Computer Engineering Commons^™

Foundations Of Artificial Intelligence In Healthcare Diagnostics: A Systematic Survey, Raghad Tariq Al-Hassani

Al-Esraa University College Journal for Engineering Sciences

Artificial Intelligence (AI) is becoming the cornerstone of the future of healthcare diagnostics, that has to ability to change the healthcare diagnostic landscape in terms of diagnostic accuracy, speed, and availability. This systematic review investigates the basic methods, tools, applications, and challenges involved in the integration of AI in diagnostic medicine. It emphasizes the using of machine learning models, deep learning networks (e.g., CNNs), NLP for clinical documentation, and smart computing infrastructures, such as edge device and IoMT. They are making possible real-time, data-driven decision making that is already at human-expert-level performance or, in some cases, even better (in the …

Deep Learning-Based Change Detection In High-Resolution Remote Sensing Imagery, Hazem Badawy

Theses and Dissertations

Remote sensing has become a key tool for monitoring Earth’s surface over time, offering valuable insights into both natural and human-driven changes. Among its many applications, change detection focuses on analyzing multi-temporal imagery to reveal how specific areas evolve across different time periods. It plays a pivotal role in Earth observation applications, including urban development monitoring, environmental degradation assessment, and disaster response. However, existing approaches often struggle with limited contextual awareness, high sensitivity to noise, and imprecise localization of change boundaries, especially with high-resolution imagery. This thesis investigates the complex problem of change detection in remote sensing imagery by proposing …

Software Engineering Approach To Enhancing Privacy Protection: Automated Face Blurring Using Deep Learning In Arab Social Media, Yasmin Makki Mohialden, Nadia Mahmood Hussien, Mostafa Abdulghafoor Mohammed

Iraqi Journal for Computer Science and Mathematics

In the age of digital media, securing personal identities in shared material, especially on social media, has become a significant challenge. This research leverages software engineering to automate face blurring in photographs of Arab social media personalities. It proposes a system that integrates sophisticated deep-learning algorithms with standard image processing within a robust software architecture. This modular system is scalable, maintainable, and compatible with digital media platforms. Gaussian blur is applied to protect privacy once convolutional neural networks (CNNs) identify faces. The system’s efficiency and accuracy are enhanced by OpenCV and NumPy. In experiments, this system consistently identifies and blurs …

Memf-Net: A Mega-Ensemble Of Multi-Feature Cnns For Classification Of Breast Histopathological Images, Alaa Hussein Abdulaal, Ali H. Abdulwahhab, Aqeel Majeed Breesam, Zahra Hasan Oleiwi, Riyam Ali Yassin, Morteza Valizadeh, Saja Nafea Mohsin

Iraqi Journal for Computer Science and Mathematics

Pathological anatomical images play a pivotal role in diagnosing diseases, notably breast cancer, which affects women globally. These images, obtained through biopsies or post-mortem examinations, are preserved to maintain their structural integrity. Software tools, like computer-aided diagnosis, aid doctors in early detection and treatment planning, contributing to reduced mortality rates. In this context, convolutional neural networks (CNNs) have emerged as valuable tools for diagnosing benign and malignant breast cancers. This paper introduces a Mega Ensemble Net method, leveraging multi-scale combination features on the breast histopathology dataset. Three fine-tuned deep learning models, namely ResNet-18, ResNet-34, and ResNet-50, are integrated into this …

Application Of Neural Networks For Intelligent Processing Of Sensor Signals In The Control Of Technological Process Parameters, N.R. Yusupbekov, Yu.Sh. Avazov, G.Kh. Rashidov

Chemical Technology, Control and Management

This scientific article investigates the problem of analyzing technological process parameters in the fields of chemistry, energy, and metallurgy based on sensor data and applying intelligent signal processing methods. The main objective is to evaluate the effectiveness of artificial intelligence and deep learning models for intelligent analysis, forecasting, and anomaly detection of data obtained from sensors. Time-series data collected from industrial sensors were analyzed using LSTM (Long Short-Term Memory) and Autoencoder neural networks, as well as the Kalman filter. At the first stage of the study, sensor signals were denoised and their true state was estimated using the Kalman filter. …

Evaluation Of Deep Learning Techniques In Road Sign Recognition, Latafat Abbas Gardashova, Haji Fakhraddin Hajiyev

Chemical Technology, Control and Management

Deep learning has transformed the computer vision field and greatly improved the performance and efficiency of road sign recognition systems. This research compares different deep learning methods, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and hybrid models, in terms of their ability to effectively detect and classify road signs under various conditions. The study compares performance measures such as accuracy, processing speed, and robustness to environmental conditions like low lighting, occlusion, and adverse weather. The results show that CNN-based methods, especially those with transfer learning and ensemble techniques, have better performance in real-time scenarios. Problems like computational …

Explaining Time Series Classifiers Through Post-Hoc Xai Methods Capturing Temporal Dependencies, Ephrem Tibebe Mekonnen

Conference papers

Time series classification is essential in domains such as healthcare and finance, where accurate predictions can have significant real-world consequences. However, in many high-stakes applications, understanding why a model makes a certain decision is just as important as the prediction itself. While deep learning models excel at capturing complex temporal patterns, their black-box nature limits transparency, making it difficult to trust and interpret their decisions. Although eXplainable AI (XAI) methods have advanced considerably for image and tabular data, applying them to time series remains challenging due to the intricate temporal dependencies and high dimensionality of the data. Post-hoc model-agnostic XAI …

Csa-Xai: Channel–Spatial Attention And Explainable Ai In A Modular Multi-Backbone Framework For Lung Cancer Classification, Omar Ibrahim Obaid, Abdulbasit Alazzawi

Iraqi Journal for Computer Science and Mathematics

Computed tomography (CT) scans require precise and early lung cancer detection to produce better clinical results. High accuracy in deep learning approaches (DL) poses an existing challenge to interpret their functionality effectively. This research presents an innovative modular multi-backbone structure that combines channel-spatial attention together with explainable AI (XAI) methods for three-class lung cancer diagnosis (Normal, Benign, and Malignant). Research was carried out to evaluate six pre-trained CNN backbones (ResNet-50, VGG19, Inception-V3, EfficientNet-B0, MobileNet-V2, DenseNet-121) which received hybrid attention enhancement on the IQ-OTH/NCCD dataset. The experimental data showed four pre-trained models reaching perfect accuracy at 100 percent whereas the others …

Iraqi’S Car License Plate Recognition Based On Deep Learning, Mushreq Abdulhussain Shuriji, Husam Al-Behadili, Hadel A. Hussain

Iraqi Journal for Computer Science and Mathematics

Vehicle license plate recognition is essential due to the rising number of operational cars, which leads to an increasing difficulty of this task even for humans. Systems for car license recognition normally consist of two branch systems, namely, license plate recognition and license plate detection. The aim of the detection part is to pinpoint the car and the position of its license plate, while the objective of the recognition part is to recognize characters on that plate. In this work, the emphasis is on Arabic car license plates. In this category of plates, there are three lines containing numerals and …

Retracted: Comparative Study Based On Continuous Analysis Of Autism Spectrum Disorder Using Advanced Deep Learning With Model Interpretability Insights, Ayan Sar, Hussain Falih Mahdi, Sumit Aich, Pranav Singh, Tanupriya Choudhury

Iraqi Journal for Computer Science and Mathematics

Hysterical conversion has similar cognition and behaviours to those in the case of ASD; it is, therefore, complex when diagnosing and classifying the condition. The majority of employed diagnostic tests are cross-sectional and fail to describe the developmental and clinical features of ASD; for this reason, they are pretty inaccurate in the diagnosis of ASD and thus cause disparities in the efficiency of the therapeutic interventions used. The present study's research contribution is a new application of deep learning that aims to analyse the spectrum of ASD with gradient-based classifications. In this case, we use a DL model trained on …

Research On Requirements And Methods For Intelligent Assessment Of Simulation Credibility, Bingheng Wang, Tingrui Liu, Fan Yang, Huan Zhang, Wei Li, Ping Ma, Ming Yang

Journal of System Simulation

Abstract: The accuracy of simulations in representing real-world systems is a critical concern for users. Simulation credibility assessment ensures trustworthiness by evaluating the correctness and effectiveness of simulations to meet application requirements. As simulation technologies are widely adopted, and new simulation paradigms emerge, traditional assessment methods are increasingly showing limitations in their dependence on experts, data processing capabilities, and assessment efficiency. This paper systematically reviewed the research demands, current progress, new technologies, and future trends of intelligent simulation credibility assessment. Based on the simulation credibility assessment process and problem analysis, the requirements for intelligent credibility assessment were discussed. Intelligent technologies …

Retracted: A Quantum Convolutional Neural Network Approach For Early And Accurate Diagnosis Of Parkinson's Disease, Aiesha Mahmoud Ibrahim, Mazin Abed Mohammed, Omar Al-Boridi

Iraqi Journal for Computer Science and Mathematics

Parkinson's disease (PD) is a progressive neurological disorder that primarily affects individuals over the age of 55. It is characterized by a range of motor and non-motor symptoms that can significantly impact various aspects of daily life. Despite notable advancements in medical science, there is currently no permanent cure or definitive treatment for PD. This therapeutic gap underscores the critical importance of early diagnosis, which remains a major focus of ongoing research. Due to the disease's gradual progression, PD symptoms may take years to fully develop, making early detection essential for improving patient outcomes and quality of life. Moreover, the …

Enhanced Detection Of Intracranial Hemorrhage: A New Hybrid Model Design Based On The U-Net Segmentation Method, Hassan F. Hassan, Hadeel K. Aljobouri, Oktay Algin

Iraqi Journal for Computer Science and Mathematics

Intracranial hemorrhage (ICH) denotes bleeding inside the skull, which can occur in or around the brain. Computed tomography (CT) has been used to detect ICH due to its high efficiency and accuracy. Nowadays, deep learning model design is introduced to allow an accurate and efficient classification of ICH in CT images. This work focused on developing U-Net-based models for the segmenting of ICH. Furthermore, the proposed model employed two transfer learning models, MobileNet and Xception, as the backbones of the U-Net topology. This approach aims to establish metrics that improve ICH treatment through precise segmentation techniques. A free dataset from …

Retracted: A Novel Benchmarking Framework For Selecting The Best Deep Learning Model Diagnosing Covid-19 Based On New Development For Dual Mcdm Methods, Mahmood M. Salih, Yousif Raad Muhsen, M.A. Ahmed, Reem D. Ismael, Moceheb Lazam Shuwandy, Z.T. Al-Qaysi

Iraqi Journal for Computer Science and Mathematics

COVID-19 was diagnosed using deep learning models by a group of studies. Evaluating and benchmarking these models are essential to achieving the most suitable model for diagnosing coronavirus. Objective: In this investigation, we offer an inclusive valuation of several deep learning models to detect the maximum appropriate and active model which gratifies doctors' requirements and assessment criteria. Method: This study combines Fuzzy decision by the opinion score method (FDOSM) and Fuzzy-Weighted Zero-Inconsistency (FWZIC). According to the advantage of Trapezoidal Intuitionistic fuzzy, we developed FWZIC into Trapezoidal Intuitionistic fuzzy named (TrIF-FWZIC) for weighting criteria and FDOSM into Trapezoidal Intuitionistic fuzzy FDOSM …

Simulation Study On Adaptive Signal Control Of Deformed Intersection Based On Lstm-Gnn, Kun Chen, Liang Chen, Jiming Xie, Fengbo Liu, Taixiong Chen, Lukuan Wei

Journal of System Simulation

Abstract: Aiming at the traffic congestion at deformed intersections, an improved adaptive traffic signal control scheme based on deep learning is designed, the scheme integrates the adaptive signal control of LSTM and GNN at deformed intersections. LSTM is used to capture the dependence between time series traffic data, while GNN is used to construct a spatial interaction model between lanes. By integrating the information of time and space dimensions, the model can dynamically adjust the phase duration of signal lights according to real-time traffic conditions. The results indicate that the LSTM-GNN adaptive control scheme improves overall traffic throughput efficiency by …

Arson Event Detection Using Yolov9, Ali Abbas Abbod, Matheel E. Abdulmunimb, Ismail A. Mageed

Journal of Soft Computing and Computer Applications

Detecting event anomalies is crucial for surveillance systems, as it enables the identification of occurrences in videos, both temporally and spatially. It can identify deviations from patterns without requiring human oversight by learning from past information to distinguish normal behavior and pinpoint irregularities. Early detection of arson fires is critical to mitigating damage, public safety, property, and the environment, as well as saving lives and aiding in law enforcement investigations. The objective of this study is to evaluate a system for detecting events using the You Only Look Once version 9 (YOLOv9) model in surveillance videos with a focus on …

Retracted: Accurate Electrocardiogram Classification Of Heart Disease Using Deep Learning Network, Hadeel M. Saleh, Sahar Hamad Ahmed, Akeel Sh. Mahmoud

Iraqi Journal for Computer Science and Mathematics

Long short-term memory networks can effectively process complex temporal patterns in electrocardiogram data. These sequential models excel at classifying heart disease from the rich signals captured by electrocardiograms. However, traditional algorithms struggle with the intricate waveforms encoded in each heartbeat. Deeper architectures such as LSTM are better equipped to untangle the subtle variations between healthy sinus rhythms and lethal arrhythmias. In this study, an LSTM model was developed to diagnose disease from the PTB dataset. The network was trained using a fusion of deep learning schemes for sequential data. The model underwent several evaluations, from a confusion matrix mapping predictions …

Mfgat: Map-Free Trajectory Prediction With Graph Attention Networks For Autonomous Vehicles, Zehra Gunindi

UNLV Theses, Dissertations, Professional Papers, and Capstones

Accurate trajectory prediction is a key component for ensuring safe and efficient navigation of autonomous vehicles in complex traffic scenarios. While traditional methods rely heavily on high-definition (HD) maps, these approaches face significant challenges, including high costs, limited availability, and susceptibility to rapid obsolescence. This thesis proposes an end-to-end, map-free trajectory prediction model that leverages Graph Attention Networks (GAT) to dynamically capture spatial-temporal interactions among road agents, eliminating the need for HD maps.The research introduces UNLVTraj, a novel LiDAR-based dataset collected around the University of Nevada, Las Vegas campus, specifically along Cottage Grove Street, Harmon Avenue, and Maryland Parkway. This …

Adaptive Noise Estimation And Denoising With Deep Learning For Nmr Spectroscopy, Naveen Asokan

McKelvey School of Engineering Graduate Student Theses & Dissertations

Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful analytical technique widely used for molecular structure elucidation in chemistry, biology, and medicine. However, spectral accuracy is often degraded by noise—particularly in low acquisition time settings—resulting in reduced resolution and obscured chemical features. While traditional noise reduction techniques such as signal averaging can improve spectral quality, they require longer acquisition times, limiting their utility in real-time and high-throughput applications.

This thesis presents a deep learning-based denoising framework designed to enhance the quality of complex-valued NMR spectra. The proposed model, built upon a U-Net architecture, incorporates both real and imaginary components of the …

Controlling A Mobile Inverted Pendulum And Optimizing Leaning Angle To Apply Force Using Reinforcement Learning, Aryan Mediratta

2025 Spring Honors Capstone Projects - Archive

Reinforcement Learning is a Machine Learning paradigm that involves simulating learning through rewards and penalties in intelligent systems. This technique is often employed in robotics when traditional control methods are insufficient or when human intuition does not provide a good solution on how to control robot systems, This project involves training a Segway-style Mobile Inverted Pendulum (MIP) robot to balance and push a box forward. The BeagleBone Blue board is used that includes a built-in Inertial Measurement Unit (IMU) and encoder ports. These sensors enable the system to measure its current state. The goal is to find the optimal leaning …

Graph Based Deep Reinforcement Learning Aided By Transformers For Multi-Agent Cooperation, Michael S. Elrod

All Theses

Mission planning for a fleet of cooperative autonomous drones in applications that involve serving distributed target points, such as disaster response, environmental monitoring, and surveil- lance, is challenging, especially under partial observability, limited communication range, and uncertain environments. Traditional path-planning algorithms struggle in these scenarios, particu- larly when prior information is not available. To address these challenges, I propose an innovative framework that integrates Graph Neural Networks (GNNs), Deep Reinforcement Learning (DRL), and transformer-based mechanisms for enhanced multi-agent coordination and collective task ex- ecution. My approach leverages GNNs to model agent-agent and agent-goal interactions through adaptive graph construction, enabling efficient …

Multimodal Emotion Recognition For Human-Robot Interaction Across Neuro-Diverse Populations., Ruchik Mishra

Electronic Theses and Dissertations

This dissertation explores the integration of multimodal data streams and artificial intelligence pipelines to understand human affect in neurotypical and children with Autism Spectrum Disorder (ASD). This dissertation captures human affect in the context of human-robot interaction. For this, multiple studies have been presented with both children with ASD and neurotypical adults. This dissertation makes four contributions: 1) The first study introduces autonomy during perspective-taking teaching sessions by making verbal content generation through large language models (LLMs). This system is the first of its kind for teaching perspective-taking in a semi-autonomous manner under the supervision of domain experts. Furthermore, this …

Liberated Arabic Handwritten Text Recognition Using Convolutional Recurrent Neural Networks, Ahmad Abdulqadir Alrababah, Mohammed Khalid Aljahdali, Abdulrahim Abdulhamid Al Jahdali, Mohammed Saleh Alghanmi, Israa Ibraheem Al_Barazanchi

Iraqi Journal for Computer Science and Mathematics

Arabic script is exhibited in a cursive style, which is a departure from the norm in many common languages, and the shapes of letters are contingent on their positions within words. The form of the first letter is influenced by the subsequent letter, middle letters are shaped by both preceding and succeeding letters, and the shape of the final letter is determined by the preceding letter. Additionally, certain letters are found to have strikingly similar shapes, making Arabic text recognition a formidable challenge in computer vision. The challenge of detecting and recognizing Arabic handwritten text is addressed in this paper …

Robust Inner Knuckle Print Recognition System Using Densenet201 And Inceptionv3 Models, Haitham Salman Chyad, Tarek Abbes

Iraqi Journal for Computer Science and Mathematics

Texture features and stability have generated significant interest in biometric recognition. The inner knuckle print is distinctive and difficult to fake, making it extensively used in individual identification, criminal investigation, and various other domains. In recent years, the rapid progress of deep learning technology has created new prospects for internal knuckle recognition. This paper proposes a robust inner-knuckle-print recognition system (RIKP-RS) depending on two deep learning (DL) models. This paper focuses on the key components of the inner surface of the hand namely the little finger, ring finger, middle finger, index finger, and thumb finger that are used for human …

Improving Heart Attack Prediction Accuracy Performance Using Machine Learning And Deep Learning Algorithms, Mosleh Hmoud Al-Adhaileh, Mohammed Ibrahim Ahmed Al-Mashhadani, Eidah M Alzahrani, Theyazn H.H. Aldhyani

Iraqi Journal for Computer Science and Mathematics

Accurate classification of cardiovascular diseases (CVDs) is of utmost importance for cardiologists to provide appropriate treatments. Diagnosing and predicting cardiovascular conditions are crucial medical responsibilities in this context. The healthcare sector is increasingly utilizing deep learning (DL) and machine learning (ML) algorithms due to their ability to identify patterns in data. Diagnosticians may reduce the number of misdiagnoses by using DL and ML techniques for the categorization of cardiovascular disease incidence. To reduce the mortality linked to CVDs, this research offers a unique model that properly predicts and classifies these problems. This research presents approaches such as deep learning, random …

Hybrid Methods For Detecting Face Morphing Attacks, Essa M. Namis, Khalid Shaker, Sufyan Al-Janabi

Iraqi Journal for Computer Science and Mathematics

The face morphing process blends two or more facial images to produce a singular morphed facial image that shows the vulnerabilities of Face Recognition Systems (FRS). The widespread use of facial recognition algorithms, especially in Automatic Border Control (ABC) systems, has elicited concerns about potential attacks, as modified passports pose a significant risk to national security. This research presents a hybrid approach for feature extraction from facial images. The suggested approach involves three stages: The initial phase involves preprocessing the image through resizing and face identification, using the Viola-Jones algorithm to detect and locate the human face in the image, …

Research On Air Target Threat Assessment Technology Based On Deep Learning, Dawei Jiang, Yangyang Dong, Lidong Zhang, Xiao Lu, Chunxi Dong

Journal of System Simulation

Abstract: In order to realize the effective assessment of air combat targets, a deep learning-based air target threat assessment method is proposed. According to threat characteristics of the air target, the threat attributes of air target faced by electronic countermeasure operation are analyzed from the two perspectives of platform layer and equipment layer, the air target threat assessment index system is constructed, and the air target threat assessment index data set is established. Based on convolutional neural network, a residual structure is introduced to optimize the network, a threat assessment model is established, and the threat ranking of air targets …

Double Dual Convolutional Neural Network (D2cnn): A Deep Learning Model Based On Feature Extraction For Skin Cancer Classification, Raya Sattar Shahadh, Belal Al-Khateeb

Iraqi Journal for Computer Science and Mathematics

Artificial intelligence, especially in the field of ``deep learning'', is still promising when it comes to skin cancer detection and diagnosis. Among deep learning algorithms, convolutional neural networks (CNNs) give a high level of accuracy in identifying and classifying different types of skin cancer. CNNs have a strong coordination due to understanding the important features from medical images that are extracted from convolutional layers. However, there is still a problem which is the high imbalance in the dataset with high noise in the images. This paper presents a new solution that combines different architectural structures of convolutional neural networks (CNNs) …

Palindrome: A Bi-Directional Multi-Object Detection Framework For Relative Navigation And Autonomous Docking, Liam A. Weinfurtner

Theses and Dissertations

This work introduces a bi-directional, multi-object detection framework that integrates pose estimates from both receiver- and tanker-mounted cameras to improve accuracy and redundancy. A modular YOLO-based detection pipeline is trained using synthetic and real imagery, leveraging a bootstrap transfer learning approach to enhance sim-to-real performance. System evaluation in both virtual and real-world environments demonstrates improved detection robustness, pose estimation accuracy, and scalability. These advancements contribute to the development of AI-driven vision systems for AAR and other autonomous docking applications.

Facial Swap Detection Based On Deep Learning: Comprehensive Analysis And Evaluation, Israa Mishkhal, Nibras Abdullah, Hassan H. Saleh, Nur Intan Raihana Ruhaiyem, Fadratul Hafinaz Hassan

Iraqi Journal for Computer Science and Mathematics

In recent years, Advancements in Artificial Intelligence (AI), particularly deep learning (DL), have made great strides in the creation of highly realistic deepfakes, which manipulate facial forensics to generate convincing fake faces or expressions. These manipulations pose significant threats to individual privacy and the integrity of legal, political, and social institutions. In fact, several existing studies have recently pursued the development of machine learning techniques for detecting deepfake content, with the overarching aim of protecting the victim's privacy or curbing the rise of picture fabrication. Despite extensive research on DL-based deepfake detection systems, challenges such as detecting facial swaps under …