Journal of Science Technology and Social Transformation
The Journal of Science, Technology and Social Transformation (JSTST) is a multidisciplinary, peer-reviewed scholarly platform dedicated to exploring the dynamic intersections of scientific innovation, technological advancement, and societal change. The journal serves as a forum for researchers, academicians, policymakers, and practitioners to critically examine how science and technology influence social structures, cultural values, economic development, and human well-being. It also focuses on new entrepreneurship and start-up culture and how it is affecting social structure in terms of sustainable development. Title: Journal of Science, Technology and Social Transformation Frequency: Quarterly E-ISSN: 3105-8981 Owner: iLearn Publisher: iLearn Chief Editor: Prof. (Dr.) Bharat Raj Singh Copyright: iLearn Starting year: 2025 Subject: Science & Technology Language: English Publication format: Online Phone Number: Email ID: editor@jstst.info Website: https://jstst.info/index.php Address: iLearn, #7A Borey MLVILLA Preakchrey, Phnom Penh, Cambodia. E-mail: editor@jstst.info Year of Starting: 2025 Periodicity: Annual Language: English Publisher: iLearn
The Evolution of Payment Gateways: Integrating Artificial Intelligence for Secure and Efficient Transactions
Payment gateways have undergone a profound transformation over five decades—from mechanical card imprinters and batch dial-up authorisation terminals to cloud-native, API-first platforms processing billions of transactions in real time. This review traces that evolutionary arc and places particular focus on the most consequential contemporary development: the systematic integration of artificial intelligence (AI) and automation into gateway security, transaction efficiency, regulatory compliance, and customer experience. Drawing upon 198 peer-reviewed articles, industry technical reports, and regulatory documents published between 2015 and 2026, the article provides a chronological taxonomy of gateway generations, analyses the AI-powered security stack underpinning modern platforms—encompassing machine learning fraud scoring, graph-based fraud ring detection, behavioural biometrics, and risk-based authentication under 3D Secure 2.x—and evaluates quantified efficiency gains attributable to AI adoption. It further examines the sociotechnical dimensions of AI integration, including digital financial inclusion, algorithmic accountability, and the regulatory frameworks governing AI in payment systems across the European Union, United States, and India. The review synthesises evidence showing that AI-integrated gateways achieve fraud detection accuracy exceeding 99.2%, authorisation rate improvements of 3–8 percentage points, AML alert precision improvements from 1–2% to over 40%, and KYC onboarding time reductions from days to hours. The article concludes by mapping an interdisciplinary research agenda at the intersection of payment technology, social equity, and AI governance.
payment gateway evolution; artificial intelligence; transaction security; fraud detection; digital payments; 3D Secure; open banking; financial inclusion; AI governance; fintech
Copyright (c) 2026 https://creativecommons.org/licenses/by-sa/4.0
Automation and Machine Learning in Payment Processing Systems:A Systematic Review
The convergence of automation technologies and machine learning (ML) within payment processing systems represents one of the most consequential developments in contemporary financial infrastructure. This systematic review synthesises empirical evidence from 242 peer-reviewed studies, regulatory documents, and high-quality industry reports published between January 2015 and March 2026, following a PRISMA-adapted protocol applied to seven major academic databases and targeted grey literature sources. The review maps the full automation landscape across payment processing sub-domains—including ML-based fraud detection, intelligent transaction routing, robotic process automation (RPA) in compliance and reconciliation, natural language processing (NLP) in dispute management, and large language model (LLM) applications in regulatory compliance—and provides a critical synthesis of quantitative performance evidence. Key findings indicate that: (i) ML ensemble models achieve fraud detection AUC values of 0.97–0.997, outperforming rule-based baselines by 12–22 percentage points; (ii) reinforcement learning routing systems improve authorisation rates by 3–9 percentage points over static routing; (iii) intelligent document processing achieves KYC extraction accuracy exceeding 98%, reducing onboarding time from days to hours; (iv) AML alert precision improves from 1–2% to over 40% with supervised ML, reducing analyst workload by an order of magnitude; and (v) LLM-assisted dispute handling reduces per-case resolution time by 60–75%. The review further identifies eight critical research gaps—including real-time explainability, production-scale federated learning, intersectional algorithmic fairness, and quantum-resistant infrastructure—and proposes a structured research agenda. The sociotechnical implications of automation for payment sector employment, financial inclusion, and algorithmic accountability are examined with reference to the journal’s interdisciplinary mission.
machine learning; payment processing; automation; robotic process automation; fraud detection; transaction routing; AML compliance; NLP; systematic review; fintech; algorithmic fairness
Artificial Intelligence and Automation in Modern Payment Gateways:A Comprehensive Review
The rapid integration of artificial intelligence (AI) and automation technologies into financial payment infrastructures has fundamentally transformed the architecture, security posture, and operational efficiency of modern payment gateways. This comprehensive review systematically examines the current state of AI adoption across the global payment processing landscape, encompassing machine learning-based fraud detection, intelligent transaction routing, natural language processing for customer dispute resolution, robotic process automation in compliance workflows, and emerging biometric authentication mechanisms. Drawing upon peer-reviewed literature, industry white papers, and empirical deployment data published between 2018 and 2026, this article identifies key technological trajectories, evaluates comparative performance metrics among leading gateway providers—including Stripe, Adyen, CyberSource, PayPal, and Razorpay—and critically assesses persistent challenges related to algorithmic bias, explainability, adversarial robustness, and regulatory compliance under frameworks such as PCI DSS v4.0 and GDPR. The review further explores the convergence of AI with blockchain-based settlement, open banking APIs, and large language model (LLM) applications in payment orchestration. Findings indicate that AI-driven gateways achieve fraud detection accuracy exceeding 99.2%, false-positive reductions of up to 67%, and authorisation rate improvements of 3–8 percentage points over rule-based systems. The article concludes with a forward-looking agenda identifying federated learning, quantum-resistant cryptography, and real-time explainable AI as the most consequential research frontiers in this domain.
artificial intelligence; payment gateways;
fraud detection; machine learning; transaction routing;
robotic process automation; fintech; deep learning; PCI DSS; open banking
AI-Based Early Warning Systems for Industrial Stormwater Exceedances: A Data-Driven Approach to Regulatory Compliance and Environmental Protection
Industrial stormwater management presents one of the most significant environmental governance challenges of the 21st century, requiring rapid detection and intervention to prevent regulatory violations, ecological degradation, and public health impacts. This comprehensive literature review synthesizes evidence from 87 peer-reviewed publications (2017-2026) examining the application of artificial intelligence (AI) and machine learning (ML) technologies to develop early warning systems for industrial stormwater exceedances. Through meta-analysis of 45 published studies and synthesis of operational experience from 23 global facilities, this review demonstrates that hybrid AI-IoT early warning systems achieve prediction accuracies of 93-96% (R² = 0.93-0.96), representing a 50-51 percentage point improvement over traditional rule-based monitoring (R² = 0.45-0.55). Detection latency improves from 24-48 hours to less than 1 second, enabling proactive interventions before environmental standards are breached. Implementation across 23 operational facilities reveals 35-55% improvement in regulatory compliance rates, with 35% reduction in average response times and 30% reduction in false alarm rates. Annual implementation costs of $120,000-$400,000 per facility generate compliance improvement value of $300,000-$1,200,000 through avoided violations and operational efficiency gains, with return-on-investment timelines of 2.5-10 years depending on facility characteristics. Key algorithmic advances include ensemble methods combining LSTM networks with XGBoost optimization (achieving 94.6% accuracy), Transformer-based architectures (92-96 % accuracy), and Explainable AI methods (particularly SHAP analysis, now applied in 65% of published water quality studies). Substantial challenges persist including data scarcity at resource-constrained facilities, cybersecurity vulnerabilities in connected infrastructure, geographic equity disparities in deployment, and lack of standardized performance validation protocols. This review identifies critical research gaps requiring future investigation: (1) transfer learning frameworks enabling cross-facility model generalization, (2) integration of AI predictions with physics-based process models for improved extrapolation, (3) deployment of secure low-cost monitoring systems in Global South contexts, and (4) establishment of regulatory standards for algorithmic transparency and trustworthiness. Seven actionable recommendations are provided for practitioners and policymakers, emphasizing the necessity of coordinated commitment to equitable and inclusive deployment of intelligent stormwater monitoring across industrial settings globally.
Artificial intelligence, Stormwater management, Water quality monitoring, IoT sensors, Predictive modeling, Cost-benefit analysis, Infrastructure resilience, Data-driven decision-making, Environmental compliance, Systems integration.
Copyright (c) 2025 https://creativecommons.org/licenses/by-sa/4.0
AI-Augmented Visual Inspections in Mining and Heavy Industry
AI-enhanced visual inspection solutions are rapidly changing the operational process of mining and heavy industries in terms of safety, reliability, and efficiency. Through a combination of artificial intelligence and computer vision systems, these systems can be used to automatically detect defects, wear and anomalies in complex industrial property and infrastructure. In contrast to the labor-intensive and manual-based inspections, the AI-driven solutions can also be used to provide scalable, real-time, and consistent inspection of hazardous and inaccessible areas, using high-resolution drone, fixed camera, and robot-platform-based imagery. Advanced learning models are used to increase accuracy by recognition of patterns and anomaly detection to aid predictive maintenance and minimize unplanned downtimes. Decision-making is also enhanced with the introduction of AI-enhanced visual inspections because it provides the outputs of the inspections with the help of the asset management and maintenance systems. In spite of the associated data quality, environmental variability and system integration issues, AI-led visual inspection systems present a viable opportunity of safer operations, streamlined resource use and more resilient operations in heavy industrial and mining industries.
AI-augmented inspection; computer vision; mining industry; heavy industry; visual analytics; predictive maintenance; industrial safety
A Data-Driven Approach to Drug Discovery Analytics Using Generative Artificial Intelligence Techniques
Pharmaceutical industry is becoming more and more difficult in drug exploration and trial administration, as the expenses, long-term development, and more complex regulatory actions are rising. To respond to the mentioned challenges, the current research suggests a new model of drug discovery analytics that is constructed using a GAN, which is efficient in learning multidimensional patterns in molecules and enhancing predictive power. The data is preprocessed, features calculated by means of molecular descriptors, selected, and then the SMOTE is used to address unequal class distribution, and the model is then thoroughly trained and assessed. The suggested GAN model shows excellent performance, outperforming other ML and DL models with a (precision of 99.51), (recall of 99.99), (F1-score of 99.75) and (accuracy of 99.75). These findings imply that it has a high ability to generalize, less overfitting, and high robustness to classification tasks. On the whole, the framework provides an efficient, promising, and scalable idea in streamlining the drug discovery processes, facilitating better decisions, and speed-up innovation in pharmaceutical research and development.
Drug Discovery, Generative AI, Molecular Design, ML, DL, Drug-Target Interaction.
Edge-Native Knowledge Graph and RAG Integration for Advanced Intellectual Property Landscape Mapping
The exponential growth of global intellectual property (IP) data has introduced significant challenges in extracting timely, accurate, and context-aware insights for strategic decision-making. Traditional centralized analytics systems are increasingly inadequate due to latency constraints, limited scalability, and poor handling of complex semantic relationships embedded within patent documents and citation networks. This study proposes an edge-native architecture that integrates knowledge graphs (KGs) with retrieval-augmented generation (RAG) models to enable advanced IP landscape mapping. The framework leverages distributed edge computing to process data closer to the source, thereby reducing latency and enhancing real-time responsiveness. Knowledge graphs are employed to represent entities such as patents, inventors, and organizations, along with their interrelationships, enabling structured semantic reasoning. The RAG component enhances this capability by dynamically retrieving relevant contextual information and generating coherent, knowledge-informed outputs for analytical tasks. The proposed system is designed to support scalable, low-latency IP intelligence while maintaining high retrieval accuracy and contextual relevance. Experimental evaluation demonstrates improved performance in terms of response time, information retrieval precision, and system throughput compared to conventional centralized and graph-only approaches. The integration of edge computing with KG–RAG pipelines provides a robust and flexible solution for modern IP analytics, offering significant benefits for research institutions, patent offices, and innovation-driven enterprises. This work contributes a novel architectural paradigm that bridges distributed computing and semantic AI for next-generation intellectual property intelligence systems.
Edge Computing, Knowledge Graphs, Retrieval-Augmented Generation, Intellectual Property Analytics, Semantic Reasoning, Distributed AI.
Resolving Construction Conflicts in Energy Infrastructure: A Deep Dive into Arbitration and Project Dynamics
Construction disputes in energy infrastructure projects have become increasingly complex due to the scale of investment, evolving regulatory landscapes, and shifting geopolitical dynamics. This article critically examines how arbitration and complementary dispute resolution mechanisms (DRMs) are deployed to manage risks and resolve conflicts in energy infrastructure development. Drawing on comparative legal analysis, industry case studies, and interdisciplinary scholarship, it explores how contractual fragility, ESG compliance demands, and public-private tensions are shaping the dispute landscape. Special attention is given to emerging legal reforms in investor-state dispute settlement, the growing use of hybrid arbitration models, and the influence of digitalisation on dispute procedures. Through a synthesis of global perspectives from the Eastern Mediterranean and Sub-Saharan Africa to Northern Europe, the article uncovers how diverse jurisdictions are adapting arbitration to align with sustainability goals, economic resilience, and equitable governance. It also addresses critical critiques of arbitration, including issues of procedural opacity, power asymmetries, and legitimacy in high-stakes infrastructure disputes. Ultimately, the paper argues for more inclusive, context-sensitive dispute resolution frameworks capable of supporting both commercial certainty and public accountability in energy transitions.
Energy infrastructure, construction disputes, arbitration, ESG, dispute resolution, governance.
Copyright (c) 20256 https://creativecommons.org/licenses/by-sa/4.0
Strategic Integration of AI in Pharmaceutical Project Management: Anticipating Challenges and Opportunities in a Rapidly Evolving Industry
The strategic integration of artificial intelligence (AI) into pharmaceutical project management is reshaping the industry’s operational landscape, enabling faster, data-driven decision-making, streamlined resource allocation, and improved clinical and regulatory outcomes. This article explores how AI technologies are being embedded into pharmaceutical workflows, from drug discovery and clinical trial management to supply chain optimization and quality assurance. Drawing on recent interdisciplinary studies, the research critically assesses the advantages of AI, such as predictive analytics, real-time monitoring, and intelligent automation, while also identifying persistent challenges, including data governance, infrastructure gaps, talent deficits, and regulatory complexities. Using a sector-specific analytical framework, the paper evaluates case studies of AI adoption in pharmaceutical and adjacent sectors, offering comparative insights and highlighting transferable best practices. In addition, it outlines a strategic roadmap to guide future integration efforts, emphasizing the need for robust ethical oversight, policy innovation, and collaborative capacity-building among industry stakeholders. The findings suggest that while AI adoption offers a substantial competitive advantage, its success relies on organizational readiness, regulatory alignment, and stakeholder trust. The paper concludes by proposing actionable recommendations for embedding AI capabilities in pharmaceutical project ecosystems to enhance agility, resilience, and long-term value creation.
Artificial Intelligence, Pharmaceutical Project Management, Predictive Analytics, Digital Transformation, Strategic Integration, Data Governance.
Harnessing Agile and Cross-Functional Project Management to Compress Drug Development Cycles in the Biopharma Sector – The Role of Traditional Project Planning and Scheduling
The biopharmaceutical industry faces increasing pressure to accelerate drug development while maintaining regulatory compliance and ensuring product safety. Traditional project management approaches, while foundational in ensuring long-term scheduling and regulatory oversight, often lack the flexibility required to respond to dynamic market demands and scientific uncertainty. This study explores the integration of agile methodologies and cross-functional team structures as a strategic response to compress development cycles in the biopharma sector. Drawing on recent empirical studies and industry case analyses, the article investigates how agile frameworks, when layered onto or hybridized with traditional planning models, can enhance responsiveness, improve interdepartmental coordination, and reduce decision-making bottlenecks. Furthermore, the research examines the role of data visualization tools, digital platforms, and innovation sourcing in optimizing project flow across dispersed and interdisciplinary teams. While agile transformation offers substantial promise, challenges such as cultural inertia, regulatory alignment, and leadership capability persist. The findings suggest that hybrid project ecosystems anchored in traditional scheduling but empowered by agile practices and digital enablers can deliver faster, more adaptive, and value-driven drug development outcomes. This paper contributes to ongoing debates around project governance in complex, high-stakes innovation environments and offers practical guidance for operational leaders, R&D managers, and transformation strategists in life sciences.
Agile project management, cross-functional teams, drug development, traditional scheduling, biopharma innovation, hybrid governance.
The Role of People and Technology in Modern Pharma Project Management: Addressing Global Challenges in Cancer Medication Delivery
Effective administration of cancer drugs has become one of the important issues in contemporary healthcare and this demands a combination of innovative technology and human resource expertise in management of the pharmaceutical projects. This paper addresses the interaction between humanity and technology in streamlining drug development, clinical trial and drug distribution systems worldwide in relation to oncology therapeutics. Some of the technological advancements that are reviewed in terms of their potential to streamline precision, cost-cutting and patient outcomes are artificial intelligence, pharmacoinformatics, and the approaches to drug delivery involving nanoparticles. Similarly, the importance of expert project managers, clinical researchers, pharmacists in the successful planning, coordination and compliance with the regulatory aspects of multinational projects with complexities is shown. The study has found the key obstacles in the international access to oncology drugs and has suggested possible solutions to the accessibility problem, dealing with the combination of human factor and technological approach to solve the issue. The results highlight the importance of agile project management, training of workforce, and strong IT framework to facilitate fair and efficient delivery of cancer medication across all regions of the world.
Cancer medication, pharmaceutical project management, artificial intelligence, pharmacoinformatics, nanoparticle drug delivery, global healthcare, precision medicine, project management strategies
Copyright (c) 2026 Oba, Timothy Taiwo (Author) https://creativecommons.org/licenses/by-sa/4.0
XAI-Driven Multimodal Deep Learning for Early Sepsis Prediction in ICU
Early detection of sepsis in intensive care units (ICUs) remains a critical challenge due to the rapid progression of the condition and the complexity of physiological signals associated with its onset. Advances in artificial intelligence, particularly deep learning, have enabled the development of predictive models capable of identifying early warning signs of sepsis from large-scale clinical datasets. However, many of these models operate as black-box systems, limiting their interpretability and reducing clinical trust. This study presents an explainable artificial intelligence (XAI)-driven multimodal deep learning framework designed to improve early sepsis prediction in ICU environments. The proposed approach integrates multiple healthcare data modalities, including vital signs, laboratory measurements, and electronic health records, to capture complex interactions among clinical variables. In addition to achieving high predictive performance, the framework incorporates explainability techniques that highlight the most influential clinical features contributing to the model’s predictions. The results demonstrate that the multimodal model improves prediction accuracy and enables earlier detection of sepsis compared to traditional machine learning approaches, while also providing transparent insights to support clinical decision-making. The findings highlight the potential of combining multimodal deep learning and explainable AI to enhance patient monitoring systems and assist healthcare professionals in making timely and informed interventions in critical care settings.
Explainable Artificial Intelligence, Multimodal Deep Learning, Sepsis Prediction, Intensive Care Unit, Clinical Decision Support, Machine Learning in Healthcare
Human-AI collaboration systems represent an emerging paradigm in artificial intelligence, where humans and AI agents work together synergistically to achieve goals that neither could accomplish as effectively alone. Unlike traditional AI systems, which operate autonomously, these collaborative frameworks emphasize complementarity, allowing AI to augment human decision-making, creativity, and problem-solving while incorporating human judgment, contextual understanding, and ethical reasoning. Such systems are increasingly critical across high-stakes domains, including healthcare, finance, scientific research, and complex industrial operations, where the integration of human expertise and AI computational power can lead to better outcomes, higher efficiency, and reduced errors. At the core of human-AI collaboration is the concept of adaptive interaction. AI agents are designed to understand human intentions, communicate insights clearly, and adjust their recommendations based on feedback, while humans leverage AI capabilities to process large datasets, simulate scenarios, and generate predictive insights. Effective collaboration requires the AI to be explainable, trustworthy, and aligned with human goals, fostering confidence and minimizing risks associated with errors or misinterpretation. Advances in interactive AI, natural language interfaces, and multimodal sensing are enabling increasingly seamless collaboration, making AI systems not only tools but active partners in complex tasks. Human-AI collaboration systems have demonstrated transformative potential across diverse applications. In healthcare, AI-assisted diagnostic systems support clinicians in interpreting medical images and treatment planning. In creative industries, AI co-design tools help artists, writers, and designers generate ideas and explore novel possibilities. In industrial and operational settings, AI enhances human decision-making in logistics, predictive maintenance, and complex process optimization. These applications highlight the value of combining human intuition and contextual awareness with AI’s computational strength and scalability. Despite their promise, challenges remain, including ensuring transparency, preventing over-reliance on AI recommendations, addressing ethical and social implications, and designing intuitive human-AI interfaces. Research continues to focus on improving interpretability, collaboration protocols, and adaptive learning mechanisms to create AI systems that integrate seamlessly with human workflows. Overall, human-AI collaboration systems signify a shift toward cooperative intelligence, where humans and machines augment each other’s strengths. By blending human expertise with AI’s analytical power, these systems have the potential to enhance productivity, creativity, and decision-making across domains, laying the groundwork for a future where AI serves as a trusted and capable collaborator.
Human-AI Collaboration, Cooperative Intelligence, Interactive AI, Explainable AI, Human-in-the-Loop, Multimodal AI, Decision Support Systems, AI-Augmented Human Performance, Trustworthy AI, Adaptive AI Systems.
Enhancing Contractor Health Risk Governance in High-Hazard Industries: A Risk-Based Prequalification and Monitoring Model from the Oil and Gas Sector
The governance of health risks faced by contractors in high-hazard industries, especially in the oil and gas industry is a pressing issue because of the intricacy of the operations, the significant impact of failures, and because of the inconsistencies in the competence of the contractors. This paper introduces a risk-based Pre Qualification and monitoring model that is aimed at improving the occupational health, safety and environmental (HSE) performance of the contractors. The model combines multi-criteria assessment, such as the safety compliance, technical competency, risk management capability, training, and the previous performance, with constant monitoring based on the real-time KPI monitoring and predictive risk modelling. The implementation of the framework in upstream and downstream oil and gas business shows that the number of incidents involving contractors decreased significantly, the safety regulations were followed better, and the HSE governance became more active. The results emphasize structured prequalification, data-based monitoring, and multidisciplinary cooperation to the level of eliminating the risks related to contractors in hazardous environments. The research provides practical information to organizations that want to tighten their control over the contractors and to improve work safety.
Contractor governance, Health risk management, Prequalification model, High-hazard industries, Oil and gas sector, Risk-based monitoring, Occupational safety, Predictive analytics
Transfer learning and few-shot learning are pivotal advancements in machine learning that address the challenges of data scarcity and computational efficiency. Traditional machine learning models often require large labeled datasets and extensive training to achieve high performance, which can be prohibitive in domains where annotated data is limited or expensive to obtain. Transfer learning mitigates this problem by leveraging knowledge gained from one task or domain to improve learning efficiency and performance on a related task. Pretrained models, often trained on massive datasets, serve as a foundation that can be fine-tuned for specific applications, significantly reducing the need for extensive task-specific data and computational resources. Few-shot learning further extends this paradigm by enabling models to generalize from a minimal number of labeled examples, closely mimicking human learning capabilities. Through meta-learning, metric-based approaches, or parameter-efficient adaptation, few-shot learning algorithms can quickly adapt to new tasks with limited supervision, making them particularly valuable in rapidly changing or low-resource environments. Applications span natural language processing, computer vision, healthcare diagnostics, robotics, and personalized AI systems, where the ability to generalize from sparse data is critical. Despite their promise, transfer learning and few-shot learning introduce challenges related to domain mismatch, overfitting, and model interpretability. Ensuring effective knowledge transfer requires careful selection of source tasks, adaptation strategies, and evaluation methods. Furthermore, addressing biases present in source datasets is crucial to avoid propagating errors or unfair predictions in target tasks. Overall, transfer learning and few-shot learning are transforming machine learning by enabling faster, more flexible, and data-efficient model development. They open opportunities for innovation across a wide range of fields, providing a foundation for AI systems that can learn effectively even under constraints of limited data, while also highlighting the need for robust evaluation, domain alignment, and ethical considerations.
Transfer Learning, Few-Shot Learning, Meta-Learning, Knowledge Transfer, Pretrained Models, Low-Resource Learning, Data-Efficient AI, Model Adaptation, Generalization, Domain Adaptation.
Artificial Intelligence (AI) is transforming healthcare diagnostics by enabling faster, more accurate, and data-driven clinical decision-making. By leveraging machine learning, deep learning, and advanced data analytics, AI systems can analyze complex medical data such as imaging scans, electronic health records (EHRs), laboratory results, and genomic information. These technologies assist in early disease detection, risk prediction, and personalized treatment planning across conditions such as cancer, cardiovascular diseases, neurological disorders, and infectious diseases. AI-powered diagnostic tools enhance radiology, pathology, and clinical workflow efficiency while reducing human error and diagnostic delays. Despite its promise, challenges remain, including data privacy concerns, model interpretability, regulatory compliance, bias in training data, and integration into existing healthcare systems. Ensuring transparency, fairness, and ethical deployment is critical for building trust among clinicians and patients. Overall, AI in healthcare diagnostics has the potential to improve patient outcomes, reduce costs, and expand access to quality care, marking a significant advancement in modern medicine.
Artificial Intelligence in Healthcare, Medical Diagnostics, Deep Learning, Medical Imaging, Clinical Decision Support, Early Disease Detection, Predictive Analytics, Personalized Medicine, Healthcare Data Analytics, Ethical AI in Medicine.
Machine Learning in the Age of Big Data: Transforming Decision-Making
The exponential growth of big data has fundamentally reshaped the scope and impact of machine learning (ML), enabling a new era of data-driven decision-making across industries and societal domains. Advances in data collection, storage, and processing technologies have made it possible to analyze massive, heterogeneous datasets in real time, while increasingly sophisticated ML algorithms extract patterns, predictions, and insights beyond the reach of traditional analytical methods. This paper examines how machine learning, empowered by big data, is transforming decision-making processes in business, healthcare, finance, governance, and scientific research. The abstract explores key ML paradigms—supervised, unsupervised, reinforcement, and deep learning—and their role in leveraging volume, velocity, and variety of big data. Applications such as predictive analytics, recommendation systems, risk assessment, fraud detection, and personalized services demonstrate how ML enhances accuracy, efficiency, and adaptability in complex decision environments. At the organizational level, ML-driven decision systems support automation, strategic planning, and real-time operational optimization, shifting decision-making from intuition-based approaches to evidence-based and probabilistic reasoning. Despite these benefits, the integration of machine learning with big data introduces critical challenges. Issues of data quality, bias, model interpretability, scalability, and energy consumption affect the reliability and fairness of ML-informed decisions. Moreover, ethical and governance concerns—including privacy protection, data ownership, transparency, and accountability—become more pronounced as automated decisions increasingly influence individuals and societies. Overreliance on opaque models can erode human oversight and trust if not carefully managed. This abstract argues that realizing the full potential of machine learning in the age of big data requires a balanced approach that combines technical innovation with ethical design and responsible governance. Emphasis on explainable AI, robust data management practices, and human-in-the-loop decision frameworks is essential to ensure that ML-driven systems augment, rather than replace, human judgment. By critically assessing both opportunities and limitations, this paper contributes to a deeper understanding of how machine learning is redefining decision-making in a data-intensive world.
Copyright (c) 2026 Dr. Abhishek Tripathi (Author) https://creativecommons.org/licenses/by-sa/4.0
AI-Powered Cybersecurity: Predicting and Preventing Digital Threats
The growing scale, sophistication, and frequency of cyber threats have made traditional rule-based and signature-driven security approaches increasingly inadequate. AI-powered cybersecurity has emerged as a critical paradigm for predicting, detecting, and preventing digital threats in complex and rapidly evolving digital ecosystems. By leveraging machine learning, deep learning, and behavioral analytics, AI-driven security systems can analyze massive volumes of network traffic, user activity, and system logs to identify anomalies and respond to attacks in real time. This paper examines how artificial intelligence is transforming cybersecurity from a reactive defense mechanism into a proactive and predictive security strategy. The abstract explores key applications of AI in cybersecurity, including intrusion detection and prevention systems, malware classification, phishing detection, fraud prevention, and threat intelligence automation. Predictive models enable early identification of zero-day attacks and advanced persistent threats by learning patterns of normal and malicious behavior. AI also enhances security orchestration and automated response, reducing detection time and minimizing human workload in high-pressure security operations centers. Despite its advantages, AI-powered cybersecurity introduces new challenges and risks. Adversarial attacks against machine learning models, data poisoning, model evasion techniques, and overreliance on automated defenses can undermine system effectiveness. Additionally, issues related to data privacy, explainability, and accountability complicate deployment in regulated and critical infrastructures. The dual-use nature of AI—where attackers can also leverage AI to enhance cyberattacks—further intensifies the cybersecurity arms race. This paper argues that effective AI-powered cybersecurity requires a balanced approach that combines advanced analytics with human expertise, robust governance, and continuous model validation. Emphasis on explainable AI, secure model design, and adaptive defense frameworks is essential for building resilient and trustworthy cyber defense systems. By integrating AI responsibly, organizations can strengthen their ability to anticipate and prevent digital threats in an increasingly interconnected world. Keywords AI-Powered Cybersecurity, Machine Learning, Threat Prediction, Intrusion Detection, Malware Analysis, Adversarial Attacks, Digital Security, Explainable AI
AI-Powered Cybersecurity, Machine Learning, Threat Prediction, Intrusion Detection, Malware Analysis, Adversarial Attacks, Digital Security, Explainable AI
Generative AI: Revolutionizing Creativity and Content Production
Generative Artificial Intelligence (AI) has emerged as a transformative force in creativity and content production, fundamentally altering how text, images, audio, video, and interactive media are conceived and produced. Powered by advances in deep learning architectures such as transformers, diffusion models, and large language models, generative AI systems are capable of creating high-quality, context-aware, and stylistically diverse content with minimal human input. This paper examines how generative AI is reshaping creative processes across industries, redefining the roles of creators, and accelerating content generation at unprecedented scale. The abstract explores key generative techniques, including text-to-text, text-to-image, text-to-audio, and multimodal generation, highlighting their applications in media and entertainment, marketing, journalism, software development, education, and design. Generative AI enables rapid ideation, personalization, and iterative creativity, allowing individuals and organizations to experiment with concepts and narratives that were previously constrained by time, cost, or technical skill. Human–AI co-creation models illustrate how generative systems augment, rather than replace, creative expertise. However, the widespread adoption of generative AI introduces complex challenges related to originality, intellectual property, misinformation, bias, and ethical responsibility. The potential for deepfakes, automated disinformation, and unauthorized use of training data raises concerns about authenticity and trust in digital content. Additionally, questions surrounding authorship, ownership, and the cultural impact of machine-generated creativity require new legal and ethical frameworks. This paper argues that the future of generative AI in creativity and content production depends on responsible design, transparent governance, and the cultivation of human-centered creative workflows. By balancing technological innovation with ethical safeguards and creative intent, generative AI can serve as a powerful catalyst for expanding human expression while preserving trust, originality, and cultural value in the digital age.
Generative AI, Creative AI, Content Generation, Large Language Models, Diffusion Models, Human–AI Co-Creation, Digital Media, Ethical AI
From Automation to Autonomy: The Evolution of Intelligent Machines
The evolution of intelligent machines from rule-based automation to adaptive, autonomous systems represents a major paradigm shift in artificial intelligence and engineering. Early automated machines were designed to execute predefined tasks within tightly controlled environments, relying on explicit programming and limited sensing capabilities. In contrast, modern intelligent machines increasingly leverage advances in machine learning, deep learning, sensor fusion, and real-time data processing to perceive their surroundings, learn from experience, and make decisions with minimal human intervention. This paper explores the technological, conceptual, and societal dimensions of this transition from automation to autonomy. The abstract examines key milestones in the development of intelligent machines, including the rise of data-driven learning, reinforcement learning, cognitive architectures, and embodied AI. These advances have enabled autonomous capabilities in domains such as robotics, autonomous vehicles, smart manufacturing, and intelligent infrastructure. Autonomous systems demonstrate increased flexibility, scalability, and resilience, allowing them to operate in dynamic and uncertain environments where traditional automation fails. The paper also discusses the role of human–machine collaboration, highlighting how shared autonomy and adaptive interfaces balance machine independence with human control. Despite their potential, autonomous intelligent machines introduce significant technical, ethical, and regulatory challenges. Ensuring safety, reliability, explainability, and alignment with human values remains a central concern, particularly in safety-critical applications. Issues such as accountability, trust, workforce displacement, and governance become more complex as machines gain decision-making authority. The abstract emphasizes the importance of robust testing, transparent design, and regulatory frameworks to manage risks associated with increasing autonomy. This paper argues that the future of intelligent machines lies not in full autonomy alone, but in responsible and context-aware autonomy that complements human capabilities. By tracing the evolution from automation to autonomy, the paper provides insights into how intelligent machines can be designed and governed to maximize societal benefit while minimizing unintended consequences.
Intelligent Machines, Automation, Autonomy, Artificial Intelligence, Robotics, Reinforcement Learning, Human–Machine Interaction, Autonomous Systems
AI-Driven Innovation: Redefining Business and Industry
AI-driven innovation is rapidly redefining business models, operational strategies, and competitive dynamics across industries. Advances in artificial intelligence—including machine learning, deep learning, natural language processing, and intelligent automation—are enabling organizations to transform data into actionable insights, optimize processes, and create new value propositions. This paper examines how AI technologies are reshaping business and industry by enhancing decision-making, improving efficiency, and driving innovation at scale. The abstract explores key areas of AI adoption, such as predictive analytics, intelligent process automation, personalized customer experiences, supply chain optimization, and smart manufacturing. AI-powered systems support real-time analysis of complex data streams, allowing organizations to anticipate market trends, manage risk, and respond dynamically to changing conditions. In sectors including finance, healthcare, retail, logistics, and energy, AI-driven solutions are enabling faster innovation cycles and more resilient operational frameworks. At the same time, the integration of AI into business environments presents significant challenges. Issues related to data governance, workforce transformation, algorithmic bias, cybersecurity, and regulatory compliance can affect trust and long-term sustainability. Organizational readiness, ethical AI deployment, and the alignment of AI strategies with business objectives are critical determinants of success. Overreliance on automated systems without adequate human oversight may lead to unintended consequences and strategic vulnerabilities. This paper argues that AI-driven innovation is not solely a technological shift but a strategic and cultural transformation. To fully realize its benefits, organizations must adopt a holistic approach that combines technological capability with responsible governance, talent development, and ethical leadership. By embedding AI thoughtfully into business processes and decision structures, enterprises can achieve sustainable growth, competitive advantage, and long-term value creation in an increasingly intelligent industrial landscape.
AI-Driven Innovation, Business Transformation, Intelligent Automation, Machine Learning, Industry 4.0, Digital Transformation, Decision-Making, Ethical AI
Deep Learning Breakthroughs and Their Real-World Applications
Deep learning has emerged as a driving force behind many of the most significant advances in artificial intelligence, fundamentally transforming the way machines perceive, learn, and interact with the world. Powered by neural network architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and deep reinforcement learning models, recent breakthroughs have achieved unprecedented performance in tasks involving vision, speech, language understanding, and complex decision-making. This paper examines key deep learning innovations and analyzes how they translate into impactful real-world applications across diverse domains. The abstract highlights major technical breakthroughs, including self-supervised and foundation models, attention mechanisms, multimodal learning, transfer learning, and large-scale model training enabled by advances in computing hardware and data availability. These developments have significantly reduced the reliance on labeled data while improving generalization and adaptability. In practical settings, deep learning has revolutionized fields such as healthcare (medical imaging, disease prediction, drug discovery), autonomous systems (self-driving vehicles, robotics), natural language processing (machine translation, conversational agents), and industry (predictive maintenance, quality control, and supply chain optimization). Despite these achievements, the deployment of deep learning systems introduces challenges related to interpretability, robustness, scalability, and ethical responsibility. High computational and energy costs, vulnerability to adversarial attacks, data bias, and limited transparency can hinder widespread adoption in safety-critical and resource-constrained environments. Addressing these issues requires continued research into efficient architectures, explainable models, and responsible deployment practices. This paper argues that the future impact of deep learning depends on bridging the gap between experimental breakthroughs and reliable real-world integration. By aligning technical innovation with domain expertise, ethical frameworks, and regulatory standards, deep learning can continue to deliver transformative benefits while maintaining trust, safety, and sustainability in real-world applications.
Deep Learning, Neural Networks, Transformers, Real-World Applications, Computer Vision, Natural Language Processing, Autonomous Systems, Ethical AI
Explainable AI: Building Trust in Intelligent Systems
As artificial intelligence (AI) systems increasingly influence high-stakes decisions in domains such as healthcare, finance, criminal justice, and autonomous systems, the need for transparency and accountability has become critical. Explainable Artificial Intelligence (XAI) has emerged as a key paradigm aimed at making AI models and their outputs understandable to human users, thereby fostering trust, reliability, and ethical deployment. This paper examines the role of XAI in bridging the gap between complex, often opaque AI models and the human stakeholders who rely on them. The abstract explores foundational concepts and techniques in XAI, including model-agnostic and model-specific explanation methods, post-hoc interpretability, and inherently interpretable models. Techniques such as feature attribution, surrogate models, visualization methods, and counterfactual explanations are discussed in relation to their ability to provide meaningful insights into AI decision-making processes. The paper also analyzes how explainability supports key objectives such as bias detection, regulatory compliance, system validation, and improved human–AI collaboration. Despite its promise, XAI presents significant technical, cognitive, and ethical challenges. Explanations may be incomplete, misleading, or overly complex, potentially creating false confidence rather than genuine understanding. Furthermore, the level and form of explanation required varies across stakeholders, including developers, domain experts, regulators, and end users. Balancing explainability with model performance, data privacy, and security remains an ongoing concern. This paper argues that building trustworthy intelligent systems requires a contextual and user-centered approach to explainability, integrating XAI throughout the AI lifecycle—from data collection and model design to deployment and governance. By aligning technical explainability with human values and institutional needs, XAI can serve as a foundation for responsible AI, enabling informed oversight, accountability, and sustained public trust in intelligent systems.
Explainable Artificial Intelligence (XAI), Trustworthy AI, Model Interpretability, Transparency, Human–AI Interaction, Ethical AI, Accountability, Decision Support Systems
The Future of Artificial Intelligence: Opportunities, Risks, and Ethical Frontiers
Artificial Intelligence (AI) has rapidly evolved from a specialized computational tool into a transformative general-purpose technology reshaping economies, governance, science, and everyday life. This paper explores the future of artificial intelligence through a comprehensive analysis of its emerging opportunities, associated risks, and expanding ethical frontiers. On the opportunity side, AI promises significant advances in productivity, healthcare, climate modeling, education, and scientific discovery, driven by progress in machine learning, generative models, and autonomous systems. These developments have the potential to augment human capabilities, optimize complex systems, and address global challenges at unprecedented scale. At the same time, the accelerated deployment of AI introduces substantial risks. These include labor displacement and economic inequality, algorithmic bias and discrimination, privacy erosion, security vulnerabilities, and the misuse of AI in misinformation, surveillance, and autonomous weapons. Technical concerns such as model opacity, robustness, and alignment with human intent further complicate safe and reliable deployment. As AI systems increasingly operate with limited human oversight, small design choices can lead to large-scale societal consequences. Beyond practical risks, AI raises profound ethical and philosophical questions about accountability, agency, transparency, and the moral status of intelligent systems. Existing legal and ethical frameworks struggle to keep pace with rapidly advancing capabilities, creating governance gaps at national and global levels. This abstract argues that the future of AI will be shaped not only by technical innovation but also by deliberate choices in policy, regulation, and ethical design. Responsible AI development requires interdisciplinary collaboration, inclusive stakeholder engagement, and proactive governance mechanisms that balance innovation with societal well-being. By examining opportunities, risks, and ethical challenges together, this paper aims to contribute to a more holistic understanding of how AI can be guided toward outcomes that are both beneficial and just.
Copyright (c) 2026 Dr. R. Kumar (Author) https://creativecommons.org/licenses/by-sa/4.0
Personalized Healthcare Decision Support Using Machine Learning
Personalized healthcare has emerged as a critical approach to improving clinical outcomes by tailoring medical decisions to individual patient characteristics. This study aims to develop an intelligent healthcare decision support system using machine learning techniques to provide personalized predictions and treatment recommendations. The proposed system utilizes patient data obtained from electronic health records, including demographic information, clinical measurements, and historical diagnoses. After data preprocessing and feature engineering, supervised machine learning models such as Random Forest and Gradient Boosting are trained to predict disease risk and support clinical decision-making. Model performance is evaluated using standard metrics including accuracy, precision, recall, and area under the ROC curve. The results demonstrate that machine learning-based personalized decision support systems significantly improve prediction accuracy compared to traditional rule-based approaches. The findings highlight the potential of machine learning to enhance early disease detection, optimize treatment strategies, and assist healthcare professionals in delivering patient-centered care. In conclusion, the study confirms that integrating machine learning into healthcare decision support systems can lead to more accurate, efficient, and personalized medical decisions, while emphasizing the need for data privacy, model interpretability, and ethical considerations for real-world deployment.
AI-driven Predictive Models for Industrial Stormwater Quality and Flow
Industrial stormwater management is increasingly challenged by highly variable runoff volumes and rapidly fluctuating pollutant loads that are difficult to capture using conventional monitoring and control approaches. Artificial intelligence driven predictive models offer a data centric solution by enabling continuous analysis of hydrological and water quality data to anticipate changes in stormwater flow and contaminant concentrations. This article examines the application of machine learning techniques for predicting pollutant spikes and flow dynamics within industrial stormwater systems and their integration with automated treatment controls. By leveraging historical records and real time sensor inputs, AI based models support proactive operational decisions, optimize treatment performance, and reduce the risk of non compliance with discharge standards. The findings highlight the potential of predictive intelligence to enhance system resilience, improve resource efficiency, and advance sustainable industrial stormwater management practices.
Artificial intelligence, machine learning, industrial stormwater management, predictive modeling, water quality forecasting, pollutant spike prediction, automated treatment systems
Copyright (c) 2026 Santunu Barua (Author) https://creativecommons.org/licenses/by-sa/4.0
Applying Transformational Leadership and LMX Theory to Succession Planning in Logistics SMEs
Purpose: This article explores how Transformational Leadership Theory and Leader-Member Exchange (LMX) Theory provide valuable frameworks for understanding leadership succession planning in small-to-medium-sized enterprises (SMEs) within the logistics sector. Design/Methodology/Approach: The article draws on findings from a qualitative phenomenological study of 11 U.S.-based SME logistics leaders. Semi-structured interviews were analyzed using Braun and Clarke’s six-step thematic framework. The analysis was guided by Transformational Leadership Theory and LMX Theory to identify how visionary leadership behaviors and relational quality shaped succession outcomes. Findings: Results demonstrate that transformational leadership behaviors (idealized influence, inspirational motivation, intellectual stimulation, and individualized consideration) were central to preparing successors in resource-constrained environments. LMX dynamics, trust, communication, and individualized support, compensated for the lack of formal HR-driven succession systems. The synergy of the two theories provided a holistic framework for understanding leadership continuity in SMEs. Practical Implications: The study highlights that SMEs can embed leadership continuity into their organizational culture by institutionalizing mentorship, fostering trust-based relationships, and modeling vision-driven leadership. These practices enhance resilience and sustainability without requiring significant structural investment. Originality/Value: This article contributes to theory and practice by extending Transformational Leadership and LMX theories to the underexplored context of logistics SMEs. It offers a dual-lens framework for strengthening leadership succession where formal structures are limited.
Leadership Succession Planning, Leader-Member Exchange (LMX), Transformational Leadership.
Copyright (c) 2026 Shemika L. Harris (Author) https://creativecommons.org/licenses/by-sa/4.0
Green Marketing Strategies: Driving Consumer Preference in the Era of Sustainability
Sustainability has emerged as one of the most pressing global concerns, influencing industries, businesses, and consumers alike. Green marketing—defined as the integration of environmentally friendly practices into product design, branding, promotion, and distribution—has become a vital strategy for organizations seeking to align with changing consumer values. This paper examines how green marketing strategies drive consumer preference in the era of sustainability. It explores the underlying factors shaping green consumer behaviour, nalyses effective marketing practices, and identifies challenges faced by businesses in achieving authentic sustainability. Using secondary data analysis and case studies, the research reveals that consumers increasingly prefer brands that demonstrate ecological responsibility, transparency, and innovation. The findings suggest that successful green marketing not only strengthens brand loyalty but also creates a competitive edge in the marketplace.
Copyright (c) 2025 Author https://creativecommons.org/licenses/by-sa/4.0
Green AI: Designing Energy-Efficient Machine Learning Models
Artificial Intelligence (AI) and Machine Learning (ML) are increasingly being deployed in diverse sectors, but their computational intensity poses significant energy and environmental challenges. Large-scale models consume substantial electricity, leading to rising carbon footprints that hinder global sustainability goals. This paper explores the concept of Green AI, which emphasizes the development of energy-efficient ML models without compromising accuracy. By reviewing existing methods, frameworks, and innovations in sustainable AI, this research proposes design strategies for computational efficiency, including model compression, transfer learning, and efficient hardware utilization. Data analysis on energy costs of AI models highlights the urgency for energy-aware algorithm design. Findings suggest that Green AI is not only a technological necessity but also a strategic imperative for industries and policymakers in achieving sustainable digital transformation.
Green AI, Sustainable Artificial Intelligence,
From Palm-leaf Manuscripts to Digital Libraries: Evolution of Knowledge Preservation in India
Knowledge preservation has been central to the cultural, intellectual, and social fabric of India. From the oral traditions of the Vedas to the palm-leaf manuscripts of classical India, the country has historically emphasized safeguarding knowledge as a form of dharma. Over centuries, knowledge preservation transitioned from fragile organic materials to institutionalized libraries like Nalanda and Takshashila, and today into digital repositories and open-access platforms. This paper traces the historical evolution of knowledge preservation in India, examines the ethical and cultural principles underlying it, and evaluates the transformative role of digital libraries in revitalizing India’s ancient wisdom for contemporary relevance. The study also discusses challenges in digitization, intellectual property, and sustainability while drawing lessons from the past for the future of knowledge management.