Browse Items (11810 total)
Sort by:
-
Securing patient information: A multilayered cryptographic approach in IoT healthcare
The increasing integration of devices utilising the of Internet of Things (IoT) in healthcare has resulted in the collection of an unparalleled volume of patient data. Personal identifiers, insurance information, medical history, and health monitoring measures are all included in a complete dataset. Ensuring security and privacy of IoT devices is crucial in the healthcare sector. The goal of this project is to combine steganography with three different cryptographic algorithms to develop a hybrid cryptographic technique. Among the algorithms under investigation are steganography, Caesar cipher, columnar transposition cipher, and one-time pad. Every encryption scheme uses three keys to encrypt patient data. The encrypted data is subsequently encoded into an image file through image-based steganography. To ensure confidentiality and authentication, an authorised user can decrypt the file through a designated decryption process, maintaining the integrity of patient data. 2025 selection and editorial matter, Keshav Kumar and Bishwajeet Kumar Pandey; individual chapters, the contributors. -
Unlocking Happiness: The Power of Spiritual Intelligence for Emerging Adults
This study investigated the relationship between spiritual intelligence (SI) and happiness among emerging adults. 163 undergraduate and postgraduate psychology students from private universities completed standardized measures of SI and subjective happiness. Results showed positive correlations between SI and happiness (r = 0.26 to 0.59, p <.01). Two SI domains - transcendental awareness and conscious state expansion - were found to be significant predictors of happiness. The findings suggest that SI plays a crucial role in promoting happiness among emerging adults, supporting the hypothesis that SI can be used as an aid in the process of achieving happiness through independent decision-making and responsibility. 2024 selection and editorial matter, Dr. Sundeep Katevarapu, Dr. Anand Pratap Singh, Dr. Priyanka Tiwari, Ms. Akriti Varshney, Ms. Priya Lanka, Ms. Aankur Pradhan, Dr. Neeraj Panwar, Dr. Kumud Sapru Wangnue; individual chapters, the contributors. -
The Efficacy of Multi-Component Intervention for Adolescents with Problematic Video Gaming in a Community-Based Setting
Video gaming is a popular leisure activity enjoyed by millions globally, helping with socialisation, interaction, and relieving stress. It may also become a maladaptive coping mechanism to evade distress and negative emotions, leading to problematic usage. Research evidence shows that problematic gaming is associated with different psychosocial issues. Video games can be a way of negative coping and escaping reality, and problematic usage can hide other problems of players in real life. Adolescents are vulnerable to problematic use due to their developmental stages, and those with specific vulnerabilities and disabilities are at greater risk. No one psychotherapy has all the answers, and the multi-component intervention technique might have better treatment utility than a solitary behaviour intervention. The research aims to show the effectiveness of the intervention for problematic video game usage in a community-based setting. The study focuses on adolescents in seventh through ninth grade who were identified as problematic video gamers (not addictive users) from a selected group of schools in Kerala. The study employed an experimental design, encompassing both intervention and control groups, to systematically assess the effects of the experimental manipulation and establish a baseline measurement. The paired t-test results showed no significant decrease in the intervention groups Gaming Addiction Scale at the post-test, but it did lower the addiction scores. By conducting the research, we provide psychological care for adolescents and help them identify and prevent problematic gaming experiences. The research underscores the significance of early identification and prevention of problematic video game usage among adolescents, advocating for a holistic approach incorporating diverse components. 2024 selection and editorial matter, Dr. Sundeep Katevarapu, Dr. Anand Pratap Singh, Dr. Priyanka Tiwari, Ms. Akriti Varshney, Ms. Priya Lanka, Ms. Aankur Pradhan, Dr. Neeraj Panwar, Dr. Kumud Sapru Wangnue; individual chapters, the contributors. -
Blockchain and the Evolving Internal Audit Function
Blockchain Technology indicates a transformative era for internal audit practices in the evolving digital finance and operations landscape. This research explores the internal audit function in a Blockchain-driven world, emphasizing the changing perspectives and methodologies necessitated by this disruptive technology. With its foundational principles of transparency, immutability, and decentralization, Blockchain presents challenges and opportunities for internal auditors. The paper delves into how Blockchain is poised to redefine traditional audit practices, moving towards more real-time and continuous auditing techniques. It examines the implications of Blockchain for risk assessment, fraud detection, and compliance, highlighting the shift towards proactive rather than reactive audit strategies. Furthermore, the research examines Blockchains opportunities and challenges to the internal audit function. This study provides insights into integrating Blockchain Technology in internal auditing through a comprehensive secondary data analysis. It proposes a roadmap for auditors to adapt and thrive in this new era. The findings underscore the importance of embracing technological advancements, advocating for a dynamic approach to audit practices that aligns with the complexities of a blockchain-driven world. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Multi-level Prediction of Financial Distress of Indian Companies Using Machine Learning
Predicting Financial Distress (FD) and shielding companies from reaching that stage is vital, even indispensable for every business. FD, if not attended to on time, ultimately leads to bankruptcy. Prediction variables are essential to forecast the wreckage in the business; however, the prediction is successful when suitable models are used. This study aims to predict FD at three levels: from mild to severe, by applying a machine learning algorithm. The study identifies modern models using the machine learning approach for predicting multi-level FD and summarises the significance of modern models through machine learning technology, to sustain the future development of the economy. The modern models are free from rigid assumptions and have proved to be the best in the prediction of FD. The results show that FD prediction is important at multiple stages. The models performance will be high when the best features are selected using the Pearson Correlation and SFS Feature selection approach. Among the ten models used in the study, LightGBM Classifier shows the highest performance of 80.43% accuracy without feature selection. However, with Pearson Correlation Approach and SFS Feature Selection methods, the accuracy is 82.68% and 86.95% respectively. This study has major implications for the stakeholders of the company to take timely decisions on their investment and for the management as a yardstick to check the performance of the business. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Initial Public Offerings (IPOs) Performance: Bibliometric Analysis of Scholarly Articles on Short-Run Performance
Initial Public offerings are used by the companies to raise capital from the public and to enter in to the public markets. To understand the concept of short-run performance and long-run performance of the company is essential for the investors, regulators and market analysts to evaluate market efficiency. there are many researches were conducted on IPO from the year 1991 to till date, shows the importance of the IPOs. This paper conducted a bibliometric analysis on IPO performance landscape. The data were collected using the Dimensions data base. The articles so collected were from the period 1991 to 2024. Total of 126 articles were identified and considered for the current research. The extracted database was analyzed using VOSview software. Using the software, the current research identified the key authors in the field of IPO research, their organizations, countrywide research contributed in the field of IPO performance both short-run and long-run, especially short-run. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Safeguarding the future through the prevention of cybercrime in the quantum computing era
Quantum computing is an emerging field that holds great promise for solving complex problems at an unprecedented speed by harnessing the principles of quantum mechanics. However, this disruptive technology also introduces new challenges, particularly in the realm of cybersecurity. Quantum computing can lead to cyberattacks such as cryptographic attacks, data breaches, blockchain vulnerabilities, social engineering, and phishing attacks. It is important to note that, at present, these risks are largely theoretical, as practical, large-scale quantum computers capable of breaking current cryptographic systems are not yet available. However, it is crucial for researchers, organisations, and policymakers to anticipate and address these potential threats in advance by developing quantum-resistant cryptographic algorithms, improving security protocols, and raising awareness about the evolving landscape of cyberthreats in the quantum computing era. There is a need for preparing safeguard measures form the quantum threat by investing in quantum-safe technologies, training cybersecurity professionals in quantum-resistant techniques, and fostering collaboration among industry, academia, and government entities. As quantum computing progresses, the landscape of cybercrime is expected to evolve, necessitating the development of robust laws to mitigate potential threats. The chapter aims at understanding the intersection of quantum computing and cybercrime, highlighting the potential implications and risks associated with quantum advancements in the context of cybersecurity. The chapter also emphasises the need for proactive measures and policies to mitigate the risks posed by quantum computing to cybersecurity. 2025 selection and editorial matter, Keshav Kumar and Bishwajeet Kumar Pandey; individual chapters, the contributors. -
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. -
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. -
Entrepreneurship Education: Experiments with Curriculum, Pedagogy and Target Groups
The book provides an overview of developments in the field of entrepreneurship education, with special reference to global perspectives on innovations and best practices, as well as research in the emerging economy context. It focuses on various experiments in curriculum design, review and reform in addition to the innovative processes adopted for developing new content for entrepreneurship courses, in many cases with an assessment of their impact on students' entrepreneurial performance. Further, it discusses the pedagogical methods introduced by teachers and trainers to enhance the effectiveness of students' learning and their development as future entrepreneurs. It explains the various initiatives generally undertaken to broaden the scope of entrepreneurship education by extending it beyond regular students and offering it to other groups such as professionals, technicians, artisans, war veterans, and the unemployed. The book is a valuable resource for researchers and academics working in the field of entrepreneurship education as well as for trainers, consultants, mentors and policy makers. Springer Nature Singapore Pte Ltd. 2017. All rights are reserved. -
(Hi)stories of desire: Sexualities and culture in modern India
(Hi)Stories of Desire situates questions of sexuality in the larger domain where they are conditioned by and, in turn, also condition historically and culturally produced landscapes of being, doing and desiring. The book draws upon multi-disciplinary frameworks of analysis - including history, anthropology, literary studies, queer studies and psychoanalysis - to provide a pan-Indian account of the making of sexual cultures. Based on original research, the chapters foreground sexuality as a significant site for the making of regional, national and personal modernities. The volume addresses the modern paradox where sexuality is assigned a central significance in human life and yet its study tends to remain unconnected from the political, religious, social and economic contexts that produce human subjectivity. It will be of interest to a wide range of readership, opening up the topic to complex yet accessible ways of understanding the culture of sexualities and the sexuality of culture. Indian Institute of Advanced Studies, Shimla 2020. -
Advanced Machine Vision Paradigms for Medical Image Analysis
Computer vision and machine intelligence paradigms are prominent in the domain of medical image applications, including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics. Medical image analysis and understanding are daunting tasks owing to the massive influx of multi-modal medical image data generated during routine clinal practice. Advanced computer vision and machine intelligence approaches have been employed in recent years in the field of image processing and computer vision. However, due to the unstructured nature of medical imaging data and the volume of data produced during routine clinical processes, the applicability of these meta-heuristic algorithms remains to be investigated. Advanced Machine Vision Paradigms for Medical Image Analysis presents an overview of how medical imaging data can be analyzed to provide better diagnosis and treatment of disease. Computer vision techniques can explore texture, shape, contour and prior knowledge along with contextual information, from image sequence and 3D/4D information which helps with better human understanding. Many powerful tools have been developed through image segmentation, machine learning, pattern classification, tracking, and reconstruction to surface much needed quantitative information not easily available through the analysis of trained human specialists. The aim of the book is for medical imaging professionals to acquire and interpret the data, and for computer vision professionals to learn how to provide enhanced medical information by using computer vision techniques. The ultimate objective is to benefit patients without adding to already high healthcare costs. 2020 Elsevier Inc. All rights reserved. -
Intelligent Environmental Data Monitoring for Pollution Management: A volume in Intelligent Data-Centric Systems
Intelligent Environmental Data Monitoring for Pollution Management discusses evolving novel intelligent algorithms and their applications in the area of environmental data-centric systems guided by batch process-oriented data. Thus, the book ushers in a new era as far as environmental pollution management is concerned. It reviews the fundamental concepts of gathering, processing and analyzing data from batch processes, followed by a review of intelligent tools and techniques which can be used in this direction. In addition, it discusses novel intelligent algorithms for effective environmental pollution data management that are on par with standards laid down by the World Health Organization. To learn more about Elseviers Series, Intelligent Data-Centric Systems, please visit this link: https://www.elsevier.com/books-and-journals/book-series/intelligent-data-centric-systems-sensor-collected-intelligence. 2021 Elsevier Inc. All rights reserved. -
Hybrid Computational Intelligence: Challenges and Applications
Hybrid Computational Intelligence: Challenges and Utilities is a comprehensive resource that begins with the basics and main components of computational intelligence. It brings together many different aspects of the current research on HCI technologies, such as neural networks, support vector machines, fuzzy logic and evolutionary computation, while also covering a wide range of applications and implementation issues, from pattern recognition and system modeling, to intelligent control problems and biomedical applications. The book also explores the most widely used applications of hybrid computation as well as the history of their development. Each individual methodology provides hybrid systems with complementary reasoning and searching methods which allow the use of domain knowledge and empirical data to solve complex problems. 2020 Elsevier Inc. -
Quantum machine learning
Quantum-enhanced machine learning refers to quantum algorithms that solve tasks in machine learning, thereby improving a classical machine learning method. Such algorithms typically require one to encode the given classical dataset into a quantum computer, so as to make it accessible for quantum information processing. After this, quantum information processing routines can be applied and the result of the quantum computation is read out by measuring the quantum system. While many proposals of quantum machine learning algorithms are still purely theoretical and require a full-scale universal quantum computer to be tested, others have been implemented on small-scale or special purpose quantum devices. New trends in Machine Learning based on Quantum Computing and Quantum Algorithms Examples on real life applications Illustrative diagrams and coding examples. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved. -
Deep learning: Research and applications
This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition. Tutorials on deep learning framework with focus on tensor flow, keras etc. Numerous worked out examples on real life applications Illustrative diagrams and coding examples. 2020 Walter de Gruyter GmbH, Berlin/Boston. All rights reserved. -
Recent Trends in Computational Intelligence Enabled Research: Theoretical Foundations and Applications
The field of computational intelligence has grown tremendously over that past five years, thanks to evolving soft computing and artificial intelligent methodologies, tools and techniques for envisaging the essence of intelligence embedded in real life observations. Consequently, scientists have been able to explain and understand real life processes and practices which previously often remain unexplored by virtue of their underlying imprecision, uncertainties and redundancies, and the unavailability of appropriate methods for describing the incompleteness and vagueness of information represented. With the advent of the field of computational intelligence, researchers are now able to explore and unearth the intelligence, otherwise insurmountable, embedded in the systems under consideration. Computational Intelligence is now not limited to only specific computational fields, it has made inroads in signal processing, smart manufacturing, predictive control, robot navigation, smart cities, and sensor design to name a few. Recent Trends in Computational Intelligence Enabled Research: Theoretical Foundations and Applications explores the use of this computational paradigm across a wide range of applied domains which handle meaningful information. Chapters investigate a broad spectrum of the applications of computational intelligence across different platforms and disciplines, expanding our knowledge base of various research initiatives in this direction. This volume aims to bring together researchers, engineers, developers and practitioners from academia and industry working in all major areas and interdisciplinary areas of computational intelligence, communication systems, computer networks, and soft computing. 2021 Elsevier Inc. All rights reserved. -
Quality of Life: An Interdisciplinary Perspective
Quality of Life: An Interdisciplinary Perspective presents the Quality of Life using a contemporary and interdisciplinary approach. Various socio-cultural, spiritual, technological, and human factors aspects, which have an immense bearing on our lives, are an integral part of this book. This book highlights cultural differences in terms of Quality of Life. It recognizes the presence of cultural differences resulting from the social status attributed to an individuals age, gender, class, race, and ethnicity. It can be used as a guide in the field of global well-being and for future research. It presents clues to complex problems and empirical materials, and attempts to bring out a more comprehensive picture of global and contemporary Quality of Life and well-being. This book can also fill a gap in teaching and research. Those who will find this book useful are researchers, academicians, practitioners, and students of management, behavioral science, human factors, psychology, health economics, sociology, public health, and politics. 2022 Taylor & Francis Group, LLC. -
Strategic Management During a Pandemic
The COVID- 19 pandemic changed world dynamics, working scenarios, as well as professional and emotional dimensions. The virus has emerged as a significant threat for the continuity of business. Keeping the gravity of the problem in mind, companies must understand the need for change and must now update their strategy to account for pandemics. The next pandemic may be more severe than the current one, meaning that organizations need to devise mechanisms and business models to fight with these situations and maintain business continuity. They should not only look forward to saving plants, machinery and infrastructure, but also concentrate on employee welfare, customer engagement and satisfaction during this crisis time. The book will not only present the evidence of various effective solutions to run a business in the time of a pandemic, but also put forward the new models and practices of business being followed by people at the time of crisis. It aims to create a bridge between existing business models and proposed business solutions, focusing on existing theories and most importantly case studies from the recent happenings. This rich collection of chapters will provide insights regarding the business challenges, opportunities and practices during pandemic situations like COVID- 19, making it particularly valuable to researchers, academics and students in the fields of strategic management, leadership and disaster management. 2022 selection and editorial matter, Vikas Kumar and Gaurav Gupta.