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All Journal Jurnal Teknoinfo JISA (Jurnal Informatika dan Sains) International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) International Journal of Research and Applied Technology (INJURATECH) The Indonesian Journal of Computer Science
Abdulazeez, Adnan Mohsin
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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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Classification of Ultra Sound Images Breast Cancer Based on Deep Learning: A review Abdulazeez, Adnan Mohsin; Alnabi, Nisreen Luqman Abd
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 2 (2024): Vol 4 No 2 (2024)
Publisher : Universitas Komputer Indonesia

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Abstract

Breast cancer is the second most common cause of mortality for women, after lung cancer. Women's death rates can be decreased if breast cancer is identified early. The artificial intelligence model has the ability to predict breast cancer with the same level of accuracy as an experienced radiology technician. For early cancer detection, an automated approach is necessary because manual breast cancer diagnosis is time-consuming. Deep learning is a type of artificial intelligence that enables software applications to predict more accurate results without being explicitly programmed. The main objective of this paper is to evaluate the performance of a general deep learning algorithm (DLS) with human readers with varying degrees of breast imaging experience in order to train it to identify cancer of the breast on ultrasound pictures. Moreover, this study will examine five deep learning methods that have aided in breast cancer prediction, these are Convolutional Neural Network (CNN), Genetic Algorithm GA-CNN, Deep Belief Network (DBN), Computer Aided Diagnosis (CAD), and Generative Adversarial Networks (GAN). Our main goal is to identify the most appropriate and accurate algorithm for the prediction of breast cancer.
Feature Selection Methods of Gene Expression Based on Machine Learning: A Review Merceedi, Karwan Jameel; Abdulazeez, Adnan Mohsin
International Journal of Research and Applied Technology (INJURATECH) Vol. 5 No. 1 (2025): Vol 5 No 1 (2025)
Publisher : Universitas Komputer Indonesia

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Abstract

This article offers a thorough analysis of feature selection strategies that use machine learning to analyze gene expression data. In order to extract significant biological insights, the explosion of high-dimensional genomic data has required the invention and use of sophisticated analysis techniques. In this situation, feature selection is essential because it finds the most pertinent genes that have a major impact on the prediction ability of machine learning models. The paper examines a range of feature selection techniques, classifying them into filter, wrapper, and embedding approaches, each having special advantages and disadvantages. The importance of gene expression data in comprehending the molecular mechanisms underlying complicated diseases and biological processes. The difficulties presented by high-dimensional datasets are next explored, with a focus on feature selection as a means of enhancing model interpretability, lowering computational cost, and raising prediction accuracy. In order to shed light on the fundamental ideas and practical uses of well-known feature selection algorithms, the writers thoroughly examine a number of them, including Mutual Information, Relief, and Recursive Feature Elimination (RFE). Additionally, the study assesses these methods' performance critically across a range of datasets and experimental situations, emphasizing important factors like interpretability, scalability, and resilience. The paper also discusses new developments in feature selection, such as the incorporation of deep learning techniques, ensemble methods, and domain expertise. In order to fully realize the promise of gene expression data for biomedical research and clinical applications, the study ends with a discussion of the present issues and prospective future directions in the field. This discussion emphasizes the significance of creating reliable and understandable feature selection techniques. This thorough study will be an invaluable tool for practitioners, researchers, and bioinformaticians in the field of genomics as they navigate the challenging terrain of feature selection techniques in the context of machine learning-based gene expression analysis.
Ocular Disease Recognition Based on Deep Learning: A Comprehensive Review Jameel, Dilan; Abdulazeez, Adnan Mohsin
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.v13i3.3976

Abstract

This review article represents a major advance in the field of medical imaging and ophthalmology by exploring the critical role of deep learning in the detection and diagnosis of eye diseases. Early and accurate diagnosis becomes essential due to the frequency of ocular disorders that pose a significant risk to vision, including diabetic retinopathy, age-related macular degeneration, glaucoma, and cataracts. The need for more reliable automated solutions is highlighted by the limitations of traditional methods, despite their benefits, which include reliance on small datasets and manual feature analysis. Deep learning, a subset of machine learning, is becoming evident as a powerful tool that can interpret complex medical images and improve diagnostic accuracy without the need to extract human features. This article explores the evolution of deep learning applications in ophthalmology, highlighting the difficulties encountered such as interpretability of models and data quality and the creative solutions that have been found to overcome them. We highlight the revolutionary impact of deep learning in eye disease detection through an in-depth analysis of recent developments, providing insight into potential future research avenues that may further improve patient care in ophthalmology.
Integration of Machine Learning with Fog Computing for Health Care Systems Challenges and Issues: A Review Abdulazeez, Ali Hussein; Abdulazeez, Adnan Mohsin
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.v13i3.3986

Abstract

Fog computing, a distributed cloud computing model, extends the traditional cloud paradigm to the network's edge, reducing latency and alleviating congestion. It addresses challenges in classical cloud architectures exacerbated by real-time IoT applications, which produce massive amounts of data that traditional cloud computing struggles to process due to limited bandwidth and high propagation delays. Fog computing is crucial in latency-sensitive applications like health monitoring and surveillance, where it processes vast volumes of data, minimizing delays and boosting performance. This technology brings computation, storage, monitoring, and services closer to the end-user, enhancing real-time decision-making capabilities. This paper presents the challenges of IoT applications. It also demonstrates the role of two emerging technologies fog computing and machine learning in health care scenarios.
A Review on Deep Reinforcement Learning for Autonomous Driving Kamil, Zheen; Abdulazeez, Adnan Mohsin
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.v13i3.4036

Abstract

Autonomous driving technology has gained significant attention, offering opportunities to modernize transportation systems worldwide. Deep reinforcement learning (DRL) has emerged as a robust approach to design smart driving policies for intricate and changeable environments. This paper provides a detailed investigation of state-of-the-art DRL methodologies that are effectively applied to autonomous driving. It begins by providing a clear explanation of the fundamental concepts of deep learning and reinforced learning, highlighting their application for control of self-driving vehicles. Consequently, the paper presents an overview of various DRL algorithms, including Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), and Actor-Critic methods, describing their structures, training approaches, and applications in autonomous driving situations. Recent advancements in DRL research, such as domain adaptation, imitation learning, and meta-learning, have also been addressed in the study, with an investigation of their potential implications for autonomous driving. Via a thorough assessment of current literature, key trends, challenges, and research directions have been identified for exploiting DRL in autonomous car development. This review intends to provide a comprehensive understanding of the current and future possibilities of DRL for self-driving vehicles to researchers, practitioners, and enthusiasts.
Leukemia Detection and Classification Based on Machine Learning and CNN: A Review Rasheed, Hakar Hasan; Abdulazeez, Adnan Mohsin
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.v13i3.4044

Abstract

Advancements in data mining methods have significantly improved disease diagnosis, particularly in the realm of leukemia detection. Leukemia, a complex cancer affecting white blood cells, poses significant challenges in diagnosis and management due to its diverse manifestations. Various machine learning algorithms, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), Random Forests (RF), Decision Trees (DTs), K-Nearest Neighbors (K-NN), Logistic regression (LR) and Naïve Bayes (NB) classifiers, have been employed to accurately classify leukemia cases based on diverse datasets and image analyses. This paper provides a comprehensive overview and comparison of these classification techniques, highlighting their effectiveness in diagnosing different leukemia subtypes. Additionally, the paper discusses the methodology and findings of several studies focusing on leukemia detection, emphasizing the significance of machine learning in enhancing diagnostic accuracy and treatment planning. Furthermore, it explores the challenges and future directions in leveraging machine learning for leukemia diagnosis, including the need for standardized datasets, algorithm refinement, and integration with clinical data for personalized treatment strategies.
Prostate Cancer: MRI Image Detection Based on Deep Learning: A Review Alhamzo, Jelan Salih Jasim; Abdulazeez, Adnan Mohsin
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.v13i3.4045

Abstract

This comprehensive study delves into the transformative role of artificial intelligence (AI) and deep learning (DL) in the realm of prostate cancer care, an issue of paramount importance in men’s health worldwide. Prostate cancer, marked by the unchecked growth of cells in the prostate gland, poses risks of tumor formation and eventual metastasis. The crux of combating this disease lies in its early detection and precise diagnosis, for which traditional screening methodologies like Prostate-Specific Antigen (PSA) tests and multiparametric Magnetic Resonance Imaging (mp-MRI) are fundamental. The introduction of AI and DL into these diagnostic avenues has been nothing short of revolutionary, enhancing the precision of medical imaging and significantly reducing the rates of unnecessary biopsies. The advancements in DL, particularly through the use of convolutional neural networks (CNNs) and the application of multiparametric MRI, have been instrumental in improving the accuracy of diagnoses, foreseeing the progression of the disease, and tailoring individualized treatment regimens. This paper meticulously examines various DL models and their successful application in the detection, classification, and segmentation of prostate cancer, establishing their superiority over conventional diagnostic techniques. Despite the promising horizon these technologies offer, their implementation is not without challenges. The requisite for specialized expertise to handle these advanced tools and the ethical dilemmas they present, such as data privacy and potential biases, are significant hurdles. Nevertheless, the potential of AI and DL to inaugurate a new chapter in prostate cancer management is undeniable. The emphasis on interdisciplinary collaboration among scientists, clinicians, and technologists is crucial for pushing the boundaries of current research and clinical practice, ensuring the ethical deployment of AI and DL technologies. This collaborative effort is vital for realizing the full potential of these innovations in providing more accurate, efficient, and patient-centric care in the fight against prostate cancer, heralding a future where the burden of this disease is significantly mitigated.
Sentiment Analysis Based on Machine Learning Techniques: A Comprehensive Review Hamid, Ari Ibrahim; Abdulazeez, Adnan Mohsin
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.v13i3.4049

Abstract

In the landscape of digital communication, sentiment analysis stands out as a pivotal technology for deciphering the vast troves of unstructured text generated online. When integrated with machine learning, sentiment analysis transforms into a powerful tool capable of distilling insights from complex human emotions and opinions expressed across social media, reviews, and forums. This review paper embarks on a thorough exploration of the integration of machine learning techniques with sentiment analysis, shedding light on the latest advancements, challenges, and applications spanning various sectors including public health, finance, and consumer behavior. It meticulously examines the role of machine learning in elevating sentiment analysis through improved accuracy, adaptability, and depth of analysis. Furthermore, the paper discusses the implications of these technologies in understanding consumer sentiment, tracking public health trends, and forecasting market movements. By synthesizing findings from seminal studies and cutting-edge research, this review not only charts the current landscape but also forecasts the trajectory of sentiment analysis. It underscores the necessity for ongoing innovation in machine learning models to keep pace with the evolving digital discourse. The insights presented herein aim to guide future research endeavors, highlight the transformative impact of machine learning on sentiment analysis, and outline the potential for new applications that could benefit society at large.
A Review on Utilizing Data Mining Techniques for Chronic Kidney Disease Detection Hassan, Shivan Hussein; Abdulazeez, Adnan Mohsin
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.v13i3.4062

Abstract

This comprehensive study delves into the application of machine learning (ML) and data mining techniques for the prognosis and diagnosis of Chronic Kidney Disease (CKD), a significant global health concern characterized by the gradual loss of kidney function. Through a detailed examination of various predictive models, the research evaluates the efficacy of different ML algorithms and data mining methodologies in classifying and diagnosing CKD. Utilizing datasets from the UCI machine learning repository and other sources, this study explores a range of ML algorithms-including logistic regression, decision trees, support vector machines, random forest, and deep learning networks-alongside feature selection techniques to enhance prediction accuracy and facilitate early diagnosis. Despite facing challenges such as dataset limitations and the need for external validation, the findings reveal remarkable potential in using ML and data mining to improve CKD diagnosis, with some models achieving accuracy rates exceeding 99%. The research underscores the critical role of technology in advancing CKD diagnosis and management, paving the way for more personalized and effective healthcare solutions.
Transfer Learning in Machine Learning: A Review of Methods and Applications Ali, Ali Hamad; Abdulazeez, Adnan Mohsin
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.v13i3.4068

Abstract

Transfer learning has gained significant traction and popularity in the field of machine learning due to its wide range of potential applications. This review article offers a thorough examination of transfer learning techniques and their wide-ranging applications in several fields. This text provides a thorough evaluation of the literature, focusing on important research and the methodology used. Furthermore, a comparative table highlighting transfer learning research across several areas provides valuable insights into the wide range of applications. The inclusion criteria were centred on recent articles published within the past five years that comprehensively examined transfer learning methodologies, applications, frameworks, problems, and future directions. The review articles highlight the widespread use of transfer learning models, the effectiveness of data augmentation strategies, and the capability of transfer learning to tackle issues particular to different domains. Nevertheless, some constraints like as biases in the dataset, difficulties in interpreting the model, and problems with scalability have been recognised. These limitations provide opportunities for future research to focus on creating transfer learning algorithms that are more resilient and easier to read.
Co-Authors Abdi, Abdo Abdulazeez, Ali Hussein Ageed, Zainab Salih Alhamzo, Jelan Salih Jasim Ali, Ali Hamad Almufti, Saman M. Alnabi, Nisreen Luqman Abd Ashqi Saeed, Vaman Fadhil, Jawaher Ferzo, Barwar Hamid, Ari Ibrahim Hasan, Dathar Hasan, Mayaf Hasan, Shereen Sadiq Hassan, Shivan Hussein Hussein, Nashwan Ibrahem, Ali Hikmat Ismaeel, Hivi Kamal Jalal, Dimen Jameel, Dilan Kamil, Zheen Merceedi, Karwan Jameel Miran, Ara Zozan Musa, Jihan Rasheed, Hakar Hasan Salih, Sardar Sulaiman , Sulaiman Muhammed Yaqoub, Karin Younis Younis, Aqeel Hanash Zeebaree, Imad Majed