Article Per Year (5 Year)
p-Index From 2021 - 2026
0.751
P-Index
This Author published in this journals
All Journal International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) The Indonesian Journal of Computer Science
Mohsin Abdulazeez, Adnan
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Detection of SQL Injection Attacks Based on Supervised Machine Learning Algorithms: A Review Salih Abdullah, Hilmi; Mohsin 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.v5i2.12731

Abstract

In the ever-changing world of cybersecurity, it is becoming more important to ensure integrity of web applications as well as securing sensitive data. Among a variety of vulnerabilities, SQL injection is considered a significant risk with severe consequences. Addressing this crucial threat has always attracted the researchers to explore various approaches to identify and detect SQL injection attacks. The machine learning has captured the attention of the researchers to explore its potential due to its success in several different fields and the limitation of other rule-based approaches. This study provides a comprehensive review on a variety of the most recent researches that have been carried out using supervised learning algorithms. The study reveals that machine learning has a huge potential in the process of identification and detection of SQL injection attacks.
A Review of Text Classification Based on ML & Data Mining Algorithms Mustafa, Ashraf Atam; Mohsin 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.v13i3.4027

Abstract

In the digital era, the field of text classification has experienced transformative growth through the application of Machine Learning (ML) and Data Mining (DM) algorithms. This review traces the evolution from traditional data mining methods to sophisticated ML strategies that significantly enhance the analysis and categorization of textual data. We discuss pivotal technologies including Bayesian classifiers, Support Vector Machines (SVM), and contemporary advances such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The integration of Natural Language Processing (NLP) techniques is highlighted for their critical role in enriching semantic analysis capabilities, a necessity for effective text classification. Additionally, the paper addresses challenges like handling high-dimensional data, dealing with imbalanced datasets, and confronting ethical issues such as bias and privacy in automated systems. By synthesizing the latest research, this review identifies current gaps, proposes practical solutions, and forecasts future trends in text classification to support ongoing research and application across various sectors.
Review paper Deep and Machine Learning Algorithms for Diagnosing Brain Cancer and Tumors Rebar, Zhehat; Mohsin 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.v13i3.4028

Abstract

In the rapidly evolving field of medical diagnostics, the integration of deep learning (DL) and machine learning (ML) technologies has dramatically advanced the accuracy and efficiency of brain cancer and tumor diagnosis using magnetic resonance imaging (MRI). This review explores the transformative impact of these technologies, highlighting their role in enhancing tumor detection, classification, and early diagnosis interventions. DL and ML algorithms have significantly improved the analysis of complex imaging data, enabling more precise and faster diagnostic decisions, which are crucial for effective patient management and treatment planning. This review encompasses a broad spectrum of studies that illustrate the capabilities of these computational techniques in handling large datasets, learning intricate patterns, and achieving a high diagnostic performance. By delving into various algorithmic approaches and their clinical implications, this study underscores the importance of continued advancements and the integration of AI technologies in the field of oncology, aiming to foster better patient outcomes through innovative diagnostic tools.
Diabetes prediction based on Ensemble Methods: A Review Mosa, Jihan; Mohsin Abdulazeez, Adnan
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 | DOI: 10.33022/ijcs.v14i5.5006

Abstract

Diabetes is a global health crisis, and early prediction is critical to preventing serious complications. Recent research shows that ensemble machine learning methods and deep learning architectures significantly improve diabetes prediction accuracy. Ensemble methods such as random forest, XGBoost, bagging, boosting, and stacking utilize multiple algorithms to capture diverse data patterns and consistently outperform traditional single classifiers. In parallel, deep learning models, such as convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and hybrid CNN-LSTM architectures, excel at identifying complex temporal and spatial relationships. These techniques are widely applied to benchmark datasets, such as the Pima Indian diabetes data and other repositories at the University of California, Irvine, and are evaluated through metrics including area under the curve (AUC-ROC), precision, and recall. Challenges remain—particularly computational cost and model interpretabilitybut both approaches deliver superior accuracy and reliability. By integrating current evidence, this overview highlights the potential of ensemble learning and deep learning methods to enable earlier and more accurate detection of diabetes and enhance personalized healthcare solutions.
Co-Authors Mosa, Jihan Mustafa, Ashraf Atam Rebar, Zhehat Salih Abdullah, Hilmi