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All Journal International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) International Journal of Research and Applied Technology (INJURATECH)
Abdulazeez, Adnan M
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Developed Clustering Algorithms for Engineering Applications: A Review Zangana, Hewa Majeed; Abdulazeez, Adnan M
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 4 No. 2 (2023): INJIISCOM: VOLUME 4, ISSUE 2, DECEMBER 2023
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v4i2.11636

Abstract

Clustering algorithms play a pivotal role in the field of engineering, offering valuable insights into complex datasets. This review paper explores the landscape of developed clustering algorithms with a focus on their applications in engineering. The introduction provides context for the significance of clustering algorithms, setting the stage for an in-depth exploration. The overview section delineates fundamental clustering concepts and elucidates the workings of these algorithms. Categorization of clustering algorithms into partitional, hierarchical, and density-based forms lay the groundwork for a comprehensive discussion. The core of the paper delves into an extensive review of clustering algorithms tailored for engineering applications. Each algorithm is scrutinized in dedicated subsections, unraveling their specific contributions, applications, and advantages. A comparative analysis assesses the performance of these algorithms, delineating their strengths and limitations. Trends and advancements in the realm of clustering algorithms for engineering applications are thoroughly examined. The review concludes with a reflection on the challenges faced by existing clustering algorithms and proposes avenues for future research. This paper aims to provide a valuable resource for researchers, engineers, and practitioners, guiding them in the selection and application of clustering algorithms for diverse engineering scenarios.
Harnessing Machine Learning for Crypto-Currency Price Prediction: A Review Ali, Zeravan Arif; Abdulazeez, Adnan M
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 1 (2024): International Journal of Research and Applied Technology (INJURATECH)
Publisher : Universitas Komputer Indonesia

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Abstract

Despite their recent inception, cryptocurrencies have become globally recognized for their dispersal, diversity, and high market capitalization. This volatility developed into a challenge for investors looking to predict price movements. Thus, it has become an attractive investment opportunity. To increase prediction accuracy, researchers integrate machine learning algorithms with technical indicators. In this review, a systematic comparison has been employed to identify efficient algorithms, and researchers have employed statistical measures to make short- and long-term forecasts of decentralized money prices. Moreover, the paper highlights the results of researchers based on machine learning and deep learning methodologies on multiple types of cryptocurrencies like Bitcoin, Ethereum, Monero, etc. Lastly, the work emphasizes the limitations, gaps, and challenges facing researchers to take advantage of existing literature for future works.
Facial Emotion Recognition Based on Deep Learning: A Review Ali, Nabeel N; Abdulazeez, Adnan M
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 1 (2024): International Journal of Research and Applied Technology (INJURATECH)
Publisher : Universitas Komputer Indonesia

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

Abstract

Numerous domains, including safety, health, and human-machine interfaces, have garnered significant attention from researchers. Within this field, there is a notable interest in developing methodologies for interpreting and encoding facial expressions, as well as extracting pertinent features for more accurate computer-based predictions. Leveraging the remarkable advancements in deep learning, various architectural approaches are explored to enhance performance outcomes. The primary objective of this paper is to conduct an examination of recent research endeavors pertaining to automatic facial emotion recognition (FER) through the utilization of deep learning techniques. We emphasize the treatment of these contributions, elucidate the architectural frameworks employed, and outline the databases that have been utilized. Additionally, we present a comprehensive assessment of the progress achieved by comparing the methodologies proposed and the corresponding results obtained. This paper aims to provide valuable insights and guidance to researchers in this field by reviewing recent developments and suggesting avenues for further enhancements
Co-Authors Ali, Nabeel N Ali, Zeravan Arif Zangana, Hewa Majeed