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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 105 Documents
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AI-Driven Cyber Threat Intelligence as a Management Information System: Integrating Cybersecurity Governance and IT Project Management for Organizational Resilience Orthi, Shuchona Malek; Chakraborty, Partha; Siam, Md Abubokor; Shan-A-Alahi, Ahmed; Al Zaiem, Abdullah; Hasan, Syed Nazmul; Kaur, Jobanpreet; Mahmud, Foysal; Goffer, Mohammad Abdul
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (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.v1i02.873

Abstract

As organizations quickly become increasingly digital, they are experiencing escalating, complex cyber threats. These challenges make cybersecurity an important area for managers and governance, and not just a technical problem. Even if you are a heavy investor in security tech, it still happens to many companies to suffer from cyber. The reasons are broken information flows, a lack of clear visibility among managers, and misalignment between the security operations and the overall decision-making. This research rethinks Artificial Intelligence (AI) based Cyber Threat Intelligence (CTI) as a Management Information System (MIS). It combines the approach of cybersecurity governance and IT project management to increase the resilience of organizations. Using MIS theory, cybersecurity governance models, and studies of IT project management, the paper derives one cohesive model for translating raw threat data into useful managerial insight. Through a design science methodology, the research chooses a lot of scholarly sources and demonstrates a layered AI-enabled CTI-MIS architecture. This is good architecture for strategic oversight, risk-based governance, and flexible project execution. The paper extends the theory of MIS to Artificial Intelligence (AI) driven cybersecurity intelligence and provides practical knowledge for companies seeking to achieve resilient digital transformation in a time of evolving cyber threats.
Performance Optimization Strategies for High-Concurrency Spring Boot Microservices in Enterprise Financial Systems Singi, Kamalakar Reddy
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 02 (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.v1i02.883

Abstract

This paper investigates performance optimization strategies for Spring Boot–based microservices deployed in high-concurrency enterprise financial transaction systems. Although microservices improve modularity and scalability, financial workloads expose bottlenecks related to database contention, synchronous execution, and Java Virtual Machine (JVM) resource management. A coordinated, multi-layer performance optimization framework is proposed, addressing application-level, data-access-level, and runtime-level challenges. The framework is validated using a simulated high-concurrency financial transaction workload. Experimental results demonstrate improved response time, higher throughput, enhanced runtime stability, and reduced error rates under peak load conditions.
Cloud-Based Information Retrieval for Big Data: A Survey of Architectures and Scalability Challenge Chowdhury, Tanay
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.v3i03.937

Abstract

Cloud computing has become a paradigm of managing, storing and retrieving large amounts of data emanating in contemporary digital applications. The mode of information retrieval (IR), which is typically insufficient in large-scale, heterogeneous, and dynamic data settings, has been severely challenged by the issue of big data, namely its high volume, high velocity, high diversity, high veracity, and high value. Cloud retrieval information systems take advantage of the elasticity, scalability and on-demand provisioning of cloud systems to facilitate effective and cost-effective access to data across distributed platforms. This work is a critical overview of the concept of big data and cloud-based IR, with a specific emphasis on the most significant models of cloud service, the specifics of data types, and the prospects of ML and DL to improve the quality of retrieval and relevance. Moreover, the paper logically examines key scalability issues, such as distributed storage management, index maintenance, query processing latency, load balancing and resource provisioning. All critical issues related to security and privacy, including leakage of data, insider threats, and vulnerability of programming interfaces, and multi-tenancy risks are also discussed. This paper, by summarizing the available literature and discovering gaps in the research, offers useful information on how scalable, secure, and intelligent information retrieval systems can be designed, as well as presents future research opportunities so as to facilitate reliable deployment of the system in data-intensive applications.
Semantic Search with Vector Database: A Comprehensive Review of Models, Indexing and Applications Chowdhury, Tanay
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.v3i03.938

Abstract

The use of semantic search with the help of vector databases has become an impressive paradigm of retrieving the pertinent information by offering the contextual and conceptual sense of the information searching more than using the conventional methods of keyword searching. This paper provides an in-depth overview of the models of vector representation, transformer-based semantic encoders, and technologies of vectors database that jointly allow efficient and error-free semantic search. Classical distributional semantics, word-level embeddings, and transformer architectures are presented as background methods of making designed generating meaningful vectors representations. The paper also looks at the contemporary databases of vectors and indexing mechanisms which enable scalable similarity search in high-dimensional data. Moreover, different distance measures, hash algorithms and indexing strategies based on graphs are evaluated to determine how they can be used to maximize retrieval. Lastly, the paper presents practical examples of semantic searching with the use of the vector databases with text, image, audio and conversational applications, outlining both the main challenges and research opportunities.
Performance Evaluation of Machine Learning Inference Workloads in Containerized Cloud Computing Environments Konda, Manisha
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.v3i02.949

Abstract

Machine learning (ML) systems are increasingly deployed in cloud-native environments where scalability, portability, and resource efficiency are essential. There are many scenarios in which the Docker and Kubernetes containerization solution are the best solution for machine learning inferencing services as the application scales, moves, and seeks every efficiency. However, the performance of machine learning inferencing services within a containerized cloud environment still needs to be explored. What is the performance of machine learning inferencing services within a containerized cloud environment? The performance of machine learning inferencing services within a containerized cloud environment needs to be explored. The aim of the exploration is to understand the performance of various machine learning models within a containerized cloud environment and to determine the major factors affecting the performance of machine learning inferencing services. Several machine learning models are implemented using Python-based frameworks and deployed as microservices in Docker containers. The experiments are performed by sending simultaneous prediction requests from multiple users to the deployed models. The study establishes baseline benchmarks, which demonstrate the impact of containerization on inference speed and efficiency. This provides useful and practical knowledge for building scalable AI systems and establishes the foundation for future work, such as optimizing ML deployment pipelines, incorporating privacy-preserving inference techniques, and improving container orchestration for AI workloads.

Page 11 of 11 | Total Record : 105

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