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STRATEGIC PARTNERSHIPS AND REGIONAL RESILIENCE: Exploring the Evolving Landscape of India-Southeast Asia Relations
India and Southeast Asia share an elusive sphere of influence, yet face formidable challenges in realising ambitious goals set for the region. Over the years, Indias foreign policy has progressed from being principled to goal driven and objective oriented. Based on analysis of secondary sources of literature, this chapter traces through the relationship between India and Southeast Asia, highlighting a shared landscape of experiences, weaving socio-cultural practices and further boosting economic and international relations. These historical references have found avenues for remodelling in contemporary times in the form of diplomatic success in varied dimensions of engagements. Drawing from these developments and taking the transformations in the geopolitics of Indo-Pacific region into cognizance, this chapter envisions the future prospects for India and Southeast Asia through the lens of building community resilience, promoting its potential to guide regional development and explore the sustainability of social, economic and environmental systems to manage change. This renewed line of thought supports a new analytic of governance which advocates that the local define the configurations and prospects for sustainability of policy frameworks and agreements in the global system. Thus, in the background of the rising traditional and non-traditional challenges, this chapter contributes to a better understanding of change and complexity through a revitalised scope for coordination, cooperation and pragmatism in partnership between the countries. 2024 Taylor & Francis. -
Machine Learning-Based Driver Assistance System Ensuring Road Safety for Smart Cities
Technologies around smart city and green computing are gaining more and more interest from diversified workforce areas. The transportation system is one of them. The transportation vehicles are operating day and night to provide proper support for the need. This is really tiring for the transportation workers, especially the drivers who are driving the vehicle. A slight negligence of a driver may cause a huge loss. The increasing number of road accidents is therefore a big concern. Research works are going on to comfort the drivers and increase the security features of vehicle to avoid accidents. In this chapter, a model is proposed, which can efficiently detect drivers drowsiness. The discussion mainly focuses on building the learning model. A modified convolution neural network is built to solve the purpose. The model is trained with a dataset of 7000 images of open and closed eyes. For testing purpose, some real-time experiments are done by some volunteer drivers in different conditions, like gender, day, and night. The model is really good for daytime and if the driver is not wearing any glass. But with a glass in the eyes and in night condition, the system needs improvements. 2025 selection and editorial matter, Yousef Farhaoui, Bharat Bhushan, Nidhi Sindhwani, Rohit Anand, Agbotiname Lucky Imoize and Anshul Verma; individual chapters, the contributors. -
AI Applications Computer Vision and Natural Language Processing
Artificial intelligence (AI) applications in computer vision and natural language processing (NLP) have made major advances in recent years, challenging a number of sectors and areas. This multidisciplinary topic combines NLP, which examines the study of human language, and computer vision, which concentrates on the understanding of visual data. This study examines the wide range of applications that are included within this convergence, highlighting the revolutionary potential of AI technology. AI has made it possible to make significant advances in autonomous systems, object identification, and image recognition in the field of computer vision. These developments have stimulated innovation and increased efficiency, revolutionizing sectors including healthcare, autonomous vehicles, and security. Meanwhile, AI-driven advances in NLP have produced strong language models that can produce, comprehend, and translate text. These approaches have been utilized to improve accessibility and efficiency of communication in chatbots, sentiment analysis, and language translation services. This chapter explores the basic ideas and advancements in these two fields, emphasizing the opportunities and novel challenges that arise from integrating computer vision and NLP. Additionally covered are data privacy, ethical issues, and the possibility of prejudice in AI applications. The study also highlights the ongoing need for these fields' advancement and investigation in order to solve real-world problems and fully utilize AI's potential in the computer vision and NLP industries. 2025 The Institute of Electrical and Electronics Engineers, Inc. -
Understanding Binary Employees Awareness Toward LGBTQ Inclusion atWorkplaces
The LGBTQ [Lesbian, Gay, Bisexual, Transgender, and Queer] community does not comply with the conventional categorization of gender identity and sexual orientation. While there are laws that provide reservations to transgender employees, the other members of the LGBTQ community still find job security as a significant career threat if the member is open about their respective gender identity or sexual orientation. Individuals who belong to the LGBTQ community are facing several forms of discrimination in the workplace. Obtaining jobs has also become problematic. The study aims to understand the gender binary employees awareness, perspective, and support toward the LGBTQ community. The study is exploratory. The sample consisted of 238 respondents; data was collected from gender-binary employees working in white collared jobs in Bengaluru City. Gender has an impact on the awareness of binary employees regarding the LGBTQ community, sexual orientation, and sexual identity. With inclusion practices, diversity policy, and pride celebration, the world is moving toward an inclusive and welcoming sphere. Still, the absence of awareness and support will hinder the development and welfare of the LGBTQ community. The findings denote the need to increase awareness and broaden the horizon of inclusion practices. Applying inclusion awareness at all employment levels is imperative to create an equity-driven and inclusive workplace. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Internet of Senses: immersive eating and traversing into the metaverse
[No abstract available] -
Energy efficiency and conservation using machine learning
This chapter explores the fascinating nexus between machine learning (ML), energy efficiency, and conservation, concentrating on a captivating case study that makes use of the oneAPI framework. Optimizing energy consumption has become crucial due to the increased interest in sustainable practices. By investigating the use of oneAPI in energy efficiency projects, we examine the possibility of ML techniques to overcome this difficulty. We demonstrate how ML algorithms can accurately model and anticipate energy usage patterns through a thorough analysis of real-world data. Additionally, we discuss the importance of feature engineering, algorithm selection, and data pretreatment in creating accurate energy consumption models. The case study emphasizes the wider implications of utilizing ML to support energy-saving initiatives in addition to demonstrating the effectiveness of oneAPI. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Educational Management System Using Hybrid Blockchain Network
This work explores integrating blockchain technology into the education system, focusing on academic Records and administrative control systems. It addresses challenges in data handling and security in centralized education administration. The proposed framework entails a Hybrid blockchain network that integrates elements of both private and public blockchains. In this architecture, the public network functions as a university network, while individual nodes within it represent distinct colleges, each equipped with its exclusive private network. Private blockchain manages registration, secure hash generation, and student assessment, while public blockchain handles record management. Adding a randomized mining algorithm to public blockchains and a proof of authority consensus mechanism strengthens transaction security. Introducing a voting miner selection algorithm in private blockchains further enhances security measures. This integration represents a strategic evolution in education management systems, unlocking new security, scalability, and performance possibilities. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Sustainable materials for urban streets: trends, challenges, and case studies
Urban planners face a growing need for efficient, smart, and sustainable projects. One of the dynamic urban elements of cities is its streets, which accommodate the majority of the public realm. This study aims to identify sustainable materials that are employed in the construction of urban streets and analyze the potential for other sustainable materials in future street design. We conduct a thorough literature review through case studies and identify sustainable materials currently in use in the construction of urban streets across the world. This study focuses on existing and potential sustainable materials for urban streets suitable for Qatar. Hence, the objectives of this study are: (1) to identify sustainable materials in the construction of urban streets; (2) to analyze challenges to using sustainable materials in making urban streets more sustainable; (3) comparative analysis of the case studies. The study concludes with sustainable urban street design guidelines derived from Qatar. 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Early stage detection of osteoarthritis of the joints (hip and knee) using machine learning
This study explores the developing relationship between health care and technology, with a special emphasis on the use of machine learning (ML) algorithms to detect early stage osteoarthritis (OA) in the hip and knee joints. OA, a substantial worldwide health problem, requires improved diagnosis techniques. In this analysis, we illuminate the limitations of traditional methods, emphasizing the inherent subjectivity of clinical assessments and the delay in detection using routine imaging techniques. The research investigates the potential of ML to bring about significant changes. It focuses on combining various algorithms with extensive datasets and highlights the need to select relevant features and prepare the data to improve the accuracy of the models. The use of ML is closely connected to ethical issues, which include the protection of data privacy and the capacity to comprehend the models used. To bridge the gap between theory and practice, the chapter presents concrete examples of ML's practical use in detecting OA, opening possibilities for customized therapy and enhanced patient results. The chapter also highlights potential areas for future study, emphasizing the urgent requirement for additional progress in ML-based early detection techniques to alleviate the worldwide impact of OA. 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Integration of Intelligent System and Big Data Environment to Find the Energy Utilization in Smart Public Buildings
Buildings are the leading consumer of energy in the setting of smart cities, and public structures such as hospitals, schools, government offices, and additional institutions have high energy needs owing to their frequent use. However, there needs to be adequate use of the latest innovations in machine learning inside the big data context in this field. Controlling the energy efficiency of public subdivisions is a crucial aspect of the smart city concept. This chapter aims to address the challenge of integrating big data platforms and machine learning algorithms into an intelligent system for this purpose to forecast how much energy various Croatian government buildings will consume, prediction models were constructed using deep learning neural networks, Rpart regression tree models, and random forests using variable reduction techniques. The evaluation of all three techniques considered critical aspects, and the random forest methodology yielded the most precise model. The MERIDA intelligent system aims to enhance energy efficiency in public buildings by integrating big data and predictive algorithms. This research examines the technological requirements for a platform that facilitates public administration in planning public building reconstruction, reducing energy consumption and expenses, and connecting intelligent public buildings in smart cities. Digitizing energy management may improve public administration efficiency, service quality, and environmental health. 2025 Scrivener Publishing LLC. All rights reserved. -
AI-based online interview bot with an interactive dashboard
In recent years, video interviews have become increasingly popular in the recruitment process due to their convenience and efficiency. However, evaluating a candidates communication skills and perceived personality traits from a video interview can be challenging. The agent utilizes natural language processing and computer vision techniques to analyze the candidates verbal and nonverbal behavior during the interview. Specifically, the agent focuses on linguistic features such as fluency, grammar, and vocabulary, as well as nonverbal cues such as facial expressions and body language. Based on these features, the agent predicts the candidates communication skills and perceived personality traits. To validate the effectiveness of the agent, a Talk was conducted with a group of participants who completed video interviews with and without the agent. The results show that the agents predictions of communication skills and perceived personality traits are highly correlated with the ratings given by human evaluators. Additionally, the agent is able to provide valuable insights into the candidates performance that may not be immediately apparent to human evaluators. Overall, the intelligent video interview agent proposed here has the potential to improve the recruitment process by providing more accurate and objective evaluations of candidates communication skills and perceived personality traits. 2025 selection and editorial matter, A. Vadivel, K. Meena, P. Sumathy, Henry Selvaraj, P. Shanmugavadivu and Shaila S. G.; individual chapters, the contributors. -
Regression Approach for Predictive Analysis in Cognitive Decline
Cognitive decline refers to the deterioration of cognitive abilities, including memory, thinking, and reasoning, often associated with aging or neurological disorders like Alzheimer's disease. Machine learning (ML) methods can be used for predicting cognitive decline. Techniques such as Generative Adversarial Networks (GANs), feed-forward neural networks, supervised, and unsupervised learning process and analyse data patterns to forecast cognitive changes. By analyzing large datasets, ML algorithms can identify subtle cognitive shifts and predict future decline, enabling early intervention and personalized healthcare strategies. These diverse ML methods provide valuable tools for understanding, detecting, and potentially mitigating cognitive decline, advancing our ability to address cognitive health challenges. Some of these methods have been discussed later. In this research paper, a model to predict cognitive decline using principles of logical regression is proposed. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Ecofriendly Approaches for Ameliorating the Adverse Effects of Cadmium in Plants by Regulating Physiological and Defense Responses: An Overview
Mitigating cadmium stress in agricultural plants becomes extremely critical in order to assure food sufficiency in the scenario of a rapidly growing population. An extensive review of environmentally friendly methods for reducing cadmium toxicity in plants is provided in this chapter, with special attention to a variety of tactics like phytohormones, polyamines, melatonin, mineral ions, nanoparticles, and transgenic techniques. Nanoparticles are capable of changing the distribution of cadmium, activating antioxidant defense mechanisms, and boosting physiological processes that are crucial for plant resilience and growth. Microorganisms greatly increase plant resistance to cadmium stress by modifying phytohormones and regulating defense-related proteins. Phytohormones can increase a plants adaptability to cadmium stress through a number of mechanisms, such as the regulation of gene expression and physiological processes. Melatonin and polyamines provide protection against oxidative stress and heavy metal toxicity, while mineral ions such as silicon, calcium, zinc, iron, and selenium increase plant resistance to cadmium, minimizing pollution-related harm. Transgenic plants that are tolerant to cadmium exhibit enhanced detoxification processes and reduced metal accumulation. These findings provide important insights for long-term plant cadmium mitigation and highlight the significance of interdisciplinary approaches in managing heavy metal stress in agricultural systems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Natural Language Processing in Medical Applications
Medical applications of machine learning are very new, and there are still several obstacles that limit their widespread use. There is still a need to address issues like high dimensionality data and a lack of a standard data schema. An intriguing way to explore the possibilities of machine learning in healthcare is to apply it to the difficult problem of cardiovascular disease diagnosis. At the present day, cardiovascular disorders account for the majority of deaths worldwide. It is often too late to adopt appropriate treatment for many of them because they progress for a long time without showing any symptoms. Because of this, its crucial to get checked up on routinely so that any developing diseases can be caught early. If the sickness is caught early enough, effective therapy can be put into place to stop the progression of the illness. This is done with the intention of analysing data from many sources and making use of NLP to overcome data heterogeneity. This paper evaluates the usefulness of several machine learning methods (such as the Naive Bayes (NB), Transductive Neuro-Fuzzy Inference, and Terminated Ramp-Support Vector Machine (TR-SVM)) for healthcare applications and suggests using Natural Language Processing (NLP) to address issues of data heterogeneity and the transformation of plain text. The implementation, testing, comparison of performance and analysis of the parameters of the algorithms used for diagnosis have simplified the process of selecting an algorithm better suited to a certain instance. TWNFI is particularly effective on larger datasets, while Terminated Ramp-Support Vector Machine is well suited to lesser datasets with a lower number of magnitudes due to performance difficulties. 2024 Scrivener Publishing LLC. -
Plant, Animal, and Microbial Sources of Dyes and Mordants
Synthetic dyes and mordants have been used by various industries, including food, cosmetics, textiles, and pharmaceuticals, for many decades. However, their potential hazards to the environment and human health, such as carcinogenicity and teratogenicity, have raised global concerns. In earlier decades, people used naturally extracted dyes and mordants from plants and insects for purposes like painting, dyeing clothes, and enhancing skin and hair, using substances like henna, turmeric, and saffron. However, chemically synthesized dyes quickly replaced natural dyes due to their easy availability and low cost. Currently, consumers are becoming more conscious of the use of synthetic dyes and their effects, which can cause allergies and toxicity. This has led to a resurgence of eco-friendly dyes and biocolors, which have gained importance. There has been advanced and increased development in utilizing naturally occurring bioresources to produce sustainable biocolors with multifunctional applications. Natural colors have not only increased their market value due to their aesthetic appeal but also for their various properties, including antibacterial, antiviral, anticancer, anti-inflammatory, and antioxidant effects. Indeed, biocolors derived from plants, animals, and microorganisms have better degradability and compatibility with the environment. These naturally occurring pigments need to be explored from various natural sources to meet the increasing global demand, using suitable techniques for their extraction. 2025 Apple Academic Press, Inc. -
Bionanomaterials in Food Applications and their Risk Assessment
Nanotechnology has increased impressively during the last decade for their diverse potential uses in food, environment, medical, sustainable energy and so forth. Nanomaterial synthesis by chemical methods has unintended properties on the ecological pollution and also effect on human welfare. To overcome these challenges green synthesized nanoparticles (NPs) has been used from plants and animals. The green synthesized NPs include gold (Au NPs), copper (Cu NPs), silver (AgNPs), iron and its oxides (Fe NPs). Abundant microbes and plants are used for the synthesizing NPs that are eco-friendly, cost effective and potentially safe. Further, these can be constructed using agri-food waste sources such as agricultural crops, fruits and vegetables, cereals, oil cakes, alcoholic beverages, and so forth, for synthesizing sustainable NPs, reducing environmental issues. These green synthesized metallic NPs needs to be further characterized for the synthesis, factors affecting the parameters and their potential applications in various fields with major challenges that needs to be researched such as toxicity and translational research. 2025 selection and editorial matter, Shakeel Ahmed; individual chapters, the contributors. -
A case study on a beacon of hope transition: India's renewable energy integration and the Ujwal DISCOM assurance Yojana (UDAY)
This case study examines the transformative impact of the Ujwal DISCOM Assurance Yojana (UDAY) on India's energy landscape, focusing on its role in facilitating renewable energy integration. India's energy sector faced daunting challenges, including financially distressed power distribution companies (DISCOMs) and high aggregate technical and commercial (AT&C) losses. UDAY's financial restructuring and operational efficiency improvements led to remarkable reductions in DISCOMs' debt burdens and AT&C losses, respectively. The policy aligned with India's renewable energy goals, driving DISCOMs to procure renewable energy sources. Consequently, India witnessed significant growth in its renewable energy capacity, environmental benefits through reduced emissions, and economic growth via job creation. This case study offers insights into the challenges faced, technological advancements incentivized, and the long-term sustainability of these reforms. Moreover, it presents broader lessons for energy sector reform and renewable energy integration, both within India and globally. 2024, IGI Global. All rights reserved. -
Bionanomaterials in Improving Food Quality and Safety
Current inventions in the area of nanotechnology opened several transformations in scientific and industrial sectors. One such rapidly developing technology gets a lot of application in the food industrys changing the culture of food cultivation to its several branches, like production, processing, packaging, preservation, detection of foodborne pathogens, transportation, shelf life and bioavailability of its valuable nutrients. Far smaller in size and in surface area is strongly related to its stability in terms of chemical and biological activities. Hence, food nanotechnology empowers advancement in several novel bio-nanomaterials with an extensive choice towards potential applications. Nanotechnology benefits the food industry in several ways: to extend and predictable for the growth due to recent and swiftly developing technology influences the characteristic of the food products, which should not get exposed to human and microbial activities. Therefore, implication of bio-nanomaterials in food-related industries pose a significant contribution for economy and also a key community concern. The involvement of nanotechnology throughout the life cycle of food processing, storage, transportation, safety, and potential benefits to mankind are also briefly reviewed in this chapter. Acceptance of nano-based ingredients by the public in various phases of the food business and their associated safety and regulatory measures pertaining to food items can be improved by many methods of nanotechnology. 2025 selection and editorial matter, Shakeel Ahmed; individual chapters, the contributors. -
Orality, Literacy, and Modernity: A Reading of The Legends of Khasak
What is the relationship between literacy and culture? It is not possible to give a simple answer to this question. Eric Havelock, while commenting on ancient Greek culture and literacy, observes that the classic culture of Greece had attained an advanced stage even before the emergence of Greek script. It continued to exist as an oral culture for a long time (Havelock 1963, 117120). A culture without a script is not uncivilized or underdeveloped. Havelock observes: One can propose with assurance that the pre-Homeric epoch the Dark Age yields for the historian what might be called a controlled experiment in non-literacy. Here, if anywhere, we can study those conditions on which a total culture, and a very complex one, relied for its preservation upon oral tradition alone. (pp. 11718) 2025 selection and editorial matter, E.V. Ramakrishnan and K.C. Muraleedharan; individual chapters, the contributors. -
Cross-disciplinary collaborations and partnerships for sustainability education: Including community-based learning, industry partnerships, and international collaborations
Multidisciplinary collaboration is crucial to sustainable education. It places a strong emphasis on combining many academic disciplines to address complex environmental issues and promote sustainable lifestyles. Students can put their knowledge into practice and raise awareness of the world through community-based learning, corporate partnerships, and international collaborations. The chapter emphasizes effective methods for evaluating the effectiveness and impact of collaborative activities. It highlights how crucial innovation, knowledge sharing, and international cooperation are to building a sustainable future. Social justice, action-oriented learning, and lifetime professional growth are all components of sustainability education. Institutional barriers, cultural differences, and resource constraints impact these cooperative initiatives. Future generations' perceptions of the Earth can be affected via interdisciplinary collaboration and partnerships. 2024, IGI Global. All rights reserved.