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Revolutionizing Healthcare with IoT: Connecting the Dots for Better Patient Outcomes
Healthcare enhances ones physical and emotional well-being via the detection, treatment, and eventual cure of disease, illness, injuries, and other debilitating conditions. The importance of information systems has increased everywhere, particularly in the healthcare sector. Information technology has long benefitted the health business, from electronic health records to cloud-based platforms. Information systems are becoming increasingly important in advancing healthcare and healthcare administration. The pandemic brought virtual space and services to all sectors of the economy, especially healthcare, which was predominantly supported through face-to-face services earlier, but due to the requirement of social distancing, hospitals started offering services in virtual mode. Also, evolution in the information system and the Internet has paved the way for the Healthcare Internet of Things (HIoT). The Healthcare Internet of Things (HIoT) is the interconnection of intelligent objects or devices that enables the development of new healthcare services and applications. HIoT can take many forms, namely medical devices, public health services, innovative technology, medication refills, and remote monitoring. This healthcare data is a new treasure for healthcare stakeholders to improve patients health and experiences while creating revenue opportunities and improving healthcare operations. Thus, HIoT is redefining healthcare by ensuring better care, improved treatment outcomes, and reduced patient costs, as well as better processes and workflows, improved performance, and patient experience for healthcare providers. HIoT devices can also be useful for asset management tasks like controlling inventory at the pharmacy, checking refrigerator temperatures, and controlling humidity and temperature in the environment. Having said the advantages, one cannot deny the challenges it has brought to safety, security, privacy, and scalability aspects. Hence, this chapter will explore the evolution of IoT in healthcare, its elements, applications, and challenges. 2024 selection and editorial matter, Alex Khang. -
Revolutionizing Healthcare: The Impact of Generative AI and Large Language Models
The chapter explores the transformative impact of generative AI and large language models (LLMs) in healthcare, emphasizing their potential to revolutionize patient care, clinical operations, and medical research. Generative AI, a subset of artificial intelligence, offers groundbreaking capabilities such as personalized medicine, virtual health assistants, and enhanced diagnostic accuracy. LLMs like Med-PaLM and BioBERT are fine-tuned to perform specific healthcare tasks, such as clinical note summarization and diagnostic support. These models also assist in drug discovery, clinical trials, and pandemic preparedness by analyzing complex medical data and predicting patient outcomes with high accuracy. The chapter also addresses the ethical and regulatory considerations associated with AI in healthcare, including data privacy, bias, and accountability. While the integration of AI technologies promises significant advancements, it also requires stringent regulatory oversight to ensure safety, efficacy, and fairness. The potential of generative AI to generate synthetic medical data offers a secure way to advance research without compromising patient privacy. Additionally, AI can optimize healthcare processes, enhance patient engagement, and accelerate medical research, contributing to a more efficient and personalized healthcare system. The chapter concludes by highlighting the need for continuous collaboration between AI developers, healthcare professionals, and regulators to maximize the benefits of these technologies while addressing the associated risks. 2025 selection and editorial matter, Sakshi Gupta, Umesh Gupta, Moolchand Sharma, Kamal Malik; individual chapters, the contributors. -
Revolutionizing High-Frequency Trading: The Impacts of Financial Technology and Data Science Innovations
This paper provides a comprehensive review of various High- Frequency Trading (HFT) strategies and the cutting- edge technologies that underpin them. It explores the latest advancements in financial data science, machine learning, and AI, highlighting their transformative role in reshaping modern trading practices. The paper delves into the algorithmic approaches employed by HFT firms and stock exchanges, including predictive analytics, time- series analysis, pattern recognition, sentiment analysis, and risk management techniques. Through a series of case studies, the paper examines successful implementations of HFT technologies at global exchanges such as the NYSE, NASDAQ, LSE, TSE, and Euronext. It also considers the regulatory and ethical implications of HFT, emphasizing the need for balance between technological innovation, market stability, and fairness. By analyzing these advancements, the paper sheds light on the future trajectory of HFT and its broader impact on financial markets. 2025 by IGI Global Scientific Publishing. All rights reserved. -
REVOLUTIONIZING HR: AI-DRIVEN TRANSFORMATION IN TALENT ACQUISITION, DEVELOPMENT, AND MANAGEMENT
Introduction: Organizations in the knowledge-based industry depend on intellectual capacity, where key talent plays a major role in their success and business sustainability. Human resource (HR) management, in its traditional form involves labour-intensive processes and practices that are prone to unconscious bias, inadequate retention measures and diversity issues creating significant challenges in talent management. Purpose: The study brings to the fore a multi-stakeholder outlook that makes the adoption of artificial intelligence (AI) very lucrative. This study aims to highlight the capabilities of AI that address its limitations and retain a human-centric approach. Scope: The study encompasses the whole gamut of talent management lifecycle, starting from recruitment automation initiatives to onboarding, career planning and development, employee engagement, performance management, and succession planning in large global organizations. Methodology: The authors carried out an extensive review of the literature and analysis of use cases encompassing the use of AI and its implementation strategies, outcomes, and best practices, spanning the myriad talent acquisition and management functions. Findings: About 40% of large organizations have plans to invest in AI-enabled skills management solutions by 2028. AI-driven talent analyticshaveenabledbettercandidateassessment,hiringbiaseshave significantly reduced, and retention rates have increased an 82%. Significant workload savings, enhanced prediction capabilities, and improved accuracy across talent management processes are some of the other benefits of AI-enabled talent management solutions. AI usage has enhanced HR professionals efficiency, leadership has improved their decision-making capabilities, and employees have experienced increased opportunities for personal and professional development. Individual chapters 2026 The authors. -
Revolutionizing Islamic finance with ethical AI: Shariah-compliant Robo-advisors
The revolutionary role that robo- advisors play in Shariah- compliant Islamic finance is examined in this essay. The first section lays out the fundamental ideas of Shariah- compliant investing, which serve as a framework for moral financial judgment. Examined is the emergence of robo-a dvisors, which show how these AI-p owered tools are improving accessibility and efficiency while enabling adherence to Shariah laws. Key characteristics of robo- advisors that adhere to Shariah are examined, along with particular difficulties including cultural sensitivity and regulatory compliance. The research integrates case studies that demonstrate effective implementations, providing valuable perspectives on their real- world uses. The paper's conclusion, which offers a look ahead, highlights how ethical AI and robo- advisors have the potential to transform Islamic finance by promoting sustainability, inclusion, and creativity in Shariah- compliant investments. 2025, IGI Global Scientific Publishing. All rights reserved. -
Revolutionizing Legal Intelligence: Advances in Neural Networks and Language Models for Legal NLP
As the legal field continues to generate vast amounts of complex text, from contracts to court rulings, machine learning and natural language processing (NLP) techniques have emerged as valuable tools to help analyze and organize this data. In this paper, a number of state-of-the-art models will be reviewed and evaluated, including transformer models like BERT, GPT, and T5, and neural network models such as LSTM and CNN-RNN hybrids. These were then tested for the legal tasks of document classification, text summarization, and entity recognition. Some of the metrics used for evaluation include Accuracy, F1-Score, ROUGE, and BLEU. Advanced models, in particular large language models (LLMs), outperform the traditional methods by a large margin since they capture the niceties of legal language and structure much more completely. Meanwhile, high-quality legal datasets remain scarce, legalese remains incomprehensible to most, and the models are still relatively unexplainable. In sum, these challenges clearly call for future research in terms of data augmentation, explainable AI techniques, and more robust training methods that would allow AI-powered tools to be integrated much more effectively within lawyers' workflows to support them in their decision-making processes. 2025 IEEE. -
Revolutionizing legal services with blockchain and artificial intelligence
[No abstract available] -
Revolutionizing lifelong learning AI, virtual, and augmented reality in education
The purpose of this chapter is to investigate the revolutionary effects that artificial intelligence (AI), virtual reality (VR), and augmented reality (AR) have had on the educational environment, specifically with regard to the revolutionization of lifelong learning. It investigates the ways in which the incorporation of cuttingedge technology is changing conventional instructional approaches, therefore providing students with individualized and immersive educational experiences. The conversation focuses on the inclusive and dynamic character of education that is made possible by artificial intelligence, virtual reality, and augmented reality, while also addressing problems such as the limitations of technology and ethical implications. It is emphasized that in order to fully realize the potential of modern technologies in the field of education, it is necessary for educators, legislators, and technologists to work together. This abstract offers a succinct overview of the ways in which artificial intelligence, virtual reality, and augmented reality are profoundly changing paradigms of lifelong learning. 2024, IGI Global. All rights reserved. -
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. -
Revolutionizing packaging sustainability through advanced materials and technologies
Consumer engagement is identified as a catalyst for positive environmental impact in the context of sustainable packaging. It probes into three fundamental aspects collectively contributing to forming a conscientious community: packaging transparency and labelling, consumer education, and packaging as a narrative medium. Education promotes advocacy for sustainability, transparent labelling enables individuals to make informed decisions, and packaging narratives establish emotional connections. Collectively, they facilitate a transition towards conscientious consumption. The potential for improved environmental impact of packaging is further enhanced by the collaborative efforts of consumers and brands, which can shape corporate strategies and contribute to preserving the planet. 2024, IGI Global. All rights reserved. -
Revolutionizing Road Traffic Management and Enforcement: Harnessing AI, ML, and Geospatial Techniques
This study investigates the synergistic application of Artificial Intelligence (AI), Machine Learning (ML), and Geospatial Technologies in optimizing traffic management systems. Through a mixed-methods research design, it evaluates the potential of these technologies to enhance urban traffic flow and reduce congestion. The research emphasizes the critical importance of data quality, ethical considerations, and the selection of appropriate technological solutions based on specific urban traffic scenarios. Findings highlight the significant role of integrated AI and geospatial analyses in improving traffic predictions and operational efficiency. Future work will focus on developing more sophisticated models that ensure privacy, equity, and adaptability to new transportation trends. 2024 IEEE. -
Revolutionizing Road Transportation: The Role of Artificial Intelligence in Smart and Efficient Systems
This chapter is about how AI is transforming road transportation by improving efficiency, safety, and comfort. By emphasising the importance of AI integration and moving on to main AI technologies such as machine learning (ML) for applications like traffic prediction and autonomous driving. Deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are essential for vehicle detection and traffic analysis. Also, natural language processing (NLP) enhances traffic planning and customer support by providing real-time information and virtual assistants. We can also know from the chapter that AI applications like self-driving cars that use AI for vision and control, intelligent traffic management systems that optimise signal timings, and predictive maintenance to avoid vehicle problems. It also discusses data privacy, technology, and ethics questions, as well as demonstrates effective real-world AI deployments and future trends. Overall, the chapter emphasises AIs disruptive impact on road transport and its potential for ongoing innovation and improvement in the pursuit of a smarter, more efficient transportation network. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Revolutionizing Schizophrenia Care for the Elderly: Blockchain-driven Predictive Models for Personalized Support
The escalation of mental health challenges encountered by the elderly community, novel research is carried out by adopting twin cutting-edge technologies, artificial intelligence (AI) and Blockchain, which are proposed in this study, focusing on schizophrenia, which is a complex mental disorder, to raise awareness of the disease, reduce negativity, and ensure positive support. symptom prediction and disease classification models have been developed using Machine learning (ML) algorithms and symptom datasets for schizophrenia prediction. Natural Language processing is also employed to find the speech patterns for mental health illness indications. The outcomes predicted with AI models are transformed into a detailed report by leveraging Llama 3.3, ensuring understanding and encouraging people to have community conversations. The generated reports are stored in the decentralized file system to achieve more security and privacy. It ensures the tamper-free nature of Blockchain, with an interactive user interface and multilingual capabilities. This research outcome allows individuals to know the early disease symptoms and get timely assistance; it ensures an illuminating way toward improved mental well-being for senior citizens. 2025 IEEE. -
Revolutionizing the financial landscape: A review on human-centric AI thinking in emerging markets
The emergence of Industry 4.0 has transformed the financial landscape by integrating unconventional technologies and artificial intelligence (AI) into consumer interactions. This chapter explores the evolving paradigm of human-centric AI-thinking in the context of emerging customer interactions in making financial decisions. The review analyses the opportunities and the challenges that arise from the integration of AI tools and human-centric approaches in addressing the diverse needs and behaviours of consumers within emerging financial markets. More specifically, the review critically examines the utilization of AI-driven technologies, such as predictive analytics, natural language processing (NLP), and machine learning algorithms, in customising the financial services to cater the emerging-market consumers. Moreover, the current study explicates how AI enables personalized customer interactions, risk assessments, and ethical decision making and financial inclusion strategies while considering the socioeconomic and cultural landscapes. The study has focussed on addressing the concerns related to data privacy, risk assessment, and transparency towards AI-powered financial solutions with ethical standards. Through an exhaustive analysis of current trends, and empirical evidence from the existing literature, this review highlights the inevitability of human-centric AI-thinking approach towards financial services decision making. It emphasizes the importance of congruent AIdriven financial solutions in the context of banking where the determinants such as empathy, financial literacy, ethical considerations, and human values plays a significant role in finding the financial services in emerging markets. This research explores the challenges and prospects and has made commendations to all the major stakeholders such as industry stakeholders, policymakers, practitioners, customers, and service providers to create a dynamic financial landscape of Industry 4.0 in AI technologies that embrace a human-centric ethos to meet the evolving needs of consumers within emerging financial ecosystems. 2024, IGI Global. All rights reserved. -
Reward Based Garbage Monitoring and Collection System Using Sensors
Most of the time in our surroundings we come across the overfilled garbage bins near the lakes. When the bins are full, people just throw the waste here and there, which eventually goes into the lakes and pollutes the water bodies. This is because of improper dumping of garbage that is practiced in our society. With the increase in population, this problem is taking really bad shape. The prime need is to maintain a clean and healthy environment with proper disposal of waste. This paper presents a small effort to reduce this garbage problem. An Android app has been created which keeps on checking whether the dustbin is full. Also, the people will be rewarded for throwing waste into the dustbins. A QR code has been attached to the dustbin which will be scanned for rewarding the people. The dustbins use an IR sensor that detects the receiver of waste in bins. Major part of this proposed system includes the proper working of mobile application and proximity sensors. Arduino is used to maintain the proper connection with sensors and application and that is done by Bluetooth sensor. The main objective of this proposed system is to lure people to put waste into the dustbin along with the contribution towards smart city vision. This paper also gives a brief overview of the technologies and work done so far in this field. 2024 River Publishers. -
Rewarding Fathers, Penalizing Mothers - A Quantitative Evidence on the Unequal Gains of Parents in Indian Labor Market
The gender discrimination is a significant issue in the labor market. Motherhood Penalty is one of the important contributors to this issue. This study aims to find the evidence of impact of parenthood on employment to population ratio and mean nominal monthly earnings concerning factors household structure and number of children under age six. Using interactive multiple linear regression models, we have derived meaningful conclusions from data collected from the International Labor Organization (ILO). Our findings reveal that there is a significant motherhood penalty in India. Womens employment probability decreases by 12.4% with one child and up to 19.09% with three or more children. Meanwhile, men experience a fatherhood bonus, with employment rates rising by up to 24.79% as they have more children. Wage disparities are also evidentmothers with two or more children earn substantially less than childless women, whereas the fatherhood wage premium is weaker than in developed economies. Mothers with two or more children earn substantially less than childless women, whereas the fatherhood wage premium is weaker than in developed economies. Through this study, we also see the probable reasons behind the results observed from the models. Lack of institutional support for working moms, workplace prejudice, and deeply rooted gender stereotypes are some of the main reasons attributing to the Motherhood Penalty. This disparity is further exacerbated by strict work rules, poor childcare facilities, and lax paternity leave regulations. Overall, the motherhood penalty is a serious phenomenon affecting the lives of many mothers and degrading their standards of living. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Rewriting Epic as a Discourse of the Marginalized: A Study of Mahasweta Devis Select Fiction
The present dissertation engages itself with an analytical study of five short stories by Mahasweta Devi, where she has rewritten episodes from the grand narrative The Mahabharata. Her stories The Five Women, Kunti and Nishadin, Souvali, Draupadi and Bhishmas thirst have been chosen for being studied in order to show how Devi counter narrates the grand epic by looking at the religious battle of Kurukshetra and the canonical epic characters from the subaltern perspective and thus creates a discourse of the marginalised. The critical framework of the study is based on a postcolonial and subaltern study of the texts as the principal characters and Devis themes are anti-canonical and anti- hegemonic. Through Devis feminist rewriting of the ancient text the subaltern sections of the society, who have been marginally represented in the canonical text, have been given a chance to speak. A readers understanding of the epic undergoes a change by going through the rewritten stories which is Devis main intention behind rewriting The Mahabharata. Through her writing she challenges the age old notions and long established truths in the epic for which it has been granted an epic stature. Thus she makes an attempt to lend a voice to the voiceless by this narrative technique and fulfils her social commitment as a journalist and activist writer. -
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RF-ShCNN: A combination of two deep models for tumor detection in brain using MRI
The tumor in the brain is the reason for jagged cell enlargement in the brain. Magnetic resonance imaging (MRI) is a common scheme to identify tumor existence in the brain. With these MRIs, the medical practitioner can examine and detect the abnormal growth of tissues and corroborate if the brain is influenced by a tumor or not. Due to the appearance of artificial intelligence models, the discovery of brain tumor is performed by adapting different models which thereby help in making decisions and selecting the most suitable diagnosis for patients. The main motivation of this work is to reduce the death rate. If they are not adequately treated, the survival rate of the patient decreases. The correct diagnoses help patients receive accurate treatments and survive for a long time. This paper develops a hybrid model, namely the Residual fused Shepherd convolution neural network (RF-ShCNN) for discovering tumor in the brain considering MRI. Thus, the Adaptive wiener filtering is adapted to filter image-commencing noise. Thereafter, Conditional Random Fields-Recurrent Neural Networks (CRF-RNN) are adapted for segmentation followed by the mining of essential features. Lastly, the features employed in RF-ShCNN for making effective brain tumor detection by means of MRI. Thus, the RF-ShCNN is built by unifying the deep residual network and Shepherd convolution neural network. The hybridization is done by adding a regression layer wherein the regression is fused with Fractional calculus (FC) to make effective detection. The RF-ShCNN provided better accuracy of 94%, sensitivity of 95% and specificity of 94.9%. 2023