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- Intrusion detection system (IDS) (2)
Articles 91 - 120 of 235
Full-Text Articles in Computer Engineering
Retracted: Evaluating The Performance Of The Dbscan Algorithm's Using Number Of External Scores Measures, Rajaa Hasan Abbas, Huda Qusay Hashim, Huda Karem Nasser
Retracted: Evaluating The Performance Of The Dbscan Algorithm's Using Number Of External Scores Measures, Rajaa Hasan Abbas, Huda Qusay Hashim, Huda Karem Nasser
Iraqi Journal for Computer Science and Mathematics
The emergence of more informative clustering methods than classical representations is important, so the density-based spatial clustering for applications with noise (DBSCAN) technique can yield an accurate statistical idea of clusters. DBSCAN is becoming more and more popular. On the other hand, if we are aware of actual datasets so that we can make comparisons with these datasets, we aim to determine the accuracy with which the partitioning is estimated using the density-based method. Therefore, in order to compare the success of the partitioning found by the density-based approach under different models, some external scores measures (Adjusted Rand, F-measure, and …
Ontology Features-Based Arabic Text Augmentation Using Word2vec, Enas Tariq Khudair, Onsa Lazzez, Mourad Zaied, Tarek M. Hamdani, Ahmed T. Sadiq, Habib Chabchoub, Adel M. Alimi
Ontology Features-Based Arabic Text Augmentation Using Word2vec, Enas Tariq Khudair, Onsa Lazzez, Mourad Zaied, Tarek M. Hamdani, Ahmed T. Sadiq, Habib Chabchoub, Adel M. Alimi
Iraqi Journal for Computer Science and Mathematics
Text augmentation plays a major role when data is scarce. In this context, there are few Arabic news texts for specific purposes, and hence, there is a dire need to generate Arabic text, especially news. This paper presents an enhanced approach to Arabic text augmentation based on Arabic ontology features. The Arabic part of speech, particularly adjectives, verbs, and prepositions, and the ontology properties regarding such parts to create new texts, make up the first stage of the system, which has multiple stages. Word2Vector (Word2Vec) plays a pivotal role in giving Arabic ontology features to the specific Arabic Part of …
Valuing Fruit Tree Lease Contracts Under Uncertainty: A Probabilistic Framework For Fair Pricing, Abdurakhman Abdurakhman, Agus Sihabuddin, Kurniawan Chandra Wijaya, Evita Purnaningrum, Di Asih I Maruddani
Valuing Fruit Tree Lease Contracts Under Uncertainty: A Probabilistic Framework For Fair Pricing, Abdurakhman Abdurakhman, Agus Sihabuddin, Kurniawan Chandra Wijaya, Evita Purnaningrum, Di Asih I Maruddani
Iraqi Journal for Computer Science and Mathematics
Fruit tree lease contracts are a prevalent economic practice in Indonesia, especially within rural communities. This study addresses the challenge of establishing equitable contract prices for both lessees and tree owners, specifically by integrating the inherent uncertainties associated with crop yield and fruit price fluctuations. To achieve this, we develop and employ two distinct models: Fixed-Time Discount Model (FTD) and the Dynamic-Time Discount Model (DTD). Each model is mathematically formulated, leveraging a Poisson distribution to capture yield uncertainty and a Uniform distribution to represent fruit price variability. Through computations, we evaluate the impact of key parameters - average yield ( …
Ada-Application Of Decision Analysis For Developing A Healthcare System To Predict Fetal Health, Melfi Alrasheedi, Theyazn.H.H Aldhyani
Ada-Application Of Decision Analysis For Developing A Healthcare System To Predict Fetal Health, Melfi Alrasheedi, Theyazn.H.H Aldhyani
Iraqi Journal for Computer Science and Mathematics
A fatal health condition involves an unborn baby that persists throughout the embryonic stage until delivery. The fetus grows and develops during each trimester of pregnancy. Obstetricians may detect fetal anomalies and select medical interventions based on cardiotocogram (CTG) data. However, the obstetrician's visual assessment of CTG data can sometimes be subjective or inaccurate. Therefore, automated analysis using machine learning approaches for CTG data is essential. This research employs decision analysis techniques, including decision trees (DT), gradient boosting (GB), and type-2 fuzzy neural networks (FNN), for prenatal analysis and prediction. The system was tested using a standard dataset consisting of …
Retracted: Hotspot Issue Handling And Reliable Data Forwarding Technique For Ocean Underwater Sensor Networks, Omar Adil Mahdi, Yusor Rafid Bahar Al-Mayouf, Sameer Sami Hassan Al-Obaidi, Bourair Al-Attar, Hamed Balogun, Suleman Khan
Retracted: Hotspot Issue Handling And Reliable Data Forwarding Technique For Ocean Underwater Sensor Networks, Omar Adil Mahdi, Yusor Rafid Bahar Al-Mayouf, Sameer Sami Hassan Al-Obaidi, Bourair Al-Attar, Hamed Balogun, Suleman Khan
Iraqi Journal for Computer Science and Mathematics
Underwater Wireless Sensor Networks (UWSNs) have emerged as a promising technology for a wide range of ocean monitoring applications. The UWSNs suffer from unique challenges of the underwater environment, such as dynamic and sparse network topology, which can easily lead to a partitioned network. This results in hotspot formation and the absence of the routing path from the source to the destination. Therefore, to optimize the network lifetime and limit the possibility of hotspot formation along the data transmission path, the need to plan a traffic-aware protocol is raised. In this research, we propose a traffic-aware routing protocol called PG-RES, …
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
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 …
Retracted: Capsule Network Model For Detecting Spoofing Attack In The Internet Of Medical Things (Iomt), Mohammad A. Alsharaiah, Mohammed Amin Almaiah, Mansour Obeidat, Rami Shehab
Retracted: Capsule Network Model For Detecting Spoofing Attack In The Internet Of Medical Things (Iomt), Mohammad A. Alsharaiah, Mohammed Amin Almaiah, Mansour Obeidat, Rami Shehab
Iraqi Journal for Computer Science and Mathematics
The Internet of Medical Things (IoMT) has transformed healthcare delivery through real-time monitoring and data exchange. However, this integration of smart medical devices has also introduced critical cybersecurity threats, particularly spoofing attacks, which can compromise patient safety and system reliability. Conventional Intrusion Detection Systems (IDS) often fail to address IoMT-specific challenges such as class imbalance, computational constraints, and the need for real-time adaptability. This study proposes a Capsule Network (CapsNet)-based IDS that leverages spatial dependency modeling and hierarchical feature relationships to detect spoofing attacks in IoMT environments. Using the CICIoMT2024 dataset, we implemented a binary classification framework where spoofing instances …
Retracted: Automated Diagnosis Of Orthopedic Patients With Vertebral Column Disorders Using Advanced Mathematical Modeling, Chen Feng, Zhenhua Sun, Xinheng Dai, Hongli Wen
Retracted: Automated Diagnosis Of Orthopedic Patients With Vertebral Column Disorders Using Advanced Mathematical Modeling, Chen Feng, Zhenhua Sun, Xinheng Dai, Hongli Wen
Iraqi Journal for Computer Science and Mathematics
Orthopedic disorders are multifactorial, making accurate diagnosis a significant challenge. This study introduces a novel method for classifying patients into three categories—normal, disc herniation, and spondylolisthesis—using biomechanical parameters derived from diagnostic datasets. To enhance classification accuracy, two meta-heuristic optimization algorithms—the Zebra Optimization Algorithm (ZOA) and Chaos Game Optimization (CGO)—are integrated with Adaptive Boosting (ADAC) and Light Gradient Boosting Machine (LGBM) classifiers. The experimental results reveal that ZOA significantly improves model performance, particularly in the ADAC classifier. The baseline ADAC model achieved a mean accuracy of 0.916, which increased to 0.952 after optimization with ZOA (referred to as the ADZO model). …
Deep Learning And Texture Analysis For Lung And Colon Cancer Predicting, Mohamed M. Neamah, Laith A. Al-Ani, Loay E. George
Deep Learning And Texture Analysis For Lung And Colon Cancer Predicting, Mohamed M. Neamah, Laith A. Al-Ani, Loay E. George
Iraqi Journal for Computer Science and Mathematics
Cancer remains a major cause of death worldwide, with lung and colon (LC) cancers presenting significant challenges to healthcare systems due to their high rates of occurrence and mortality. Early and precise diagnosis is essential for better patient outcomes. This research utilizes recent advances in deep learning (DL) and texture analysis (TA) to create a reliable predictive model for detecting LC cancer through histopathological images (HPI). A hybrid method is proposed that combines a gray-level co-occurrence matrix (GLCM) for extracting texture features with an adaptive modified EfficientNet B2 model (AM-EfficientNet B2) for deep feature extraction. These features are used to …
Retracted: Software Engineering-Oriented Text Generation And Analysis Using Gpt-2, Nadia Mahmood Hussien, Aumama Mohammed Farhan, Yasmin Makki Mohialden, Qabas Abdal Zahraa Jabbar, Shahbaa Mohammed Abdulmaged, Asmaa Hatem Arif
Retracted: Software Engineering-Oriented Text Generation And Analysis Using Gpt-2, Nadia Mahmood Hussien, Aumama Mohammed Farhan, Yasmin Makki Mohialden, Qabas Abdal Zahraa Jabbar, Shahbaa Mohammed Abdulmaged, Asmaa Hatem Arif
Iraqi Journal for Computer Science and Mathematics
The research focuses on developing an improved system for generating and analyzing text by combining GPT-2, LSTM, and CNN models to address challenges in automated content creation for software engineering tasks. The system targets specific applications such as requirements engineering, software documentation, and code comment generation. It generates 150-token text samples based on over 100 user-provided prompts. These generated texts are first processed through an LSTM layer to capture semantic meaning, then passed through a CNN module to extract syntactic and semantic features. All outputs are stored in structured CSV files to support future analysis. Evaluation results demonstrate positive impacts …
The Innovative Technique For Obtaining The Solutions Of Jeffery Hamel Nano-Fluids Flow Problem, Haedir Abd Alrazak Namoos, Abeer Majeed Jasim
The Innovative Technique For Obtaining The Solutions Of Jeffery Hamel Nano-Fluids Flow Problem, Haedir Abd Alrazak Namoos, Abeer Majeed Jasim
Iraqi Journal for Computer Science and Mathematics
The study of heat transfer in nanofluid flows is increasingly important in many engineering, medical, and industrial applications. These fluids offer enhanced thermal cooling properties compared to conventional fluids. The research problem lies in the challenges of solving the Jeffrey-Hammel flow model for nanofluids, which includes coupled nonlinear differential equations that describe the thermal and hydrodynamic behavior of this type of flow, taking into account the influence of multiple factors such as the type, size, and concentration of nanoparticles. This research aims to propose a new hybrid analytical technique that combines the Laplace transform and the q-homotopy analysis technique with …
Optimized Hybrid Watermarking: Dual-Scheme Strategies For Enhanced Robustness, Ooi Jessie, Liew Siau Chuin, Syifak Izhar Bt Hisham, Khor Hui Liang, Khoo Bee Ee, Jasni Mohamad Zain
Optimized Hybrid Watermarking: Dual-Scheme Strategies For Enhanced Robustness, Ooi Jessie, Liew Siau Chuin, Syifak Izhar Bt Hisham, Khor Hui Liang, Khoo Bee Ee, Jasni Mohamad Zain
Iraqi Journal for Computer Science and Mathematics
Digital watermarking is crucial in content identification and copyright protection, particularly multimedia and medical imaging. This paper introduces two novel hybrid watermarking methods, Entropy-Guided Singular Embedding (EGSE) and Entropy-Guided Hybrid Embedding (EGHE), that improve upon existing techniques by integrating entropy-based adaptive block selection with Particle Swarm Optimization (PSO) for dynamic embedding strength determination. Unlike traditional methods, which rely on fixed embedding regions or manual parameter tuning, the proposed approaches automatically identify high-entropy regions to embed watermark signals, ensuring stronger resistance to distortion while maintaining image quality. EGSE employs Integer Wavelet Transform (IWT) and Singular Value Decomposition (SVD), whereas EGHE enhances …
Retracted: Efficient Multi-User Computation Offloading And Reducing Latency In Mobile-Edge Computing For Iot Applications, Sarmad T. Abdul-Samad, Osamah Al-Hwaidi, Ali Abd Al-Rasool Muslem
Retracted: Efficient Multi-User Computation Offloading And Reducing Latency In Mobile-Edge Computing For Iot Applications, Sarmad T. Abdul-Samad, Osamah Al-Hwaidi, Ali Abd Al-Rasool Muslem
Iraqi Journal for Computer Science and Mathematics
Mobile Edge Computing (MEC) is an inventive paradigm for computing that has the potential to notably diminish latency and energy consumption by transferring computationally demanding jobs to edge clouds near intelligent mobile users. This investigation aims to reduce offloading and latency between multiple users and edge computing in the context of Internet of Things (IoT) applications in the fifth generation (5G) by utilizing an optimization algorithm called the Bald Eagle Search Optimization Algorithm. Although employing deep learning methods might increase time consumption and computational complexity, an edge computing system enables devices to transfer their demanding jobs to edge servers, decreasing …
Retracted: Iot Flow Parameters Classification Based On Machine Learning Techniques, El-Sayed M. El-Kenawy, Marwa M. Eid, Ban Salman Shukur, Amel Ali Alhussan, Doaa Sami Khafaga
Retracted: Iot Flow Parameters Classification Based On Machine Learning Techniques, El-Sayed M. El-Kenawy, Marwa M. Eid, Ban Salman Shukur, Amel Ali Alhussan, Doaa Sami Khafaga
Iraqi Journal for Computer Science and Mathematics
In recent years, there has been a highly remarkable convergence of artificial intelligence (AI) and the Internet of Things (IoT), which has made rapid progress in smart city initiatives by developing smart devices for such cities. Since these devices are increasingly diversified, they require a resilient communication network to demonstrate high performance in managing consistent traffic flows. A machine learning model intended for identifying network parameters from diverse devices, in addition to proposing modifications meant for network performance enhancement, is developed in this study. In relation to packet data as a network traffic parameter, employing gateway devices can facilitate its …
Retracted: Metaguard: A Federated Learning Approach To Hybrid Xgboost And Meta-Learning Models For Proactive Cyber Threat Hunting, Shatha H. Jafer Al-Khalisy, Ghada Al-Kateb
Retracted: Metaguard: A Federated Learning Approach To Hybrid Xgboost And Meta-Learning Models For Proactive Cyber Threat Hunting, Shatha H. Jafer Al-Khalisy, Ghada Al-Kateb
Iraqi Journal for Computer Science and Mathematics
In an increasingly interconnected world, cybersecurity threats have become more sophisticated, necessitating advanced, scalable, and privacy-preserving solutions. MetaGuard emerges as a novel framework that integrates federated learning with hybrid machine learning models, specifically XGBoost and meta-learning, to enhance proactive cyber threat detection. This framework offers a robust, distributed approach to cybersecurity, ensuring high detection accuracy while preserving user privacy through the implementation of differential privacy and homomorphic encryption. MetaGuard leverages distributed nodes to collaboratively train a global model, enabling rapid adaptation to new threats without the need for centralized data aggregation. Experimental evaluations using the CYBER-2024 dataset demonstrate that MetaGuard …
Csa-Xai: Channel–Spatial Attention And Explainable Ai In A Modular Multi-Backbone Framework For Lung Cancer Classification, Omar Ibrahim Obaid, Abdulbasit Alazzawi
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 …
Class Weighting In Imbalanced Data For Leukocyte Classification Using Fine-Tuning Inception-V3, Wahyudi Setiawan, Meidya Koeshardianto, Eka Mala Sari Rochman, Aeri Rachmad, Tutut Herawan
Class Weighting In Imbalanced Data For Leukocyte Classification Using Fine-Tuning Inception-V3, Wahyudi Setiawan, Meidya Koeshardianto, Eka Mala Sari Rochman, Aeri Rachmad, Tutut Herawan
Iraqi Journal for Computer Science and Mathematics
This study investigates the classification of leukocyte images in an imbalanced dataset using deep learning techniques. The dataset consists of 14,514 images, categorized into five leukocyte types: basophils (301), neutrophils (8,891), lymphocytes (3,461), monocytes (795), and eosinophils (1,066). To address class imbalance, we applied class weighting alongside transfer learning and fine-tuning using the Inception-v3 architecture. The dataset was split into 80% for training and 20% for testing, and 5-fold cross-validation was conducted to evaluate model robustness. Hyperparameters were set with a learning rate of 0.0001, batch size of 32, and 30 training epochs, optimized using the Adam optimizer. Fine-tuning was …
Symmetric Toeplitz Matrices For A Class Of New Subclass Functions And Bazilevič Functions, Nihad H. Shehab, Abdul Rahman S. Juma
Symmetric Toeplitz Matrices For A Class Of New Subclass Functions And Bazilevič Functions, Nihad H. Shehab, Abdul Rahman S. Juma
Iraqi Journal for Computer Science and Mathematics
In this work, we describe and investigate a novel collection of analytic functions, including the new functions and the Bazilevič functions. An important component of analytic functions, Bazilevič functions have numerous uses in both pure and practical mathematics. For this class of functions, we concentrate on building Toeplitz matrices, examining their structural characteristics, and evaluating their eigenvalues and trends. We utilise these studies to draw sophisticated mathematical conclusions on the stability and convergence characteristics of Bazilevič functions, as well as possible uses in geometry and differential equations. This work aims to determine coefficient estimates for the functions in this family …
Retracted: Mathematical Properties And Simulations Of The Neutrosophic Gompertz-Inverse Burr-X Distribution With Application To Under-Five Mortality, Mustafa Hassan Jumaa, Asmaa S. Qaddoori, Sara A. Khalaf, Nooruldeen A. Noori, Mundher A. Khaleel
Retracted: Mathematical Properties And Simulations Of The Neutrosophic Gompertz-Inverse Burr-X Distribution With Application To Under-Five Mortality, Mustafa Hassan Jumaa, Asmaa S. Qaddoori, Sara A. Khalaf, Nooruldeen A. Noori, Mundher A. Khaleel
Iraqi Journal for Computer Science and Mathematics
Despite significant progress in the development of statistical distributions, there remain clear gaps in modelling complex datasets, such as those involving uncertainty or requiring flexible representations of multidimensional variables. This study introduces a new distribution the Neutrosophic Gompertz-Inverse Burr-X (NGoIB-X) distribution to address these challenges. The model is based on the Neutrosophic Gompertz family (NGo-G), which itself employs the T-X method in its formulation. Characterised by four Neutrosophic parameters and a Neutrosophic random variable, the NGoIB-X distribution offers enhanced flexibility for representing and analysing uncertain data. The theoretical properties of the NGoIB-X distribution are explored, including its Neutrosophic probability density …
A New Lightweight Encryption Method Based On The Dna-Rc4 Substitution For Resource-Constrained Iot Devices, Athraa J. H. Witwit, Ahmed Fanfakh, Ali Kadhum Idrees
A New Lightweight Encryption Method Based On The Dna-Rc4 Substitution For Resource-Constrained Iot Devices, Athraa J. H. Witwit, Ahmed Fanfakh, Ali Kadhum Idrees
Iraqi Journal for Computer Science and Mathematics
Technology's impact on daily life necessitates increased data protection. Cryptographic systems improve security, ensuring confidentiality and legitimacy of Internet of Things systems. However, resource-constrained devices face challenges like memory, battery life, processing power, and small size. In light of this, lightweight cryptography (LWC) provides techniques specifically tailored to the constraints of resource-constrained Internet of Things devices. However, the presence of a fixed S-Box in some LWC algorithms, such as Advanced Encryption Standard, or the absence of one in others, such as Speck and Tiny Encryption Algorithm, renders them more susceptible to attacks. In this paper, we suggest a new lightweight …
Kernel Principal Component Analysis And Convolutional Neural Network-Based Approach For Obstructive Sleep Apnoea Detection Using Electrocardiogram, Aida Noor Indrawati, Nuryani Nuryani, Wiharto Wiharto, Diah Kurnia Mirawati, Trio Pambudi Utomo, Nanang Wiyono
Kernel Principal Component Analysis And Convolutional Neural Network-Based Approach For Obstructive Sleep Apnoea Detection Using Electrocardiogram, Aida Noor Indrawati, Nuryani Nuryani, Wiharto Wiharto, Diah Kurnia Mirawati, Trio Pambudi Utomo, Nanang Wiyono
Iraqi Journal for Computer Science and Mathematics
Obstructive Sleep Apnoea (OSA) is a prevalent sleep disorder characterised by repeated episodes of partial or complete upper airway obstruction during sleep, primarily due to the relaxation and collapse of soft tissues in the throat. These interruptions lead to disrupted sleep patterns and reduced oxygen saturation, increasing the risk of cardiovascular complications. Although Polysomnography (PSG) is considered the gold standard for diagnosing OSA, it is often uncomfortable for patients due to the extensive use of sensors and prolonged monitoring duration. As a result, there is a growing need for alternative diagnostic methods that are more efficient, comfortable, and cost-effective. This …
Iraqi’S Car License Plate Recognition Based On Deep Learning, Mushreq Abdulhussain Shuriji, Husam Al-Behadili, Hadel A. Hussain
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: High-Performance System For Predicting Icu Patient Durations Using Artificial Neural Networks With Transfer Learning, Mahmood. K. Awsaj, Yousif Al Mashhadany, Lamia Chaarifourati
Retracted: High-Performance System For Predicting Icu Patient Durations Using Artificial Neural Networks With Transfer Learning, Mahmood. K. Awsaj, Yousif Al Mashhadany, Lamia Chaarifourati
Iraqi Journal for Computer Science and Mathematics
In the wake of disease outbreaks such as COVID-19, real-time health monitoring and prediction systems have become essential for ensuring effective patient care. These systems rely on sensors to monitor biometric parameters such as blood pressure, body temperature, and heart rate, providing continuous and accurate data that medical staff cannot collect manually around the clock. This study presents a robust framework for managing Intensive Care Unit (ICU) patients using Artificial Neural Networks (ANN) with Transfer Learning. The data is analyzed across five distinct time windows, each representing a period of ICU stay based on vital signs and medical test results. …
Retracted: Dynamics And Stability Analysis Of 8-Dimensional Hyperchaotic Systems: Study Of Lyapunov Exponents, Abdulsattar Abdullah Hamad, Nida Muhsin Ali, Muayyad Mahmood Khalil
Retracted: Dynamics And Stability Analysis Of 8-Dimensional Hyperchaotic Systems: Study Of Lyapunov Exponents, Abdulsattar Abdullah Hamad, Nida Muhsin Ali, Muayyad Mahmood Khalil
Iraqi Journal for Computer Science and Mathematics
This research explores the complex dynamics of an 8D hyperchaotic system, focusing on its trajectory stability and behavior under various control parameters. By using numerical simulations, we investigate the relationship between the Lyapunov components and the stability of the system. It provides a quantitative measure of the chaos within the model of the matrix, whose derivation consists of the system's sensitivity to perturbation, simulating chaotic systems in high dimensions faces challenges such as high computational resource demands. Difficulty in ensuring numerical convergence and stability. The results highlight the profound impact of control parameters on the dynamic behavior of hyperchaotic systems. …
Corneal Elevation Maps Patterns Classification Using Correlation Method, Sura M. Ahmed, Nebras H. Ghaeb, Salman Yussof, Noor T. Al-Sharify, Husam Yahya Nser, Zainab T. Al-Sharify, Ong Hang See, Leong Yeng Weng
Corneal Elevation Maps Patterns Classification Using Correlation Method, Sura M. Ahmed, Nebras H. Ghaeb, Salman Yussof, Noor T. Al-Sharify, Husam Yahya Nser, Zainab T. Al-Sharify, Ong Hang See, Leong Yeng Weng
Iraqi Journal for Computer Science and Mathematics
The ophthalmologist uses various techniques to diagnose corneal abnormalities, including corneal topography and tomography devices. Currently generated color corneal elevation surface maps from topographic imaging devices are essential for detecting ocular diseases, while accurately classifying these maps to differentiate between different shapes remains an issue. This study aims to assess and compare parameters of the front and back corneal surface elevation map patterns of normal/abnormal corneas. Two hundred cases were randomly taken (100 normal and 100 abnormal) with a single normal reference image, and then an additional 25 cases were added later for the optimization process. The preprocessing of all …
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
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 …
Enhanced Restoration Of Covid-19 Ct Scan Images Utilizing Advanced Wiener Filtering Techniques, Warqaa Shaher Alazawee, Marwa Subhi Ibrahim, Raghda Salam Al Mahdawi, Ali Albu-Rghaif, Ahmed Sabri Altaie
Enhanced Restoration Of Covid-19 Ct Scan Images Utilizing Advanced Wiener Filtering Techniques, Warqaa Shaher Alazawee, Marwa Subhi Ibrahim, Raghda Salam Al Mahdawi, Ali Albu-Rghaif, Ahmed Sabri Altaie
Iraqi Journal for Computer Science and Mathematics
COVID-19, caused by the SARS-CoV-2 virus, was declared a global pandemic by the World Health Organization (WHO) and rapidly spread worldwide from late 2019. While Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the primary diagnostic tool, its sensitivity ranges from only 60% to 70%, leading to false negatives. Computed Tomography (CT) imaging has emerged as a valuable alternative for accurate diagnosis; however, the quality of CT images is often degraded by motion-induced blur and additive noise, particularly in children, individuals with mental health conditions, or those with phobias of CT scans. This study aims to enhance COVID-19 CT image quality …
Motionfusion: A Robust Ensemble Learning Framework For Accurate Sensor-Based Human Activity Recognition, Hussein K. Almulla, Hussam J. Mohammed, Alaa S. Al-Waisy, Shumoos Al-Fahdawi, Ahmed Adnan Had, Bourair Al-Attar
Motionfusion: A Robust Ensemble Learning Framework For Accurate Sensor-Based Human Activity Recognition, Hussein K. Almulla, Hussam J. Mohammed, Alaa S. Al-Waisy, Shumoos Al-Fahdawi, Ahmed Adnan Had, Bourair Al-Attar
Iraqi Journal for Computer Science and Mathematics
Human activity Recognition (HAR) has emerged as an important research area due to its potential applications in health, sport, and recreation. The widespread availability of smartphone sensors has facilitated data collection for HAR systems. Although machine learning and deep learning models have proven to be effective in detecting human activity from sensor data, their performance may be limited, this study proposes MotionFusion which is an ensemble learning model to increase HAR accuracy utilizing accelerometer and gyroscope data from a smartphone. By combining Histogram-Based Gradient Boosting, Random Forest, and Extra Trees models with a Support Vector Machine classifier and using feature …
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
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 …
Retracted: Solving Time-Fractional Nonlinear Variable-Order Delay Pdes Using Feedforward Neural Networks, Hala S. Alruhaili, Adel S. Hussain, Abdullah M. S. Ajlouni, Funda Türk, Emad A. Az-Zo’Bi, Mohammad A. Tashtoush
Retracted: Solving Time-Fractional Nonlinear Variable-Order Delay Pdes Using Feedforward Neural Networks, Hala S. Alruhaili, Adel S. Hussain, Abdullah M. S. Ajlouni, Funda Türk, Emad A. Az-Zo’Bi, Mohammad A. Tashtoush
Iraqi Journal for Computer Science and Mathematics
This study presents an innovative application of Feedforward Neural Networks ‘FNNs’ to solve Variable-Order Fractional Partial Differential Equations ‘VO-FPDEs’ with time delays. Utilizing the Caputo definition, the variable-order fractional derivatives are approximated in terms of integer-order derivatives. The problem is reformulated as a system of partial differential equations with delay terms, which is then addressed using ‘FNNs’ to achieve explicit approximate solutions. Comprehensive error and convergence analyses validate the method’s precision and reliability. The effectiveness of the proposed approach is highlighted through numerical examples, with graphical and tabular representations showcasing minimal absolute errors and robust convergence. These results demonstrate the …