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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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Detection of Hate-Speech Tweets Based on Deep Learning: A Review Miran, Ara Zozan; Abdulazeez, Adnan Mohsin
JISA(Jurnal Informatika dan Sains) Vol 6, No 2 (2023): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v6i2.1813

Abstract

Cybercrime, cyberbullying, and hate speech have all increased in conjunction with the use of the internet and social media. The scope of hate speech knows no bounds or organizational or individual boundaries. This disorder affects many people in diverse ways. It can be harsh, offensive, or discriminating depending on the target's gender, race, political opinions, religious intolerance, nationality, human color, disability, ethnicity, sexual orientation, or status as an immigrant. Authorities and academics are investigating new methods for identifying hate speech on social media platforms like Facebook and Twitter. This study adds to the ongoing discussion about creating safer digital spaces while balancing limiting hate speech and protecting freedom of speech.   Partnerships between researchers, platform developers, and communities are crucial in creating efficient and ethical content moderation systems on Twitter and other social media sites. For this reason, multiple methodologies, models, and algorithms are employed. This study presents a thorough analysis of hate speech in numerous research publications. Each article has been thoroughly examined, including evaluating the algorithms or methodologies used, databases, classification techniques, and the findings achieved.   In addition, comprehensive discussions were held on all the examined papers, explicitly focusing on consuming deep learning techniques to detect hate speech.
Network Intrusion Detection Based on Machine Learning Classification Algorithms: A Review Younis, Aqeel Hanash; Abdulazeez, Adnan Mohsin
JISA(Jurnal Informatika dan Sains) Vol 7, No 1 (2024): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v7i1.2056

Abstract

The worldwide internet continues to spread, presenting numerous escalating hazards with significant potential. Existing static detection systems necessitate frequent updates to signature-based databases and solely detect known malicious threats. Efforts are currently being made to develop network intrusion detection systems that can utilize machine learning techniques to accurately detect and classify hazardous content. This would result in a decrease in the overall workload required. Network Intrusion Detection Systems are created with a diverse range of machine learning algorithms. The objective of the review is to provide a comprehensive overview of the existing machine learning-based intrusion detection systems, with the aim of assisting those involved in the development of network intrusion detection systems.
Empowering Diagnosis: A Review On Deep Learning Applications for COVID-19 and Pneumonia in X-Ray Images Yaqoub, Karin Younis; Abdulazeez, Adnan Mohsin
JISA(Jurnal Informatika dan Sains) Vol 7, No 1 (2024): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v7i1.2024

Abstract

The emergence of COVID-19, a highly contagious virus capable of infecting both the upper and lower respiratory tracts, has led to one of the deadliest pandemics in modern history, claiming millions of lives worldwide. Early and accurate detection of this rapidly spreading disease is crucial for effective containment and saving lives. Chest X-ray (CXR) stands out as a promising diagnostic tool due to its accessibility, affordability, and long-term sample preservation. However, distinguishing COVID-19 pneumonia from other respiratory ailments poses a significant challenge. This article delves into various approaches utilized for COVID-19 detection and the hurdles encountered in this endeavor. The imperative for developing automated detection systems to mitigate virus transmission via contact is underscored. Notably, deep learning architectures such as ResNet, Inception and Googlenet have been deployed for COVID-19 detection, albeit with a focus on identifying pneumonia cases. Discriminating between COVID-19-induced pneumonia and pneumonia caused by other pathogens remains a formidable task, demanding innovative solutions for accurate and timely diagnosis.
Face Emotion Recognition Based on Machine Learning: A Review Abdulazeez, Adnan Mohsin; Ageed, Zainab Salih
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 5 No. 1 (2024): INJIISCOM: VOLUME 5, ISSUE 1, JUNE 2024
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v5i1.12145

Abstract

Computers can now detect, understand, and evaluate emotions thanks to recent developments in machine learning and information fusion. Researchers across various sectors are increasingly intrigued by emotion identification, utilizing facial expressions, words, body language, and posture as means of discerning an individual's emotions. Nevertheless, the effectiveness of the first three methods may be limited, as individuals can consciously or unconsciously suppress their true feelings. This article explores various feature extraction techniques, encompassing the development of machine learning classifiers like k-nearest neighbour, naive Bayesian, support vector machine, and random forest, in accordance with the established standard for emotion recognition. The paper has three primary objectives: firstly, to offer a comprehensive overview of effective computing by outlining essential theoretical concepts; secondly, to describe in detail the state-of-the-art in emotion recognition at the moment; and thirdly, to highlight important findings and conclusions from the literature, with an emphasis on important obstacles and possible future paths, especially in the creation of state-of-the-art machine learning algorithms for the identification of emotions.
Classification of Heart Diseases Based on Machine Learning: A Review Abdulazeez, Adnan Mohsin; Hasan, Shereen Sadiq
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 6 No. 1 (2025): INJIISCOM: VOLUME 6, ISSUE 1, JUNE 2025
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v6i1.13600

Abstract

The article emphasizes the critical need for early and accurate diagnosis of cardiovascular disease (CVD), a leading cause of global mortality. Recent advancements in machine learning (ML) have shown promising results in classifying cardiac disorders, aiming to enhance healthcare practices. It discusses both the benefits and limitations of current ML algorithms used in this field, highlighting their role in improving the management of cardiac diseases through accurate diagnosis. The study evaluates various supervised learning techniques like support vector machines, decision trees, and neural networks, illustrating their effectiveness in handling diverse datasets and identifying significant patterns. Furthermore, it explores unsupervised learning methods such as clustering algorithms, which uncover hidden patterns in cardiac data. The research also investigates the potential of ensemble approaches and deep learning to further enhance classification accuracy. In conclusion, the study provides an overview of the current state of ML-based heart disease classification research, aiming to inform policymakers, physicians, and researchers about the transformative potential of ML in advancing heart disease diagnosis and treatment, ultimately aiming for improved patient outcomes and reduced healthcare costs.
LUNG CANCER DETECTION AND CLASSIFICATION BASED ON DEEP LEARNING: A REVIEW Ismaeel, Hivi Kamal; Abdulazeez, Adnan Mohsin
Jurnal Teknoinfo Vol 18, No 2 (2024): Juli 2024
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v18i2.4309

Abstract

AbstractLung cancer is a significant health problem worldwide because it is difficult to treat and often caused by factors such as smoking and lifestyle choices. Early detection and accurate classification are crucial for assisting patients. Lung cancer remains a major global health challenge due to its late detection and the complexity of its treatment options. Advancements in deep learning, a form of artificial intelligence that mimics the way humans learn, are offering new hopes for earlier detection and more accurate classification of this disease through the analysis of medical images. This review paper explores recent progress in the use of deep learning techniques, specifically focusing on how these methods are applied to improve lung cancer diagnostics. Our study delves into several types of neural networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Graph Neural Networks (GNNs), which have been adapted to analyze complex medical imaging data effectively. These networks help in identifying and classifying cancerous tissues from lung scans with a higher degree of accuracy than traditional methods, which rely heavily on human interpretation. We review a variety of models and approaches that demonstrate significant improvements in detecting lung cancer features from imaging studies like CT scans. These models not only enhance the accuracy but also reduce the time needed for diagnosis, which is crucial in improving patient outcomes. The paper discusses the specific roles of these models in automating the detection processes, their efficiency, and how they overcome some of the common challenges in lung cancer diagnosis, such as dealing with ambiguous or incomplete images. Furthermore, we address the challenges still facing deep learning applications in this field, including the need for large, annotated datasets and the computational demands of training complex models. Despite these challenges, the future looks promising due to the continuous improvements in computational power and the increasing availability of medical data.
Lung Segmentation from Chest X-Ray Images Using Deeplabv3plus-Based CNN Model Hasan, Dathar; 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.v13i1.3700

Abstract

As a result of technological advancements, a variety of medical diagnostic systems have grown rapidly to support the healthcare sectors. Over the past years, there has been considerable interest in utilizing deep learning algorithms for the proactive diagnosis of multiple diseases. In most cases, Coronavirus (COVID-19) and tuberculosis (TB) are diagnosed through the examination of pulmonary X-rays. Deep learning algorithms can identify tuberculosis with an almost medical-grade level of consistency by extracting the lung regions in the X-ray images. The probability of tuberculosis detection is increased when classification algorithms are applied to segmented lungs rather than the entire X-ray. The main focus of this paper is to execute lung segmentation from X-ray images using the deeplabv3plus CNN-based semantic segmentation model. In other CNN architectures, the feature resolution diminishes as the network becomes deeper due to the use of sequential convolutions with pooling or striding within the down-sampling stage. To tackle this drawback, deeplabv3plus incorporates "Atrous Convolution" in addition to modifying the pooling and convolutional striding components of the backbone. The experimental results were: an accuracy of 97.42%, a Jaccard index of 93.49%, and a dice coefficient of 96.63%. We also conduct an extensive comparison between the deeplabv3plus segmentation model and other benchmark segmentation architectures. The results prove the ability of the deeplabv3plus model to achieve precise lung segmentation from X-ray images.
Credit Card Fraud Detection using KNN, Random Forest and Logistic Regression Algorithms : A Comparative Analysis Ashqi Saeed, Vaman; 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.v13i1.3707

Abstract

Because credit cards are utilized so frequently, fraud appears to be a significant concern in the credit card industry. It is challenging to quantify the effects of misrepresentation. Globally, credit card fraud has cost institutions and consumers billions of dollars. Despite the existence of numerous anti-fraud mechanisms, fraudsters continue to seek out novel methods and strategies to commit fraud. An additional challenge in the estimation of credit card fraud loss is that the magnitude of unreported or undetected forgeries cannot be determined, only losses associated with those frauds that have been detected can be measured. Implementing effective fraud detection algorithms through the utilization of machine-learning techniques is crucial in order to mitigate these losses and provide support to fraud investigators. This paper presents a machine learning-based method for the detection of credit card fraud. Three methodologies are implemented on the raw and pre-processed data. Python is used to implement the work. By comparing the accuracy-based performance evaluations of k-nearest neighbor and logistic regression with Random Forest, it is determined that the former exhibits superior performance.
Classification of Cancer Microarray Data Based on Deep Learning: A Review Fadhil, Jawaher; 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.v13i1.3711

Abstract

This review article delves into applying deep learning methodologies in conjunction with microarray data for cancer classification. The study provides a comprehensive overview of recent advancements in utilizing deep learning techniques to accurately categorize cancer types based on intricate patterns discerned from microarray datasets. Various aspects are covered, including integrating deep learning algorithms, exploring diverse cancer types, and analyzing microarray data to enhance classification accuracy. The review synthesizes findings from recent research, highlighting the efficacy of deep learning in uncovering subtle and complex relationships within microarray data that contribute to improved classification outcomes. Key insights into the strengths and limitations of employing deep learning in this context are discussed, offering a critical appraisal of the field's current state. This review aims to provide a valuable resource for researchers, clinicians, and practitioners interested in cutting-edge developments in cancer classification methodologies by exploring the intersection of deep learning and microarray technology. The synthesis of knowledge presented herein contributes to a deeper understanding of the potential and challenges associated with harnessing deep learning for enhanced classification accuracy in the realm of cancer research.
Leveraging of Gradient Boosting Algorithm in Misuse Intrusion Detection using KDD Cup 99 Dataset Sulaiman , Sulaiman Muhammed; 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.v13i1.3720

Abstract

This study addresses the persistent challenge of intrusion detection as a long-term cybersecurity issue. Investigating the efficacy of machine learning algorithms in anomaly and misuse detection. Research employs supervised learning for misuse detection and explain anomaly detection. Focused on adaptability and continual evolution the study explores the application of ensemble learning models AdaBoost, LightGBM, and XGBoost. Applying these algorithms in the context of intrusion detection. Utilizing the KDD Cup 99 dataset as a benchmark the paper assesses and compares the performance of these models. Besides, illuminating their effectiveness particularly in identifying smurf attacks within the cybersecurity landscape.
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