Article Per Year (5 Year)
p-Index From 2021 - 2026
0.882
P-Index
This Author published in this journals
All Journal International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) International Journal of Research and Applied Technology (INJURATECH) The Indonesian Journal of Computer Science
M. Abdulazeez, Adnan
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Healthcare Diseases Classification Based on Machine Leaning Algorithms: A Review Mohammed, Ahmed Jameel; M. Abdulazeez, Adnan
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 5 No. 2 (2024): INJIISCOM: VOLUME 5, ISSUE 2, DECEMBER 2024
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v5i2.13581

Abstract

Researchers have increasingly focused on applying machine learning algorithms to enhance healthcare operations in the past few years. Machine learning has become increasingly popular and has shown to be a viable strategy for raising the standard of healthcare, preventing disease transmission and early disease detection, reducing hospital operational expenses, aiding government healthcare programs, and enhancing healthcare efficiency. This review offers a succinct and well-structured summary of machine learning research that has been done in the field of healthcare. Specifically, the emphasis is placed on the examination of non-communicable illnesses, which pose a significant risk to public health and rank among the primary contributors to global mortality. Moreover, the COVID-19 pandemic, which is among the world's deadliest illnesses and has recently been formally declared a public health emergency, is included. This study aims to assist health sector researchers in choosing appropriate algorithms. After conducting a comprehensive investigation, it was shown that the Decision Tree (DT), Gaussian Naive Bayes (GNB), and Random Forest (RF), algorithms had the highest performance in healthcare classification, achieving a remarkable accuracy rate of 100%. In most tests, the Random Forest (RF) and Support Vector Machine (SVM) demonstrated consistently better performance
Machine Learning Approaches for Heart Disease Detection: A Comprehensive Review A. Taher, Hanan; M. Abdulazeez, Adnan
International Journal of Research and Applied Technology (INJURATECH) Vol. 3 No. 2 (2023): International Journal of Research and Applied Technology (INJURATECH)
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injuratech.v3i2.12052

Abstract

This paper presents a comprehensive review of the application of machine learning algorithms in the early detection of heart disease. Heart disease remains a leading global health concern, necessitating efficient and accurate diagnostic methods. Machine learning has emerged as a promising approach, offering the potential to enhance diagnostic accuracy and reduce the time required for assessments. This review begins by elucidating the fundamentals of machine learning and provides concise explanations of the most prevalent algorithms employed in heart disease detection. It subsequently examines noteworthy research efforts that have harnessed machine learning techniques for heart disease diagnosis. A detailed tabular comparison of these studies is also presented, highlighting the strengths and weaknesses of various algorithms and methodologies. This survey underscores the significant strides made in leveraging machine learning for early heart disease detection and emphasizes the ongoing need for further research to enhance its clinical applicability and efficacy.
Comprehensive Classification of Iris Flower Species: A Machine Learning Approach Renas Rajab Asaad; M. Abdulazeez, Adnan
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.v13i1.3717

Abstract

This study employs robust machine learning techniques to comprehensively assess the classification of Iris flower species. This study investigates the effectiveness of several machine learning algorithms in reliably classifying Iris flower species by utilizing a dataset that includes crucial morphological attributes such as sepal length, sepal width, petal length, and petal width. The algorithms under consideration are Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost. Every algorithm has its own distinct methodology for classification, where Decision Trees offer clear interpretability and Random Forest and XGBoost offer strong and complex ensemble techniques. The primary aim of this study is to assess and contrast different algorithms, considering not only their classification accuracy but also significant performance metrics including precision, recall, F1-score, ROC AUC, and specificity. This research provides valuable insights into the capabilities and constraints of each methodology when implemented on a meticulously organized and defined botanical dataset. It is expected that the results of this study will contribute significantly to the fields of artificial intelligence and botanical taxonomy, highlighting the capacity of these methods to accurately identify and categorize plant species.
A Review of Bitcoin Price Prediction Based on Deep Learning Algorithms Tayib, Hanaa; M. Abdulazeez, Adnan
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.v13i2.3858

Abstract

This study provides a comprehensive analysis of the existing body of work on predicting the price of Bitcoin using deep learning techniques. It discusses the fundamental concepts behind deep learning and Bitcoin, including recurrent neural networks, convolutional neural networks, and long short-term memory networks. The study also examines the data sources used in training these models, including historical Blockchain transaction data, social media sentiments, and Bitcoin prices. The report also highlights the importance of metrics like mean absolute error, mean squared error, and root mean squared error for evaluating the effectiveness of various models. It also discusses future research topics, such as incorporating external factors into prediction models. The article offers valuable insights for academics, practitioners, and policymakers interested in cryptocurrency prediction.
Face Recognition Based on Deep Learning: A Comprehensive Review Dakhil, Nasreen; M. Abdulazeez, Adnan
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.v13i3.4037

Abstract

Face recognition technology has undergone transformative changes with the advent of deep learning techniques. This review paper provides a comprehensive examination of the development and current state of face recognition techniques influenced by deep learning. We begin by discussing the fundamental deep learning models that have dramatically enhanced the accuracy and efficiency of face recognition, highlighting pivotal architectures such as convolutional neural networks (CNNs) and autoencoders. Subsequent sections delve into the application of these models in various environments and challenges, such as different lighting conditions, occlusion, and facial expressions. We also address the integration of deep learning with emerging technologies such as 3D facial reconstruction and multimodal biometrics. Furthermore, the review explores the ethical, privacy, and bias concerns inherent in facial recognition systems, focusing on the need for responsible and fair practices in AI. Finally, future directions are suggested, focusing on the need for robust, adaptable, and ethical face recognition systems. This paper aims to provide an important resource for researchers and practitioners in the field of computer vision, providing insight into the technological advances and ongoing challenges in deep learning-based face recognition.
Co-Authors A. Taher, Hanan Dakhil, Nasreen Mohammed, Ahmed Jameel Renas Rajab Asaad Tayib, Hanaa