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All Journal The Indonesian Journal of Computer Science
Ihsan, Rasheed
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Computer Science & IT Electrical & Electronics Engineering Engineering

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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 (IJCS)
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.
A Facial Expression Prediction Based on Pre-Trained ResNet50 and SVM Ihsan, Rasheed; Abdulazeez , Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 14 No. 5 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

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

Abstract

Facial expression prediction has become a vital area in computer vision, with applications spanning healthcare, security, and human-computer interaction. This study proposes a robust system for binary facial expression prediction using a combination of classical computer vision techniques and deep learning. The system employs the Haar Cascade algorithm for face detection and ResNet50, a 50-layer deep residual network, for feature extraction. Support Vector Machines (SVM) with a radial basis function kernel are used for classification. Using the 4,000 tagged images from the GENKI dataset, preprocessing and data augmentation improved the model's capacity for generalization. Experimental results demonstrate the system’s effectiveness, achieving a test accuracy of 94.65%. The robust integration of classical and modern techniques ensures computational efficiency while maintaining high performance. For real-world applications, this method provides a scalable solution that tackles issues including lighting fluctuation, position, and expression variation.
Co-Authors Abdulazeez , Adnan Mohsin Zeebaree, Subhi R. M.