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The Eastasouth Journal of Information System and Computer Science
Published by Eastasouth Institute
ISSN : 30266041     EISSN : 3025566X     DOI : https://doi.org/10.58812/esiscs
Core Subject : Science,
Computer Science & IT
ESISCS - The Eastasouth Journal of Information System and Computer Science is a peer-reviewed journal and open access three times a year (April, August, December) published by Eastasouth Institute. ESISCS aims to publish articles in the field of Enterprise systems and applications, Database management systems, Decision support systems, Knowledge management systems, E-commerce and e-business systems, Business intelligence and analytics, Information system security and privacy, Human-computer interaction, Algorithms and data structures, Artificial intelligence and machine learning, Computer vision and image processing, Computer networks and communications, Distributed and parallel computing, Software engineering and development, Information retrieval and web mining, Cloud computing and big data. ESISCS accepts manuscripts of both quantitative and qualitative research. ESISCS publishes papers: 1) review papers, 2) basic research papers, and 3) case study papers. ESISCS has been indexed in, Crossref, and others indexing. All submissions should be formatted in accordance with ESISCS template and through Open Journal System (OJS) only.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 143 Documents
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Security and Compliance Issues in Cloud-Based Deployments of Content and Workflow Management Systems Ravi Kiran Kanneganti
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 01 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v2i01.1122

Abstract

The spread of cloud-based content and workflow management systems has increased boisterously within industries, and usage has subjected organizations to severe security and compliance threats. This paper discusses these risks based on a process of systematic analysis of literature on twenty peer-reviewed sources. We are able to categorize five types of threats, analyze five hypothetical compliance models and provide a quantitative security assurance model based on weighted scoring. Conclusions indicate that data breaches and identity management successes and failures occur in content and workflow environments and can explain about 50% of reported incidents of cloud security. The three frameworks of compliance vary in terms of coverage, ease of implementation and integration of workflow. The article suggests a layered security architecture and quantitative assurance metrics to enable decision clouds to select cloud services and design policies based on that selection. Companies with coordinated compliance strategies have significantly low numbers of violations, and improved overall security status.
Federated Machine Learning for Privacy-Preserving Cyber Threat Intelligence in Global Cloud-Integrated MIS Platforms Md Kazi Tuhin; Md Talha Bin Ansar; Mohammad Somon Sikder; Hemayet Uddin Himel; Harleen Kaur; Hasan Imam; Ahmed Shan-A-Alahi
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 02 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v2i02.1123

Abstract

Cyber Intelligence (CI) is an advanced security system that defends networks against cyberattacks by using ML models. In conventional centralized machine learning tools used to detect cyber threats, critical organizational information must be shared, which imposes severe privacy and security issues. To overcome this issue, this paper suggests a privacy-aware cyber threat detection system based on Federated Learning combined with a Convolutional Neural Network (FL-CNN). The proposed framework is tested on CIC-IDS-2017 dataset, where the methods of preprocessing like: missing value imputation, MinMax normalization, one-hot encoding, and attack label remapping are used. Experiment findings show that the suggested FL-CNN model outperforms the centralized CNN model in most metrics, including accuracy (96.8%), precision (96.2%), recall (96.7%), and F1-score (96.5%). Also, a false positive rate of the model stands at 0.025, which is much lower meaning that the model is better at identifying a cyber threat in a distributed cloud system.
Artificial Intelligence-Driven Clinical Decision Support Systems for Improving Diagnostic Accuracy and Personalized Treatment Planning in Physical Therapy Dhirenbhai Kalal
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 03 (2025): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v2i03.1146

Abstract

As physical therapy practice moves toward the increasingly large and complex multimodal patient data stream (imaging, gait kinetics, wearable-sensor streams, and patient-reported outcomes), relying on unaided clinical judgment is insufficient for pattern recognition. Twenty-five papers were retrieved between 2018 and 2025 for artificial intelligence (AI) clinical decision support systems (CDSS) related to diagnostic accuracy and personalized treatment planning in physical therapy.Twenty-five papers were identified between 2018 and 2025 for AI clinical decision support systems (CDSS) for diagnostic accuracy and personalized treatment planning in physical therapy. The deep-learning models for knee osteoarthritis grading, low back pain classification, and sarcopenia-related gait screening are analyzed, as well as the large language model-based clinical reasoning models, multi-sensor rehabilitation-monitoring platforms, and myoelectric control systems for upper-limb recovery. The reported diagnostic accuracy of imaging-based models ranges from 86.2% to 92.5% and the evidence from the network meta-analysis suggests that the improvement of pain and ROM outcomes by AI-assisted rehabilitation is greater than conventional rehabilitation. The main barriers to the adoption are clinician trust, burden of integration to the workflow, and data-privacy concerns; while the facilitators are explainability and demonstrated diagnostic benefit. Ethical, legal, and regulatory issues related to the use of AI-CDSS in rehabilitation are also explored in the synthesis. The results confirm the hybrid model (clinician in the loop) of integrating AI-CDSS within the task of physical therapist judgment.
Carbon-Aware Intelligent Electrical Distribution Network Using Digital Twins and Edge Artificial Intelligence Milan Bharatkumar Makwana
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 03 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v1i03.1147

Abstract

The escalating integration of distributed energy resources and the urgency of decarbonization targets have exposed the limitations of conventional, centrally managed electrical distribution networks. This paper synthesizes findings from twenty-three studies to examine how digital twin technology and edge artificial intelligence can be combined to form a carbon-aware intelligent distribution network. The problem addressed concerns the inability of legacy supervisory control and data acquisition architectures to deliver the millisecond-scale responsiveness, emission transparency, and adaptive reconfiguration required by high-penetration renewable grids. A layered conceptual framework is proposed, integrating field sensing, edge inference, digital twin synchronisation, and carbon-weighted reinforcement learning control. Evidence drawn from the literature indicates that hybrid edge-cloud architectures reduce control-loop latency from several hundred milliseconds to below 100 milliseconds relative to cloud-only deployment, while safe deep reinforcement learning controllers for Volt-VAR optimisation converge within 300 to 500 training episodes and reduce voltage violations substantially. Carbon emission flow modelling combined with temporally shifted, carbon-aware scheduling is shown to yield emission reductions ranging from approximately 10 per cent to more than 25 per cent when co-optimised with digital twin state estimation. The synthesis further identifies bandwidth reduction of up to 82 per cent and inference accuracy gains of nearly 5 percentage points for hybrid configurations. Implications include improved grid resilience, measurable decarbonization, and a pathway toward regulatory-compliant, self-optimising distribution networks, with future work directed toward standardised digital twin interoperability and federated edge learning.
Development and Evaluation of an Internet of Things Based Wearable Sensor System for Real-Time Monitoring and Remote Management of Musculoskeletal Rehabilitation Patients Dhirenbhai Kalal
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 01 (2025): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v3i01.1148

Abstract

Musculoskeletal rehabilitation needs people to keep track of how patients are moving after they leave the clinic. The usual way of following up with patients after they leave the clinic has some problems. Patients can only visit the clinic often they have to tell us how they are feeling and some patients live really far from the clinic. This paper looks at twenty-one studies that were published between 2019 and 2025. These studies are about using Internet of Things based sensor systems to monitor musculoskeletal rehabilitation patients in real time. The patients in these studies were recovering from things like knee arthroplasty, post-stroke upper-limb impairment and hip and knee osteoarthritis. The systems that were studied have three parts: sensing, edge or network and cloud or application layers. The paper examines how these systems operate, such as how they detect movement, how they communicate with one another and how they interpret data. The paper also examines the tracking capabilities of these systems – such as movement, muscle activity, patient feelings and how they communicate this information to doctors. The paper compares these systems and discovers that some systems are more adept at tracking leg motion, and others, arm motion. Systems with inertial measurement units, for instance, are able to follow leg motion, while systems with surface electromyography and accelerometers are able to follow arm motion in stroke patients. In one study, they looked at many studies and found that patients who used remote monitoring systems were less likely to require rehospitalization, and they were not in the hospital as long. They were also not required to come into the clinic frequently, and were more likely to adhere to their treatment plan. In a few small studies, which used an inertial platform to track the patients after knee replacement surgery, it was determined that these devices were effective and there was no loss of accuracy if utilized within the home environment. The ability to interact with other computers, and learn from the result of the data collected, is the key to making these systems effective, it concludes the paper. The biggest problems that are still preventing these systems from being widely used are that they do not work well with systems they are not secure and there are problems, with how they will be paid for through 2025. Although the rehabilitation of musculoskeletal and the Internet of Things based sensor systems are improving, they still have some challenges to be addressed. In the future, the Internet of Things (IoT) sensor systems will continue to be employed in musculoskeletal rehabilitation.
Edge Intelligence-Enabled Self-Calibrating Smart Instrumentation for Autonomous Process Industries Milan Bharatkumar Makwana
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 01 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v2i01.1149

Abstract

A dense network of smart instrumentation is essential to autonomous process industries, with the accuracy of these sensors constantly being compromised by sensor drift, fouling, temperature changes and degradation over time. Routine manual recalibration is expensive, disruptive and is not able to cope with realtime degradation. Based on sixteen literature references dealing with edge computing, edge intelligence, TinyML, soft sensing and concept drift management, this paper proposes a combined approach for the development of self-calibrating smart instrumentation for autonomous plants. The synthesis results show that the edge intelligence architectures achieve a latency of ~420ms with cloud-only processing, but a latency of <40ms when quantized models are processed on-device, and a reduction in energy consumption per inference from 2.85 joules to 0.31 joules. Cluster-based statistical drift detectors and optimal-transport transfer learning are demonstrated to maintain calibration accuracy 90 percent or higher for more than a year of use without manual corrections. TinyML quantization drastically reduces the footprint of neural drift estimators to fit microcontroller-class devices with less than 256 kilobytes of static random-access memory. The proposed layered architecture integrates field sensing, edge-resident calibration controllers, and federated retraining in the cloud to support the measurement traceability while reducing the bandwidth requirement by up to 87 percent compared to raw data streaming. Results also show that self-calibrating instrumentation decreases the number of unplanned maintenance visits and total measurement uncertainty. The paper finds that edge intelligence and self-calibration represent a technically mature and economically viable roadmap for achieving full autonomy of process instrumentation, though there are still remaining challenges related to standardization, cybersecurity and long-term model drift.
Federated Learning-Based Adaptive Control Architecture for Autonomous Smart Manufacturing Systems Milan Bharatkumar Makwana
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 02 (2025): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v3i02.1150

Abstract

The machine learning architectures for autonomous, smart manufacturing systems must guarantee data privacy across geographically distributed production cells and be able to continuously adapt to the non-stationary process conditions. In this paper, recent advancements in federated learning, secure aggregation, blockchain-based trust management, digital twin simulation, and reinforcement learning are brought together to design a layered adaptive control framework for autonomous smart manufacturing. The architecture incorporates a novel edge-resident local training method, a drift-aware adaptive aggregation mechanism, a digital-twin-validated reinforcement learning control policy, and a blockchain ledger to trace the provenance of the model for auditability. Both results suggest that drift-aware weighted aggregation achieves about 0.958 global model accuracy after 100 communication rounds, while standard federated averaging achieves about 0.887 global model accuracy in the same number of rounds. When adaptive scheduling and secure aggregation are combined, the estimated reduction in communication overhead is around 60% compared to a default schedule. A comparative assessment along privacy, scalability, latency resilience, robustness, auditability and adaptivity dimensions shows that the proposed architecture is superior compared to centralized control and standard federated learning baselines, especially in terms of auditability and robustness to non-independent and non-identically distributed data. The results indicate that a viable path towards confidential, resilient and continuously adaptive control of autonomous manufacturing equipment could be achieved by combining federated optimization with blockchain-verified digital twin validation.
Mining Production Behavior Without Mining Customer Data: An AI Approach to Test Generation in Regulated Environments Tanvi Mittal
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 02 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v2i02.1151

Abstract

Structural issues arise for regulated industries like banking, insurance, and healthcare where the need for accurate production data to verify the behaviour of software and the legal obligation to limit customer exposure to the software are in conflict. This paper combines recent developments in privacy-preserving data publishing, generative modelling, process mining, and search-based software testing into a single framework that allows one to mine production behaviour without mining customer data. It combines anonymisation primitives (like k-anonymity and l-diversity) with differential privacy techniques to limit the re-identification risk, uses generative adversarial architectures (conditioned tabular GANs) to produce statistically faithful synthetic event logs and injects the artefacts into whole-suite and mutation-guided test generation engines. Synthesized evidence shows that downstream analytical utility of differentially private generators is between 78 and 88 percent, while the residual re-identification risk is kept below 12 percent as compared to 31 to 42 percent for the classical k-anonymity and l-diversity approaches. Synthetic behavioral logs generate more than 90 percent branch coverage and 80 percent mutation score in 10 iterations of refinement. Federated learning also spreads the information of production patterns, but without transmitting raw data. Results indicate that privacy engineering alongside generative synthesis and search-based testing provides an appropriate and scalable alternative to production-data-dependent pipelines for quality assurance, which is audit and regulation compliant.
Artificial Intelligence-Based Cyber Threat Detection and Response for Critical Infrastructure Security Reily Kaium; Lizi Alasa; Kurtz Robert; Okuma Kaium; Kurtz Diana
The Eastasouth Journal of Information System and Computer Science Vol. 4 No. 01 (2026): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v4i01.1166

Abstract

The rapid digital transformation of critical infrastructure has significantly increased its exposure to complex and continuously evolving cyber threats, creating an urgent need for intelligent and adaptive cybersecurity solutions. Conventional security mechanisms, such as signature-based and rule-based intrusion detection systems, often struggle to identify novel attack patterns and provide timely responses to emerging threats. To address these limitations, this study proposes an artificial intelligence (AI)-driven framework for cyber threat detection and automated response that strengthens the security, resilience, and operational reliability of critical infrastructure environments. The experimental evaluation demonstrates that AI-based techniques substantially outperform traditional cybersecurity methods in terms of detection performance. Conventional rule-based systems achieve an average detection accuracy of approximately 68%, whereas machine learning and deep learning models improve the accuracy to nearly 80% and 88%, respectively. The proposed AI-driven framework delivers the highest performance, achieving an overall detection accuracy of approximately 94%. This superior performance highlights its capability to accurately identify both previously known attacks and sophisticated zero-day threats. Beyond detection accuracy, the study evaluates response time, which plays a crucial role in limiting the impact of cyber incidents. The findings reveal that the proposed AI-enabled response mechanism reduces the average response time to approximately 35 seconds, compared with around 150 seconds for manual response processes and 90 seconds for conventional rule-based automation. Such improvements enable faster threat containment, minimize operational disruption, and enhance the resilience of critical infrastructure systems. The framework also demonstrates notable improvements in reducing false positive alerts. The AI-driven approach achieves a false positive rate of approximately 5%, significantly lower than the 20% observed in signature-based systems and the 12% reported for anomaly-based detection methods. By minimizing false alarms, the proposed framework improves the efficiency of security operations, reduces alert fatigue among cybersecurity analysts, and enables security teams to prioritize genuine threats more effectively.
Blockchain-Driven Cybersecurity Framework for Smart Digital Ecosystems Marion Kayla; Crispinus Ode; Marion Sanaipei
The Eastasouth Journal of Information System and Computer Science Vol. 4 No. 01 (2026): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The rapid growth of smart digital environments, driven by cloud computing, the Internet of Things (IoT), artificial intelligence (AI), and interconnected cyber-physical systems, has significantly increased the frequency and sophistication of cyber threats. Conventional cybersecurity approaches often struggle to ensure data integrity, trust, and resilience in decentralized and highly dynamic environments, highlighting the need for more robust security frameworks. This review paper examines the role of blockchain technology in enhancing cybersecurity for smart digital ecosystems through decentralized authentication, immutable data storage, secure information sharing, and intelligent threat mitigation. The study reviews blockchain architectures, consensus mechanisms, security protocols, and their integration with emerging technologies such as AI, machine learning, edge computing, and cloud platforms. A comparative analysis of consensus algorithms, including Proof of Work (PoW), Proof of Stake (PoS), and Practical Byzantine Fault Tolerance (PBFT), is presented to evaluate their effectiveness in improving data integrity, scalability, and security. The review further analyzes hybrid blockchain-AI cybersecurity frameworks, demonstrating that integrating blockchain with AI-based intrusion detection systems significantly improves threat detection accuracy, reduces successful cyberattacks, and enhances system resilience. The paper also discusses major implementation challenges, including scalability, computational overhead, interoperability, energy consumption, and privacy preservation, which continue to hinder large-scale deployment. Finally, future research directions are identified, emphasizing explainable artificial intelligence (XAI), federated learning, zero-trust architectures, edge intelligence, and privacy-preserving blockchain solutions to strengthen next-generation cybersecurity systems. Overall, this review demonstrates that blockchain-based cybersecurity frameworks provide a secure, transparent, and resilient foundation for protecting smart digital environments against increasingly sophisticated cyber threats while supporting trustworthy and scalable digital transformation.

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