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Future Perspectives of Microplastic towards Environmental Assessment
Microplastic (MP) pollution is an outcome of the widespread use of non-biodegradable plastic and improper disposal. This leads to contamination of environmental resources, such as landfills, and all kinds of water reservoirs including but not limited to sea, fresh water, drinking water, and even wastewater. Recent reports have highlighted the presence of MPs in the human body, including blood, lungs, placentas, and breast milk, indicating the severity of the issue. It is thus crucial to eliminate these hazardous contaminants from the environment. One of the effective methods to address the concern while reducing the adverse effects is to remove the MPs at their discharge points. Nanomaterials with exceptional properties like high surface area, ease of functionalization, and high affinity toward various pollutants act as excellent adsorbents. In this chapter, we present an overview of emerging nanomaterial-based adsorbents, such as photocatalysts, metal-organic frameworks, carbon-based nanomaterials, and nanocomposites, for effective removal of MPs from aqueous media via adsorption, photo-catalysis, and membrane filtration. However, considering that the research in the area of MP pollution is still in its infant stage, we aim to provide a brief account of the strengths, weaknesses, and future research dimensions of nanomaterial-based adsorbents for removing MPs from aqueous media. 2025 selection and editorial matter, Nirmala Kumari Jangid and Rekha Sharma; individual chapters, the contributors. -
Navigating Post-Pandemic Mental Health Challenges: Unleashing the Potential of Artificial Intelligence
In the wake of the COVID-19 pandemic, the landscape of healthcare, particularly in the realm of mental health, has undergone unprecedented shiftsThis chapter delves into the pivotal role of artificial intelligence (AI) in shaping post-pandemic mental healthcare deliveryThis chapter comprehensively explores AI-driven solutions, including predictive modeling, sentiment analysis, and virtual assistants; this chapter underscores how AI technologies can revolutionize mental health diagnosis, treatment, digitalized psychometric assessment and support care websites, and automated consultation or recommendation of specific mental issuesAdditionally, it discusses integrating AI-powered tools in remote monitoring, teletherapy, and digital interventions to address the increasing demand for mental health services and bridge existing gaps in accessibility and conversion of digital platforms to provide mental health services and appointments, case records, and easy accessibilityMoreover, ethical considerations, data privacy concerns, and the importance of maintaining human-centric approaches in AI-enabled mental healthcare are examined per the Information Act and Mental Healthcare ActBy shedding light on the transformative potential of AI, this chapter aims to empower healthcare stakeholders to leverage cutting-edge technologies in fostering resilience and recovery in the post-pandemic era. 2025 selection and editorial matter, Philip Eappen and Narasimha Rao Vajjhala; individual chapters, the contributors. -
Breast Cancer Classification Using Machine Learning A Study
Nowadays, breast cancer is the most common disease found in women. Although many researchers and experts have aimed to discover the solution to this widespread disease, they have not determined it. In this study, the techniques that are used to find the early signs of breast cancer with the use of machine learning (ML) are discussed. ML is an emerging technology in the field of computer science and information technology, especially in disclosing medical diagnoses. ML is also used, for example, in image recognition, speech recognition, traffic prediction, virtual personal assistants, and online fraud detection. There are plenty of algorithms and techniques that are used in ML. Some of the most popular techniques are discussed in this study. 2025 selection and editorial matter, A. Malini, Surbhi Bhatia Khan, S. Kayalvizhi, and Mohammed Saraee; individual chapters, the contributors. -
Precision agriculture takes flight: Drone technology in crop management
[No abstract available] -
Ethical AI in Humanitarian Contexts: Challenges, Transparency, and Safety
This chapter elaborates on how emerging technologies for artificial intelligence (AI) can help create social change and solve worldwide problems. The chapter brings to light the issue of ethical matters and responsible AI practices that should be considered to avoid technology usage by the vulnerable population to harden already present inequalities. This chapter also examines the role of AI in ensuring that quality education is accessible to all, in addressing poverty through innovative approaches, and in the amplification quest of human rights advocacy by marginalized groups. This chapter presents a complete picture of the impact of AI on humanitarianism, exemplifying the devices of new horizons and emphasizing the necessity of responsible and inclusive applications. This chapter provides findings and advice for researchers, practitioners, policymakers, and all interested parties who are involved in using the new technologies to make their world fairer and well-sustained. The chapter aims to comprehend the AI-humanitarianism nexus and simultaneously proclaim safety measures and transparency for the sake of social upheaval. 2025 selection and editorial matter, Adeyemi Abel Ajibesin and Narasimha Rao Vajjhala; individual chapters, the contributors. -
Stability and Effciency Enhancement of Perovskite Solar Cells
The greatest notable efficiency increases in recent years have been observed in perovskite solar cells (PSCs). With an ABX3 crystal structure, perovskite is an organic-inorganic hybrid chemical that generally has an arrangement similar to that of BaTiO3-. In this configuration, X stands for halogens, such as oxygen (O), iodide (I?), bromide (Br?), or chloride (Cl?), while A and B are variously sized cations that coordinate 12-fold and 6-fold, respectively, with X anions. Cations such as formamidine and methylammonium alter the lattice parameters, with the bandgap growing as the lattice parameters increase, but they have no direct effect on the valence band maxima. Comparable to the body-centered cubic lattice with extra anions on a unit cell's faces is the ideal perovskite structure. To achieve high power conversion efficiency (PCE), perovskite absorbers and PSC device topologies must have high charge. Consequently, increasing electron mobility, prolonging carrier life span, and lowering defect density all depend on improving the perovskite absorber's material quality. 2026 selection and editorial matter, T.D. Subash, J. Ajayan, and Leong Wai Yie; individual chapters, the contributors. -
Assessing the Efficacy of Artificial Intelligence (AI) Applications in Predictive Policing: A Systematic Review Method
Artificial intelligence (AI) has gained attention for its potential to improve law enforcement operations through proactive policing. Advancements in data science have shown the potential benefits of applying machine learning (ML) in the criminal justice sector. Therefore, research in improving methods to forecast the likelihood of criminal reoffending is quickly growing. Creating a cutting-edge model for using ML to predict recidivism is challenging. We picked 12 out of 79 studies from Scopus and PubMed online databases in a comprehensive review that ensures the models can be replicated across various datasets and are suitable for predicting recidivism. Using two specific measures, the 12 research compared different datasets and machine learning algorithms. This study demonstrates that each approach achieves strong performance, with an average accuracy score of 0.81 and an average area-under-the-curve score of 0.74. This systematic research emphasizes essential factors that could enable criminal justice professionals to consistently utilize forecasts of recidivism risk generated by machine learning approaches. The factors include performance indicators, transparent algorithms or explainable AI approaches, and high-quality input data. 2026 Sofia Khatun, K. Sivananda Kumar. All rights reserved. -
Tracing the Evolution of Digital Strategy with AI, Blockchain, Cloud, and Cryptocurrencies
This chapter explores the transformative role of key technologies - artificial intelligence (AI), blockchain, cloud computing, and cryptocurrencies - in shaping contemporary digital strategies. It traces the historical evolution of these technologies and highlights their individual and synergistic contributions to business, governance, and society. AI has progressed from theoretical concepts to practical applications across diverse industries, enhancing decision-making, automation, and operational efficiency. Initially conceived for cryptocurrencies, blockchain technology now plays a pivotal role in securing and streamlining finance, healthcare, and supply chain management transactions. Cloud computing has democratized access to advanced technologies, accelerating the integration and scalability of AI and blockchain. Cryptocurrencies, built on blockchain frameworks, are reshaping global financial systems through decentralization and security. The chapter also addresses the challenges and opportunities of technological convergence, including ethical considerations, regulatory challenges, and the strategic need for multidisciplinary collaboration. By analyzing these intersections, this article provides a comprehensive understanding of how AI, blockchain, cloud computing, and cryptocurrencies drive digital strategies' future. 2026 Manjari Sharma, Sharad Gupta. All rights reserved. -
Innovation and Governance in the Digital Era: Exploring the Complexities of the Digital Supply Chain
This chapter investigates multiple factors and dynamics related to the digital supply chain and the involvement of national institutions, government authorities, and various stakeholders. In the highly advanced era of technological innovations and globalization, the digital supply chain has become decisive for modern economies. An interdisciplinary focus addresses the implications of digitalization for female workers, industrialization tendencies, global supply chains, and the enforcement of corporate codes of conduct. Given that modern digital technologies alter traditionally accepted methods of production and supply, it is important to understand the social and economic effects on female workers and the changes in opportunities and conditions for them at the current point. The purpose of the current research is to identify gender gaps and access to working opportunities and investigate the role national institutional frameworks and government authorities play in supporting womens empowerment in the digital supply chain. Primarily, the chapter aims at assessing the implications of digitalization on industrialization in the developed and developing world. Additional focus will be made on the opportunities and obstacles associated with automation, data analytics, or artificial intelligence, and ways of applying them to ensure sustainable development. Case studies and empirical research are likely to offer a comprehensive picture of the strategies governments, international organizations, and stakeholders can use to address the challenges of digital industrialization and address issues of social equity and just exposure to the opportunities opened through innovative tools and techniques. The relatively new concept of globalization is also closely connected to digital tools and technologies that are believed to facilitate the flow of goods and services and improve the conditions for efficient and fast supplies. However, on the other hand, global chains of supply are associated with specific challenges, such as disruptions or surveillance. Digitalization is also likely to boost unethical behavior and human rights violations. Hence, an important achievement of the current chapter is to investigate the interaction between digitalization and global supply chains and provide suggestions for the collaborative governance strategies that would promote openness, transparency, and better interaction. An alternative idea for the research is the evaluation of the efficiency of corporate codes of conduct employed to address human, civil, or product rights violations and guarantee consistency with environmental standards or regulations. To accomplish the goal, the chapter will focus on the evidence available to investigate the enforcement strategies and monitoring programs or approaches that companies rely on. An analysis of these two options is likely to result in a comprehensive understanding of the roles government authorities, institutions or organizations, and stakeholders play in preserving just working conditions and ethical standards in digital supply management. A tendency to consider the critical effects of digitalization on different administrative levels and stakeholders can be observed. That is why it is important to concentrate on the contributions of such approaches and the development of joint policies to ensure a balance between receiving the benefits of digitalization and avoiding its detrimental effects. 2025 selection and editorial matter, Saurabh Tiwari and Richa Goel; individual chapters, the contributors. -
Internet of Medical Things-Based Smart System for the Mental Health Care Challenges in 21st Century: Trends and Progress
Depression is a major mental health challenge in the 21st century and the factors that contribute to depression are rising on a day-to-day basis. Rapid urbanization and modernization brought in drastic changes in the way of life, including family type, nature of relationships, eating habits, pattern of socialization, entertainment, and various other aspects. Sometimes, this often leads to isolation and alienation in a well-connected world. In addition to the above challenges, war and climate change also pose major threats to mental health and well-being of the world population. According to the WHO, around 3.8% of the world population experienced depression in 2023 and this includes 5% of the adult population globally. Depression is considered as a leading cause of suicide and a major mental health issue among individuals in the age range of 15-29 years. This chapter reviews the applications of Internet of Medical Things (IoMT)-based systems to effectively manage depression and the related mental health care challenges in the present world. The study gives an overview of the existing trends by exploring working systems and prototypes for their service features, ethical aspects, privacy, and confidentiality. 2026 selection and editorial matter, Balakrishnan C, Jayapriya J, Vinay M, Sanjeev Kumar Singh, Nadarajah Manivannan individual chapters, the contributors. -
Revolutionizing Mental Health Care: The Transformative Power of Internet of Medical Things (IoMT): A Comprehensive Overview and Case Studies
This study proposes the detection of humans in disaster-affected areas from the images obtained from unmanned aerial vehicle (UAV). Specialized rescue teams do such search and rescue operations to search a vast area for missing persons. The degrading effect of blur induced by camera movement during image capture is one of the primary difficulties in data processing of UAV photography. This can be caused by the UAVs natural flying movement, severe winds, turbulence, or unexpected operator inputs, all of which reduce accuracy. This blur disturbs the visual analysis and interpretation of the data, causes errors, and can degrade the accuracy. As a result, this approach proposes a method based on contrast-limited adaptive histogram equalization (CLAHE) and deblurring using Gaussian blur kernel as the solution. A variety of UAV datasets were used to verify the methods speed and reliability. This method proves to be fast and efficient, making the algorithm applicable for UAV dataset. 2026 selection and editorial matter, Balakrishnan C, Jayapriya J, Vinay M, Sanjeev Kumar Singh, Nadarajah Manivannan individual chapters, the contributors. -
Revolutionizing Mental Health Care: The Transformative Power of Internet of Medical Things (IoMT): A Comprehensive Overview and Case Studies
This study proposes the detection of humans in disaster-affected areas from the images obtained from unmanned aerial vehicle (UAV). Specialized rescue teams do such search and rescue operations to search a vast area for missing persons. The degrading effect of blur induced by camera movement during image capture is one of the primary difficulties in data processing of UAV photography. This can be caused by the UAVs natural flying movement, severe winds, turbulence, or unexpected operator inputs, all of which reduce accuracy. This blur disturbs the visual analysis and interpretation of the data, causes errors, and can degrade the accuracy. As a result, this approach proposes a method based on contrast-limited adaptive histogram equalization (CLAHE) and deblurring using Gaussian blur kernel as the solution. A variety of UAV datasets were used to verify the methods speed and reliability. This method proves to be fast and efficient, making the algorithm applicable for UAV dataset. 2026 selection and editorial matter, Balakrishnan C, Jayapriya J, Vinay M, Sanjeev Kumar Singh, Nadarajah Manivannan individual chapters, the contributors. -
AI-Driven Health Coach for Diabetes Management
Artificial intelligence (AI) is transforming diabetes care through innovative approaches that enhance monitoring, prediction, and treatment. AI-powered health coaches exemplify this progress by automating various aspects of patient care, such as creating personalized dietary plans and managing medication schedules, thereby optimizing resource utilization with minimal human intervention. In India, where diabetes affects over 77 million people and significantly elevates the risk of complications like heart disease and stroke, AI-driven tools offer immense potential. Food recognition and nutritional apps powered by AI can revolutionize diabetes management by tracking dietary intake and providing tailored recommendations. However, widespread adoption faces barriers, including challenges related to localization, cultural relevance, and integration with healthcare systems. This chapter examines the role of AI in diabetes management, evaluating the benefits and limitations of current applications. It also proposes a framework for an AI-driven health coach tailored to the Indian context. The proposed solution aims to bridge existing gaps by delivering accurate, culturally sensitive, and integrated diabetes management tools, ultimately improving long-term health outcomes for Indian patients. 2026 selection and editorial matter, Balakrishnan C, Jayapriya J, Vinay M, Sanjeev Kumar Singh, Nadarajah Manivannan individual chapters, the contributors. -
Tapestry of Promise and Peril: NLP, AI, and IoMT in Healthcare Transformation - A Review
This chapter investigates the intersection of NLP, AI, and IoMT in healthcare, as the former two will unlock the medical jargon to create accessible communication, while the latter enables real-time monitoring and decentralized clinical trials, thus providing predictive insights into personalized care plans. A systematic literature search was carried out using keywords in important academic databases that include PubMed, Scopus, and Google Scholar from 2022 to 2024. Shadows lurk amidst this optimism. Security and privacy concerns pertaining to IoMT data loom large. Decentralized trials are clouded by ethical concerns and regulatory hurdles. The specter of inequality threatens to increase the digital divide. To bridge this gap, a multipronged approach is crucial. Secure, privacy-preserving NLP and AI algorithms are the foundation. Robust IoMT infrastructure with blockchain-based security and interoperability standards is the framework. Clear, ethical frameworks for decentralized trials are the guiding threads. Ultimately, inclusivity is key. Bridging the digital divide and empowering patients and healthcare workers alike will ensure this future benefits all. 2026 selection and editorial matter, Balakrishnan C, Jayapriya J, Vinay M, Sanjeev Kumar Singh, Nadarajah Manivannan individual chapters, the contributors. -
Adverse Childhood Experiences, Psychological Well-wBeing, and Grit: A Comparative Study between LGBTQIA+ and Cis-Heterogeneous Sample of India
Adverse childhood experiences (ACEs) is a major concern that has been related to serious health consequences. Moreover, lesbian, gay, bisexual, transgender, intersex, asexual, and queer (LGBTQIA+) individuals are more likely to experience ACEs than cis-heterosexual individuals, especially in India. However, research in India has been scarce. This study compared these variables between Indian LGBTQIA+ individuals (n = 102) and cis-heterosexual individuals (n = 118) aged between 18 and 25. The findings of this comparative study reveal significant differences between LGBTQIA+ and cis-heterogeneous groups in terms of ACEs and grit levels. Notable differences were also discovered in three domains of psychological well-being: environmental mastery, positive interpersonal relationships, and self-acceptance. However, the vulnerability of LGBTQIA+ individuals in India reveals itself in descriptive statistics that report they are susceptible to negative outcomes in mental health. This study further emphasizes the importance of implementing focused interventions and support to increase psychological well-being and grit in the LGBTQIA+ community. 2026 selection and editorial matter, Balakrishnan C, Jayapriya J, Vinay M, Sanjeev Kumar Singh, Nadarajah Manivannan individual chapters, the contributors. -
Healing beyond Beliefs: A Posthumanist Reading of IoMT Advancements
Healthcare procedures powered by the Internet of Medical Things (IoMT) blur the boundaries between human and non-human entities, contesting the naturalness of human beings. It aligns with posthumanist philosophy, which challenges narratives of human identities by emphasizing technologys role in human existence and progress. Contextually, the exemplary technological advancements in healthcare services have challenged popular belief systems nurtured by religious perspectives. It has shifted the focus of healing prayers from seeking miraculous healing to praying for reception and efficiency of treatments, thus encouraging people to embrace scientific remedies unconsciously. This chapter explores the uses of IoT sensors, devices, and procedures from a sociocultural posthumanist lens that includes Donna Haraways Cyborg Manifesto, " Katherine Hayles How We Became Posthuman, " and Bruno Latours Actor-Network Theory. It also examines the initial religious resistance to IoMT applications and their eventual success against the pastoral power fostered by religious narratives. In addition, the chapter contests the normalization of pseudoscientific faith healing as opposed to embracing scientific solutions, which can be hazardous to public health and lead to the exploitation of the ignorant masses. The chapter advocates for normalizing a progressive scientific temper that endorses and employs effective, ethical, equitable, and democratized access to scientific healthcare solutions. 2026 selection and editorial matter, Balakrishnan C, Jayapriya J, Vinay M, Sanjeev Kumar Singh, Nadarajah Manivannan individual chapters, the contributors. -
Data- Driven Insights for Decision- Making in the Stock Market by Using Meta- Analyses
The stock market structure is complex, dynamic, and continually evolving. This makes it harder for investors to make smart decisions. Using data-driven insights to inform investment decisions has become increasingly prevalent in recent years. This research study focused on two main parameters: investors behavioral biases and initial public offering (IPO) pricing. Forty-five past studies from 2010 to 2022 were analyzed using meta-analyses. The study initially delves into the difficulties investors face in choosing suitable stock market investments. It then covers the various types of data that are available to investors. The paper proceeds to examine the techniques for analyzing stock market data. Finally, the article concludes by discussing the advantages of implementing data-based insights in investment decision-making. 2025 CRC Press. -
An Application of Improved Support Vector Machine Classifier for the Study of Breast Cancer Detection
Breast cancer is known to be a major global health challenge, necessitating effective early detection strategies to improve patient outcomes and reduce mortality rates. This research focuses on the application of machine learning algorithms for the detection of breast cancer. The dataset considered includes a wide array of features extracted from breast tissue samples, enabling the evaluation of five different machine learning algorithms. These algorithms were chosen for their proven efficacy in medical diagnostics and their potential to complement traditional diagnostic methods. Among the algorithms evaluated, the support vector machine (SVM) emerged as particularly noteworthy, achieving an impressive accuracy rate of 98.27%. SVM demonstrated robust capabilities in accurately categorising breast cancer cases, effectively distinguishing between benign and malignant tumours with high precision. This underscores SVMs potential as a valuable tool for enhancing breast cancer detection accuracy, thereby aiding clinicians in making informed decisions. Furthermore, this research highlights the importance of leveraging large-scale datasets like WBCD to train machine learning models effectively. Such datasets provide a comprehensive set of features that enable algorithms to discern complex patterns and correlations, which may not be apparent through conventional methods alone. This data-driven approach not only enhances diagnostic accuracy but also lays the groundwork for personalised medicine approaches tailored to individual patient profiles. To summarise, the following study emphasises the transformative role of machine learning in oncology, specifically in early breast cancer detection. Continued research and validation of these algorithms across diverse datasets will be crucial in further improving their effectiveness and applicability in real-world healthcare settings, ultimately benefiting patients globally. 2026 selection and editorial matter, Ravichander Janapati, Usha Desai, Steven Fernandes, Rakesh Sengupta, Shubham Tayal; individual chapters, the contributors. -
E-Learning Recommender System for Deaf and Hard of Hearing Learners
People with disabilities, including deaf and hard of hearing (DHH), face numerous resources online and need support to choose the right learning materials according to their preferences in communication and learning style. Content recommendation engines may help the DHH learners by suggesting the best possible matching resources to find out the suitable learning materials according to the preferences of learners. Content recommenders that use tag-based clustering techniques reduce the search space by filtering learning objects that match users search keywords at the first level and then present the learning objects with the specified accessibility preferences in terms of communication and learning style in the next level. This chapter presents a detailed study focusing on the tag-based content recommender systems in the e-Learning domain that support learners with sensory impairment, especially DHH learners. 2025 selection and editorial matter, Urmila Shrawankar and Prerna Mishra; individual chapters, the contributors.