Journal of Computer Science and Technology Studies
type: channel
description: 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.
publisher: Al-Kindi Center for Research and Development
language: en
publication name: Journal of Computer Science and Technology Studies
issn: 2709-104X
items: A Secure and Privacy-Preserving Architecture for Web-Based Remote Learning Systems
items: NLP Framework to Summarize p53–FBW7 Pathway Alterations and Associated miRNAs in CRC
items: Big Data Analytics Using Hadoop and Spark: Applications, Challenges, and Future Direction
items: Stress-Level Classification for Students with Recommendation Outputs
items: A Systematic Self-Review of Electronic Searching Studies (2002–2021): Training, Infrastructure, and Institutional Contexts
items: Advanced Models & Multimodal Reasoning: A Conflict Resolution–Centric Architecture
items: From Monolithic Models to Cognitive Hives: A Framework for Multimodal Reasoning Systems
items: Regulating the Algorithmic Bloodhound: Modernizing US Financial Regulations for the AI Era of Counter-Terrorism
items: The Digital Shield: An Analysis of AI's Role in Protecting US Financial Infrastructure from Cyberattack
items: Research on the Application of Machine Learning in Fault Diagnosis Technology
items: Overview of AI-Driven New Drug Development Skills
items: Privacy-Preserving Chargeback Intelligence for Tokenized Payment Systems
items: Machine Learning-Based Hospital Readmission Prediction and Risk Analysis in the United States Healthcare System
items: Human-in-the-Loop Reinforcement Learning for Next-Generation Adaptive Interfaces
items: Risk-Aware Rework Prevention in Personalized Hearing Aid Manufacturing
items: PAN-Less Refunds and Privacy-Preserving Chargeback Evidence for Tokenized Payment Gateways
items: Transforming Undergraduate Education Through Modern Pedagogical Innovations
items: An AI-Driven Classification System for Early Detection of Customer Churn in E-Commerce Platforms
items: A Review of Sensor Data Acquisition Methods for Accurate and Timely Detection of Faults in Mechanical Systems
items: Forecasting Household Economic Hardship, Financial Fragility, and Vulnerability Through Artificial Intelligence and Predictive Analytics
items: IoT-Enabled Cognitive Manufacturing Systems: A Conceptual Framework for Real-Time Autonomous Decision-Making in Industry 4.0
items: A Survey of Blockchain-Powered Digital Payment Systems and Their Influence on Global Financial Inclusion
items: AI-Assisted Design for Manufacturability (DFM): A Conceptual Framework for Intelligent Heavy Fabrication Systems in Smart Manufacturing
items: Real-Time and Near-Real-Time Analytics in Healthcare Data Ecosystems
items: Explainable AI for Institutional Fraud Decisions: A Cross-Sector Empirical Study Using Public Healthcare and Financial Transaction Data
items: Evidence-Gated Search: Controlling Operational Search Explosion in LLM-Driven Incident Response
items: AI-Driven Hyper-Personalization, Consumer Sovereignty Loss, and the Transition Toward Subscription Economy: An Information Foraging Autonomy Perspective: An Information Foraging Perspective
items: Machine Learning-Driven Early Warning Analytics for Identifying Market Manipulation, Irregular Trading Activity, and Suspicious Market Signals in U.S. Stock Markets
items: A Generative AI–Driven Clinical Decision Support Framework Using Large Language Models
items: Securing Enterprise Data for LLM-Powered Applications: A Reference Architecture for Inference-Time Data Protection

A Secure and Privacy-Preserving Architecture for Web-Based Remote Learning Systems
type: item
description: 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.
subject: Research Article
  topic: section
subject: A Secure and Privacy-Preserving Architecture; Web-Based Remote Learning Systems
  topic: keywords
creator: Akib Rahman
creator: Sharmin Sultana
creator: Ismail Hossain Pranto
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-04-15
publication date: 2026-04-15
starting page: 38
ending page: 48
doi: 10.32996/jcsts.2026.8.6.4

NLP Framework to Summarize p53–FBW7 Pathway Alterations and Associated miRNAs in CRC
type: item
description: 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.
subject: Research Article
  topic: section
subject: Natural Language Processing, p53–FBW7 Pathway, Colorectal Cancer, microRNA Regulation, Biomedical Text Mining
  topic: keywords
creator: Swati Kulshrestha
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-04-15
publication date: 2026-04-15
starting page: 29
ending page: 37
doi: 10.32996/jcsts.2026.8.6.3

Big Data Analytics Using Hadoop and Spark: Applications, Challenges, and Future Direction
type: item
description: 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.
subject: Research Article
  topic: section
subject: AI–Big Data Analytics, Apache Hadoop, Apache Spark, Streaming Analytics, Data Mining, MapReduce
  topic: keywords
creator: Surya Veera Brahmaji Rao Sunnam
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-04-15
publication date: 2026-04-15
starting page: 16
ending page: 28
doi: 10.32996/jcsts.2026.8.6.2

Stress-Level Classification for Students with Recommendation Outputs
type: item
description: 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 …
subject: Research Article
  topic: section
subject: Stress-Level Classification; Students; Recommendation Outputs; academic life
  topic: keywords
creator: Chandrasekar Adhithya Harsha Pasumarthi
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-04-15
publication date: 2026-04-15
starting page: 01
ending page: 15
doi: 10.32996/jcsts.2026.8.6.1

A Systematic Self-Review of Electronic Searching Studies (2002–2021): Training, Infrastructure, and Institutional Contexts
type: item
description: 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 institution…
subject: Research Article
  topic: section
subject:
  topic: keywords
  value: 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
creator: Reima Al-Jarf
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-04-14
publication date: 2026-04-14
starting page: 179
ending page: 194
doi: 10.32996/jcsts.2026.8.5.16

Advanced Models & Multimodal Reasoning: A Conflict Resolution–Centric Architecture
type: item
description: 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.
subject: Research Article
  topic: section
subject: Multimodal Reasoning, Conflict Resolution Layer, Service-Oriented AI, Temporal Semantics, Expert Models, and Human-in-the-Loop
  topic: keywords
creator: Inesh Hettiarachchi
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-04-14
publication date: 2026-04-14
starting page: 165
ending page: 178
doi: 10.32996/jcsts.2026.8.5.15

From Monolithic Models to Cognitive Hives: A Framework for Multimodal Reasoning Systems
type: item
description: 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.
subject: Research Article
  topic: section
subject: Multimodal Reasoning, Distributed AI Systems, Cognitive Architectures, Conflict Resolution in AI, Modular Artificial Intelligence
  topic: keywords
creator: Inesh Hettiarachchi
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-04-14
publication date: 2026-04-14
starting page: 150
ending page: 164
doi: 10.32996/jcsts.2026.8.5.14

Regulating the Algorithmic Bloodhound: Modernizing US Financial Regulations for the AI Era of Counter-Terrorism
type: item
description: 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 ho…
subject: Research Article
  topic: section
subject: Terrorist Financing Artificial Intelligence Data Analytics Fraud Detection U.S. Financial System PaySim Dataset
  topic: keywords
creator: Mohammad Kowshik Alam
creator: Md Lutfur Rahman Fahad
creator: Md Sabbir Hossen Shuvo
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2023-07-30
publication date: 2023-07-30
starting page: 66
ending page: 87
doi: 10.32996/jcsts.2023.5.2.6

The Digital Shield: An Analysis of AI's Role in Protecting US Financial Infrastructure from Cyberattack
type: item
description: 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 transfo…
subject: Research Article
  topic: section
subject: Artificial Intelligence (AI) Cybersecurity Analytics Financial Fraud Detection Machine Learning Models Cybersecurity Readiness Indexes. U.S. Financial Institutions Defense
  topic: keywords
creator: Mohammad Kowshik Alam
creator: Md Lutfur Rahman Fahad
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2022-06-25
publication date: 2022-06-25
starting page: 112
ending page: 133
doi: 10.32996/jcsts.2022.4.1.14

Research on the Application of Machine Learning in Fault Diagnosis Technology
type: item
description: 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.
subject: Research Article
  topic: section
subject: Machine learning; fault diagnosis; deep learning; predictive maintenance; explainable artificial intelligence
  topic: keywords
creator: Chen Yao
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-04-09
publication date: 2026-04-09
starting page: 144
ending page: 149
doi: 10.32996/jcsts.2026.8.5.13

Overview of AI-Driven New Drug Development Skills
type: item
description: 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.
subject: Research Article
  topic: section
subject: Artificial intelligence; drug discovery; machine learning; deep learning; vaccine development
  topic: keywords
creator: Xiongpeng Jiang
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2026-04-09
publication date: 2026-04-09
starting page: 140
ending page: 143
doi: 10.32996/jcsts.2026.8.5.12

Privacy-Preserving Chargeback Intelligence for Tokenized Payment Systems
type: item
description: 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 practi…
subject: Research Article
  topic: section
subject: Payment tokenization
  topic: keywords
subject: chargeback intelligence
  topic: keywords
subject: privacy-preserving evidence
  topic: keywords
subject: selective disclosure
  topic: keywords
subject: tokenized payment systems
  topic: keywords
subject: dispute workflows
  topic: keywords
creator: Vimal Teja Manne
rights: Copyright (c) 2023 Journal of Computer Science and Technology Studies
date: 2023-06-30
publication date: 2023-06-30
starting page: 54
ending page: 65
doi: 10.32996/jcsts.2023.5.2.5

Machine Learning-Based Hospital Readmission Prediction and Risk Analysis in the United States Healthcare System
type: item
description: 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 technolog…
subject: Research Article
  topic: section
subject: Machine Learning, Hospital Readmission, XGBoost, Healthcare Analytics, IoT Healthcare, Explainable AI
  topic: keywords
creator: Mostafizur Rahman Shakil
creator: Mousumi Akter
creator: Sadia Afrin Dipa
creator: Farmina Sharmin
creator: Hamim Islam Hellol
creator: SK Rakib Ul Islam Rahat
creator: Mustafizur Rahaman
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2024-12-19
publication date: 2024-12-19
starting page: 369
ending page: 384
doi: 10.32996/jcsts.2024.6.5.32

Human-in-the-Loop Reinforcement Learning for Next-Generation Adaptive Interfaces
type: item
description: 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.
subject: Research Article
  topic: section
subject:
  topic: keywords
  value: Adaptive user interfaces, reinforcement learning, human-in-the-loop, reward modeling, preference learning, policy optimization, explainable AI, personalization, multi-agent systems, human-computer interaction
creator: Prashanth Reddy Vontela
creator: Prudhvi Naayini
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2024-02-25
publication date: 2024-02-25
starting page: 325
ending page: 339
doi: 10.32996/jcsts.2024.6.1.35

Risk-Aware Rework Prevention in Personalized Hearing Aid Manufacturing
type: item
description: 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.
subject: Research Article
  topic: section
subject: Hearing aid manufacturing · Rework prevention · Manufacturing execution · Predictive analytics · Personalized healthcare devices · Risk-aware production
  topic: keywords
creator: Sudhakavya Bodapati Venkata
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2022-09-25
publication date: 2022-09-25
starting page: 215
ending page: 230
doi: 10.32996/jcsts.2022.4.2.25

PAN-Less Refunds and Privacy-Preserving Chargeback Evidence for Tokenized Payment Gateways
type: item
description: 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 repeat…
subject: Research Article
  topic: section
subject: Payment tokenization, PAN-less refunds, chargeback evidence, selective disclosure, tokenized pay- ment gateways, privacy-preserving payments
  topic: keywords
creator: Vimal Teja Manne
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2021-02-28
publication date: 2021-02-28
starting page: 37
ending page: 49
doi: 10.32996/jcsts.2021.3.1.6

Transforming Undergraduate Education Through Modern Pedagogical Innovations
type: item
description: 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.
subject: Research Article
  topic: section
subject: Undergraduate Education, Modern Pedagogy, Experiential Learning, Big Data Analytics, Student-Centered Learning, Technology-Enhanced Learning
  topic: keywords
creator: Ajay Dasari
creator: Venkata Kishore Chilakapati
creator: Srikanth Reddy Keshireddy
creator: Venkata Teja Nagumotu
creator: , Harsha Vardhan Reddy Kavuluri
creator: Akhil Kumar Pathani
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2025-12-29
publication date: 2025-12-29
starting page: 547
ending page: 556
doi: 10.32996/jcsts.2025.7.12.61

An AI-Driven Classification System for Early Detection of Customer Churn in E-Commerce Platforms
type: item
description: 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.
subject: Research Article
  topic: section
subject: Customer Churn Prediction, Business Analytics, E‑Commerce, Machine Learning, List Online Dataset, CNN Model
  topic: keywords
creator: Dhuli Shyam
creator: Prabu Manoharan
creator: Muzaffer Hussain Syed
creator: Uday Kumar Ragireddy
creator: Prasanth Varma Addepalli
creator: Sridhar Reddy Bandaru
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0
date: 2025-11-30
publication date: 2025-11-30
starting page: 452
ending page: 462
doi: 10.32996/jcsts.2025.7.11.42

A Review of Sensor Data Acquisition Methods for Accurate and Timely Detection of Faults in Mechanical Systems
type: item
description: 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.
subject: Research Article
  topic: section
subject: Sensor Data Acquisition, Fault Detection, Mechanical Systems, Real-Time Monitoring, Machine Learning, Industrial Diagnostics, Accuracy, Timeliness
  topic: keywords
creator: Prathik Kumar Jannu
creator: Javed Ali Mohammad
creator: Sri Harsha Panchali
creator: Usha Mohani Kavirayani
creator: Krishna Bhardwaj Mylavarapu
creator: Jenitha Pilli
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2025-09-25
publication date: 2025-09-25
starting page: 796
ending page: 803
doi: 10.32996/jcsts.2025.7.9.92

Forecasting Household Economic Hardship, Financial Fragility, and Vulnerability Through Artificial Intelligence and Predictive Analytics
type: item
description: 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.
subject: Research Article
  topic: section
subject: household economic hardship; financial fragility; vulnerability forecasting; machine learning; predictive analytics; SHED; CFPB; Household Pulse Survey; food insecurity; explainable AI
  topic: keywords
creator: Shah Farhan Rabbani
creator: Yusuf Oli Rahat
creator: Md Kamrul Islam
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2025-09-28
publication date: 2025-09-28
starting page: 689
ending page: 702
doi: 10.32996/jcsts.2025.7.10.69

IoT-Enabled Cognitive Manufacturing Systems: A Conceptual Framework for Real-Time Autonomous Decision-Making in Industry 4.0
type: item
description: 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.
subject: Research Article
  topic: section
subject: AI, Fabrication, Industry 4.0, Industry 5.0, IoT
  topic: keywords
creator: Venkata Naga Kishore Thota
rights: Copyright (c) 2026 Journal of Computer Science and Technology Studies
date: 2026-04-03
publication date: 2026-04-03
starting page: 133
ending page: 139
doi: 10.32996/jcsts.2026.8.5.11

A Survey of Blockchain-Powered Digital Payment Systems and Their Influence on Global Financial Inclusion
type: item
description: 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.
subject: Research Article
  topic: section
subject: Blockchain Technology, Digital Payment Systems, Financial Inclusion, Cryptocurrencies, Decentralized Finance (DeFi), and Digital Finance.
  topic: keywords
creator: Deepak Reddy Suram
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2020-03-25
publication date: 2020-03-25
starting page: 33
ending page: 41
doi: 10.32996/jcsts.2020.2.1.5

AI-Assisted Design for Manufacturability (DFM): A Conceptual Framework for Intelligent Heavy Fabrication Systems in Smart Manufacturing
type: item
description: 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.
subject: Research Article
  topic: section
subject: AI-assisted DFM
  topic: keywords
subject: Smart Manufacturing
  topic: keywords
subject: Heavy Fabrication
  topic: keywords
subject: Digital Twin
  topic: keywords
subject: Industry 4.0
  topic: keywords
subject: Industry 5.0
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Manufacturability Optimization
  topic: keywords
subject: Welding Design
  topic: keywords
subject: Supply Chain Integration
  topic: keywords
creator: Rohan Joshi
creator: Sairaj Prakash Desai
creator: Geshna Raghvan
creator: Sankarshan Deshpande
creator: Sujata L Patekar
rights: Copyright (c) 2026 Journal of Computer Science and Technology Studies
date: 2026-04-02
publication date: 2026-04-02
starting page: 128
ending page: 132
doi: 10.32996/jcsts.2026.8.5.10

Real-Time and Near-Real-Time Analytics in Healthcare Data Ecosystems
type: item
description: The fact that connected medical gadgets, wearable sensors and digital health platforms are rapidly expansive has added to pressure on the requirement of real-time and near-real-time analytics in healthcare data ecosystems. With newer changes in the healthcare systems, where the aspect of retrospective reporting is being replaced with the concept of continuous monitoring and acting upon the available data in real-time, the capacity to process, analyse and respond to streaming data in a minimal balance of latencies has gained significant importance. Nevertheless, poor system architecture, lack of interoperability and true data governance and scale are still in the way of the smooth assimilation of real-time analytics into clinical and organizational functions. This paper analyzes the architectural principles, interoperability solutions and governance needs that makes real-time and near real-time healthcare analytics a reality. Based on the latest publications in the field of stream processing, IoT-supported healthcare, edge-cloud processing, AI-based analytics, and interoperability standards like FHIR, the paper carries synthesis of both technical and ecosystem views to create a cohesive framework of real-time healthcare data ecosystem. The analysis proposed four mutually supporting layers in data generation by means of multimodal sensaging infrastructure, stream processing or event-based analytics infrastructure, adaptive Artificial Intelligence (AI)-provided intelligence to aid clinical decision-making, and governance infrastructures to achieve privacy, compliance with regulations, and system reliability. The paper also identifies trade-offs that are very crucial between latency, accuracy, scalability and security, especially in deployed distributed environment as also in edge-enabled environment. Through the alignment of technical architectures and ecosystem governance issues, the present paper presents a comprehensive model of how to operationalize real-time healt…
subject: Research Article
  topic: section
subject:
  topic: keywords
  value: Real-time analytics; Near-real-time processing; Healthcare data ecosystems; Stream processing architectures; Edge–cloud computing; Internet of Things (IoT); Clinical decision support systems; FHIR interoperability; Healthcare data governance; Privacy-preserving analytics
creator: JAGADEESWAR ALAMPALLY
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2024-02-25
publication date: 2024-02-25
starting page: 314
ending page: 324
doi: 10.32996/jcsts.2024.6.1.34

Explainable AI for Institutional Fraud Decisions: A Cross-Sector Empirical Study Using Public Healthcare and Financial Transaction Data
type: item
description: Institutional fraud detection operates at the intersection of predictive analytics, regulatory accountability, and human judgment. While machine learning models can identify fraudulent behavior, their organizational value depends on their ability to support institutional decision-making through interpretable and stable explanations. In regulated environments, analytical outputs must be justified and integrated into investigative workflows. This study empirically examines explainable machine learning as a decision support mechanism across two institutional domains: public healthcare payment systems and financial transaction systems. Using the CMS-derived Healthcare Provider Fraud Detection dataset and the UCI Credit Card Fraud dataset, logistic regression, random forest, and gradient boosting models were developed and evaluated under realistic class imbalance conditions. Explainability was assessed using SHAP-based feature attribution to examine explanation stability and institutional interpretability. Results show that healthcare fraud models produce stable and institutionally meaningful explanations aligned with billing behavior, while financial fraud models generate accurate predictions but less stable explanations. These findings indicate that explainability is shaped by institutional data structure rather than model architecture alone. The study contributes to business analytics research by demonstrating how explanation stability influences decision relevance in institutional fraud detection.
subject: Research Article
  topic: section
subject: Business Analytics; Fraud Detection; Explainable Artificial Intelligence; Decision Support Systems; Healthcare Analytics; Financial Fraud; SHAP; Institutional Risk Management
  topic: keywords
creator: Imran Hossain Rasel
creator: Md Nurul Huda Razib
creator: Muhaimin Ul Zadid
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2026-01-09
publication date: 2026-01-09
starting page: 97
ending page: 106
doi: 10.32996/jcsts.2025.8.1.7

Evidence-Gated Search: Controlling Operational Search Explosion in LLM-Driven Incident Response
type: item
description: Large language model (LLM) assistants are increasingly used during outages and failed deployments, yet their remediation behavior can degrade into long lists of loosely justified changes such as restarts, redeploys, rollbacks, scaling changes, and configuration edits. In practice, this ap- pears as operational search explosion (OSE), where the branching factor of candidate fixes grows faster than teams can validate them, increasing change risk, time-to-recovery, and operator distrust. This paperformulates incident response as a bounded search problem over a constrained library of remediation primitives, where each primitive carries explicit preconditions, risk and cost meta- data, and evidence requirements that must be satisfied beforeexecution. The proposed mechanism, Evidence-Gated Search (EGS), blocks any state-changing action unless the required evidence is present in a normalized incident state. Missing evidence forces bounded read-only evidence gath- ering, such as logs, metrics, traces, deployment diffs, Terraform plan outputs, DNS checks, and access-control verification, before another action is considered. Across 175 incident episodes, EGS reduces executed remediation actions by 13.47% and lowers the action explosion rate (AER) by 16.65%, while maintaining a recovery success rate of 98.86%. The results show that requiring ev- idence before irreversible operational steps can significantly reduce operational search complexity without materially degrading recovery outcomes.
subject: Research Article
  topic: section
subject: Incident response automation, large language models, operational search explosion, evidence- gated search, safe remediation, budgeted search control, reliability engineering, DevOps
  topic: keywords
creator: Sudhakavya Bodapati Venkata
rights: Copyright (c) 2026 Journal of Computer Science and Technology Studies
date: 2026-03-19
publication date: 2026-03-19
starting page: 106
ending page: 120
doi: 10.32996/jcsts.2026.8.5.8

AI-Driven Hyper-Personalization, Consumer Sovereignty Loss, and the Transition Toward Subscription Economy: An Information Foraging Autonomy Perspective: An Information Foraging Perspective
type: item
description: Artificial intelligence-driven hyper-personalization has revolutionized marketing efficiency but simultaneously erodes consumer sovereignty through structural constraints on information exploration pathways. This study proposes the Information Foraging Autonomy Score (IFAS) - a novel metric operationalizing consumer decision autonomy within algorithmic recommendation ecosystems. Analysis draws from Cisco Consumer Privacy Survey 2024 (n=3,400 global consumers), Korea Consumer Commerce Association survey 2025-2026 (n=1,000 Korean consumers), EU Digital Services Act consumer impact study (n=2,300), and comparative firm cases including ZARA (SVD+RNN inventory optimization), Sephora Virtual Artist (AR personalization), and Coupang Rocket Membership (domestic subscription benchmark). A Difference-in-Differences model (n=1,247 matched pairs, 2024Q4-2026Q1) demonstrates subscription economies increase IFAS by +23.5 points (p<0.01) versus performance marketing environments. Key findings reveal hyper-personalization reduces choice autonomy by 30% through daily ad exposures exceeding 5,000 impressions (CTR decline -25%, brand avoidance +76%, privacy distrust 91%), while subscription models recover +40% autonomy at modest efficiency cost (ROAS 5.5x→4.9x). Performance marketing exhibits structural exploration suppression (IFAS=35), whereas subscription architectures occupy optimal Autonomy-Efficiency Matrix quadrant (IFAS=70, ROAS=4.9x). Korean food e-commerce applications demonstrate viability: regional specialty producers can implement "default box + 3 alternatives" structures maintaining diversity while leveraging subscription lock-in benefits. Theoretical contributions include IFAS framework extending information foraging theory to algorithmic marketing, practical Autonomy-Efficiency Matrix for strategy positioning, and Korea-specific PIPA/AI Basic Act 2026 compliance roadmap linking consumer rights (explanation/opt-out) to marketing KPIs. Findings challenge the personalizat…
subject: Research Article
  topic: section
subject: Hyper-personalization; Consumer sovereignty; Information foraging; Subscription economy; Algorithmic marketing; Consumer autonomy; Digital advertising; AI marketing; Choice overload
  topic: keywords
creator: changbeum yang
rights: Copyright (c) 2026 Journal of Computer Science and Technology Studies
date: 2026-03-19
publication date: 2026-03-19
starting page: 99
ending page: 105
doi: 10.32996/jcsts.2026.8.5.7

Machine Learning-Driven Early Warning Analytics for Identifying Market Manipulation, Irregular Trading Activity, and Suspicious Market Signals in U.S. Stock Markets
type: item
description: This paper develops a machine learning-driven early warning framework for identifying market manipulation, irregular trading activity, and suspicious market signals in U.S. stock markets, while grounding the argument in public regulatory evidence rather than purely illustrative examples. The study is motivated by three practical problems. First, conventional rule-based surveillance remains necessary but often produces large alert volumes, high false-positive burdens, and limited capacity to prioritize weak but meaningful signals. Second, suspicious trading increasingly spans multiple channels, including order flow, cross-venue behavior, public narratives, and relational patterns that are poorly captured by isolated tabular indicators. Third, the strongest public evidence available to researchers is fragmented across enforcement summaries, disciplinary statistics, and whistleblower reporting, which means rigorous early warning research must integrate official sources even when transaction-level labels remain limited. Accordingly, the manuscript combines scholarly synthesis with descriptive evidence derived from Securities and Exchange Commission and Financial Industry Regulatory Authority datasets and reports covering the 2021-2023 period.
subject: Research Article
  topic: section
subject: Market manipulation; abnormal trading; suspicious market signals; U.S. stock markets; machine learning; anomaly detection; graph neural networks; surveillance analytics; explainable AI
  topic: keywords
creator: Nusrat Jahan
creator: Anika Anjum Pritty
creator: Md Ibrahim
creator: Muhaimin Ul Zadid
creator: A S M FAHIM
creator: Sakib Mahmud
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by-nc/4.0
date: 2024-04-06
publication date: 2024-04-06
starting page: 257
ending page: 283
doi: 10.32996/jcsts.2024.6.2.26

A Generative AI–Driven Clinical Decision Support Framework Using Large Language Models
type: item
description: Early disease detection aids in the correct diagnosis and treatment of illnesses. A Clinical Decision Support System (CDS) helps identify illnesses and choose the best course of therapy. This paper presents a Generative AI-powered Clinical Decision Support Architecture based on Large Language Models (LLMs) to predict diseases and support diagnoses. The suggested architecture incorporates both structured clinical information and high-level preprocessing, feature selection, and class-balancing algorithms to increase the predictive accuracy. Experiments were conducted on 400 patient records from the UCI Chronic Kidney Disease (CKD) dataset. GPT-4o was used to learn more complex clinical patterns and aid in diagnostic decision-making. The recommended framework performed well, as evidenced by the accuracy of 99.17, sensitivity of 99.98, specificity of 98.70, F1-score of 98.85, Matthews Correlation Coefficient (MCC) of 98.21, and AUROC of 0.996. These findings are far more effective than conventional ML models and currently available LLM-based clinical methods. The high sensitivity yields a low rate of false negatives, which is essential in the early detection of disease, whereas the high specificity lowers the wrong diagnosis of healthy patients. Altogether, the suggested generative AI-based solution is powerful, consistent, and effective in clinical contexts, which underscores the potential of large language models (LLM) in medical decision support systems of the next generation.
subject: Research Article
  topic: section
subject: Clinical Decision Support System, Chronic Kidney Disease, Electronic Health Records, Machine Learning, Disease Prediction, Large Language Models
  topic: keywords
creator: Jahnavi Anilkumar Kachhia
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
date: 2024-12-25
publication date: 2024-12-25
starting page: 359
ending page: 368
doi: 10.32996/jcsts.2024.6.5.31

Securing Enterprise Data for LLM-Powered Applications: A Reference Architecture for Inference-Time Data Protection
type: item
description: Enterprises are rapidly adopting large language models (LLMs) by connecting them to internal data sources through approaches like retrieval-augmented generation (RAG), natural language database querying, and API based tool calling. In most enterprise deployments, the LLM itself is not hosted with in the company’s cloud tenant, but hosted by a third-party provider, which means the company data including customer records, financial information and proprietary documents must leave the organization’s, controlled environment every time a query is processed. This applies to the adoption of Saas products as well that leverage GenAI capabilities, where often times the data leaves the organization, the Saas platforms and are sent to the LLM vendors. Contractual agreements with the LLM providers help, but they cannot fully address risks like prompt injection, unintended data exposure, or regulatory non-compliance. This paper presents a practical reference architecture for securing enterprise data at inference time, organized around three main layers. The first layer addresses the data sources including unstructured content accessed through RAG, structured databases queried through SQL or graph languages, semi-structured records from SaaS APIs, and real-time event streams. The second layer covers the access mechanisms that connect these sources to LLMs, including function and tool calling, the Model Context Protocol, and code execution, along with agentic orchestration that combines multiple mechanisms autonomously. The third layer provides the governance controls such as data classification, role-based and context-aware access control, monitoring, audit trails, and regulatory compliance. The architecture draws on established industry standards including the NIST AI Risk Management Framework and the OWASP Top 10 for LLM Applications. This paper consolidates proven industry security practices into a coherent, practical, layered model that any organization can adapt when deployi…
subject: Research Article
  topic: section
subject: Enterprise data protection
  topic: keywords
subject: LLM security
  topic: keywords
subject: Retrieval-augmented generation (RAG)
  topic: keywords
subject: Large Language Models (LLMs)
  topic: keywords
subject: Inference-time security
  topic: keywords
subject: MCP
  topic: keywords
subject: Prompt attacks
  topic: keywords
subject: Tools calling
  topic: keywords
subject: AI Governance
  topic: keywords
subject: Cybersecurity
  topic: keywords
creator: Sathiesh Veera
rights: Copyright (c) 2026 Journal of Computer Science and Technology Studies
date: 2026-03-25
publication date: 2026-03-25
starting page: 121
ending page: 127
doi: 10.32996/jcsts.2026.8.5.9