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All Journal Sistemasi: Jurnal Sistem Informasi International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Indonesian Journal of Education and Social Science Jurnal Ilmiah Ilmu Terapan Universitas Jambi The Indonesian Journal of Computer Science
Zeebaree, Subhi R. M.
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Computer Science & IT Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Distributed Systems for Artificial Intelligence in Cloud Computing: A Review of AI-Powered Applications and Services Zangana, Hewa Majeed; Zeebaree, Subhi R. M.
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 5 No. 1 (2024): INJIISCOM: VOLUME 5, ISSUE 1, JUNE 2024
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v5i1.11883

Abstract

The synergy of distributed frameworks with Artificial Intelligence (AI) is pivotal for advancing applications in cloud computing. This review focuses on AI-powered applications in distributed systems, conducting a thorough examination. Analyzing foundational studies and real-world applications, it extracts insights into the dynamic interplay between AI and distributed frameworks. Quantitative measures allow a nuanced comparison, revealing diverse contributions. The survey provides a broad overview of the state-of-the-art, spanning applications like performance optimization, security, and IoT integration. The ensuing discussion synthesizes comparative measures, significantly enhancing our understanding. Concluding with recommendations for future research and collaborations, it serves as a concise guide for professionals and researchers navigating the challenging landscape of AI-powered applications in distributed cloud computing platforms.
SMART HOME ENERGY SAVING WITH BIG DATA AND MACHINE LEARNING Ahmad, Hawar Bahzad; Asaad, Renas Rajab; Almufti, Saman M; Hani, Ahmed Alaa; Sallow, Amira Bibo; Zeebaree, Subhi R. M.
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 8 No. 1 (2024): Volume 8, Nomor 1, June 2024
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v8i1.32598

Abstract

In response to escalating energy consumption, particularly within the housing sector, a global imperative to reduce energy usage has emerged, propelling the concept of "smart houses" to the forefront of innovation. This paradigm shift owes its genesis to the convergence of advancements in energy conversion, communication networks, and information technology, catalyzing the emergence of the Internet of Things (IoT). The IoT facilitates seamless connectivity of devices via the World Wide Web, enabling remote management, monitoring, and detection capabilities. Capitalizing on this technological synergy, the integration of IoT, big data, and machine learning with home automation systems holds immense promise for enhancing energy efficiency. This paper introduces HEMS-IoT, a groundbreaking energy control system for intelligent homes, underpinned by big data analytics and machine learning algorithms, prioritizing security, convenience, and energy conservation. Leveraging J48 neural network technology and the Weka API, the study illuminates user behaviors and energy consumption patterns, enabling household classification based on energy usage profiles. Moreover, to ensure user comfort and safety, RuleML and Apache Mahout are deployed to customize energy-saving recommendations tailored to individual preferences. By presenting a practical demonstration of smart home monitoring, this paper validates the effectiveness of the proposed approach in enhancing security, comfort, and energy conservation. This pioneering research not only showcases the transformative potential of IoT-driven energy management systems but also sets the stage for a sustainable and interconnected future.
INTELLIGENT HOME IOT DEVICES: AN EXPLORATION OF MACHINE LEARNING-BASED NETWORKED TRAFFIC INVESTIGATION Almufti, Saman M; Hani, Ahmed Alaa; Zeebaree, Subhi R. M.; Asaad, Renas Rajab; Majeed, Dilovan Asaad; Sallow, Amira Bibo; Ahmad, Hawar Bahzad
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 8 No. 1 (2024): Volume 8, Nomor 1, June 2024
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v8i1.32767

Abstract

In the rapidly evolving landscape of smart homes powered by Internet of Things (IoT) devices, the twin specters of safety and privacy loom large, exacerbated by pervasive security vulnerabilities. Confronted with a heterogeneous array of devices each with unique Value of Service (QoS) requirements, devising a singular network management strategy proves untenable. To mitigate these risks, device categorization emerges as a promising avenue, wherein rogue or vulnerable devices are identified and network operations are automated based on device type or function. This novel approach not only fortifies IoT security but also streamlines network management, offering a multifaceted solution to the burgeoning challenges. Recognizing the burgeoning interest in leveraging machine learning for traffic analysis in IoT environments, this study delves deep into the potential and pitfalls of such techniques. Beginning with a comprehensive framework for categorizing IoT devices, the research meticulously examines methodologies and remedies across every stage of the workflow. Key focal points include the categorization of public datasets, nuanced analysis of IoT traffic data collection methodologies, and the exploration of feature extraction techniques. Through a rigorous evaluation of machine learning algorithms for IoT device classification, the study elucidates emerging trends and highlights promising avenues for future exploration. The culmination of this investigation manifests in meticulously crafted taxonomies, offering insights into prevailing patterns and informing future research trajectories. Moreover, the study identifies and advocates for uncharted territories within this burgeoning domain, propelling the discourse forward and catalyzing innovation in IoT security and management.
DATA ANALYSIS AND MACHINE LEARNING APPLICATIONS IN ENVIRONMENTAL MANAGEMENT Majeed, Dilovan Asaad; Ahmad, Hawar Bahzad; Hani, Ahmed Alaa; Zeebaree, Subhi R. M.; Abdulrahman, Saman Mohammed; Asaad, Renas Rajab; Sallow, Amira Bibo
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 8 No. 2 (2024): Volume 8, Nomor 2, December 2024
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v8i2.32769

Abstract

The rapid expansion of data on air contaminants and climate change, particularly concerning public health, presents both opportunities and challenges for traditional epidemiological methods. This study aims to address these challenges by exploring advanced data collection, pattern identification, and predictive modeling techniques in the context of air pollution research. The focus is leveraging data mining and computational methods to enhance the understanding of air pollution's impact on public health, specifically ozone exposure. A comprehensive review of the scientific literature was conducted, utilizing databases such as Professor, Scholar, Embl, and Nih to identify relevant studies on air pollution epidemiology. The review highlights the integration of data mining, machine learning, and spatiotemporal modeling to improve the detection, analysis, and forecasting of air pollution-related health issues. The findings reveal a growing trend in applying data mining techniques within the field of air pollution epidemiology. Advanced methods, such as spatiotemporal analysis and geographic data mining, enable more precise tracking and forecasting of pollution-related health risks. Continuous advancements in artificial intelligence and the development of more sophisticated sensors and data storage technologies are enhancing the accuracy and reliability of air quality monitoring and public health predictions. This study highlights the transformative potential of integrating data mining and AI techniques into air pollution epidemiology. Exploring emerging technologies like spatiotemporal mining and next-generation sensors paves the way for more accurate, timely, and scalable solutions to monitor air quality and predict its impact on public health, opening new avenues for research and policy interventions.
COMPARATIVE ANALYSIS OF STATE-OF-THE-ART CLASSIFIERS FOR PARKINSON'S DISEASE DIAGNOSIS Hani, Ahmed Alaa; Sallow, Amira Bibo; Ahmad, Hawar Bahzad; Abdulrahman, Saman Mohammed; Asaad, Renas Rajab; Zeebaree, Subhi R. M.; Majeed, Dilovan Asaad
Jurnal Ilmiah Ilmu Terapan Universitas Jambi Vol. 8 No. 2 (2024): Volume 8, Nomor 2, December 2024
Publisher : LPPM Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/jiituj.v8i2.32771

Abstract

Parkinson's disease (PD) presents a growing global health challenge, with early detection being crucial for effective management and treatment. This study seeks to develop an innovative machine learning (ML) framework for the early detection of PD by integrating advanced techniques for data preprocessing, dimensionality reduction, feature selection, and ensemble classification, aiming to significantly improve detection accuracy and timeliness. The research employs a robust ML pipeline, beginning with data preprocessing using mean imputation, standardization, min-max scaling, and SMOTE (Synthetic Minority Over-sampling Technique) to handle imbalanced data. Dimensionality reduction is achieved through Principal Component Analysis (PCA), while feature selection is performed using SelectKBest coupled with the ANOVA F-test to identify the most relevant features. Four ensemble methods—Random Forest, Gradient Boosting, XGBoost, and Support Vector Machine (SVM)—are evaluated for classification. Among the classifiers tested, the Gradient Boosting model stands out with an impressive accuracy of 0.9487, demonstrating its superior performance in PD detection. Integrating multiple preprocessing, dimensionality reduction, and feature selection techniques proves essential in optimizing model performance, highlighting the importance of a multifaceted approach in handling complex datasets. This research introduces a comprehensive ML framework that combines multiple advanced techniques in a streamlined process, significantly improving the early detection of Parkinson's disease. Ensemble methods, combined with strategic feature selection and data balancing techniques, offer a novel approach that could be applied to other neurodegenerative disorders, expanding its potential impact beyond PD detection.
Transforming Public Management: Leveraging Distributed Systems for Efficiency and Transparency Zangana, Hewa Majeed; Ali, Natheer Yaseen; Zeebaree, Subhi R. M.
Indonesian Journal of Education and Social Sciences Vol. 4 No. 1 (2025)
Publisher : Papanda Publishier

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56916/ijess.v4i1.783

Abstract

This paper explores the integration of distributed systems in public management and its implications for governance, service delivery, and innovation. Drawing on a review of existing literature and case studies, the paper examines the applications, benefits, challenges, and opportunities associated with distributed systems in the public sector. Key findings indicate that distributed systems, such as blockchain technology, offer the potential to enhance transparency, accountability, and efficiency in public management. By providing immutable and auditable records of transactions and interactions, distributed systems can reduce the risk of corruption and fraud while streamlining operations and improving service delivery. However, challenges such as interoperability issues, data privacy concerns, and regulatory complexities pose significant hurdles to adoption. Nevertheless, the adoption of distributed systems presents opportunities for innovation and collaboration, enabling governments to develop novel solutions for governance, resource management, and service delivery. Moreover, distributed systems raise important ethical and societal considerations, emphasizing the need for inclusivity, equity, and social justice in their design and deployment. At last, while challenges remain, the integration of distributed systems holds promise for building more resilient, responsive, and inclusive governance systems that better serve the needs of citizens and society as a whole.
Parallel Processing in Distributed and Hybrid Cloud-Fog Architectures: A Systematic Review of Scalability and Efficiency Strategies Ihsan, Rasheed; Zeebaree, Subhi R. M.
The Indonesian Journal of Computer Science Vol. 14 No. 1 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i1.4661

Abstract

In distributed computing, hybrid cloud-fog architectures have become a revolutionary concept for tackling the pressing issues of latency, scalability, and energy efficiency. These systems allow real-time data processing closer to end users by fusing the localized capabilities of fog computing with the centralized capacity of cloud computing. This makes them especially useful for latency-sensitive applications like smart cities, healthcare, and the Internet of Things. The technological developments, application areas, and difficulties related to hybrid systems are all examined in this study's methodical analysis of the body of existing research. With a focus on utilizing technologies like SDN, NFV, and AI-driven optimization frameworks, key focus areas include resource management, dynamic job allocation, privacy-preserving procedures, and scaling tactics. Although hybrid designs show great promise for increasing system responsiveness and efficiency, unresolved problems including resource allocation complexity, privacy concerns, and interoperability underscore the need for more study. This work offers actionable recommendations to address these gaps, including standardization of communication protocols, integration of advanced AI techniques, and the development of energy-efficient designs. The findings lay a strong foundation for advancing hybrid cloud-fog systems and ensuring their broader adoption across diverse industries.
Bridging the Gap: Integrating Organizational Change Management with IT Project Delivery Zangana, Hewa Majeed; Ali, Natheer Yaseen; Zeebaree, Subhi R. M.
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4450

Abstract

In today's rapidly evolving technological landscape, the successful implementation of IT projects is increasingly contingent upon effective organizational change management (OCM). This research paper explores the intersection of OCM and IT project delivery, proposing a comprehensive framework that integrates these two critical domains. Through a review of existing literature and analysis of case studies, we identify key challenges and best practices for synchronizing OCM strategies with IT project management processes. Our findings reveal that the alignment of OCM with IT project delivery not only enhances project success rates but also promotes sustainable organizational transformation. This integrated approach ensures that technological advancements are supported by a well-prepared workforce, thereby minimizing resistance and maximizing adoption. The paper concludes with practical recommendations for practitioners aiming to bridge the gap between OCM and IT project delivery, ultimately fostering a more agile and resilient organizational environment.
Systematic Review of Decentralized and Collaborative Computing Models in Cloud Architectures for Distributed Edge Computing Zangana, Hewa Majeed; Mohammed, Ayaz khalid; Zeebaree, Subhi R. M.
Sistemasi: Jurnal Sistem Informasi Vol 13, No 4 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i4.4169

Abstract

This systematic review paper delves into the evolving landscape of cloud architectures for distributed edge computing, with a particular focus on decentralized and collaborative computing models. The aim of this systematic review is to synthesize recent advancements in decentralization techniques, collaborative scheduling, federated learning, and blockchain integration for edge computing. As edge computing becomes increasingly vital for supporting the Internet of Things (IoT) and other distributed systems, innovative strategies are needed to address challenges related to latency, resource management, and data security.The key findings highlight the benefits of latency-aware task management, autonomous serverless frameworks, and the collaborative sharing of computational resources. Additionally, the integration of federated learning and blockchain technologies offers promising solutions for enhancing data privacy and resource allocation. The versatility of edge computing is showcased through its applications in diverse domains, including healthcare and smart cities. Future research directions emphasize the need for optimized resource management, improved security protocols, standardization efforts, and application-specific innovations. By providing a comprehensive review of these developments, this paper underscores the critical role of decentralized and collaborative models in advancing the capabilities and efficiency of edge computing systems.
Enhancing AdaBoost Performance: Comparative Analysis of CPU Parallel Processing on Breast Cancer Classification Ashqi Saeed, Vaman; Zeebaree, Subhi R. M.
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3793

Abstract

The implementation of time-sharing across processes in a real-time way has the potential to increase the execution efficiency of multiprocessor systems like the one described above. The system is able to carry out tasks that make use of a large number of processors in an effective way as a result of this. The aim of this research is to design a system with two primary goals: to enhance accuracy and to minimise the amount of time necessary with processing. This will be accomplished by integrating the ADABoost model with the decision tree algorithm. Furthermore, the statistics unambiguously demonstrate that the accuracy remains the same regardless of whether or not the central processing unit (CPU) makes use of parallel processing, which suggests that there is no variation in parallelization. As a consequence of this, there is a direct connection between the amount of time that is spent and an increase in the amount of parallel processing that is carried out by the central processing unit pertaining to the breast cancer dataset that is being investigated. This research was carried out using Python, which was the programming language that was used for the coding technique that was carried out during the course of its execution.
Co-Authors Abd Alnabe, Nisreen Abdi, Abdo Abdulrahman, Saman Mohammed Ahmad, Hawar Bahzad Al-Atroshi, Chiai Ali, Nabeel N. Ali, Natheer Yaseen Almufti, Saman M Asaad, Renas Rajab Ashqi Saeed, Vaman Atrushi, Diler Fadhil, Jawaher Ferzo, Barwar Hani, Ahmed Alaa Ihsan, Rasheed Majeed, Dilovan Asaad Merseedi, Karwan Jameel Mohammed, Ayaz Khalid Sadiq, Bareen Haval Sallow, Amira Bibo Taher, Hanan Zangana, Hewa Majeed