Garuda - Garba Rujukan Digital

Article Per Year (5 Year)

All

The Eastasouth Journal of Information System and Computer Science

esiscs
Website

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 198 Documents

16 17 18 19 20

ThreatWeave-X: A Systematic Review of Graph Neural Learning for Predicting Latent Multi-Stage Cyberattack Paths from Fragmented Enterprise Security Signals Sujatha Lakshmi Narra
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 01 (2023): 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.v1i01.1289

The telemetry being used by enterprise security operations is increasingly endpoint, network, identity and cloud-based, with each individually exposing only part of an adversary's actions, and multi-stage intrusions, like those stemming from advanced persistent threats, very hard to reconstruct from any single source. We present a systematic review of the methodological heritage from deterministic attack-graph generation to provenance-graph and log-based intrusion analytics, and graph neural network representation learning that spans twenty-six papers published over the past 20 years (2002-2022). It chronicles the journey from the relational and infrastructure-aware formulations of attack graph research to the inductive, attentional and relation-aware formulations of graph neural architectures, that emerged as a direct result of their scalability challenges, and the evolution of provenance-graph analytics from a purely sequence-based treatment of system behavior to a more explicit causal and relational treatment. On the basis of this synthesis, the review suggests a five-stage conceptual framework called ThreatWeave-X, which integrates multi-source signals from various sources into a single heterogeneous provenance graph, relational and attentional graph-neural embedding and a sequence-aware attention mechanism with an analyst feedback loop to rank candidate multi-stage attack paths. The review also outlines four cross-cutting challenges that appear in the reviewed literature that are still not completely addressed: 1) incompleteness of multi-modal heterogeneity handling, 2) scarcity of labels, 3) limited interpretability, and 4) inconsistent evaluation protocols, which all suggest directions for future empirical validation of the graph-neural multi-stage attack-path prediction.

Verifiable Transaction-State Reconciliation for AI-Orchestrated Financial Workflows Under Partial Failure and Duplicate Events Anil Mandloi
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.v2i03.1290

AI agents are increasingly tasked with orchestrating multi-step financial workflows that span internal services and external processors. These workflows are exposed to partial failures and duplicate events caused by retries and at-least-once messaging. This paper describes a reconciliation protocol that pairs a deterministic transaction state machine with idempotency controls, compensating actions, and a hash-chain ledger supporting Merkle proofs. On a synthetic workload of 50,000 workflow instances under controlled failure injection, the design reduced unresolved states and improved consistency relative to a simple retry baseline and a saga-only approach, at the cost of modest added latency. The resulting evidence chain is intended to support operational audit in regulated environments.

Autonomous Thermo-Mechanical Co-Design of Embedded Liquid-Cooling Structures for High-Power Mobility Platforms: A Systematic Review Rupesh Rameshrao Dhore
The Eastasouth Journal of Information System and Computer Science Vol. 3 No. 03 (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.v3i03.1291

For electric traction inverters and onboard electronics that adopt wide-bandgap switches, bus bars and capacitors to increasingly shrink packages, embedded liquid-cooling structures are a key enabler for power density. Based on 20 peer-reviewed papers published from 2005 to 2025, this systematic review aims to explore the merging of autonomous, optimization-based design approaches with thermo-mechanical requirements for these structures. The corpus was coded on the basis of considering the following aspects: physics fidelity, design representation, optimization algorithm, validation route and application context. Combined with adjoint sensitivity analysis, pseudo-three-dimensional reduced-order models and extensions to the adjoint model for transient and turbulent flow, the density-based topology optimization with Brinkman penalization will emerge as the predominant autonomous design engine. Results indicate that the optimised fin array reduced pressure loss by almost 53.6% compared with circular fins, the pseudo-three dimensional heat sink lowered the thermal resistance by up to 13.6% compared with optimised size parallel fin, the transient design reduced the pumping power by 66.7% and the hybrid topology-microchannel cold plate reduced the peak chip temperature by 4.28K with a pressure drop of 319.42Pa. Traditional cooling technology uses 14% to 33% of the traction-inverter volume, making it evident that the value of compact, co-designed structures is high. There are still some structural compliance, thermal-cycling fatigue, manufacturability in optimization loops and limitation in treatment. A co-design framework with closed loop coupling is proposed to guide future development: mission profiles, reduced order solvers, adjoint updates and experimental verification.

Scalable CRM Workflow Automation for High-Volume Customer Onboarding: An Event-Driven Salesforce Architecture Chakra Dhari Gadige
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 01 (2023): 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.v1i01.1295

Customer Relationship Management (CRM) systems face issues when onboarding customers to the system with high volumes and when processing large groups of employees, precision of data and system dependability. In the current paper we provide a proposed Salesforce architecture which will be based on scalable, event-driven architecture which will be used to automate the process of customer onboarding by asynchronously processing and distributing workloads across the facilities and by utilizing structured validation processes. It is proposed that Queueable Apex and Batch Apex would be combined in the suggested architecture to address the workloads of various scales, synchronous processing limitations, and give an avenue to carry out large-scale onboarding operations with the assistance of the concise execution. Integration services enable Salesforce to communicate data with other systems in businesses, and validation features identify incomplete records, duplicate records, and inconsistency and then bar data persistence. A structured error management system helps in managing exceptions, retries with locally controlled failure handling, localizing failures and performance monitoring. Event driven coordination is also provided in the architecture to decouple processing phases to make it responsive and dynamic flow of transactions. Scalability, processing dependability, data integrity, the use of resources, and user experience are the main assessment aspects in its evaluation framework. The solution proposed will decrease the response times of interfaces, and provide clear onboarding status and actionable feedback, by decoupling the user-facing UI and resource consumptive background processes. The framework offers a useful structural system to organizations aspiring to revamp Salesforce-built onboarding procedures, to ensure that they are efficient and capable of operating with a high number of employees without compromising the quality and maintainability of the data in the end.

Data-Driven Network Operations: Framework for Telemetry, Analytics, and Automated Remediation Manevannan Ramasamy
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 01 (2023): 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.v1i01.1296

The transition to data-driven decision-making, particularly when it comes to contemporary network operations, can not be achieved without an efficient form of automation, yet to approach it, it is required that more than high-volume data collection by telemetry is involved. This paper outlines a design of reliable network operations that includes telemetry ingestion, data quality management, temporal modeling and topology awareness, analytics and automated remediation as a part of a controlled data lifecycle. The architecture suggests a canonical event envelope to offer consistent identity, event time, semantics and provenance to heterogeneous sources. Topology model and temporal entity assist in giving operational context and purpose-specific data store and controlled transformation pipeline are useful in assisting assurance, capacity planning, anomaly detection and remediation processes. A feature registry makes reusable analytical representations and the evidence provenance keeps track of the provenance between the source observations, transformations, analytical outputs and operational actions. The framework specifically addresses the problem of incomplete, late, repetitive, and semantically incoherent information by incorporating measures of quality and decision processes that are conscious of the confidence. Framework assessment is a framework appraisal that gauges completeness, freshness, semantic consistency, lineage coverage, reproducibility, and downstream quality of choice. Based on the proposed architecture, credible automated remediation is achievable, as indicated by the capabilities to reuse the evidence of all decisions to improve the transparency, operational soundness and regulated reuse of network data throughout the transforming analytic and automation processes.

AI-Powered Analytics Architectures for Enterprise Security and Operational Intelligence Karthikeyan Selvarajan
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.v2i03.1297

Cloud services, applications, networks, endpoints, and business services all generate large amounts of non-uniform security and operational data that can reside as well as be utilized by and reach the environment. These variegated streams of data cannot be related by the traditional analytics design to identify looming risks and avail timely operational insights. The current research paper introduces an AI-based analytics platform to enterprise level operations and security intelligence platform which embraces machine learning, smart data processing and real-time analytical functions under one platform. The proposed architecture will integrate with multi-source data ingestion, data normalization, feature engineering, anomaly detection, which are founded on AI, behavioral analysis, the threat intelligence correlation, and operational event processing. The hierarchical analysis framework will enable the security and operation data to be transformed to the contextualized data and make appropriate choices and continuous monitoring. Machine learning models can be used to identify abnormal behavior, prioritization of security events, identification of potential threats and establishing a relationship between infrastructure and business processes. The architecture is also seen to have cloud-native observability, centralized observability, automated feedback and alerting to enhance analytical flexibility. This paper discusses the aspects of architecture, the flow of data and the prospects of AI, integration principles, as well as the benefits of the functionality of the proposed approach. Based on the findings, AI-based analytics will be able to increase the visibility of various business security activities, increase the promptness of detecting incidents, resultant heightened awareness of the operations, and manage complex and dynamic enterprise landscapes more proactively.

Cloud-Based Smart Retail System Using AI-Driven Recommendations Mallikarjun Bellundagi
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.v3i01.1298

The rapid advancement of cloud computing and artificial intelligence (AI) has significantly transformed the retail industry. This paper presents a cloud-based smart retail system that leverages AI-driven recommendation techniques to enhance customer experience, optimize inventory management, and improve sales performance. The proposed system integrates machine learning algorithms, real-time analytics, and scalable cloud infrastructure to deliver personalized product recommendations. The study adopts a conceptual-analytical methodology supported by literature synthesis and system design modeling. Results indicate that AI-powered retail systems improve customer engagement, increase conversion rates, and enable data-driven decision-making. The proposed framework contributes to the development of intelligent, scalable, and adaptive retail ecosystems.

Digital Transformation Framework for Smart Enterprises Using AI and Cloud Computing Mallikarjun Bellundagi
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.v3i02.1299

Digital transformation has become essential for modern enterprises aiming to improve efficiency, scalability, and competitiveness. The integration of Artificial Intelligence (AI) and Cloud Computing provides a powerful foundation for building smart enterprises capable of data-driven decision-making and automation. However, many organizations lack a structured framework to guide this transformation. This paper proposes a comprehensive Digital Transformation Framework (DTF) that integrates AI capabilities with cloud-based infrastructure. The framework emphasizes scalability, security, data analytics, and intelligent automation. The study also discusses applications, challenges, and implementation strategies. The proposed model supports organizations in achieving sustainable digital growth and improved operational performance.

Deep Reinforcement Learning-Driven Engineering Change Sequencing for Complex Product Development Programs Yogesh Gawade
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.v1i03.1302

The order of the Engineering changes (ECs) that are acted upon in complex product development programs is tightly coupled to the ordering of the component, the parameters and the activities, and this ordering significantly affects rework, cost, and lead time. This systematic review combines 24 peer-reviewed papers from four research streams: design structure matrix (DSM) and process architecture, engineering change management and propagation, deep reinforcement learning (DRL) foundations and DRL-based production scheduling. The evidence is used to build EC sequencing as a Markov decision process with the propagation indices generated from DSM as the state, change or sequencing rules as the action, and penalties for cost growth, schedule growth and triggered rework as the reward. The synthesis revealed that changes require between 1/3 and 1/2 of engineering time, 48.2% of the 41,500 change requests that were raised in an 8-year period for a sensor program were implemented, and changes were propagated no further than three engineering interfaces. In learning, a deep Q-network solved 43 of 49 benchmark games while previous methods were unable to solve the same number of games. A search-augmented network architecture beat other algorithms in 99.8% of games in the benchmark. The results show that rule-selecting deep Q-network agents achieve the best trade-off between scalability and verifiability, while search-based planners have a much greater look-ahead capability but at a significantly higher computational cost. The simulator fidelity, limited change histories, explainability and certification traceability are key challenges. A reference architecture and a research agenda are suggested for the development of the transfer of DRL from production scheduling to engineering change control.

Policy-Evolving IAM Architecture for Just-in-Time Privilege Control in Multi-Account Cloud Enterprises Kedar Pradhan
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.v2i02.1303

Multi-account cloud enterprises spread workloads across multiple accounts, each of which is managed separately, but privileged access is often given to users within these accounts as default and is built up over time increasing the attack surface. This systematic review combines 23 studies and standards into a policy-evolving identity and access management (IAM) architecture for the following: risk-based access control, formal policy verification, identity federation, just-in-time (JIT) elevation, role and attribute mining, multi-tenancy authorization, and zero trust architecture. The proposed design is to perform risk-scoring on each elevation request, validate it with a trust-safety property using satisfiability modulo theories (SMT) reasoning and issue a per-session credential that only lasts long enough for a specific session; and record the credential in a log for log-driven policy mining, followed by regression tests to test the candidates before promotion. A formal check of the results shows that the tests are sufficiently quick to be used in an inline mode, with 99% of the production policy questions answered within 160 milliseconds; in addition, the tests are able to handle a task that the other algorithms were not able to handle within 24 hours, while the attribute based miners scored an area under the curve of 0.9973, compared to 0.9269 for the role based miners. The use of a modeled scenario further suggests that four two-hour JIT sessions a week provide 95.3% less privilege exposure than standing access, over the course of the year. Approval fatigue, proportionality monitoring, scalability of mining, and continuity of break glass are mentioned as the major challenges to adoption.

Page 19 of 20 | Total Record : 198

16 17 18 19 20

Filter by Year

2023 2026

Filter By Issues

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
Abdul Aziz

Contact Email
abdulazizbinceceng@gmail.com

Phone
+6282180992100

Journal Mail Official
journaleastasouth@gmail.com

Editorial Address
Grand Slipi Tower, level 42 Unit G-H Jl. S Parman Kav 22-24, RT. 01 RW. 04 Kel. Palmerah Kec. Palmerah Jakarta Barat 11480

Location
Kota adm. jakarta barat,

Dki jakarta

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Abdul Aziz

Contact Email abdulazizbinceceng@gmail.com

Phone +6282180992100

Journal Mail Official journaleastasouth@gmail.com

Editorial Address Grand Slipi Tower, level 42 Unit G-H Jl. S Parman Kav 22-24, RT. 01 RW. 04 Kel. Palmerah Kec. Palmerah Jakarta Barat 11480

Location Kota adm. jakarta barat, Dki jakarta INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Abdul Aziz

Contact Email
abdulazizbinceceng@gmail.com

Phone
+6282180992100

Journal Mail Official
journaleastasouth@gmail.com

Editorial Address
Grand Slipi Tower, level 42 Unit G-H Jl. S Parman Kav 22-24, RT. 01 RW. 04 Kel. Palmerah Kec. Palmerah Jakarta Barat 11480

Location
Kota adm. jakarta barat,

Dki jakarta

INDONESIA