Journal of Computer Science and Technology Studies
The Journal of Computer Science and Technology Studies (ISSN: 2709-104X) is a double-blind peer-reviewed, open-access journal published by Al-Kindi Center for Research and Development. It covers the latest developments in the broad areas of computer science and information technology. The journal offers readers free access to all new research issues relevant to computer science and information technology. While the journal strives to maintain high academic standards and an international reputation through the suggestions of the international advisory board, it welcomes original, theoretical and practical submissions from all over the world.
Al-Kindi Center for Research and Development
Transforming Pharma Manufacturing Supply Chains Through AI and ERP Analytics
The pharmaceutical manufacturing business has a complex, international supply chain, constrained by strict regulatory measures, rising costs, and growing pressure on product quality, supply, and efficacy. Conventional planning and control strategies have not been effective at managing demand volatility, equipment reliability, and inventory risk at a large scale. To this end, artificial intelligence and enterprise resource planning systems are emerging as transformative technologies that can revamp supply chain processes in pharmaceutical companies. The next stage of analytics in demand prediction, predictive maintenance, inventory optimization, and production planning is reachable due to the integration of AI and ERP solutions, increasing the efficiency of operations and improving decision-making quality. Based on empirical data by proven pharmaceutical manufacturers, up to 30% of forecast errors, 20-30% of unplanned unforeseen downtime, and ten to 20% of holding inventory costs are reduced after AI-facilitated adoption of ERP. Nevertheless, it has to be well run in data governance, inter-functional collaboration, and change management to facilitate the effective use of the technologies and ensure the reliability of the models, regulatory compliance, and workforce acceptance. This paper combines the empirical evidence base in the industry, practical case study applications, and analysis findings to deliberate on the feasibility of AI ERP integration in the pharmaceutical manufacturing process. It suggests an analytics-driven decision-making model with governance to make supply chains more resilient.
AI in pharmaceutical supply chain, ERP analytics, Pharma manufacturing, Predictive maintenance, Regulatory compliance
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A National-Scale AI-Driven Cyber Defense Framework for Protecting U.S. Critical Infrastructure Against Nation-State Attacks
The Cybersecurity and Infrastructure Security Agency (CISA) has predicted a 140% rise in high-impact attacks between 2022 and 2024, signalling an increase in nation-state adversary cyberattacks that affect US critical infrastructures. The sophistication, persistence and ever-evolving nature of nation-states' hacking has outgrown current cybersecurity, which primarily focuses on reactive and perimeter-based approaches. In this paper, we present and test a National-Scale AI-Driven Cyber Defense Framework (NAICDF) a multi-layer, intelligence-based framework with machine learning-based threat detection, federated learning for data sharing across critical infrastructure sectors, Zero Trust Architecture (ZTA) and automated response systems. We compare intrusion detection accuracy, mean time to respond (MTTR) and resilience with traditional approaches using incident reports of 847 confirmed nation-state intrusions in 11 critical infrastructure sectors (2020-2024), publicly available threat intelligence and simulation data from the National Cyber Exercise Program (NCEP) of the Cybersecurity and Infrastructure Security Agency (CISA). Results demonstrate the NAICDF achieves a 94.3% accuracy in detecting intrusions (2.1% false positives) and a 67% reduction in the mean time to respond (MTTR) when compared to conventional security operations center (SOC) systems. We also explore governance and public-private partnership models, and compatibility with existing policy frameworks such as CIRCIA (2022) and the National Cybersecurity Strategy (2023). The framework offers a policy-friendly, scalable model to secure our critical systems in the 21st century.
Critical infrastructure protection, AI-driven cybersecurity, nation-state attacks, Zero Trust Architecture, federated learning, intrusion detection, national cybersecurity policy, advanced persistent threats
An Author-Bounded Systematic Review of Studies on Social Media in EFL Teaching and Learning (2008–2025): Skills, Platforms, and Pedagogical Insights
This study presents a systematic review (SR) of the author’s research on the use of social media (SM) in language teaching and learning published between 2008 and 2025. The SR synthesizes 33 studies distributed across 11 thematic clusters: pronunciation development using YouTube and AI generated videos; listening skill development through TED Talks and news podcasts; reading skill development; writing and creativity; grammar practice through podcasts; translation skills development; multi skill learning through Facebook; communication in instructional contexts; cultural learning through wikis; delivering academic lectures via live social media streaming; and teachers’ professional development on Facebook. The review provides a longitudinal account of how SM has been integrated into language teaching and learning across multiple platforms (Facebook, X, Skype, YouTube, Periscope, blogs, wikis, podcasts) and multiple skills (listening, pronunciation, reading, writing, culture, translation, and multi skill learning), highlighting the pedagogical potential, purposeful instructional designs, and evolving nature of SM tools in EFL education. Across the 33 studies, SM platforms offered accessible, flexible, and interactive learning environments that enhanced students’ engagement, motivation, and exposure to real world language use. Across clusters, students benefited from multimodal input, opportunities for self regulated learning, and increased interaction with peers, instructors, and global communities. SM also served as a powerful supplementary environment for EFL instruction, providing authentic content, learner autonomy, and diverse pedagogical strategies. This SR offers the first longitudinal, author bounded synthesis of 33 studies conducted over 18 years, documenting how one researcher systematically explored and advanced the use of SM in EFL education. It contributes a unified pedagogical perspective that connects platforms, tasks, and outcomes, and provides a rare insider account tracing the evolution of platforms, instructional practices, and innovations across diverse skills and learning environments. It also highlights the need for continued research that keeps pace with rapidly changing technologies and emerging digital practices. Future research may examine how emerging platforms, such as TikTok, Instagram Reels, Threads, vlogs, vodcasts and AI enhanced tools can be integrated into EFL instruction using evidence based pedagogical frameworks and how learners interact with AI mediated social environments in ways that shape language development, engagement, and digital literacy.
Author bounded systematic review, social media in EFL, Facebook, X (Twitter), YouTube, podcasts, wikis, blogs, EFL skills development, pedagogical practices.
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A Review of Evolution of Internet of Things (IoT) and its Significant Impact in the Field of Precision Agriculture
Agriculture is swiftly moving towards a digital transformation from conventional mechanization to a prescriptive "Agriculture 4.0" model. This survey offers a critical assessment of the evolution of the Internet of Things (IoT) and its application in Precision Agriculture (PA) systems. We assess the technological evolution from early Wireless Sensor Networks (WSN) to new Low-Power Wide-Area Networks (LPWAN) like NB-IoT and LoRaWAN, which solve the long-standing deployment issue of battery life in rural coverage. This review critically examines more than 50 impactful publications, centering on novel deployments of multi-modal sensors to support site-specific farming, adaptive irrigation and real-time livestock monitoring. Our report differs from existing surveys in explicitly considering the architectural evolution to Edge-Fog computing, driven by the rapid expansion in data volumes of high-resolution soil sensing and atmospheric monitoring. We benchmark the merits of connectivity protocols, showcasing their impact on resource-efficient practices, such as reported reductions in water and fertilizer use. Finally, we idensify key challenges, such as platform interoperability, data security in remote nodes, and the likely integration of 5G-powered robotic swarms. The review provides a guide to researchers and industry partners about the current state and evolution of the agricultural IoT landscape.
Internet of Things (IoT), Precision Agriculture, LPWAN, NB-IoT, Edge Computing, Smart Irrigation, Sustainable Agriculture
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Securing the Next Generation of Digital Payments: A Threat Model for Virtual Card Ecosystems
The rapid adoption of virtual cards in business-to-business (B2B) payments has created a complex digital ecosystem involving buyers, suppliers, issuers, acquirers, and increasingly, automated processing platforms. While virtual cards offer inherent security benefits, the ecosystem's interconnectedness and reliance on digital channels introduce a new and expanded attack surface. This paper presents a comprehensive threat model for this next generation of digital payments. Using the STRIDE framework, we systematically analyze the security threats inherent in the end-to-end virtual card lifecycle, with a particular focus on novel attack vectors targeting the automated data ingestion and processing stages of platforms like Visa AR Manager. Based on this analysis, we propose a multi-layered, defense-in-depth mitigation strategy. This strategy integrates foundational controls such as PCI DSS compliance, technical solutions including payment tokenization and secure API gateways, and advanced AI-powered fraud detection to create a resilient and secure virtual payment environment capable of withstanding modern threats.
Next Generation of Digital Payments; Threat Model; Virtual Card Ecosystems
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Machine learning based clinical decision support for heart disease prediction using structured patient data
Heart disease remains a leading cause of mortality worldwide, necessitating reliable and efficient predictive models for early diagnosis and clinical decision support. This study presents a comprehensive machine learning framework for heart disease prediction using a structured clinical dataset comprising 920 patient records with diverse demographic, physiological, and diagnostic attributes. The dataset includes both numerical and categorical features, requiring careful preprocessing and encoding to ensure compatibility across different model architectures.A range of classification algorithms is systematically evaluated, including Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, Extreme Gradient Boosting, and Light Gradient Boosting Machine. Model performance is assessed using multiple evaluation metrics, including accuracy, precision, recall, F1-score, and receiver operating characteristic area under the curve, along with five-fold cross-validation to examine stability and generalization behavior.The experimental results demonstrate consistently high predictive performance across most models, with several approaches achieving near-perfect classification metrics and minimal variation across cross-validation folds. In contrast, K-Nearest Neighbors exhibits slightly lower performance, highlighting differences in sensitivity to local data structure. Analysis of feature distributions and pairwise relationships indicates strong separability between classes, particularly driven by clinically relevant variables such as chest pain type, exercise-induced angina, ST depression, and maximum heart rate.Further evaluation using confusion matrices, receiver operating characteristic curves, and precision–recall curves confirms the robustness of the predictive models and their ability to distinguish between diseased and non-diseased cases with high reliability. Despite the strong performance, the study acknowledges potential dataset-specific characteristics that may influence model behavior and emphasizes the importance of external validation for clinical deployment.
heart disease prediction, clinical decision support, machine learning, classification models, structured clinical data, cross-validation, ROC AUC, precision recall analysis, medical diagnosis, predictive analytics
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Two Decades of LMS-Supported EFL Instruction: A Systematic-Review of Implementation, Use, Infrastructure, and Success Factors
This study conducted a systematic review (SR) of the author’s research on the use of Online Learning Management Systems (LMSs) in teaching English language skills between 2001 and 2021. The corpus consists of 44 studies categorized into three thematic clusters: implementation of blended learning in teaching language skills; LMS infrastructure, use, and utilization; and success factors in blended learning. The corpus covers experimental studies using LMS; reading and writing studies; investigations of the differential effects of online instruction on multiple EFL skills; studies using iRubrics for assessment; research employing web conferencing tools; extensive reading studies; online collaborative learning and cross cultural communication via LMS; explicit, strategy based online reading instruction; feasibility studies of different LMSs; LMS infrastructure and use; cases in which online learning failed; and studies on success factors. Results showed that the author’s research provides a comprehensive account of how blended learning was implemented across different English language skills (reading, writing, vocabulary, grammar, ESP, translation) using Blackboard, WebCT, Moodle, Nicenet, RCampus, and Elluminate. The studies documented instructional procedures, task types, assessment methods, and patterns of student engagement in online and blended environments. Across twelve experimental studies, students who received a combination of online and textbook based instruction achieved higher gains than those who received textbook only instruction, and active participants consistently outperformed passive participants. Overall, students expressed positive attitudes toward online instruction, and LMS supported learning, enhanced autonomy, motivation, collaboration, and engagement. Additionally, the author’s studies on LMS infrastructure and use identified institutional, technical, and training related factors that affected the utilization of online learning. Studies on success factors emphasized the importance of online collaboration, active participation, motivation, timely feedback, well designed tasks, and supportive learning environments. Notably, collaboration succeeded in the Saudi–Ukrainian–Russian study because interaction occurred in a culturally neutral, non threatening international space, whereas collaboration between King Saud University and Umm Al Qura University failed due to students’ discomfort with cross university interaction in a gender segregated local context, which suppressed participation. In conclusion, the author’s contributions to LMS based EFL instruction provide a foundation for understanding how online and blended learning evolved across different platforms, skills, and pedagogical contexts.
Systematic review (SR), Al-Jarf research program, online courses, Learning Management Systems (LMS), LMS infrastructure, EFL/ESL online instruction, web based tools, technology enhanced learning, online engagement, online collaboration
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A Secure and Privacy-Preserving Architecture for Web-Based Remote Learning Systems
The rapid proliferation of web-based remote learning systems (WBRL) has introduced critical security and privacy challenges that threaten the integrity of educational data and the confidentiality of student information. This paper presents SP-WBRL, a Secure and Privacy-Preserving architecture designed for web-based remote learning environments. The proposed multi-layered framework integrates advanced encryption standards (AES-256), role-based access control (RBAC), differential privacy mechanisms, and a zero-trust network model to provide end-to-end security. We formalize the privacy guarantees using (ε, δ)-differential privacy and derive theoretical bounds on information leakage. Extensive experiments conducted over a 12-week deployment with 2,847 participants across three institutions demonstrate that SP-WBRL achieves a 97.3% threat detection rate with only 6.8% average latency overhead compared to non-secure baselines, while maintaining FERPA and GDPR compliance. Comparative evaluation against five state-of-the-art systems shows significant improvements in security coverage (23.1% improvement), privacy preservation (31.5% improvement), and user satisfaction (92.4% approval rating). The results confirm that comprehensive security can be integrated into remote learning platforms without substantially degrading the user experience.
A Secure and Privacy-Preserving Architecture; Web-Based Remote Learning Systems
NLP Framework to Summarize p53–FBW7 Pathway Alterations and Associated miRNAs in CRC
The increasing intricacies of molecular pathways involved in CRC, especially focusing on the p53-FBW7 pathway network and the microRNAs (miRNAs) that regulate them, present significant difficulties when synthesizing knowledge from the exploding body of biomedical literature. Conventional methods of manual literature review have proven inadequate for capturing and analyzing such multidimensional interactions in a timely fashion. In this paper, we describe an innovative NLP-based approach for mining CRC-related pathway changes and miRNA interactions. The architectural framework uses domain-driven data extraction methods along with sophisticated pre-processing, named entity recognition using transformers, and relation extraction techniques to extract important biological entities and their relationships. The relations extracted by the framework are then encoded within a knowledge graph capturing pathway-level dynamics of gene-protein-miRNA relationships, which can provide an integrated perspective on gene-protein-miRNA relationships. Furthermore, the transformer-based summarizer also provides summarized insights related to p53-FBW7 axis. The model aims to bring together computational intelligence and molecular oncology through a comprehensive, automated, and interpretable process for knowledge integration in biomedical sciences. Expected benefits will involve greater efficiency in research, the ability to identify key regulatory mechanisms, and contributions to precision medicine and drug discovery initiatives. Despite being conceptual, the paper introduces a novel method that combines NLP with cancer pathways, holding promise for extensive development in the future.
Natural Language Processing, p53–FBW7 Pathway, Colorectal Cancer, microRNA Regulation, Biomedical Text Mining
Big Data Analytics Using Hadoop and Spark: Applications, Challenges, and Future Direction
Big data describes large datasets that are difficult to manage owing to their diversity, size, and complexity in terms of storage, analysis, and visualisation for future operations. Artificial intelligence (AI) based big data analytics have revolutionised the processing of massive data sets by leveraging distributed computing platforms like Apache Spark and Apache Hadoop. Both HDFS and MapReduce leverage the 5Vs of Hadoop—Volume, Velocity, Variety, and Veracity—to store files and perform batch analysis on massive, heterogeneous datasets. Spark expands upon these capabilities with libraries for graph analytics, streaming, machine learning, in-memory computing, and directed acyclic graphs (DAGs). This paper gives a general outline of the Hadoop and Spark ecosystem, as well as their architecture and how they are used in distributed machine learning, real-time analytics, NLP, and anomaly detection. It also addresses such critical issues as scalability limitations, data protection, resource administration, and complexity of infrastructures. Lastly, the future directions are discussed with a focus on cloud-native designs, edge computing, and hardware acceleration as well as intelligent resource optimization to improve the performance, efficiency, and flexibility of the next generation Big Data systems.
AI–Big Data Analytics, Apache Hadoop, Apache Spark, Streaming Analytics, Data Mining, MapReduce
Stress-Level Classification for Students with Recommendation Outputs
Student mental health has deteriorated measurably and continuously over the past decade. A 2025 survey by SRM University AP, published in the Asian Journal of Psychiatry, found that nearly 70 percent of 1,628 college students across eight major Indian cities reported moderate to high anxiety levels, while over 60 percent exhibited clinically significant depression symptoms. These numbers are not outliers. Cross-national data shows Japan at 80 percent stress prevalence, the United States at 67 percent, and Pakistan at 71 percent. What unites these figures is not geography but the structure of modern academic life: excessive workload, unrelenting performance pressure, financial strain, social comparison amplified by digital platforms, and institutional support systems that were simply not built for this scale of need. The clinical response to this crisis has been slow and resource-constrained. Counseling services are stretched thin. Most students who need help do not seek it, largely because of stigma, lack of awareness, or long wait times. Only 7 percent of college students in the United States access professional mental health support in any given year (EssayPro, 2025). This gap between need and access defines the problem this article addresses. This article presents a machine learning-based classification framework that predicts student stress levels across three tiers: low, medium, and high. The model draws on academic, lifestyle, physiological, and psychometric data collected through structured surveys. It evaluates five classification algorithms, including Logistic Regression, Random Forest, XGBoost, and ensemble methods, with accuracies ranging from 86.2 to 97.92 percent in peer-reviewed studies. The framework also includes a recommendation engine that generates targeted, evidence-based intervention guidance for each stress tier. The goal is to give educational institutions a practical, scalable tool for identifying students at risk before their stress reaches crisis level.
Stress-Level Classification; Students; Recommendation Outputs; academic life
A Systematic Self-Review of Electronic Searching Studies (2002–2021): Training, Infrastructure, and Institutional Contexts
This study conducted a systematic review (SR) of the author’s research program on electronic searching published between 2002 and 2021. A total of seventeen studies were identified and organized into five thematic clusters: electronic information infrastructure, factors affecting electronic searching, electronic searching skills and training, integration of electronic resources in teaching and research, and policies, intellectual property, and e transformation. Together, these studies provide a comprehensive account of the conditions, competencies, and institutional arrangements that shape effective electronic searching in higher education contexts. Findings across the corpus indicate that successful electronic searching involves a combination of individual skill development, supportive institutional environments, and efficient technological infrastructure. Gaps in connectivity, system design, metadata quality, and resource availability directly influence users’ ability to locate and retrieve information. Fragmented databases, limited access to full text materials, and inconsistent archiving practices, were found to hinder researchers’ performance. Results also demonstrate that electronic searching skills do not emerge spontaneously but require explicit instruction, guided practice, and exposure to diverse digital tools that appear to consistently improve users’ confidence, accuracy, and efficiency. Training programs that emphasized search strategies, evaluation of digital sources, and searching specialized databases yielded noticeable gains among faculty, graduate students, and library staff. Additionally, integrating electronic resources into teaching and research both enhances and depends on improved searching practices. When electronic resources are embedded within coursework and research tasks, learners demonstrate stronger engagement and more sophisticated information handling behaviors. However, such integration is only effective when supported by institutional policies, training initiatives, and technological systems that facilitate access and encourage sustained use. Moreover, the SR highlights the role of policy frameworks, intellectual property regulations, and institutional leadership in shaping the broader digital environment. Alignment between pedagogy, infrastructure, and policy was shown to be critical for enabling effective electronic searching across academic settings. Finally, this study recommends that researchers use AI supported search tools. However, they should be aware of their limitations. AI systems may generate incomplete or biased search results, overlook domain specific sources, or misinterpret queries that require disciplinary nuance. They should critically evaluate AI generated outputs, verify retrieved sources through authoritative databases as Google Scholar.
Systematic review (SR), Al-Jarf research program, electronic searching, electronic searching literacy, electronic searching skills, electronic searching infrastructure, intellectual property of digital resources, access to digital resources, electronic databases, specialized database search
Advanced Models & Multimodal Reasoning: A Conflict Resolution–Centric Architecture
Multimodal artificial intelligence has evolved at a fast rate, and currently, advanced models can process vision, language, audio, and sensor data on the scale and accuracy not seen before. Regardless of these developments, multimodal reasoning systems are not well adopted in the real-life context and high-stakes situations. This paper holds the view that the fundamental impediment is not perceptual performance rather than architectural weakness where there is disagreement between heterogeneous modalities. We suggest an architecture-first view where the conflict is not viewed as an exception and must be inhibited but is a normal feature of a multimodal system that must be dealt with explicitly. The key conceptual element of this school of thought is the Conflict Resolution Layer (CRL), which is a system-level architectural primitive that identifies, assesses, and resolves conflicts between expert models of specific specialization acting over common, time-based evidence. In contrast to end-to-end multimodal fusion methods or ensemble learning methods, the CRL will not only allow decision policies to be made deterministically, auditable, and by humans in the loop; it will also allow inference to be completely separated from arbitration. We give formalization and roles of the CRL, examine the performance features of the CRL, and demonstrate that conflict-conscious reasoning is more coordination-bound than it is compute-bound. The paper also shows how CRL-based architectures can facilitate incremental adoption of the industry, starting with advisory systems and then because of conditionally automated decision pipelines, all being vendor-neutral and hardware-agnostic. This contribution by highlighting shift of focus towards model-centric optimization to system level design establishes conflict resolution as a lacking architectural primitive to robust, interpretable, deployable multimodal reasoning systems.
Multimodal Reasoning, Conflict Resolution Layer, Service-Oriented AI, Temporal Semantics, Expert Models, and Human-in-the-Loop
From Monolithic Models to Cognitive Hives: A Framework for Multimodal Reasoning Systems
The increasing fast development of multimodal foundation models has greatly contributed to the development of artificial intelligence systems that have the ability to process and integrate text, vision, audio, and structured information in single architectures. Although they have been performing admirably, these models are becoming more limited in nature as they grow in size especially in performance regarding efficiency, transparency and flexibility. Existing monolithic multimodal architectures demand a lot of computational and financial resources, have inflexible structure of design, and are not that interpretable. Moreover, they also use implicit fusion processes that are blind to modal conflict and inconsistency detection and resolution. The given paper suggests another paradigm, known as cognitive hives, which views intelligent multimodal systems as distributed assemblies of specialised expert models instead of single end-to-end networks. The approach to multimodal reasoning, in this context, is viewed as an issue in distributed systems, in which autonomous domain-specific experts interact via shared representations and explicit coordination protocols. One of the main parts of the suggested architecture is a special conflict resolution layer that would identify, arbitrage, and clarify disputes between expert models. The major contributions that were made in this work are the formal definition of cognitive hive architecture, systematic approach on cross-modal conflict resolution, and analysis of deployment strategies, latency trade-offs and failure modes in distributed reasoning systems. The framework is proposed to be hardware-agnostic, modular and future-proof, and is a scalable and explainable alternative to monolithic multimodal models.
Multimodal Reasoning, Distributed AI Systems, Cognitive Architectures, Conflict Resolution in AI, Modular Artificial Intelligence
Regulating the Algorithmic Bloodhound: Modernizing US Financial Regulations for the AI Era of Counter-Terrorism
The issue of terrorist financing is a major threat to international stability, because rogue actors take advantage of vulnerabilities of financial systems to direct funds towards illegal operations. The U. The S. financial system, though highly regulated and robust, is still a prime target because of its complexity, size and accessibility. Structural frameworks of monitoring that are based on predetermined rules and thresholds are usually ineffective in identifying advanced ways of hiding the money through structuring, layering, and distributing the money on a variety of accounts. This study examines how artificial intelligence and data analytics can be used to enhance counter-terrorist financing actions by exposing concealed patterns of financial transactions. With the PaySim dataset, which is a massive synthetic dataset of mobile money transactions, the study examines how such fraudulent behavior, as a proxy of terrorist funding, can be identified using sophisticated computational methods. This data offers very useful transaction-specific information, such as account balances, the types of transactions, and red flags of suspicious behavior, allowing the creation of models that detect anomalies with the potential of being used to commit illegal financial transactions. It is shown that fraudsters have a concentrated history of fraudulent operations in transfer and cash-out operations and this is commonly concentrated around small to mid-value cycles that replicate real world methods of hiding larger amounts of money by making smaller and less noticeable transactions. Time-related and network analyses further indicate that fraudulent activities also create networks and chains that are reminiscent of terrorist funding. In addition to the technical discoveries, the study also considers the ethical issues of privacy, transparency, and equity in implementing AI in the financial systems. In general, the study indicates that AI-based data analysis can profoundly improve how financial institutions and regulators can identify and disrupt possible terrorist financing and offers an effective model towards improving national security without violating ethical principles in financial surveillance.
Terrorist Financing Artificial Intelligence Data Analytics Fraud Detection U.S. Financial System PaySim Dataset
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The Digital Shield: An Analysis of AI's Role in Protecting US Financial Infrastructure from Cyberattack
The complexity of cyberattacks is a consistent challenge to the stability and credibility of financial institutions in the United States. Increasing vulnerability of financial systems to cyber-based criminal activities, including fraud, data breach and identity theft, has been observed as a result of the rapid pace of digital transformation. This study examines how data analytics facilitated by Artificial Intelligence (AI) can improve cybersecurity conditions in financial institutions by forecasting, identifying, and addressing possible threats on a real-time basis. I used two data sets to complete this research: the IEEE-CIS Fraud Detection dataset, which includes anonymized transactional information about the U.S. financial systems, and the Cyber Security Indexes dataset, which gives the world information about cybersecurity preparedness and the exposure. To train and test AI models of Random Forest, XGBoost, and Neural Networks, the IEEE-CIS dataset was used to find out the fraudulent transactions and test their accuracy of detection. Conversely, the dataset of Cyber Security Indexes was processed to compare the cybersecurity preparedness of the United States to the situation in other nations and investigate the relationships between cyber exposure, country preparedness, and digital development. The combined analysis reveals that AI-based models can be useful to identify fraudulent activities with high precision and recall and minimize financial loss and operational risks. The evidence shows that countries that have a better framework of cybersecurity governance and higher levels of Global Cybersecurity Index (GCI) experience less financial cybercrime exposure. This paper has reached the conclusion that AI-based analytics coupled with a strong cybersecurity framework forms a holistic protection scheme in the financial sector, which is more resilient, compliant with regulations, and trusted by the population. These findings highlight the potential of AI to transform the current state in empowering micro-level financial defenses as well as macro-level national cybersecurity infrastructures.
Artificial Intelligence (AI) Cybersecurity Analytics Financial Fraud Detection Machine Learning Models Cybersecurity Readiness Indexes. U.S. Financial Institutions Defense
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Research on the Application of Machine Learning in Fault Diagnosis Technology
Modern industrial equipment is increasingly characterized by large scale, high complexity, and continuous operation. Traditional fault diagnosis methods exhibit significant limitations when dealing with massive volumes of data and various nonlinear relationships. Machine learning, which is capable of autonomously discovering patterns and identifying structures from data, provides a novel pathway for fault diagnosis. This paper systematically reviews the fundamental theories, commonly used algorithms, and practical application scenarios of machine learning in fault diagnosis, with particular attention to the applications of supervised learning and unsupervised learning in the fields of machinery, electric power, and transportation. It then discusses the current challenges, including poor data quality, limited model interpretability, and insufficient fault samples. Finally, future development directions are explored from the perspectives of explainable artificial intelligence, generative models, edge computing, and physics-informed data fusion. Overall, machine learning is driving fault diagnosis from a “repair-after-failure” paradigm toward “predictive maintenance.” However, for robust deployment in industrial environments, further efforts are still required in algorithm transparency, data efficiency, and cross-domain adaptability.
Machine learning; fault diagnosis; deep learning; predictive maintenance; explainable artificial intelligence
Overview of AI-Driven New Drug Development Skills
In traditional drug development, long development cycles, high costs, and low efficiency are often unavoidable. With the rise of artificial intelligence (AI), all aspects of traditional pharmaceutical industry have undergone unprecedented changes. Through model training, drug development cycles can be significantly shortened and efficiency improved. This article reviews the changes brought about by AI to the drug development industry in the past three years, outlining the improvements AI has made to the pharmaceutical process, and contributing to future research in the pharmaceutical field.
Artificial intelligence; drug discovery; machine learning; deep learning; vaccine development
Privacy-Preserving Chargeback Intelligence for Tokenized Payment Systems
Chargeback handling in modern payment systems remains heavily dependent on post-transaction workflows that expose more payment data than necessary and often rely on PAN-linked retrieval paths. This paper presents a privacy-aware post-transaction architecture for tokenized payment sys- tems that combines chargeback intelligence, selective evidence disclosure, and audit-oriented work- flow design. Beyond system integration, the paper introduces a formal role-based selective-disclosure policy model, a token-lineage risk formulation for dispute scoring, and an evidence-quality framework that separates completeness, relevance, and verifiability. The proposed framework contains a token- aware chargeback intelligence layer, a privacy-preserving evidence layer, and a compliance-aware audit layer to support PAN-less dispute handling wherever feasible. Public transaction datasets are used as the empirical basis, with token-related post-transaction structures constructed through controlled augmentation because the source datasets do not natively contain tokenized dispute workflows. The evaluation uses PaySim as the base transaction backbone, the ULB/Worldline credit-card fraud benchmark for fraud-context support, and a chargeback-labeled dataset for dis- pute modeling. In the primary chargeback prediction experiment, the token-aware model achieved an AUC of 0.9317 and an F1-score of 0.6710, outperforming both a static rule baseline and a ba- sic machine-learning baseline. Additional analysis includes ablation testing, sensitivity analysis, repeated-trial workflow evaluation, and failure-oriented threat scenarios. The proposed workflow reduced the weighted privacy-exposure score by 79.1% relative to a PAN-oriented baseline while improving evidence quality at the cost of higher operational overhead. These findings suggest that token-aware post-transaction intelligence can improve dispute prediction and reduce sensitive-data exposure simultaneously. The paper contributes a practical architecture, formalized workflow logic, explicit metric definitions, and a stronger evaluation framework for privacy-preserving chargeback operations in tokenized payment systems.
Copyright (c) 2023 Journal of Computer Science and Technology Studies
Machine Learning-Based Hospital Readmission Prediction and Risk Analysis in the United States Healthcare System
Hospital readmission is a significant challenge in the United States healthcare system, leading to increased healthcare costs, hospital overcrowding, and reduced quality of patient care. Early prediction of hospital readmission risk can help healthcare providers identify high-risk patients and take preventive measures before discharge. This study presents an explainable machine learning-based hospital readmission prediction framework using patient demographic and clinical data from the United States healthcare system. The dataset includes important patient information such as age, length of hospital stays, previous admission history, comorbidities, insurance status, disease type, diabetes, heart disease, and follow-up visit status. Several machine learning algorithms, including Logistic Regression, Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Network, were used to predict hospital readmission risk. The performance of the models was evaluated using accuracy, precision, recall, F1-score, and Receiver Operating Characteristic (ROC) curve. Among all models, XGBoost achieved the highest prediction performance compared to other machine learning models. Feature importance analysis showed that length of hospital stay, comorbidities, insurance status, previous admission history, and follow-up visit were the most significant factors influencing hospital readmission in the United States. In addition to the machine learning prediction model, this study proposes an IoT-based post-discharge patient monitoring system to reduce hospital readmission risk through continuous patient health monitoring and early medical intervention. The proposed system can help healthcare providers monitor patients remotely, identify high-risk patients in real time, and take preventive actions to reduce hospital readmission rates. This research contributes to the development of intelligent healthcare prediction systems and demonstrates how machine learning and IoT technologies can improve patient care, reduce readmission rates, and support data-driven decision-making in the United States healthcare system.
Machine Learning, Hospital Readmission, XGBoost, Healthcare Analytics, IoT Healthcare, Explainable AI
Human-in-the-Loop Reinforcement Learning for Next-Generation Adaptive Interfaces
Most adaptive interface research lands in one of two places: fully automated systems that ignore the user's evolving intent, or static personalization that freezes in place after an initial configuration pass. Neither age well. This paper takes a different approach. We propose a Human-in-the-Loop Reinforcement Learning (HITL-RL) framework where the interface policy continues to evolve through a combination of behavioral observation and direct user input, without requiring the user to become an unwitting trainer. The core architecture pairs a reinforcement learning agent with a hybrid feedback channel that fuses implicit signals (click patterns, dwell time, abandonment) with explicit corrections users make. Drawing on reward modeling techniques from large-scale language model alignment work, we adapt the preference learning paradigm to interface-level decisions: layout, component prioritization, and interaction sequencing. Policy updates run continuously but remain interpretable, giving users meaningful visibility into why the interface changed. We evaluate the framework against static personalization baselines and fully automated RL approaches across three interface contexts. The results show measurably faster convergence to preferred layouts, higher rated usability scores, and lower correction frequency over time, which suggests the system is learning rather than just adapting noisily. Beyond the metrics, HITL-RL shifts the design philosophy: the interface is not a product delivered to the user, it is a policy negotiated with them.
Adaptive user interfaces, reinforcement learning, human-in-the-loop, reward modeling, preference learning, policy optimization, explainable AI, personalization, multi-agent systems, human-computer interaction
Risk-Aware Rework Prevention in Personalized Hearing Aid Manufacturing
The manufacturing of custom hearing aids is done under a degree of variability that is hard to manage using standard production logic since each order is an expression of an individual anatomy, fitting needs, and design-specific decisions about the product. This means that it is frequently found out too late, after line time, operator effort and material have been used, resulting in avoidable delay and unnecessary burden on manufacturing resources. This paper builds a risk aware execution model to detect probable rework-prone orders earlier in the production process and to take action on the risk before downstream cost is incurred. The framework integrates order-context capture, risk profiling, rework-risk prediction, intervention selection, execution rout- ing and feedback-based updating in a closed operational loop. Actions that are triggered by the system may include parameter checking, expert checking, alternate routing, or controlled release, depending on the estimated risk level before the order is sent to more costly downstream stages. The main point is that prediction can only be valuable when it alters the way the order is man- aged. In that perspective, rework-risk estimation is included in manufacturing control, as opposed to an independent analytical output. The anticipated advantage is reduced rework load, enhanced turnaround performance, increased attention to specialists, minimized material waste, and more stable production flow in the personalized production of healthcare devices.
Hearing aid manufacturing · Rework prevention · Manufacturing execution · Predictive analytics · Personalized healthcare devices · Risk-aware production
Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
PAN-Less Refunds and Privacy-Preserving Chargeback Evidence for Tokenized Payment Gateways
Tokenization reduces routine merchant exposure to primary account numbers, yet refund handling and chargeback evidence preparation often remain dependent on PAN-linked or PAN-adjacent re- trieval paths. This paper presents an exploratory systems-design and workflow-evaluation study of a token-linked post-transaction architecture for PAN-less refunds and privacy-preserving charge- back evidence generation. The proposed design links payment, refund, and dispute records through explicit token lineage, confines PAN-linked authority to a token-service boundary, and applies role- based selective disclosure during evidence release. The empirical study uses uploaded public datasets and documented lifecycle augmentation because the source data do not natively contain tokenized refund and dispute chains. Validation was strengthened through 30 repeated trials on random sub- sets of 15,000 PaySim-derived base events per run, with chargeback-oriented calibration from the uploaded df.csv file and anomaly-context support from the uploaded fraud benchmarks. Across the repeated trials, the proposed workflow reduced the weighted privacy-exposure score from 1.48 to 0.31, improved the evidence-quality score from 0.5946 to 0.7344, and increased the operational- overhead index from 1.00 to 1.67. Mean local refund-processing time increased from 0.00394 ms per case to 0.00448 ms per case, while mean local evidence-assembly time increased from 0.01882 ms per case to 0.07069 ms per case. These timings reflect only local in-memory workflow steps and should not be interpreted as production gateway or payment-network latency. Sensitivity analy- sis shows that the reduction of privacy is between 65.6% and 86.0% under alternative weighting schemes, and the evidence-quality advantage is positive under all weighting scenarios tested. The paper therefore makes a bounded contribution: explicit architecture, reproducible augmentation rules, custom-metric definitions with sensitivity checks, and repeated-trial exploratory evidence, rather than a claim of live network or production scale validation.
Payment tokenization, PAN-less refunds, chargeback evidence, selective disclosure, tokenized pay- ment gateways, privacy-preserving payments
Copyright (c) 2021 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
Transforming Undergraduate Education Through Modern Pedagogical Innovations
The undergraduate education is experiencing a paradigm change whereby common teaching methodologies are finding it difficult to cope with the changing needs of a society that is dynamic and knowledge-oriented society. Innovations in modern pedagogy are characterized by focus on active learning, experiential learning, and use of technology in order to impart in students the skills of critical thinking, problem-solving, and collaboration aspect of life that are necessary to succeed in the workplace. The paper examines how education has been developed and what pedagogical needs are emerging, which can be discussed as the shift to the traditional lecture-based learning and the use of technology-focused, student-oriented learning methods. New techniques, such as project-based learning, flipped classes, collaborative techniques, and reflective practices are discussed in their contribution to the development of technical skills, analytical intelligence, and applied knowledge. A case study conducted at the Federal Institute of Amazonas shows that the combination of experiential learning and project-based exercises had a considerable impact on student motivation and performance as well as the practical acquisition of skills. The results indicate that the gap between theory and practice can be reduced by incorporating contemporary pedagogical approaches and equipping graduates with skills to address complex professional environments and facilitate lifelong learning and flexibility in an ever-evolving environment.
Undergraduate Education, Modern Pedagogy, Experiential Learning, Big Data Analytics, Student-Centered Learning, Technology-Enhanced Learning
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
An AI-Driven Classification System for Early Detection of Customer Churn in E-Commerce Platforms
Customer churn is a key issue for e-commerce organizations since it lowers revenue and hampers long-term growth. E-commerce companies may reduce expensive customer attrition and implement proactive retention measures by predicting client churn. This study proposes an AI-driven classification system for early customer churn detection using the List online retail dataset. The proposed CNN has a higher ability in capturing complex and nonlinear patterns of customer behavior as opposed to the conventional machine learning methods. The results of the experiment display excellent performance, with 99.62% accuracy, 99.32% precision, 98.66% recall, and 98.78% F1-score and with the AUC of 0.9893. The findings demonstrate that the model is robust, has generalization and small misclassification with respect to the baseline models like Random Forest, Logistic Regression, AdaBoost and ANN-MLP. In addition to predictive strength, the paper highlights an importance of DL in extracting latent features representations and provides actionable information on customer retention strategies. The results determine CNNs as a strong and justified solution in detecting churn that provides academic value and practical applicability to the e-commerce industry.
Customer Churn Prediction, Business Analytics, E‑Commerce, Machine Learning, List Online Dataset, CNN Model
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
A Review of Sensor Data Acquisition Methods for Accurate and Timely Detection of Faults in Mechanical Systems
The fault detection of mechanical systems is essential to the reliability of such systems in the industrial environment and it is enabled by the efficient method of sensor data acquisition. This review research studies the importance of different sensors and acquisition techniques to detect defects of mechanical parts with reference to current developments and applications. The paper identifies some of the important sensor types including temperature, motion, proximity, and chemical sensors and discusses the application of these sensors in real-time monitoring and diagnostics. More emphasis is put on the contemporary techniques of data acquisition such as synchronization, signal preprocessing, and smart systems, which are able to improve the accuracy of decision-making. It also examines how machine learning and deep learning models can be integrated into fault detection models that enhance efficiency and minimize the need to rely on manual inspection in diagnostic procedures. The review also provides the comparative results of recent open-access research to assess the strengths, issues, and opportunities of the future. The significance of the accuracy and timeliness is described with references to the impact of the late or wrongful detection on the productivity and safety of industry. In general, the review can be considered as a reference point for researchers and engineers who are interested in creating or enhancing sensor-based fault detection systems in mechanical systems.
Sensor Data Acquisition, Fault Detection, Mechanical Systems, Real-Time Monitoring, Machine Learning, Industrial Diagnostics, Accuracy, Timeliness
Forecasting Household Economic Hardship, Financial Fragility, and Vulnerability Through Artificial Intelligence and Predictive Analytics
Household economic hardship in the United States is increasingly shaped by interacting pressures rather than a single income shortfall. Inflation, rent burdens, revolving debt, medical costs, labor-market volatility, and fraud losses combine to produce financial fragility that conventional scorecards often miss. This paper develops a publication-ready research framework for forecasting household economic hardship, financial fragility, and vulnerability through artificial intelligence and predictive analytics using public data through 2024. The framework integrates survey microdata and contextual indicators from the Federal Reserve’s Survey of Household Economics and Decisionmaking, the Consumer Financial Protection Bureau’s Making Ends Meet surveys and Financial Well-Being Scale, the U.S. Census Bureau’s Household Pulse Survey, USDA food security reports, the Federal Reserve’s Survey of Consumer Finances, Bureau of Labor Statistics expenditure and inflation statistics, and Federal Trade Commission fraud-loss data. Building on the supplied literature and broader scholarship in household finance, explainable machine learning, and welfare measurement, the paper proposes a multimodal architecture that combines gradient boosting, temporal models, and graph-informed neighborhood context to predict three linked outcomes: immediate hardship, near-term fragility, and medium-horizon vulnerability. Data trends through 2024 show that the share of adults doing at least okay financially declined after 2021, emergency liquidity weakened, food insecurity rose, and reported fraud losses climbed sharply. The paper argues that explainable, fairness-audited models can improve targeting, consumer protection, and preventive intervention without reducing households to opaque risk labels. It concludes that AI can strengthen public-interest forecasting when embedded in transparent validation, governance, and human-centered implementation.
household economic hardship; financial fragility; vulnerability forecasting; machine learning; predictive analytics; SHED; CFPB; Household Pulse Survey; food insecurity; explainable AI
Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
IoT-Enabled Cognitive Manufacturing Systems: A Conceptual Framework for Real-Time Autonomous Decision-Making in Industry 4.0
The accelerated evolution of Industry 4.0 has further accelerated the adoption of the Internet of Things technology in manufacturing infrastructure, which enables real-time data collection and improved operational transparency. However, smart manufacturing infrastructure currently relies on human intervention and predetermined decision algorithms, which limit its ability to adapt to complex and dynamic manufacturing environments. This study aims to address this limitation by presenting a conceptual framework for an IoT-based cognitive manufacturing infrastructure that enables real-time autonomous decision-making. This infrastructure will leverage IoT-based sensing technology, edge computing technology to process data in real-time, and cognitive intelligence technology based on artificial intelligence and machine learning algorithms. The proposed architecture utilizes a multi-layer structure that includes data acquisition, processing, cognitive analytics, decision-making, and execution layers with the help of continuous feedback and learning mechanisms. This allows the manufacturing systems to sense the environment and process the data to optimize the processes with minimal human involvement. The study also presents the applications of the proposed framework, such as predictive maintenance, real-time quality management, production optimization, and energy management, which show the flexibility and applicability of the proposed framework for smart manufacturing systems. The paper also covers the challenges faced by the proposed architecture and the future scope for the proposed framework. The proposed framework provides a flexible and scalable platform for the development of intelligent and self-optimizing manufacturing systems and hence contributes to the development of next-generation manufacturing systems.
AI, Fabrication, Industry 4.0, Industry 5.0, IoT
Copyright (c) 2026 Journal of Computer Science and Technology Studies
A Survey of Blockchain-Powered Digital Payment Systems and Their Influence on Global Financial Inclusion
The blockchain technology has adhered to becoming a radical innovation in the area of digital finance, in which it has delivered secure, decentralized and transparent methods of transacting business. This paper examines the application of digital payment systems based on blockchain and how it enhances financial inclusion in the world. It describes the fundamental concepts of blockchain, such as transaction formats, block architecture, and digital signatures, as well as how cryptocurrencies can support peer-to-peer digital payments. Payments and settlements, smart contracts, asset tokenization, trade financing, and other notable applications of blockchain technology in the financial sector are included in the study. Furthermore, the paper examines the concepts of financial inclusion and the applicability of digital payments to the magnitude of supplying underserved groups with formal financial services. It is assessed that digital finance that utilizes blockchain technology might offer a viable alternative.to several prominent financial inclusion challenges, including high transaction costs, a lack of digital identity, and mistrust of financial institutions. Lastly, the study concludes blockchain technology's impact on financial inclusion globally by discussing how it improves financial system accessibility, efficiency, and transparency while also addressing new challenges in infrastructure, cybersecurity, and regulation.
Blockchain Technology, Digital Payment Systems, Financial Inclusion, Cryptocurrencies, Decentralized Finance (DeFi), and Digital Finance.
AI-Assisted Design for Manufacturability (DFM): A Conceptual Framework for Intelligent Heavy Fabrication Systems in Smart Manufacturing
The paper proposes a conceptual framework for the integration of Artificial Intelligence (AI) technologies with Design for Manufacturability (DFM) for heavy fabrication systems with the objective of creating an environment for smart manufacturing. DFM methods and tools have been found to depend significantly on guidelines and expert knowledge based on rules, which are often found to be inadequate for the complexity associated with heavy fabrication systems and assemblies. This paper proposes an AI-based DFM framework for heavy fabrication systems using machine learning and real-time data analytics for improving the process of decision-making regarding manufacturability during the initial stages of the design process. The proposed framework focuses on improving the parameters associated with heavy fabrication processes such as material selection, welding feasibility, structural integrity, costs, and supply chain constraints. In the proposed framework, a multi-layer architecture for DFM integration with AI technologies has been proposed for heavy fabrication systems with feedback for continuous improvement. In addition, the paper explores the challenges associated with the implementation of an AI-based DFM framework for heavy fabrication systems and proposes an interface for bridging the capabilities associated with Industry 4.0 and the human-centric vision for Industry 5.0 for improving the efficiency and productivity of heavy fabrication processes and assemblies through collaborative decision-making involving humans and computers. This paper proposes a framework for improving heavy fabrication systems and assemblies through the integration of AI technologies with DFM for the development of an intelligent heavy fabrication system for improving efficiency and productivity while reducing costs and improving the robustness of designs.