Browse Items (16488 total)
Sort by:
-
Leveraging AI And Blockchain for Secure Digital Right Management in Libraries
The urge to develop the Digital rights management systems that are well-established, protecting the intellectual property, and still facilitating the reasonable access to information is supported by the faster rate of digitization of library materials. The given paper investigates how Artificial Intelligence and Blockchain can be combined to support the safety, accountability, and effectiveness of Digital Rights Management (DRM) systems utilized in modern libraries. AI allows advanced content recognition, behavioral analytics, and authorization, the most important features of intelligent and dynamic rights enforcement. Conversely, Blockchain offers an immutable and decentralized historical document into which the administration of licensing agreements, property, and transactions can be documented and which will reduce the chances of illegal access and piracy. Smart contracts are issued in Blockchain platforms that allow libraries to provide license compliance with the administered copyright licensing, real-time auditing control of access, and numerous others. Licensing requirements can be forecasted, irregularities detected, and fair use policies recommended using automated algorithms formulated using content metadata and usage patterns. When these technologies are integrated, it forms a very accurate and trustworthy digital rights management (DRM) system, which is advantageous to the authors, libraries, and users. The study will present a model that has a conceptual framework aimed at showing what AI and Blockchain technology can become in library DRM systems. In the model design, the privacy of the data, data accuracy, and the ability of the system to expand and the legal issues are considered to be major concerns. Besides, the deliberation focuses on the complexity of the constituent parts as well as interoperability and technology ethics. The introduction of other new technologies leads to an unparalleled change in the way digital resources are organized, accessed, and secured in the library setting and introduces increasingly powerful and flexible information systems. The Research Publication. -
Leveraging AI and digital solutions to enhance sustainability in green and blue economies
This research explores how digital solutions, including artificial intelligence (AI), drive sustainability in terrestrial (green) and aquatic (blue) economies. By examining case studies and technological implementations, it highlights the potential and challenges of digital innovations, particularly AI, in promoting environmental sustainability, economic growth, and social well-being. The study covers the current state of green and blue economies, digital interventions like smart agriculture, renewable energy, marine monitoring, and sustainable fisheries, and their impacts. The research uses both qualitative and quantitative methods, gathering data through interviews, surveys, and literature. Results will provide success stories, challenges, and best practices, offering recommendations for policymakers and stakeholders. The study aims to contribute to the theoretical and practical understanding of how digital technologies, especially AI, can advance sustainability in these economies, with future research directions identified. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Leveraging AI and Machine Learning for Healthcare Accessibility: Enhancing Clinical Decision Support Systems in Rural Africa
Healthcare in rural Africa is hindered by resource scarcity, limited infrastructure, and a shortage of trained professionals, contributing to high mortality and morbidity rates. This study examines the transformative potential of artificial intelligence (AI) and machine learning (ML) in clinical decision support systems (CDSS) to address these challenges. Focusing on diseases prevalent in the region, such as malaria, HIV/AIDS, and noncommunicable illnesses like diabetes, the research develops and evaluates AI-enhanced CDSS to improve diagnostic accuracy, treatment planning, and healthcare accessibility. This research contributes a framework for deploying AI-driven CDSS in resource-limited settings, with implications for enhancing global health outcomes. 2026 selection and editorial matter, Wasswa Shafik, Adel Ben Youssef, Chithirai Pon Selvan and Pushan Kumar Dutta; individual chapters, the contributors. -
Leveraging AI for personalized customer experiences
This chapter discusses the disruptive nature of artificial intelligence in strategic brand management, particularly how it can enable a personalized engagement with customers. It considers just how AI allows for easy navigation of complex consumer data to deliver a tailored experience aligned with changing customer expectations. This chapter describes AI- driven tools such as machine learning, predictive analytics, and recommendation engines that enable brands to segment audiences in real- time, predict behaviors, and tailor content for higher resonance. Operational efficiency through AI is also detailed-how it can use chatbots in automating customer interactions and develop data- driven decisions that enhance responsiveness as well as customer satisfaction. In fact, the author touched upon the long- term effects that artificial intelligence will cause on loyalty and trust towards a brand, with the emphasis on the continued refinement of personalization strategies. 2025, IGI Global Scientific Publishing. All rights reserved. -
Leveraging and Deployment of AI / ML to Simplify Business Operations among Diverse Sectors during Covid-19 Battle
During the evolution of the COVID-19 outbreak, the necessity for companies to re-evaluate and restructure themselves is still not greater. It will make sense for things to change in the business operations. Most companies redesigned current existing ways of running business operations and capacity to make choices to benefit. The present condition sees Artificial Intelligence as a significant facilitator for companies to make their existing situation better (recover from their economic crisis), reconsider (prepare for a long-term change) and reinvent (completely re-engineer) their business model for long-term gain. Automated bots that could identify items and carry out duties that were previously reserved for people would make companies and other infrastructures operational around the clock, through more significant numbers, and at a lower cost. Simulated actual working conditions, including labour forces, would be created by using Artificial intelligence platforms. Businesses would use machine learning and sophisticated business intelligence to use artificial intelligence to explore better market dynamics and provide consumers with "hyper-personalized" goods. Some of the most compelling case studies can have human intelligence and expertise mixed with AI. Many firms should revamp current business processes and capacity to benefit the company in the near future. In this research paper, we have showcased how artificial intelligence would benefit businesses as they adopt with these current developments and during a condition of pandemic without inhibiting their activities. The research is carried in a descriptive way, choosing the diverse sectors in the economy like Banking & Finance, Manufacturing, Education, Retail, Telecommunications, Entertainment and media to make the research more robust and reliable. 2022 American Institute of Physics Inc.. All rights reserved. -
Leveraging artificial intelligence for predictive financial risk management in emerging markets
This chapter examines how AI is changing the management of financial risk in emerging markets. It discusses how AI is applied, what challenges it encounters, and what the future may hold. Fast changes in global financial markets and the growing complexity of economies in developing countries require better ways of managing risks than the old methods offer. This chapter focuses on how AI technologies change risk assessment and management practices. They offer new capabilities in analyzing data, patterns, and even predicting. The discussion begins by explaining why the old ways of managing risk are not effective in new markets. It then zooms in on how AI-powered solutions can rectify these issues. The chapter explores various uses of AI in managing financial risk, including the assessment of credit risk, detection of fraud, analysis of market risk, and optimization of portfolios. It takes a view of what technical infrastructure is required to implement AI successfully and the steps to do so. It focuses on the challenges of updating old systems in new market financial institutions. 2025 by IGI Global Scientific Publishing. -
Leveraging artificial intelligence for sustainable development in agriculture: An exploratory study
In a world where sustainability has been given utmost priority, agriculture plays a pivotal role. Artificial Intelligence in the agricultural sector has changed the landscape of agriculture across the globe. 'Agvolution' (evolution in agriculture) including AI supported precision farming methods, data analytics, and robotics is a novel strategy which increases crop yields using less fertilizers, and energy. AI in agriculture supports ethical farming, boost revenue, and lessen negative environmental effects. AI systems aggregate data from weather stations, sensors, and satellites to produce improved weather forecasts. This mechanism enhances environmental sustainability. Despite numerous advantages with AI, the farming community face challenges like data security and privacy, high cost of machines and tools. In light of the above, the authors explore the usage of AI in agriculture to attain sustainability, and analyze the need to establish governance structures for increasing food security and systems to overcome the challenges faced by the farmers. 2025, IGI Global Scientific Publishing. All rights reserved. -
Leveraging Big Data Analytics and Hadoop in Developing India's Healthcare Services
International Journal of Computer Applications, Vol-89 (16), pp. 44-50. ISSN-0975-8887 -
Leveraging Big Data Analytics for Sustainable and Ethical Supply Chains
This chapter explores the transformative role of Big Data Analytics in advancing sustainable and ethical supply chains. It examines how data- driven decision- making enables companies to enhance transparency, reduce environmental footprints, and uphold labour and human rights across multi- tier supply networks. The chapter addresses challenges in data integration, ethical AI use, and balancing cost with sustainability objectives, supported by real- world case studies from fashion, food, and electronics sectors. It highlights technological innovations such as digital twins, AI, and blockchain that empower circular economy practices and supply chain resilience. Offering critical insights and future directions, this chapter serves as a comprehensive guide for academia and practitioners committed to responsible supply chain management. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Leveraging Big Data and AI for Optimizing Health Insurance Claims and Risk Assessment in Healthcare Financing
This research elucidates the transformative potential of big data analytics and artificial intelligence in optimizing health insurance claims and risk assessment by employing an empirically robust framework encompassing reliability and validity metrics, Heterotrait-Monotrait Ratio (HTMT) analysis, and bootstrapping to unravel the intricate interdependencies among constructs such as AI model accuracy, claims processing efficiency, cost efficiency, data quality, fraud detection accuracy, system usability, and user trust interface, thereby advancing a comprehensive understanding of the systemic synergies that enhance predictive precision, operational scalability, and equitable resource allocation within the healthcare financing paradigm. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Leveraging Blockchain Technology forInternet ofThings Powered Banking Sector
Banking sector contributes to 70% of Indian Gross Domestic Product (GDP) and for India to meet its economic aspirations, it should enable this vivacious sector to grow at 810 times of its current pace, in the next ten years. This pace of active growth requires a double engine of sophisticated technology and a tech enabled, scalable, and a secured banking system. Implementing BlockchainTechnology (BCT) in the banking sector, provides a realistic solution which when coupled with devices connected by the Internet of Things(IoT), will result in secured, fast-paced, cost effective, and transparent growth of the sector. The prevalence of personalized banking, secured banking, connected banking, and digital banking are use cases, made possible through interface with IoT. This chapter delves into the opportunities in the banking sector to be explored and challenges to be met in the BCT-IoT implementation process. BCT- and IoT-based opportunities such as peer-to-peer lending, Know Your Customer (KYC) updation, Cross-border transfer payments, syndicate lending, fraud reduction are some of the banking operations that are elaborated. To strengthen the banking network, the consensus algorithm of Blockchainnetwork is much required and the use of IoT devices to act as nodes is pertinent. The blend of both in the banking space has to be further reinforced. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Leveraging Business Intelligence to Explore AI-Driven Transformation in the VUCA and BANI World
Artificial Intelligence (AI)-driven technological advancements often create competitive business models especially in the unpredictable BANI and VUCA environments. This chapter focuses on the role AI in transforming Business Intelligence (BI) while allowing efficient decision-making and strategic planning. The AI can transform a business using predictive analytics for volatility, automation to address complexity, decision support in non-linear scenarios, personalized user experiences, reliance on adaptive systems, improving employee productivity, and promoting sustainability. A detailed plan is implemented including resource allocation and timeline for the tasks. Using a mixed class agile methodology, a case study is developed for managing the pandemic crisis in the future for the BANI world. The BI-driven insights regarding mortality rates, infection spread, and economic impact help policymakers form informed and effective decisions. The results demonstrate that AI-driven BI offers improved efficiency, accuracy, scalability, and cost reduction in the BANI world. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Leveraging Circular Economy Principles and Emerging Technologies for Future Trends in Sustainable Business Models: A Roadmap to Net-Zero Emissions
This study examines the influence of circular economy concepts and emerging technology on the development of sustainable business models aimed at achieving net- zero emissions. It underscores the significance of resource efficiency, waste reduction, and lifecycle consideration in promoting sustainability. We analyse critical technologies such as blockchain, artificial intelligence, and the Internet of Things for their potential to enhance transparency, simplify processes, and foster stakeholder collaboration. We suggest a detailed roadmap that guides organisations in incorporating these ideas and technologies to establish robust, environmentally sustainable business practices for a sustainable future. 2026 by IGI Global Scientific Publishing. -
Leveraging cloud technology for sustainable innovation ecosystem: An ethico-legal perspective
Technological advancements affect the environment at great cost. The shift to cloud computing has intensified ethical and legal tensions over privacy, jurisdiction and regulatory requirements. The attention towards the socio-environmental consequences of economic and industrial interference is growing as corporations stress on the sustainable aspects of supply chain and product creation. The transformation of cloud technology to a sustainable innovation ecosystem depends on several ethical practices. The objective of this study is to analyse the critical factors required for the adoption of ethical, legal, and regulatory compliance for cloud technology in a sustainable innovation ecosystem. Design/Methodology/Approach: Under the doctrinal approach, the authors used an analytical method to analyse the ethical and legal perspective for the use of cloud technology in the innovation ecosystem. The authors critically analyse the international, regional and national regulatory guidelines concerning cloud technology. Findings: The study shows the lack of universally accepted framework to regulate the usage of cloud technology in a sustainable innovation ecosystem. Keeping in mind the ethical and legal impact of technology on the society and environment, the study insists global attention. Limitations: The analysis of the ethical and legal implications on the managerial and administrative aspects supports the stakeholders to comprehend the issues (ethical, legal and social) in relation to cloud technology. The study limits to focus on ethical and legal implications concerning usage of cloud technology in innovation ecosystem. Research Originality: This study critically explores the factors responsible for the rising ethical and legal issues in the usage of cloud technology in the tech-driven world. 2025 Ananya Pandey, Jipson Joseph, Amit Joshi and Vikas Kumar. All rights reserved. -
Leveraging Cybersecurity in the Context of Digital Arrest, Fraud, and Identity Theft: An Ethico-Legal Perspective
The cyber world is becoming increasingly complex due to the diverse nature of cybercrimes. Although crimes like fraud, harassment, and identity theft exist in both the cyber and physical worlds, their magnitude is unpredictably high in the cyber world. However, digital arrest fraud is unique to cyberspace. Digital arrest fraud and identity theft are the top-listed cybercrimes, causing unimaginable monetary loss, anxiety, depression, fear, and isolation among the victims. The current cybersecurity measures and tools, as well as the legal and ethical framework, fail to address the evolving nature of cybercrimes. Such a scenario demands more collaborative efforts and a renewed approach to ensure global cybersecurity. From this perspective, this chapter critically examines the evolving nature of cybercrimes and the available legal and ethical framework to underline the importance of leveraging cybersecurity through some required changes in the ethico-legal landscape. It argues for a globally accepted regulatory system and international cooperation to ensure cybersecurity. Copyright 2026, IGI Global Scientific Publishing. -
Leveraging data sharing for enhanced experiences in service industries: Role of experience orientation and privacy calculus
In the context of customer experience, the acquisition of personal and behavioural data plays an integral role, yet it raises significant privacy concerns. This study suggests employing the privacy calculus framework to craft a meaningful customer experience while addressing these privacy apprehensions. Drawing from an extensive review of literature, the study formulates propositions, subsequently validated through case studies. Beyond existing literature, this research introduces novel factors pertinent to the service industry, expanding the understanding of privacy-tradeoff dynamics. Factors such as brand reputation, transaction frequency, campaign design, grievance redressal mechanisms, ethical conduct, and marketer behaviour emerge as pivotal triggers influencing privacy-tradeoff decisions. However, empirical validation is required to consolidate these propositions. The study highlights the imperative of bridging the privacy calculus concept with customer experience, particularly within the service sector, shedding light on factors that can effectively navigate this terrain. By clarifying the relationships between privacy considerations and customer experience, this chapter contributes to a deeper comprehension of the intricacies involved. It not only identifies key factors but also underlines their potential to shape and optimize the customer journey amidst privacy concerns. The proposed framework offers a better understanding of how businesses can navigate the delicate balance between data privacy and delivering exceptional service. The study recommends further empirical scrutiny to validate and refine the proposed framework, ultimately fostering a more robust understanding of privacy dynamics in the service industry. 2025 Elangovan N., Aishwarya Nagarathinam, Sundaravel Elangovan, Aarthy Chellasamy and Sangeetha Rangasamy. Published under exclusive licence by Emerald Publishing Limited. All rights reserved. -
Leveraging Deep Autoencoders for Security in Big Data Framework: An Unsupervised Cloud Computing Approach
Abnormalities recognition in bank transaction big data is the number one issue for stability of financial security system. Due to the rate digital transactions are increasing it is vital to have effective ways. Encryption with deep autoencoder model should be explored as it involves trained neural networks that learn such patterns from the complex transaction data. The following paper demonstrates application of anomaly detection using deep autoencoders in the banking big data transactions. It focuses on the theoretical bases, network design, preparedness and the testing measures for deep autoencoders. On the other hand, it solves problems such as high dimensionality and imbalanced dataset. This research paper shows deep autoencoders effectiveness in deep learning and how the network identifies different fraudulent big data transactions, money laundry and unauthorized access. It also encompasses recent developments of cloud environments and future methods using deep autoencoders including the fact that constant search for new possible solutions is a must. The insights delivered contribute to the discourse in financial security community, which incorporates researchers, practitioners, and policymakers involved in anomaly detection in cloud. 2024 IEEE. -
Leveraging Deep Learning for Early Detection of Alzheimer's Disease from MRI Scans
Alzheimer's disease (AD) remains shrouded in mystery, with its early detection posing a significant challenge. This research paper delves into the cutting-edge realm of deep learning, exploring its potential to explore the brain's secrets and revolutionize AD diagnostics using Magnetic Resonance Imaging (MRI) data. Upon comprehensively reviewing the performance of six state-of-the-art models and studying their strengths and limitations on MRI data, this paper proposes a novel deep-learning architecture based on the InceptionV3 model for Alzheimer's Disease prediction using MRI data. The proposed architecture leverages convolutional neural networks (CNNs) to extract subtle brain structure and function patterns, potentially identifying early AD signatures before noticeable cognitive decline. The proposed model is validated on a large-scale MRI dataset that comprises four stages of dementia, demonstrating more insights. Inception V3 base model yielded 82% accuracy, measured using the metric Area Under the Curve (AUC), on the dataset, and an improved AUC of 87% was achieved by performing data augmentation to remove the class imbalance in the dataset. The proposed deep learning model built on top of Inception V3 exhibited an improved performance with an AUC of 88% underlining the potential of deep learning models in early AD detection. The paper's findings will contribute to the ongoing effort to revolutionize AD diagnosis and accelerate the development of personalized treatment strategies. Grenze Scientific Society, 2025. -
Leveraging Deep Learning for Early Detection of Autism Spectrum Disorder in Augmented and Virtual Reality Mental Healthcare Environments
The advent of digital health interventions offers new vistas for the early detection and management of autism spectrum disorder (ASD). This paper explores the innovative application of deep learning algorithms within augmented reality (AR) and virtual reality (VR) environments to enhance the early detection of ASD. Integrating AR and VR in mental healthcare provides immersive, controlled settings in which individuals behaviors and responses can be observed and analyzed in real time. We propose a novel framework that utilizes deep learning techniques to process and analyze the data collected from these digital environments. Our approach leverages facial expression analysis, eye tracking, and behavioral pattern recognition to identify early signs of ASD. By capturing subtle nuances in behavior often overlooked in traditional diagnostic settings, our method offers a non-invasive, engaging, and efficient alternative for early ASD detection. The potential of this technology extends beyond diagnosis, offering possibilities for personalized intervention strategies that can be adapted to each individual's unique needs. Our findings indicate that deep learning in AR and VR environments could significantly enhance the accuracy and timeliness of ASD diagnosis, paving the way for earlier and more effective interventions. This study underscores the importance of interdisciplinary collaboration in developing innovative healthcare solutions, combining the fields of artificial intelligence, psychology, and digital media to improve outcomes for individuals with ASD. 2026 Scrivener Publishing LLC. -
Leveraging Deep Learning in Hate Speech Analysis on Social Platform
The scope and usage of the Internet have surpassed the expected growth and have proven beyond the basic purpose of being used for networking and telecommunications. It serves as the backbone of the web, and one of the predominant domains that uses the Internet is social media. The concept was conceived in the early 1990s and went on to grow as a powerful medium of people networking along with the Internet. Social networking sites (SNS) acquired a predominant element of the Internet owing to their use and services they offer through the Internet. A few of the most used social networking sites include Twitter and Facebook, which are used synonymous to expressions of text. These SNS allow the users to post photos, videos, and other multimedia content along with text and voice messages that are shared among other users. As with any technology or application, these also have the risk of users posting offensive material and textual content. Hate is being spread through messages, which are in the form of text and also through other materials posted. There is no control to check for the message for the hate content as and when it is posted, and by the time it is deleted by admins, it could have already reached millions of users. This chapter proposes a technique for detecting hate texts in reviews from registered users in the Twitter dataset. The proposed work makes use of improved principle component analysis (IPCA) and modified convolution neural network (MCNN) for detecting hate texts. The advantage of natural language processing is used for building an automated system for the analysis of syntax and semantics of the words. The proposed methodology consists of phases like pre-processing, feature extraction, and process to classify the text. The white spaces in the text are removed through normalization in the pre-processing phase, and also remove special characters such as question marks, punctuations, and exclamatory symbols to remove stop words. The features that are pre-processed are then subjected to feature extraction using IPCA. A set of correlated features are made used for identifying more important features in the data set under consideration. Next, the classification is done for identifying the hate text or for any language abuse. MCNN is applied for the classification of the text into HATE and NON-HATE from the text with better accuracy. The experiments prove that the proposed method has a high level of accuracy even for a large dataset. The results show that the proposed method has better performance in terms of precision, recall, and F-measure when compared with other state-of-the-art methods. 2024 Taylor & Francis Group, LLC.