Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Deep learning (22)
- Machine learning (14)
- Classification (5)
- CNN (4)
- Computer vision (4)
-
- Convolutional neural network (4)
- Copyright protection (4)
- Cybersecurity (4)
- Optimization (4)
- Social media (4)
- COVID-19 (3)
- Deep Learning (3)
- Healthcare (3)
- Image processing (3)
- Internet of things (3)
- IoT (3)
- Watermarking (3)
- ANN (2)
- Artificial Intelligence (2)
- Artificial intelligence (2)
- Attention mechanisms (2)
- Breast cancer (2)
- Energy efficiency (2)
- Ensemble model (2)
- Feature extraction (2)
- Feature selection (2)
- Federated learning (2)
- Image denoising (2)
- Internet of Things (IoT) (2)
- Intrusion detection system (IDS) (2)
Articles 61 - 90 of 235
Full-Text Articles in Computer Engineering
Retraction Notice: Adaptive Crossover And Mutation Mechanisms For Enhanced Lpb Algorithm Performance, Iraqi Journal For Computer Science And Mathematics
Retraction Notice: Adaptive Crossover And Mutation Mechanisms For Enhanced Lpb Algorithm Performance, Iraqi Journal For Computer Science And Mathematics
Iraqi Journal for Computer Science and Mathematics
NOTICE OF RETRACTION FOR: Ahmed, Abbas M. and Rashid, Tarik A. (2025) ``Adaptive Crossover and Mutation Mechanisms for Enhanced LPB Algorithm Performance,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 3, Article 48. DOI: https://doi.org/10.52866/2788-7421.1323.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/48.
Retraction Notice: Accurate Electrocardiogram Classification Of Heart Disease Using Deep Learning Network, Iraqi Journal For Computer Science And Mathematics
Retraction Notice: Accurate Electrocardiogram Classification Of Heart Disease Using Deep Learning Network, Iraqi Journal For Computer Science And Mathematics
Iraqi Journal for Computer Science and Mathematics
NOTICE OF RETRACTION FOR: Saleh, Hadeel M.; Ahmed, Sahar Hamad; and Mahmoud, Akeel Sh. (2025) ``Accurate Electrocardiogram Classification of Heart Disease Using Deep Learning Network,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 2, Article 18. DOI: https://doi.org/10.52866/2788-7421.1259.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss2/18.
Retraction Notice: A Review Of Breast Cancer Histological Image Classification: Challenges And Limitations, Iraqi Journal For Computer Science And Mathematics
Retraction Notice: A Review Of Breast Cancer Histological Image Classification: Challenges And Limitations, Iraqi Journal For Computer Science And Mathematics
Iraqi Journal for Computer Science and Mathematics
NOTICE OF RETRACTION FOR: Jassam, Israa Faisal; Mukhlif, Abdulrahman Abbas; Nafea, Ahmed Adil; Tharthar, Mustafa Adnan; and Khudhair, Ahmed Isam (2025) ``A Review of Breast Cancer Histological Image Classification: Challenges and Limitations,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 1, Article 1. DOI: https://doi.org/10.52866/2788-7421.1232.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss1/1.
Retraction Notice: A Quantum Convolutional Neural Network Approach For Early And Accurate Diagnosis Of Parkinson's Disease, Iraqi Journal For Computer Science And Mathematics
Retraction Notice: A Quantum Convolutional Neural Network Approach For Early And Accurate Diagnosis Of Parkinson's Disease, Iraqi Journal For Computer Science And Mathematics
Iraqi Journal for Computer Science and Mathematics
NOTICE OF RETRACTION FOR: Ibrahim, Aiesha Mahmoud; Mohammed, Mazin Abed; and Al-Boridi, Omar (2025) ``A Quantum Convolutional Neural Network Approach for Early and Accurate Diagnosis of Parkinson's Disease,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 3, Article 13. DOI: https://doi.org/10.52866/2788-7421.1288.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/13.
Retraction Notice: A Novel Scheme To Optimize Lsb Steganography Based On A Logistic Chaotic Map And Genetic Algorithm, Iraqi Journal For Computer Science And Mathematics
Retraction Notice: A Novel Scheme To Optimize Lsb Steganography Based On A Logistic Chaotic Map And Genetic Algorithm, Iraqi Journal For Computer Science And Mathematics
Iraqi Journal for Computer Science and Mathematics
NOTICE OF RETRACTION FOR: Laila, Dena Abu; obeidt, Ibrahim Moh'd; Aljaidi, Mohammad; Almaiah, Mohammed Amin; AlBourini, Muneer; Al-Na'amneh, Qais; Samara, Ghassan; Shehab, Rami; and Momani, Khaled (2025) ``A Novel Scheme to Optimize LSB Steganography Based on a Logistic Chaotic Map and Genetic Algorithm,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 2, Article 24. DOI: https://doi.org/10.52866/2788-7421.1265.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss2/24.
Retraction Notice: A Novel Benchmarking Framework For Selecting The Best Deep Learning Model Diagnosing Covid-19 Based On New Development For Dual Mcdm Methods, Iraqi Journal For Computer Science And Mathematics
Retraction Notice: A Novel Benchmarking Framework For Selecting The Best Deep Learning Model Diagnosing Covid-19 Based On New Development For Dual Mcdm Methods, Iraqi Journal For Computer Science And Mathematics
Iraqi Journal for Computer Science and Mathematics
NOTICE OF RETRACTION FOR: Salih, Mahmood M.; Muhsen, Yousif Raad; Ahmed, M.A.; Ismael, Reem D.; Shuwandy, Moceheb Lazam; and Al-qaysi, Z.T. (2025) ``A Novel Benchmarking Framework for Selecting the Best Deep Learning Model Diagnosing COVID-19 Based on New Development for Dual MCDM Methods,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 3, Article 2. DOI: https://doi.org/10.52866/2788-7421.1276.
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/2.
Retraction Notice: A Group Decision-Making For Selecting Multi-Deep Face Recognition Models, Iraqi Journal For Computer Science And Mathematics
Retraction Notice: A Group Decision-Making For Selecting Multi-Deep Face Recognition Models, Iraqi Journal For Computer Science And Mathematics
Iraqi Journal for Computer Science and Mathematics
NOTICE OF RETRACTION FOR: Alazzawi, Abdulbasit; Yas, Qahtan M.; and Albayati, Burhan (2025) ``A Group Decision-Making for Selecting Multi-Deep Face Recognition Models,'' Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 2, Article 21. DOI: https://doi.org/10.52866/2788-7421.1262. Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss2/21.
A Contextual Attention-Based Transformer Model For Enhanced Hate Speech Detection On Twitter, Mira Mansour, Nancy Akoum, Seifedine Kadry
A Contextual Attention-Based Transformer Model For Enhanced Hate Speech Detection On Twitter, Mira Mansour, Nancy Akoum, Seifedine Kadry
Iraqi Journal for Computer Science and Mathematics
Hate speech on social media poses significant societal challenges, necessitating accurate and context-sensitive automated detection. Traditional machine learning (ML) models typically rely on lexical or superficial features, limiting their ability to capture nuanced or contextually ambiguous expressions of hate speech. Recent transformer-based methods (e.g., RoBERTa) provide improved contextual understanding but often lack explicit mechanisms guiding the model’s attention to critical semantic tokens, thereby reducing interpretability and sensitivity to nuanced linguistic contexts. This paper introduces a novel contextual attention-guided transformer model that explicitly incorporates lexicon-guided attention supervision into RoBERTa fine-tuning, significantly enhancing semantic precision in hate speech detection on Twitter. Evaluations …
A Proposed Secure Video Using Lightweight Chacha20 Based On Chaotic Map, Hadeel Mohammed Taher, Ali Makki Sagheer
A Proposed Secure Video Using Lightweight Chacha20 Based On Chaotic Map, Hadeel Mohammed Taher, Ali Makki Sagheer
Iraqi Journal for Computer Science and Mathematics
Multimedia content has become a necessary part of our daily lives in many domains, including medical imaging, surveillance, and entertainment. Among all multimedia categories, video is mainly critical because of its high content density and large size. For that, ensuring the security of video has become a necessity for transmission and storage. This paper proposed an enhancement to the Chacha algorithm integration with a hybrid chaotic map for generating keys to secure video. The main motivation for the selection ChaCha algorithm is that it is fast, simple, and appropriate for widespread applications, whereas the motivation for using chaos is to …
Application Of Adversarial Volumetric Cnns To 3d Face Generation Using Latent Space Gaussian Embeddings, Ali Raad Abdulkareem, Marwa Jabberi, Islem Jarraya, Tarek M. Hamdani, Khmaies Ouahada, Adel M. Alimi
Application Of Adversarial Volumetric Cnns To 3d Face Generation Using Latent Space Gaussian Embeddings, Ali Raad Abdulkareem, Marwa Jabberi, Islem Jarraya, Tarek M. Hamdani, Khmaies Ouahada, Adel M. Alimi
Iraqi Journal for Computer Science and Mathematics
Although 3D face generation is extensively studied in computer vision, most existing methods prioritize reconstructing 3D geometry from available 2D or 3D inputs rather than generating novel faces directly from latent representations. To bridge this gap, we present the application of Adversarial Volumetric Convolutional Neural Networks (AVCNN), a tailored adaptation of the vanilla 3D Generative Adversarial Network (3D-GAN), to 3D face generation using latent space Gaussian embeddings. We first assemble a custom 3D facial dataset to provide the requisite facial characteristics and to ensure sufficient coverage of geometric variation across identities. The generator, implemented as a decoder, maps latent space …
Retracted: Fuzzy Backstepping Approach To Stabilizing Fuzzy Parabolic Partial Differential Equations, Zainab John, Fadhel S. Fadhel, Samsul Ariffin Abdul Karim, Teh Yuan Ying
Retracted: Fuzzy Backstepping Approach To Stabilizing Fuzzy Parabolic Partial Differential Equations, Zainab John, Fadhel S. Fadhel, Samsul Ariffin Abdul Karim, Teh Yuan Ying
Iraqi Journal for Computer Science and Mathematics
In this work,\floatquery[-14pc]AU: Please provide ORCID ID for remaining authors. we study the solvability and stability of the fuzzy reaction-diffusion equation and the fuzzy diffusion equation with nonhomogeneous boundary conditions. Firstly, we derive the fuzzy exact solutions by converting the nonhomogeneous boundary condition to a homogeneous condition, then we prove the instability of the solution by using simulation approach with the help of Maple program. From this, we have decided to implement the fuzzy backstepping approach to developed the stabilization of the fuzzy parabolic partial differential equations. By using this approach together with the generalized Hukuhara (gH) derivative, we were …
Improving Road Safety Through Multimodal Deep Learning For Driver Drowsiness Detection, Hadel A. Hussain, Mohammed A. Subhi, Ahmed S. Al Tmeme, Ahmed D. Radhi, Marwan Ali Albahar
Improving Road Safety Through Multimodal Deep Learning For Driver Drowsiness Detection, Hadel A. Hussain, Mohammed A. Subhi, Ahmed S. Al Tmeme, Ahmed D. Radhi, Marwan Ali Albahar
Iraqi Journal for Computer Science and Mathematics
One of the most common causes of road accidents globally is driver drowsiness and it needs solutions that are reliable and can be applicable in numerous real-life situations. We present this paper with the aim of developing a deep-learning system that is capable of reliably detecting drowsiness in diverse and varied conditions across different drivers, environments, and sensor types. Our system is known as Multimodal Attention Network (MMAN), which combines information of eye and head movement, heart-rate and breathing pattern, and vehicle-dynamics signal. MMAN has a gradient-reversal layer that enables the layer to be domain-adaptive such that it does not …
Deep Learning-Based Fog-Cloud Approach Intrusion Detection System In Iomt, Yahya Rbah, Mohammed Mahfoudi, Mohammed Fattah, Younes Balboul, Said Mazer, Moulhime Elbekkali
Deep Learning-Based Fog-Cloud Approach Intrusion Detection System In Iomt, Yahya Rbah, Mohammed Mahfoudi, Mohammed Fattah, Younes Balboul, Said Mazer, Moulhime Elbekkali
Iraqi Journal for Computer Science and Mathematics
The Internet of Medical Things (IoMT) creates an interconnected environment linking humans, devices, sensors, and systems, enhancing healthcare services through advanced technologies. Nonetheless, these IoMT devices are susceptible to cyberattacks, which can endanger patient safety and healthcare services. To identify and mitigate cyberattacks in IoMT, techniques such as threat intelligence, log monitoring, and intrusion detection systems are employed. As attackers evolve their strategies, there is a growing trend towards leveraging artificial intelligence to achieve more predictive and accurate attack detection. Since IoMT devices are inherently low-power, they require minimal computing resources. Existing intrusion detection systems are generally trained in the …
Multitaskvenationnet: A Multi-Task Deep Neural Network With Strip Pooling And Hybrid Upsampling For Leaf Vein Segmentation, Ishak Ariawan, Ahmad Ashari, Moh. Edi Wibowo
Multitaskvenationnet: A Multi-Task Deep Neural Network With Strip Pooling And Hybrid Upsampling For Leaf Vein Segmentation, Ishak Ariawan, Ahmad Ashari, Moh. Edi Wibowo
Iraqi Journal for Computer Science and Mathematics
Leaf vein segmentation is a critical task in plant phenotyping and species classification, yet it remains challenging due to the hierarchical, curvilinear nature of veins and interference from complex backgrounds. Existing methods face three key limitations. First, they lack directional context modeling, leading to blurred vein boundaries and the omission of fine venation. Second, they fail to effectively capture global dependencies, limiting semantic coherence across spatial regions. Third, they do not incorporate explicit mechanisms for detecting vein discontinuities, which is essential for complete topological understanding. To address these challenges, we propose MultiTaskVenationNet (MTV-Net), a multi-task deep segmentation framework that integrates …
Artificial Intelligence In Higher Education: Opportunities And Challenges, Maytha Al-Ali, Adam Marks, Jasur Umirzokov, Noura Metawa
Artificial Intelligence In Higher Education: Opportunities And Challenges, Maytha Al-Ali, Adam Marks, Jasur Umirzokov, Noura Metawa
Iraqi Journal for Computer Science and Mathematics
While AI is often presented as a panacea for the challenges facing higher education, there is limited empirical evidence supporting its effectiveness in improving student learning and institutional performance. This gap between expectation and reality emphasizes the need for rigorous research, realistic goal-setting, and careful planning to ensure that AI technologies deliver on their promises in higher education. This study contrasts the potential utilization of AI technologies in Higher Education from the literature, against actual utilization in universities. The study also investigates the key barriers of AI implementation in higher education. This study uses a mixed-research methods approach, including case …
Retracted: Ai-Driven Flood Prediction, Monitoring, And Warning Systems: Design, Evaluation, And Simulation, Abdel Rahman A. Alkharabsheh, Lina M. Momani
Retracted: Ai-Driven Flood Prediction, Monitoring, And Warning Systems: Design, Evaluation, And Simulation, Abdel Rahman A. Alkharabsheh, Lina M. Momani
Iraqi Journal for Computer Science and Mathematics
The Climate is becoming increasingly unpredictable, while the incidence of extreme weather is on the rise — both are contributing to surging global demand for advanced flood forecasting and monitoring services. This paper introduces an AI-powered Flood Monitoring and Warning System (FMWS) through an IoT sensor network, scalable real-time data analytics, and Machine Learning (ML) models to enhance the accuracy of prediction, risk analysis, and early warning dissemination in the notified areas. Hydrological and meteorological data would be collected by an ultrasonic sensor, a radar sensor, and a pressure sensor interfacing via GSM/GPRS, Wi-Fi, LoRa, or satellite network links. Machine …
Blind Medical Image Watermarking Method Using Combined Nsct/2d-Dct Domains, Ali Kouadri, Ali Benziane, Abdelhalim Rabehi, Mohamed Lebcir
Blind Medical Image Watermarking Method Using Combined Nsct/2d-Dct Domains, Ali Kouadri, Ali Benziane, Abdelhalim Rabehi, Mohamed Lebcir
Iraqi Journal for Computer Science and Mathematics
This paper presents a novel blind watermarking framework that combines Non-Subsampled Contourlet Transform (NSCT) and 2D Discrete Cosine Transform (DCT) domains. The proposed method embeds the watermark’s data within low-frequency coefficients of sub-vectors extracted from a concatenated NSCT/2D-DCT transforms processing. The embedding procedure involves simple differential processing which ensures a straightforward watermark extraction. Extensive testing across medical imaging modalities (X-ray, CT, MRI, ultrasound) confirmed strong imperceptibility and robustness against attacks like JPEG compression, noise, filtering, and geometric manipulations. Compared to contemporary techniques, our NSCT/2D-DCT method shows stronger robustness against attacks without compromising diagnostic quality, proving its viability for medical image …
An Enhanced Cyber Security For Finger Knuckle Print Recognition System Using Rubik’S Cube With Rabbit Encryption Algorithm, Haitham Salman Chyad, Tarek Abbes
An Enhanced Cyber Security For Finger Knuckle Print Recognition System Using Rubik’S Cube With Rabbit Encryption Algorithm, Haitham Salman Chyad, Tarek Abbes
Iraqi Journal for Computer Science and Mathematics
Cybersecurity in biometric systems is an urgent requirement due to their increasing use in identity verification, especially in smartphones, surveillance systems, and electronic transactions. These systems depend on distinctive and immutable biological features, such as fingerprints, Knuckles and facial features, making them potential targets for cyberattacks. In this context, the need to develop advanced security mechanisms, including encryption, forgery detection, and multi-factor authentication, has emerged to guarantee the confidentiality of biometric data and protect it from identity theft or manipulation. This trend emphasizes the need to integrate cybersecurity and biometric technologies to secure and ensure the reliability of systems in …
Recognition Of Bangla And English Words In Bengali Texts Using A Modified Bert-Base-Ner Model, Md Parvez Hossain, Ohidujjaman Ohidujjaman, Mohammad Shorif Uddin, Mohammad Nurul Huda, Tetsuya Shimamura
Recognition Of Bangla And English Words In Bengali Texts Using A Modified Bert-Base-Ner Model, Md Parvez Hossain, Ohidujjaman Ohidujjaman, Mohammad Shorif Uddin, Mohammad Nurul Huda, Tetsuya Shimamura
Iraqi Journal for Computer Science and Mathematics
A fusion of Bangla and English words is frequently utilized, especially on social media. This phenomenon significantly hampers the learning and preservation of the Bengali language among future generations. This paper proposes a model to recognize Bangla and English words in Bengali texts. In addition, this study converts the detected English words into standard Bangla words. In this work, we redesign the BERT-base-NER model using the training input dataset. BERT is chosen for its strong contextual representation capabilities, which are well-suited to noisy and informal text. BERT-base-NER provides strong contextual embeddings but treats token labels independently, lacking explicit modeling of …
A Novel Hybrid Intrusion Detection Model: A New Metaheuristic Approach For Feature Selection Based On Ai Techniques For Cyber Threat Detection, Maryam Mahdi Alhusseini, Alireza Rouhi
A Novel Hybrid Intrusion Detection Model: A New Metaheuristic Approach For Feature Selection Based On Ai Techniques For Cyber Threat Detection, Maryam Mahdi Alhusseini, Alireza Rouhi
Iraqi Journal for Computer Science and Mathematics
The rapid increase in internet usage, digital transformation, and the rise of interconnected devices have greatly expanded the attack surface, introducing new and evolving cybersecurity challenges. Conventional security solutions frequently have difficulty adjusting to complex threats and the vast dimensionality of network traffic data, particularly in the case of imbalanced datasets. To tackle these challenges, this research introduces a Hybrid Intrusion Detection System (HyIDS-EVO) that combines the Energy Valley Optimizer (EVO) for feature selection and dimensionality reduction with machine learning classifiers, which include Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbors (KNN). The system’s effectiveness …
A Robust And Energy-Efficient Federated Ids For Iiot Using Spiking Neural Networks And Differential Evolution With Adversarial Resilience, Mohammad Othman Nassar
A Robust And Energy-Efficient Federated Ids For Iiot Using Spiking Neural Networks And Differential Evolution With Adversarial Resilience, Mohammad Othman Nassar
Iraqi Journal for Computer Science and Mathematics
The Industrial Internet of Things (IIoT) is faced with increasing cybersecurity threats that require lightweight, fast, and resilient intrusion detection systems (IDS). This study presents a novel federated IDS framework that integrates federated learning (FL), Spiking Neural Networks (SNNs), and differential evolution (DE). The use of SNNs within a federated context is a rare and innovative contribution that enables effective temporal feature extraction from IIoT traffic. DE is employed as a global optimization mechanism, enhancing robustness and generalization beyond conventional federated aggregation. To further strengthen resilience, synthetic adversarial noise is injected during training, allowing evaluation in realistic poisoning scenarios. The …
Retracted: Analysis Of Energy Sector Co2 Emanations Using Wavelet-Based Numerical Technique, Yeshwanth R., Kumbinarasaiah S.
Retracted: Analysis Of Energy Sector Co2 Emanations Using Wavelet-Based Numerical Technique, Yeshwanth R., Kumbinarasaiah S.
Iraqi Journal for Computer Science and Mathematics
This study aims to present the modified Chebyshev wavelet collocation method (CWCM) to investigate and obtain the numerical approximation of $CO_2$ emissions from the energy sector utilizing the fractional mathematical model. The need for energy rises as the population grows. Burning fossil fuels produces a significant portion of the world's energy, which raises the atmospheric concentration of $CO_2$ and causes global warming. The combination of mathematical modeling studies and numerical simulations allows us to understand the $CO_2$ emissions from the energy sector. Our objective is to build an operational matrix of integration (OMI) based on Chebyshev wavelets and use it …
Study Of ~-Topological Space By ~-Binary Relation In Cluster System, Mustafa Hasan Hadi, Luay Abd Al Haine Al Swidi
Study Of ~-Topological Space By ~-Binary Relation In Cluster System, Mustafa Hasan Hadi, Luay Abd Al Haine Al Swidi
Iraqi Journal for Computer Science and Mathematics
In this paper, we introduced a new type of operators, which we called ~-operator by cluster system, and studied its properties without conditions and with certain conditions. Through our study of this operator, we defined a new topology, which we called ~-topological space, since it does not, in general, constitute a regular topological space. We also presented ~-interior points, ~-closure, ~-exterior points and reviewed some theories and examples about this concept.
Retracted: Adaptive Crossover And Mutation Mechanisms For Enhanced Lpb Algorithm Performance, Abbas M. Ahmed, Tarik A. Rashid
Retracted: Adaptive Crossover And Mutation Mechanisms For Enhanced Lpb Algorithm Performance, Abbas M. Ahmed, Tarik A. Rashid
Iraqi Journal for Computer Science and Mathematics
This study proposes a more effective concept of Learner Performance-based Behavior (LPB). It is a new metaheuristic algorithm based on how the university admission process is done for high school students in various departments. The adaptive crossover and mutation methods were incorporated into the LPB algorithm as part of an investigation. The goal is to enhance convergence and significantly improve the quality of the solutions. The aLPB (adaptive-learner performance-based Behaviour) method stands out because it sets the crossover and mutation parameters based on the performance of the parent solutions. Thus, the proposed technique achieves a balance between exploration and exploitation …
Iot-Enabled Machine Learning Framework For Prediction Of Eutrophication, Hocine Dai, Akli Abbas, Houssam Eddine-Othman Lachemat, Aicha Aid
Iot-Enabled Machine Learning Framework For Prediction Of Eutrophication, Hocine Dai, Akli Abbas, Houssam Eddine-Othman Lachemat, Aicha Aid
Iraqi Journal for Computer Science and Mathematics
This study presents an innovative predictive monitoring framework that integrates the Internet of Things (IoT) with advanced machine learning (ML) techniques to model the relationship between oxidized nitrate (NOX)—employed as the sole predictor—and chlorophyll a (CHLA), a key proxy for algal biomass. By utilising a single optimally selected parameter, the approach significantly reduces sensor deployment complexity and instrumentation costs, while minimising data acquisition and computational requirements. Logarithmic and Yeo-Johnson transformations were applied to the predictor and target variables, respectively, to address distributional skewness and enhance variance homogeneity. An optimised Random Forest model demonstrated strong predictive performance, achieving a coefficient of …
Multi Features Data Clustering Using Novel Statistical Method With Application To Color Images, Husham Y. A. Alameen, Ali Karah Bash
Multi Features Data Clustering Using Novel Statistical Method With Application To Color Images, Husham Y. A. Alameen, Ali Karah Bash
Iraqi Journal for Computer Science and Mathematics
The efficiency and performance of the color image clustering algorithms are determined by various factors, including accuracy, data size, speed, and reliability (the absence of randomness in the results). Some applications, like microscopes analyzing images of biological objects or telescopes observing planetary motion prioritize accuracy over execution time. In contrast, surveillance cameras and moving object tracking prioritize speed and reliability over accuracy. This study introduces a novel algorithm that balances these four factors by clustering data with multiple features linked through specific relationships. The proposed algorithm has been practically applied to RGB color images. Traditional clustering methods, such as K-means, …
A New Approach For Multiprocessor System-On-Chip Application Scheduling In Multi-Objective Flow Shops, Tahani Jabbar Khraibet, Bayda Atiya Kalaf, Ahmed Abbas Jasim
A New Approach For Multiprocessor System-On-Chip Application Scheduling In Multi-Objective Flow Shops, Tahani Jabbar Khraibet, Bayda Atiya Kalaf, Ahmed Abbas Jasim
Iraqi Journal for Computer Science and Mathematics
The flow shop scheduling problem in Multiprocessor-System-on-Chip (MPSoC) architectures presents challenges for traditional optimization algorithms, especially when addressing multiple conflicting objectives. Hence, advanced optimization approaches are required to tackle these objectives simultaneously. Therefore, this research aims to propose and evaluate a new optimization approach based on the integration of the Fire Hawk Optimizer with the Smart Battery Scheduling Algorithm (FHO-SBSA) to address the multi-objective (make span, CPU time, global average delay, network throughput, and total energy consumption) flow shop scheduling problem in MPSoC systems. To evaluate the performance of the FHO-SBSA optimization approach, two benchmark applications were selected, with ten …
Comparison Of Liu-Type Estimator For Multicollinearity In Fuzzy Logistic Regression Model, Ayad Habib Shemail, Ahmed Razzaq Al-Lami, Amal Hadi Rashid
Comparison Of Liu-Type Estimator For Multicollinearity In Fuzzy Logistic Regression Model, Ayad Habib Shemail, Ahmed Razzaq Al-Lami, Amal Hadi Rashid
Iraqi Journal for Computer Science and Mathematics
This article addresses the fuzzy logistic regression model under conditions of multicollinearity, which causes instability and inflated variance in parameter estimation. In this model, both the response variable and parameters are represented as fuzzy triangular numbers. To overcome the multicollinearity problem, various Liu-type estimators were employed: Fuzzy Maximum Likelihood Estimators (FMLE), Fuzzy Logistic Ridge Estimators (FLRE), Fuzzy Logistic Liu Estimators (FLLE), Fuzzy Logistic Liu-type Estimators (FLLTE), and Fuzzy Logistic Liu-type Parameter Estimators (FLLTPE). Through simulations with various sample sizes and application to real fuzzy data on kidney failure, model performance was evaluated using mean square error (MSE) and goodness of …
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
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 …
Two-Factor Authentication Software For Bluetooth Pairing Between Mobile And Pc Operating Systems, Sundos A. Hameed Alazawi, Abbas A. Abdulhameed, Mostafa Abdulghafoor Mohammed, Thekra Abbas
Two-Factor Authentication Software For Bluetooth Pairing Between Mobile And Pc Operating Systems, Sundos A. Hameed Alazawi, Abbas A. Abdulhameed, Mostafa Abdulghafoor Mohammed, Thekra Abbas
Iraqi Journal for Computer Science and Mathematics
Bluetooth devices actively broadcast software when pairing to connect. Even during the connection process, the connection can be monitored to view information about the transmission. Using this information, anyone can hijack your existing connection and steal data. Bluetooth connections can be single or multiple. Thus, while connecting to a device, the same device could be simultaneously connected to another device. To avoid this problem, a new software is proposed to support the ID-based authentication process for paired devices by integrating an authentication method based on the biometric features of the device owner. The proposed two-factor security authentication system for pairing …