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
Progression or Regression: Jazz Over Time an Interview with Mike Richmond
The modern jazz world has changed in a substantial way in many respects socially, economically, and educationally which has reignited discussion of whether the changes are evidence of the evolution or decline of the art form and/or its professional support network. The musical discourse of jazz has been changing, but so have the situations of the actual musicians because of the growing problems of economic sustainability, employment, music education, and the infrastructure of performances. This study examines these issues through a qualitative case study with an in-depth semi-structured interview of world-renowned American Jazz bassist, composer and educator Mike Richmond, who has a broad and extensive career in the United States which gives him a unique historical perspective on the development of Jazz in the U.S. The study explores Richmond’s thoughts about five interrelated aspects of the existing jazz ecosystem, namely music education, professional competition, wage-to-living cost inequities, inflation, and the overall state of jazz performance and practice, through thematic qualitative analysis. They are supported by scholarly articles and industry reports to give a full picture of the structural shifts in the profession. The results show that jazz, as a musical art form, has evolved over time, with greater harmonic sophistication, rhythmic complexity, and melodic sophistication, in part through greater educational opportunities and international musical exchange. The study also uncovers the toughening of the professional landscape in which jazz lives and exists: rising living expenses, diminishing musician pay, fewer performance spaces, greater competition, and evolving economic conditions. Despite ongoing innovations, these developments have imposed significant obstacles in the way of the emerging musicians. The aim of this research is to add to the body of knowledge regarding the development of jazz as an art form and jazz music education by separating out the changes in the jazz-artistic form from the changes in the jazz-professional form. It also reflects the importance of sustainable education, cultural and economic policies that will enable the long-term sustainability of jazz artists and the continuous artistic development of jazz itself.
Jazz studies, Jazz education, Professional jazz musicians, Qualitative research, Thematic analysis, Music industry, Jazz economy.
Copyright (c) 2026 https://creativecommons.org/licenses/by-sa/4.0
Big Data Analytics in Air Traffic Management: Enhancing Aviation Safety, Operational Efficiency, and Predictive Decision-Making
Air Traffic Management (ATM) is undergoing rapid transformation as increasing global air traffic places unprecedented demands on operational safety, airspace capacity, and decision-making efficiency. Conventional ATM systems, which largely depend on radar-based surveillance and manual control processes, face significant limitations in managing complex and dynamic aviation environments. This study examines the role of Big Data analytics in enhancing the performance of ATM systems by integrating heterogeneous data sources, including aircraft sensor data, flight schedules, weather information, surveillance systems, and air traffic control communications. A mixed-methods research design was adopted, combining an extensive review of contemporary literature with qualitative insights, quantitative analysis, predictive modeling, and case-based evaluation to assess the effectiveness of Big Data applications in aviation operations. The findings demonstrate that Big Data-driven predictive analytics can significantly improve traffic flow optimization, reduce flight delays, strengthen situational awareness, enhance predictive maintenance, and support more efficient allocation of operational resources. Machine learning-based prediction models achieved high accuracy in forecasting traffic disruptions and identifying potential safety risks, while data-driven decision support contributed to measurable improvements in operational efficiency and infrastructure utilization. Despite these benefits, the study identifies persistent challenges related to data interoperability, cybersecurity, privacy protection, infrastructure investment, and the integration of diverse aviation data sources. The research concludes that the successful adoption of scalable Big Data architectures, supported by artificial intelligence, machine learning, cloud computing, and edge computing technologies, will play a critical role in developing resilient, intelligent, and sustainable ATM systems. The study provides practical recommendations for aviation authorities, airport operators, airlines, and policymakers seeking to accelerate digital transformation and improve the safety, efficiency, and sustainability of future air traffic management operations.
Machine Learning; Traffic Flow Optimization; Predictive Maintenance
Copyright (c) 2025 https://creativecommons.org/licenses/by-sa/4.0
Demystifying important issues concerning management of global IT resources in the Public Transit Sector in the US
The public transit industry has undergone a dramatic shift in its digital landscape, impacting its operations, infrastructure management, and stakeholder engagement in the United States. The effective management of global IT resources is now an imperative, with many transit systems now relying on technologies that are interconnected, including cloud, big data analytics, Internet of Things (IoT), artificial intelligence and advanced communication networks. But a host of governance, cyber security, data management, resource allocation, interoperability, workforce skills and vendor dependency challenges remain at the forefront of transit agency challenges. This article illuminates the key challenges confronting the management of global IT resources in the U.S. public transit sector by exploring the technological, organizational and operational challenges of digital transit ecosystems. The study investigates how IT governance ensures technology investments are in alignment with organizational goals and the importance of cybersecurity resiliency, enterprise data governance, and strategic sourcing practices. At the same time, the paper explores new issues which are associated with financial limitations, social justice of services, public-private cooperation, and digital innovation management. The article summarizes findings from previous studies and current practices, and suggests key recommendations for shaping more effective IT governance policies, developing digital skills, optimizing operations and fostering sustainable and resilient transport. The results support the academic body and policy makers in their actual and future efforts to effectively manage global IT resources to support the modernization and long-term sustainability of public transportation services in the U.S.
Global IT Resource Management, Public Transit, Digital Transformation, IT Governance, Cybersecurity, Big Data Analytics, Internet of Things, Public Transportation, Smart Mobility, United States
An AI-Driven Evidence-Gated Search Architecture for Intelligent Knowledge Discovery
The exponential growth of digital information repositories has rendered conventional keyword-based search architectures progressively inadequate for intelligent knowledge discovery. This paper introduces the Evidence-Gated Search Architecture (EGSA), a novel AI-driven framework that augments traditional retrieval pipelines with multi-stage evidential reasoning, semantic graph traversal, and confidence-calibrated answer generation. EGSA employs a hierarchical gate mechanism — comprising Query Intent Classification, Evidence Sufficiency Scoring, Semantic Relevance Filtering, and Contradiction Resolution — to ensure that retrieved knowledge passes rigorous epistemic thresholds before being synthesized into responses. Evaluated against four benchmark knowledge discovery datasets (MS-MARCO, TriviaQA, NaturalQuestions, and a proprietary enterprise corpus of 2.4 million documents), EGSA demonstrates a 34.7% improvement in answer faithfulness, a 29.1% reduction in hallucinated content, and a 41.3% gain in retrieval precision@10 compared to standard Retrieval-Augmented Generation (RAG) baselines. This architecture represents a significant step toward epistemically responsible AI search systems capable of supporting high-stakes knowledge work in scientific, legal, medical, and enterprise domains.
Evidence-Gated Search, Retrieval-Augmented Generation, Knowledge Discovery, Semantic Graph, Epistemic AI, Information Retrieval, Large Language Models, Hallucination Mitigation
Copyright (c) 2026 Priya Banerjee (Author) https://creativecommons.org/licenses/by-sa/4.0
AI-Driven Intrusion Detection Systems for Cybersecurity
Artificial Intelligence (AI)-based Intrusion Detection Systems (IDS) have become a vital part of the cybersecurity landscape, offering intelligent, adaptive, and real-time solutions to detect malicious activities in various computing environments. Traditional signature-based detection methods have been found to be limited in their ability to detect new and changing attack patterns as cyber threats grow in complexity, numbers and sophistication. AI-powered IDS solve these issues by utilizing machine learning, deep learning, neural networks, and hybrid analytical methods to effectively identify anomalies, classify threats, and automate incident responses with greater accuracy and efficacy. This study analyzes the development of AI-driven intrusion detection systems, their components, the key AI techniques used and implementation in enterprise networks, cloud computing systems, Internet of Things (IoT) environments, critical infrastructure and host-based systems. The research also examines the performance benefits of AI-powered detection models, such as better detection accuracy and lower false positive rates, and how these models can be scaled up to handle large-scale network monitoring. Besides, it explores the key challenges that are typically encountered in the implementation of AI systems, like adversarial attacks, data privacy, computational complexity, and model interpretability, and identifies some recent trends, such as explainable AI, federated learning, edge intelligence, and predictive threat detection. Overall, the results show that AI-enabled intrusion detection systems can greatly improve cybersecurity resilience, allowing for proactive, adaptive, and intelligent defense strategies that can tackle increasingly complex cyber threats.
Artificial Intelligence, Intrusion Detection Systems, Cybersecurity, Machine Learning, Deep Learning, Network Security, Threat Detection, Anomaly Detection, Internet of Things, Cloud Computing.
Artificial Intelligence and Machine Learning in Higher Education: Evaluating Strategies to Reduce Academic Disparities
Academic disparities — the systematic divergence in educational outcomes across student populations differentiated by socioeconomic background, geographic region, first-generation status, gender, and ethnicity — remain among the most persistent structural inequities in global higher education. Despite decades of institutional policy and targeted student support investment, the GPA gap between the highest- and lowest-risk student quintiles in large universities has narrowed by less than 8% over the past two decades using conventional approaches. Artificial Intelligence (AI) and Machine Learning (ML) now offer a fundamentally new toolkit for addressing this challenge: predictive modelling capable of early disparity identification, adaptive systems that personalise learning at scale, and automated intervention workflows that route support resources with a speed, precision, and continuity that human-only systems cannot sustain. This paper presents a comprehensive evaluation of five AI and ML strategy categories deployed across 36,400 students at six higher education institutions spanning India, Ghana, and Mexico over four academic years (AY 2022–26), constituting the longest and largest multi-site longitudinal evaluation of AI-ML disparity reduction strategies in the JSTST literature.
Artificial Intelligence, Machine Learning, Higher Education, Academic Disparities, Early Warning Systems, Adaptive Learning, Predictive Analytics
Copyright (c) 2026 Ramesh Iyer, Kavitha Sundaram (Author) https://creativecommons.org/licenses/by-sa/4.0
AI and SAP Integration for Intelligent Stormwater Management in Smart Cities
Municipal stormwater management is increasingly data-intensive, drawing on sensor telemetry, geographic information systems, maintenance records, and capital planning data that, in many cities, remain fragmented across departmental systems rather than integrated with the enterprise resource planning (ERP) platforms that manage the city’s assets, work orders, and finances. This paper examines how artificial intelligence (AI) and machine learning (ML) can be integrated with SAP enterprise systems, particularly SAP S/4HANA and SAP Enterprise Asset Management (EAM), to support intelligent stormwater infrastructure management within a smart city context. We describe an integration architecture in which stormwater sensor data, GIS asset records, and inspection findings feed AI-driven predictive models whose outputs are surfaced directly within SAP EAM as prioritized maintenance notifications and work orders, closing the gap between infrastructure condition insight and the enterprise systems municipal operations and finance teams already use to execute and fund maintenance work. The paper discusses relevant SAP modules and integration mechanisms, including SAP EAM, SAP Analytics Cloud, and IoT integration through SAP Business Technology Platform, alongside the AI/ML techniques applied to stormwater deterioration forecasting and flood-risk anomaly detection. A discussion of governance, data quality, and change-management considerations specific to municipal SAP deployments is included, along with directions for future research on interoperability between municipal GIS ecosystems and enterprise ERP platforms.
Stormwater management; artificial intelligence; machine learning; SAP; enterprise asset management; smart cities; SAP S/4HANA; predictive maintenance; GIS integration; IoT.
Data Engineering Architectures for AI-Based Digital Repair Twins in Financial Cybersecurity
Financial institutions face a growing volume of cyber threats — credential-stuffing attacks, account-takeover attempts, insider misuse, and infrastructure intrusions — that must be detected and contained within tight operational windows to prevent financial and reputational harm. This paper examines the data engineering architectures required to support AI-based Digital Repair Twins (DRTs): live, diagnostic, and action-capable virtual models that detect security anomalies, localize root causes, and initiate bounded remediation within financial infrastructure. We present a five-layer reference architecture spanning ingestion and streaming, feature and twin-state storage, AI/ML model serving, reasoning and policy, and action and remediation, and we describe the data pipeline design decisions specific to financial cybersecurity, including entity-graph modeling of accounts and sessions, lineage tracking for regulatory audit, and strict data governance for sensitive financial and personally identifiable information. A table-based comparison of pipeline layers, technologies, and data artifacts is provided, alongside a second table of evaluation metrics for operational assessment. Two figures illustrate the layered architecture and the closed-loop detection-to-repair workflow. A simulated case study involving a credential-stuffing attack against an online banking portal demonstrates how the architecture supports rapid detection, root-cause correlation, and automated session-level containment. The paper closes with a discussion of governance, explainability, and open data engineering challenges for AI-driven self-healing in financial cybersecurity.
Data engineering; digital repair twin; financial cybersecurity; machine learning; anomaly detection; self-healing systems; SIEM; feature store; incident response.
Architecting AI Governance & ‘Agent-ready’ data layer: Semantic Governance for Autonomous AI workflows
In recent years, the development of autonomous AI agents has revolutionized the way enterprises use data, making these systems capable of reasoning, acting, and managing complex processes independently. But, the quality, structure and governance of the data on which they are built plays a critical role in the effectiveness and reliability of these systems. Conventional data governance frameworks, which are more suited to human-led analysis, are insufficiently semantic and able to adapt rapidly enough for autonomous AI use. In this study, a comprehensive framework for architecting AI governance by developing an ‘agent-ready’ data layer based on semantic governance principles is presented. The goal of the proposed approach is to embed contextual meaning and relationships into data ecosystems using metadata intelligence, ontology driven modeling and knowledge graph structures. This allows AI agents to understand the data in a way that goes beyond its syntax, facilitating more accurate reasoning and decision making. The model also introduces policy driven data pipelines and continuous monitoring systems for data health and compliance, ensuring proper data integrity and transparency throughout AI workflows. The architecture allows for the governance processes to be aligned to the AI lifecycle, which enables dynamic governance enforcement and real-time data quality validation. The results prove that the adoption of semantic governance in an agent-ready data architecture can vastly improve the accuracy of decisions, efficiency of automation, and compliance with governance rules. The research underscores the need for an adaptive, context-aware governing framework that enables autonomous AI environments, replacing the traditional rule-driven approach.
AI Governance, Semantic Governance, Agent-Ready Data Layer, Autonomous AI Agents, Data Observability, Knowledge Graphs, Metadata Management, Policy-Aware Data Pipelines, Enterprise AI Architecture
From Reactive cleanup to Real-time observability: Modernizing Enterprise Data Quality for RAG pipelines
As enterprises implement Retrieval-Augmented Generation (RAG) pipelines, data quality has become even more important for maintaining the reliability and meaningfulness of outputs generated by the large language model. The traditional methods of data quality management, which primarily focus on reactive data cleansing and regular data validation, are not enough to meet the demand of real-time and changing data requirements in modern AI driven systems. This paper explores how to move from reactive data quality to real-time data observability in the context of RAG architectures. The study is a modernized enterprise data quality paradigm which includes continuous monitoring, anomaly analysis, and feedback-driven correction mechanisms that are directly built into data pipelines. This approach also allows organizations to detect issues such as data inconsistencies, latency, and semantic drift in the ingestion, transformation, and retrieval layers before they affect downstream AI applications. The study also proposes a conceptual architecture that correlates data quality metrics with RAG performance metrics like retrieval relevance, response accuracy, and latency. A prototype implementation shows that real-time observability can help make data more reliable and the system more responsive. The results show that the observability-driven models outperform the classic reactive ones in terms of data integrity and high-quality AI outputs. The results underscore the need to reimagine enterprise data quality as an ongoing intelligent system that is deeply embedded in the behavior of the AI system.
Data Quality, Data Observability, Retrieval-Augmented Generation (RAG), Enterprise Data Management, Real-Time Analytics, Data Pipelines, AI Systems
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
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.
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