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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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A Hybrid Bird Mating Optimizer for Welded Beam Design Optimization Problem: Design Optimization Ibrahem, Ali Hikmat; Abdulazeez, Adnan Mohsin
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.v13i1.3721

Abstract

This study introduces a hybridization of the Bird Mating Optimizer (BMO) with Differential Evolution (DE). The Bird Mating Optimizer exhibits certain limitations, such as a slow convergence rate and a tendency to become trapped in local optima. To address these issues, a new method, BMO-DE, is proposed to enhance the performance of the BMO swarm intelligence algorithm. BMO-DE is a versatile swarm intelligence algorithm applicable to various engineering problems. In this research, it is specifically employed in the optimization of welded beam design, a type of problem characterized by numerous constraints. The penalty function approach is used to handle the constraints associated with welded beam design. Comparative analysis indicates that the proposed BMO-DE method outperforms other swarm intelligence algorithms in tackling this category of problems. Notably, the method demonstrates efficacy in finding optimal solutions with a low number of objective function evaluations, making it a potent and promising approach for addressing such problems.
Image Denoising Techniques Using Unsupervised Machine Learning and Deep Learning Algorithms: A Review Ferzo, Barwar; Abdulazeez, Adnan Mohsin
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.v13i1.3724

Abstract

The continuous evolution of imaging technologies has accentuated the demand for robust and efficient image denoising techniques. Unsupervised machine learning algorithms have emerged as promising tools for addressing this challenge. This review scrutinizes the efficacy, versatility, and limitations of various unsupervised machine learning approaches in the area of image denoising. The paper commences with a clarification of the foundational concepts of image denoising and the pivotal role unsupervised machine learning plays in enhancing its efficacy. Traditional denoising methods, encompassing filters and transforms, are briefly outlined, highlighting their insufficiencies in handling complicated noise patterns prevalent in modern imaging systems. Subsequently, the review delves into an exploration of unsupervised machine learning techniques tailored for image denoising. This includes an in-depth analysis of methodologies such as clustering deep learning. Each technique is surveyed for its architectural variation, adaptability, and performance in denoising diverse image datasets. Additionally, the review encompasses an evaluation of prevalent metrics used for quantifying denoising performance, discussing their relevance and applicability across varying noise types and image characteristics. Furthermore, it delineates the challenges faced by unsupervised techniques in this domain and charts prospective avenues for future research, emphasizing the fusion of unsupervised methods with other learning paradigms for heightened denoising efficacy. This review merges empirical insights, critical analysis, and future perspectives, serving as a roadmap for researchers and practitioners navigating the landscape of image denoising through unsupervised machine learning methodologies.
Bitcoin Price Prediction Using Hybrid LSTM-GRU Models Hussein, Nashwan; Abdulazeez, Adnan Mohsin
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.v13i1.3725

Abstract

Cryptocurrency price prediction is a challenging task due to the inherent volatility and complexity of the market. In this research, we propose a hybrid Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural network model for predicting Bitcoin prices. The model is implemented using the TensorFlow and Keras libraries and is evaluated on historical Bitcoin price data obtained from Yahoo Finance. Our approach aims to leverage the strengths of both LSTM and GRU architectures to enhance the accuracy of price predictions. The results suggest that the proposed hybrid LSTM-GRU model holds promise for effectively capturing the complex dynamics of cryptocurrency markets, addressing the challenges associated with traditional time-series analysis techniques.
Classification of Diabetic Retinopathy Images through Deep Learning Models - Color Channel Approach: A Review Salih, Sardar; Abdulazeez, Adnan Mohsin
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.v13i1.3726

Abstract

On a global scale diabetic retinopathy, or DR, is the most common cause of vision loss. Blindness can be prevented with prompt treatment and early identification with retinal screening. Automated analysis of fundus imagery is growing prominently as a means of increasing screening efficiency, thanks to the development of deep learning. This work focuses on deep learning methods for automatic DR severity grading using color channel information. First, we give some basic information on the etiology and color features of DR lesions. Next, a novel support for deep learning technique that use unprocessed color photos as input for comprehensive feature learning. A review is mentioned on color space encodings, data augmentation methods. A summary of the evaluation parameters and public databases that were utilized to benchmark DR techniques are provided. The objective of how color channel information in retinal pictures can be efficiently utilized by deep learning models for automated DR screening has been discussed with statistical support.
OCT Images Diagnosis Based on Deep Learning – A Review Abdi, Abdo; Abdulazeez, Adnan Mohsin
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.v13i1.3731

Abstract

The recent advancements in deep learning technology have significantly transformed the field of medical imaging, namely in the diagnosis of ocular illnesses. The progress made in this field has improved the capacity to extract and evaluate intricate characteristics in images, with Optical Coherence Tomography (OCT) playing a crucial role. OCT has become known for its safe qualities and its high level of detail, rendering it an essential instrument in the diagnosis of eye diseases. The interesting improvement in research is centered around the integration of deep learning with OCT for the purpose of automating the detection of eye diseases. We conducted a comprehensive study that explores several diagnostic methods and the wide-ranging uses of OCT. Additionally, it addresses the accessibility of publicly available datasets that are specifically tailored to optical coherence tomography (OCT). The paper provides a detailed review of the most recent advancements in computer-assisted diagnostic methods for diseases of the eye, such as age-related macular degeneration, glaucoma, and diabetic macular edema, with a particular focus on the effective use of OCT. Moreover, the paper systematically analyzes the primary challenges that deep learning faces in OCT image interpretation, emphasizing the intricate nature and possibilities of this field.
An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier Almufti, Saman M.; Abdulazeez, Adnan Mohsin
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.v13i1.3735

Abstract

Gesture-based systems have emerged as a prominent breakthrough in the field of smart access control, effectively integrating security measures with user comfort. This study presents a novel gesture detection framework for smart entry systems that combines the computational capabilities of a Random Forest Classifier with the practicality of Arduino-based hardware. Central to methodology is the utilization of MediaPipe, an advanced computer vision library, to extract hand motion landmarks from live video streams. The selected landmarks function as a comprehensive dataset for training a Random Forest Classifier, which has been specifically chosen due to its high level of accuracy and efficiency in managing intricate classification jobs. The model exhibits outstanding competence in the categorization of gestures in real-time, attaining high levels of accuracy that are crucial for ensuring dependable entrance control. The Arduino microcontroller plays a vital role in the execution of the entry mechanism as it serves as the intermediary between the gesture detection software and the tangible entry control hardware. The incorporation of gesture recognition technology facilitates a cohesive and prompt user experience, wherein identified motions are directly converted into input commands. The system's practical use is demonstrated through a series of detailed tests, which highlight its dependability and efficiency across diverse climatic circumstances. The findings underscore the system's capacity as a flexible and safe solution for contactless access in many environments, including both private homes and highly protected establishments. Furthermore, the study makes a substantial contribution to the larger domain of human-computer interaction by showcasing the practicality of advanced gesture detection systems in many everyday contexts. The suggested framework presents a novel approach to smart entry systems and also paves the way for further investigation in the domains of smart home automation and interactive systems. In these areas, gesture-based interfaces have the potential to deliver user experiences that are both intuitive and efficient.
A Review on Diabetes Classification Based on Machine Learning Algorithms Musa, Jihan; Abdulazeez, Adnan Mohsin
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.v13i2.3886

Abstract

Diabetes, a chronic metabolic disorder, is a significant global health concern affecting millions of individuals worldwide. Early and accurate diagnosis of diabetes is crucial for effective management and prevention of complications. Machine learning (ML) techniques have emerged as powerful tools for analyzing diabetes-related data, aiding in the classification and prediction of diabetes types. This review provides a comprehensive overview of recent advancements in diabetes classification using ML algorithms, highlighting their strengths, limitations, and future directions. Various ML algorithms, including but not limited to support vector machines, decision trees, random forests, artificial neural networks, and ensemble methods, are discussed in details. Furthermore, data preprocessing techniques, feature selection methods, and evaluation metrics employed in diabetes classification studies are examined. Additionally, challenges such as data imbalance, interpretability, and generalization across diverse populations are addressed. Finally, potential avenues for future research to enhance the accuracy and applicability of ML-based diabetes classification systems are proposed.
A Review on Heart Disease Detection Classification Based on Deep Learning Algorithm Jalal, Dimen; Abdulazeez, Adnan Mohsin
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.v13i2.3921

Abstract

Heart disease it is one of the main causes of death in the globe. Heart illness encompasses a spectrum of disorders that impact the heart, its blood arteries, and its overall functionality. Also referred to as cardiovascular disease. This paper investigates the potential benefits of deep learning (DL) architectures for improving diagnostic accuracy addressing the critical need for improved diagnosis of cardiac disease, and the difficulties associated with applying DL methods for heart disease identification. This survey study highlights the important role that DL plays in cardiovascular diagnostics from a number of tasks like as diagnosing, predicting, and classifying heart diseases. Convolutional Neural Networks (CNNs), a type of deep learning, are being used in the context of heart illness with the primary goal of creating accurate and dependable models for the identification, diagnosis, and prognosis of various heart-related disorders.
A Review of Heart Disease Classification Base on Machine Learning Algorithms Hasan, Mayaf; Abdulazeez, Adnan Mohsin
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.v13i2.3923

Abstract

Heart disease is currently the leading cause of death. This problem is acute in developing countries. Predicting heart disease helps patients avoid it in its early stages and can also help medical practitioners find out the main causes. Machine learning has proven over time to play an important role in decision making and forecasting through massive data sets created by the healthcare sector. This review provides an overview of heart disease prediction using applied machine learning algorithms such as Naïve Bayes, Random Forest, Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression, and K-Nearest Neighbour (KNN). And these differences in the techniques are a reflection of many strategies for predicting heart disease. We present a synopsis of classification techniques that are primarily used in the predicted of heart disease. Additionally, we review several previous studies that conducted over the past four years, that used machine learning algorithms to predict cardiovascular.
Classification of Medical Images Based on Unsupervised Algorithms: A Review Zeebaree, Imad Majed; Abdulazeez, Adnan Mohsin
International Journal of Research and Applied Technology (INJURATECH) Vol. 4 No. 2 (2024): Vol 4 No 2 (2024)
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Artificial intelligence models are becoming increasingly essential in biomedical research and healthcare services. Various healthcare organizations utilize information-based machine learning and image-processing methods for the diagnosis of diseases. This review delves explicitly into elucidating the challenges and considerations of developing unsupervised learning for clinical decision support systems in real-world contexts. In recent years, supervised and unsupervised deep learning have demonstrated promising medical imaging and image analysis outcomes. Unsupervised learning gathers data, draws insights from it, and makes data-driven judgments without bias, unlike supervised learning, which requires manual class labeling. A systematic review of unsupervised medical image analysis methods is presented here. This extensive review introduces diverse methodologies rooted in unsupervised classification for detecting diseases and analyzing images. Moreover, we offer insights into publicly available image benchmarks, datasets, and performance measurement details. Each method's strengths and weaknesses are thoroughly discussed, complemented by tabular summaries illuminating each category's outcomes. Additionally, the article furnishes detailed descriptions of the frameworks employed by each approach and the image datasets utilized.
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