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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.
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 (IJCS)
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.
Blockchain for Distributed Systems Security in Cloud Computing: A Review of Applications and Challenges Fadhil, Jawaher; Zeebaree, Subhi R. M.
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3794

Abstract

The blockchain is a technology that utilizes a decentralized and distributed ledger system to enhance security in cloud computing for distributed systems. It has gained significant attention in various applications, including the Internet of Things (IoT) and cloud computing. However, the blockchain has scalability limitations that restrict its ability to handle different types of transactions effectively. On the other hand, cloud computing provides the availability of shared computer system resources on demand, but it faces challenges related to automation, process management, policy, and others. By combining blockchain technology with cloud computing in a unified system, it is possible to improve data integrity, resource management, pricing, fair compensation, and resource allocation. This article examines the applications and challenges of blockchain, emphasizing how it ensures data integrity, transparency, and resistance to tampering. It also explores various use cases to address obstacles like scalability issues and interoperability concerns, providing a comprehensive overview of the intersection between blockchain, distributed systems, and cloud computing security. The integration of cloud computing and blockchain is important for business applications because it offers advantages in terms of privacy, security, and service support. This review provides an extensive and up-to-date summary of the integration of cloud computing and blockchain, highlighting its significance in business contexts.
Parallel Processing Impact on Random Forest Classifier Performance: A CIFAR-10 Dataset Study Sadiq, Bareen Haval; Zeebaree, Subhi R. M.
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3803

Abstract

Using the CIFAR-10 dataset, this research investigates how parallel processing affects the Random Forest method's machine learning performance. Accuracy and training time are highlighted in the study as critical performance indicators. Two cases were studied, one with and one without parallel processing. The results show the strong prediction powers of the Random Forest algorithm, which continues to analyze data in parallel while retaining a high accuracy of 97.50%. In addition, training times are notably shortened by parallelization, going from 0.6187 to 0.4753 seconds. The noted increase in time efficiency highlights the importance of parallelization in carrying out activities simultaneously, which enhances the training process's computational efficiency. These results provide important new information about how to optimize machine learning algorithms using parallel processing approaches.
Embracing Distributed Systems for Efficient Cloud Resource Management: A Review of Techniques and Methodologies Abdi, Abdo; Zeebaree, Subhi R. M.
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3806

Abstract

The development of parallel computing, distributed computing, and grid computing has introduced a new computing model, combining elements of grid, public computing, and SaaS. Cloud computing, a key component of this model, assigns computing to distributed computers rather than local computers or remote servers. Research papers from 2017 to 2023 provide an overview of the advancements and challenges in cloud computing and distributed systems, focusing on resource management and the integration of advanced technologies like machine learning, AI-centric strategies, and fuzzy meta-heuristics. These studies aim to improve operational efficiency, scalability, and adaptability in cloud environments, focusing on energy efficiency and cost reduction. However, these advancements also present challenges, such as implementation complexity, adaptability in diverse environments, and the rapid pace of technological advancements. These issues necessitate practical, efficient, and forward-thinking solutions in real-world settings. The research conducted between 2017 and 2023 highlights the dynamic and rapidly evolving field of cloud computing and distributed systems, providing valuable guidance for ongoing and future research. This body of work serves as a crucial reference point for advancing the field and emphasizing the need for practical, efficient, and forward-thinking solutions in the ever-evolving landscape of cloud computing and distributed systems.
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