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p-Index From 2021 - 2026
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All Journal The Indonesian Journal of Computer Science
Abdulazeez , Adnan M.
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering

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Facial Beauty Standards Predictions Based on Machine Learning: A Comparative Analysis Sadiq, Bareen Haval; Abdulazeez , Adnan M.
The Indonesian Journal of Computer Science Vol. 13 No. 1 (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.v13i1.3709

Abstract

This study uses a variety of machine learning and classification methods to anticipate the Facial Beauty Standards. The Accuracy of five different models—Random Forest, Logistic Regression, Support Vector Machine (SVM), KNN, and decision tree—were used to analyses each one. There were noticeable differences in the models' performances. In particular, the Logistic Regression and SVM methods demonstrate almost perfect accuracy, followed closely by random forest and KNN. This study gives insight into how well different models perform in comparison and emphasizes the benefits and drawbacks of each in terms of predicting face beauty standards.
A Review on Decision Tree Algorithm in Healthcare Applications Abdulqader, Hozan Akram; Abdulazeez , Adnan M.
The Indonesian Journal of Computer Science Vol. 13 No. 3 (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.v13i3.4026

Abstract

Decision tree algorithms have emerged as a pivotal tool in healthcare, offering substantial benefits in diagnostics, prognosis, and health monitoring. This paper provides a comprehensive review of decision tree applications in medical settings, highlighting their ability to simplify complex decision-making processes and improve accuracy in disease diagnosis and outcome prediction. By dissecting various research studies and clinical implementations, we demonstrate the versatility of decision trees in handling diverse datasets—from genetic markers to electronic health records and real-time patient data. This review also explores the integration of decision trees with machine learning techniques to enhance diagnostic procedures and prognostic evaluations, underscoring the significant role of these algorithms in advancing personalized medicine and public health strategies. Challenges such as data sensitivity, privacy concerns, and the need for large annotated datasets are discussed to provide a balanced perspective on the capabilities and limitations of decision tree algorithms in healthcare. Through this analysis, we aim to illuminate the transformative potential of decision trees in improving patient care and streamlining healthcare operations.
Machine Learning-Based Prediction of Thalassemia: A Review Abdulkarim, Dawlat; Abdulazeez , Adnan M.
The Indonesian Journal of Computer Science Vol. 13 No. 3 (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.v13i3.4035

Abstract

This article presents a comprehensive systematic review of recent advancements in machine learning (ML) applications for diagnosing Thalassemia, a genetic hematologic disorder. Focusing on studies from the last five years, this review highlighted significant technological advancements in ML, including the use of predictive modeling, image analysis, and deep learning algorithms, which have considerably improved the accuracy and efficiency of Thalassemia diagnosis. The review evaluates the application of various ML models in analyzing extensive biomedical data, which significantly enhances patient management and treatment outcomes. Key challenges such as data diversity, model transparency, and the need for robust training datasets are discussed, along with the integration of ML into existing clinical workflows. The potential transformative impact of ML in hematology is underscored, critically evaluating its effectiveness and ongoing developments in the field. This review aims to provide insights into the current research trends and future directions in the use of ML for the diagnosis and management of Thalassemia and other similar hematological disorders.
Co-Authors Abdulkarim, Dawlat Abdulqader, Hozan Akram Sadiq, Bareen Haval