and development in the Fintech job market over the years in the Indian context. To complement the trend analysis conducted in the paper, a computational modeling approach is used to predict future job trends in the Indian Fintech sector. The model relies on data from the years 2015 to 2023 on job openings, web searches, and geographical distribution. Therefore, the Autoregressive Integrated Moving Average (ARIMA) model has been used to understand the future patterns of job opportunities and skill requirements accordingly. This research will be helpful for companies and business owners to improve their financial operations in the long run. 2025, Bentham Books imprint. -
Advancements in Computational Modeling for Enhancing Financial Risk Management: Applications, Challenges, and Future Directions
The advancement in risk management with deeper insights and more accurate predictions amidst complex data landscapes is attributed to computational modeling. It offers sophisticated tools to analyze, forecast, and mitigate risks in the dynamic financial market. This research article discusses integrating machine learning, network analysis, and other techniques to enhance risk identification, scenario analysis, and decision support in financial institutions. This article also addresses the importance of data quality, model validation, and transparency in ensuring the reliability and effectiveness of computational models. The application of machine learning techniques in credit risk assessment, market risk analysis, stress testing, scenario analysis, sensitivity analysis, portfolio management, and optimization is discussed. The study has demonstrated the conceptual model where identifying the type of risks is the first step, followed by sourcing the data internally and externally, considering the accuracyand reflection of current market conditions. Choosing the right computational techniques occupies an important stage due to the availability of both traditional and modern techniques. Traditional techniques are equally important to modern techniques, but this comes with challenges. Further risk management processes can be initiated to address the identified risks proactively and reduce potential financial losses. Finally, the study outlines future trends and technological advancements that promise to shape the future of computational modeling in financial risk management. 2025, Bentham Books imprint. -
Role of AI in Computational Risk Modeling of Financial Stability and Portfolio Risk: A New Perspective
The need to assess climate change-related risks and their impact on the financial stability of banks is imperative. Innovations in technology, especially AI andML algorithms, have improved the efficiency and accuracy of risk analysis models. The obstacle for banks is assessing the climate risk exposure due to their lending portfolio. The climate data are uncertain and unavailable, and the granularity of the data is questionable. To overcome these issues, in this chapter, a hybrid risk predictive model is proposed. It uses a combination of ResNet-50 (to analyze and quantify spatial image data) and CoViaR (risk prediction) models. Using the ResNet-50 model, a climate change risk score is developed from images and feature extraction, which is correlated with the emission volume of the borrower firms. Then, using the proposed model, the impact of climate change-related risk on the lending portfolio is evaluated to understand the financial stability of banks through capital. 2025, Bentham Books imprint. -
A study on remote sensing image segmentation and classification
The image is a composition of many pixels. These pixels include two pieces of information: coordinate or position and intensity value. The image includes several objects; extracting the crucial objects from the image is critical. Based on the similarity of patterns, classes, groups, and segments of contained objects in the image can be created. Assigning the labels to the pixels is necessary to make the image more informative for analyzing features and decision-making. This study addresses segmentation techniques and classifying images pertaining to remote sensing images. Thereafter, Land Use Land Cover (LULC) mapping is discussed, which classifies the remote sensing images. 2025 Bentham Science Publishers. All rights reserved. -
Mental Health
[No abstract available] -
Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction
Early detection of chronic diseases like diabetes is very important for early treatment and effective management. This chapter describes a machine learning (ML) solution for predicting diabetes risk from clinical structured data and a case study is constructed on the PIMA Indian Diabetes dataset. The solution caters to the entire ML pipeline: problem formulation, preprocessing of data, feature selection (FS), model training, validation, and deployment issues. Different preprocessing techniques including missing value imputation, detection of outliers, and feature normalization were used for improving data quality. FS techniques like correlation analysis, recursive feature elimination, and selection based on domain knowledge were utilized to decrease the dimensionality of the data as well as model interpretability. Extensive comparison was conducted among widely used classification models like logistic regression (LR), random forest, support vector machine, and XGBoost. It was suggested to adopt a stacked ensemble model of LR, RF, SVM, and XGBoost that achieved better performance in terms of accuracy, precision, recall, and F1-score. The findings confirm the tremendous potential of ML to enable early diabetes diagnosis as an unobtrusive, data-driven, and scalable decision-making supporting system for physicians. This is the groundwork for the further development of clinically applicable artificial intelligence-based prediction models within real-world healthcare settings. 2026 Walter de Gruyter GmbH, Berlin/Boston, Genthiner Stra 13, 10785 Berlin. -
Adversarial networks in image generation: A detailed approach to manage datasets and to analyze discriminator and generator losses using GANs
Image production has been transformed by generative adversarial networks (GANs), which have made unprecedented realism and diversity possible. Still, there are significant hurdles in managing datasetsdatasets managing and analyzing lossesloss analysis. This book chapter focusses on dataset administration and loss analysis, while providing a thorough method for using adversarial networks for image production. A thorough approach for selecting and preparing datasets, while maintaining optimal GAN performance is put forth by researchers. The proposed research approach enables the effective training of GANs, resulting in high-quality image generationhigh-quality image generation. Experimental results demonstrate the efficacy of the current method, showcasing improved image realism and diversity. The suggested strategy also presents a fresh way to examine discriminator and generator lossesgenerator losses, offering new perspectives on the convergence and stability of GANs. This study advances the field of GAN-based image productionGAN-based image production and offers professionals and academics who wish to use adversarial networks a priceless tool. 2026 Walter de Gruyter GmbH, Berlin/Boston, Genthiner Stra 13, 10785 Berlin. -
A novel approach to optimize power utilization and scheduling in dynamic networks through generative adversarial network-based prediction of network parameters
The infrastructure-less network communication has been in an ever-increasing demand to cater to the needs of effective communication while the network dynamism exists. The quality of service (QoS)quality of service (QoS) demands increasing the efficiency of network by reducing the time taken for a data packet to reach the destination, increasing the probability of successful data transmissiondata transmission, minimizing packet loss,packet loss and optimizing power utilizationpower utilization. In this study, a generative adversarial network-based learning modelgenerative adversarial network-based learning model has been developed that considers the previous network statistics, as realized data, to predict future network patterns by the generatorgenerator to make such predictions, called as unrealized data, as near to the realized data. Further, the proposed model uses penalty-award criteria by the discriminatordiscriminator, to fine-tune the predicted network parameters. Now, having the set of realized and unrealized data, the model uses Markov decision processMarkov decision process to perform power scheduling and effective utilization of buffer space. The buffer utilization in the intermediate nodes necessitates the model to stochastically schedule the data transmission, depending on the percentage of utilization of buffer. Simulation results denote the effective utilization of buffer that makes continued transmission of data, whenever possible, without having data packet lossdata packet loss. Also, power scheduling, by the use of goodput function and increased transmission probability improves the power utilization that ultimately increases the lifetime of the network. 2026 Walter de Gruyter GmbH, Berlin/Boston, Genthiner Stra 13, 10785 Berlin.