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Credit Card Fraud Detection Based on Machine Learning Classification Algorithm Naman, Bareq Mardan; Adnan Mohsin Abdulazeez
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.v13i3.3996

Credit risk analysis is a critical process in the financial industry, as it helps lenders assess the likelihood of borrowers defaulting on their loans. With the advent of machine learning algorithms, there has been a growing interest in leveraging these techniques for more accurate and efficient credit risk prediction. Traditional credit risk models often rely on manual processes and limited data sources, resulting in potential biases and inaccuracies. Additionally, the rapid growth of credit card usage and the increasing complexity of financial transactions have made it challenging to accurately assess credit risk using conventional methods. This review paper aims to provide a comprehensive overview of machine learning algorithms used for credit risk prediction in the context of credit card lending. It explores classification techniques and their applications in credit risk analysis. The paper also discusses the challenges and limitations associated with these algorithms, including data quality, overfitting, and interpretability.

Human Gait Recognition Based on Deep Learning: A Review Atrushi, Diler; Adnan Mohsin Abdulazeez
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.v13i1.3719

Human gait recognition as a branch of biometric identification, has witnessed remarkable progress in recent years, thanks to the integration of deep learning techniques. This paper presents a comprehensive review of the latest advancements in the field, specifically focusing on the transformative role of deep learning methodologies. Recent research papers highlight novel approaches in gait recognition, including deferent models proposed that is consisted of using more than one approach together to increase the accuracy. Subsequently, we undertake a comprehensive investigation of the most relevant literature and present an analysis of gait recognition techniques employing deep learning. We discuss the models, systems, accuracy, applications, and datasets utilized in these studies, aiming to outline and structure the research landscape and literature in this domain. Methods for acquiring gait data are distinguished between capturing video frame, radar signals, or from wearable sensors as well as from the available online datasets that are large-scale and significantly contributed to the advancement of deep learning models. The study also shows the verity applications that can utilize human gait recognition to achieve certain goals.

Predictions of Early Hospitalization of Diabetes Patients Based on Deep Learning: A Review: Machine Learning Al-Atroshi, Chiai; Adnan Mohsin Abdulazeez
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.v13i1.3738

Unmanaged diabetes can result in a number of complications that need to be hospitalised. Diabetes is a chronic disorder. With preventive treatment, outcomes may be improved through early prediction of diabetes-related hospitalisation using data-driven algorithms. Here, we examine recent advances in deep learning methods for anticipating readmissions and unexpected hospital stays in adult patients with diabetes. Firstly, we present an overview of the main factors that indicate the need for hospitalisation due to diabetic complications. The research on hospitalisation risk prediction using structured health data, such as demographics, prescriptions, test results, etc., using conventional machine learning techniques is then summarised. Using data from insurance claims and electronic health records, we then examine current research that has used deep learning models. It is emphasised that longitudinal data can be included using recurrent neural networks. Model architectures, training methods, and important data modalities are covered. The assessment also addresses deployment difficulty and the model's performance assessment on real-world datasets. Ultimately, potential paths forward include hybrid models that integrate data diversity, explainable predictions, and clinical knowledge. In order to provide evidence-based predictions of the risk of hospitalisation and readmission for diabetes patients, we examine the potential and constraints of recently developed deep learning algorithms in this review.

Enhanced Intrusion Detection System Using Deep Learning Algorithms : A Review Andy Victor Amanoul; Adnan Mohsin Abdulazeez
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.v13i3.4002

Intrusion Detection Systems (IDS) are crucial for protecting network infrastructures from advanced cyber threats. Traditional IDS, largely reliant on static signature detection, fail to effectively counter novel cyber attacks, leading to high false positive rates and missed zero-day exploits. This study investigates the integration of deep learning technologies into IDS to enhance their detection capabilities. By employing advanced deep learning frameworks, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) and other algorithms , the research explores their efficacy in identifying complex data patterns and anomalies. Furthermore, the use of big data analytics is assessed for its potential to significantly augment the predictive power of these systems, aiming to set new benchmarks in cybersecurity defenses tailored for contemporary threats.

A Comparative Study of Generative Adversarial Networks (GANs) in Medical Image Processing: A Review Marwa M Abdulqadir; Adnan Mohsin Abdulazeez
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): 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.v15i1.5047

Generative Adversarial Networks (GANs) have come to be as powerful in medical image processing with massive benefits in image quality, fusion, classification and segmentation tasks. This paper displays an in-depth analysis of GAN structures and their use cases in medical image analysis for data augment, anomaly detection, cross- modality synthesis, super-resolution, and image reconstruction. As the demand for automated diagnostic systems has increased, GANs provide an efficient way to synthesize realistic medical images, particularly in domains with limited data availability. In this review, we discuss new developments and issues on how to apply GANs to accurate and effective medical image analysis. Moreover, this work explores the strengths, limitations, and comparative performance of different GAN models across diverse datasets and clinical tasks. By identifying key differences among the GAN models, and analysing performance, this review will be a roadmap for future studies in developing GAN-based models for better diagnosis and health applications.

A Review on White Blood Cell Classification for Leukemia Diagnosis Using Deep and Transfer Learning Techniques Thamer, Dilan; Adnan Mohsin Abdulazeez
The Indonesian Journal of Computer Science Vol. 15 No. 1 (2026): 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.v15i1.5083

Leukemia is a severe hematological malignancy that disrupts normal blood cell function, primarily affecting white blood cells (WBCs). Early and accurate Classification of white blood cells (WBCs) is essential for facilitating the accurate diagnosis of leukemia, thereby improving patient outcomes and reducing treatment costs. This paper provides a comprehensive review of recent deep learning and transfer learning approaches applied to WBC classification and leukemia diagnosis. Various models, including Convolutional Neural Networks (CNNs), Vision Transformers (ViT), and hybrid techniques combining handcrafted and learned features, are examined. Performance metrics such as accuracy, sensitivity, specificity, and F1-score are discussed across multiple datasets like BCCD, ALL-IDB, and Kaggle repositories. The study highlights the strengths of different models, addresses challenges such as class imbalance and data scarcity, and outlines future directions like the integration of multimodal data and real-time deployment. This review serves as a valuable resource for researchers and clinicians aiming to develop intelligent, automated systems for hematological disease diagnosis.

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