Meta-reinforced Dual-layer Non-fragile Control for Resilient Load Frequency Regulation Under Hybrid Cyber-physical Attacks International Journal of Intelligent Engineering and Systems, 2026 The existence of com-modern power systems that are more susceptible to com-modern communications means that they can be affected by hybrid cyber-attacks like DDoS, False Data Injection (FDI), and replay attacks.Conventional load frequency control (LFC) systems have a hard time keeping up in these dynamic adversarial conditions.The presented paper proposes an alternative approach of improving robustness, flexibility, and efficiency of cyber-physical LFC systems using a Meta-Learning-Dual-layer Non-Fragile Control (MDL-NFC) framework.This proposed framework combines (1) a Non-Fragile PI controller (NFPI) at the bottom layer to ensure stability to perturbations of the controller parameters and delays, and (2) a Meta-Learning Reinforcement Layer at the top level that is based on the Model-Agnostic Meta-Learning (MAML) algorithm and can acquire new attack patterns fast by adjusting controller parameters and sampling periods.The results of the simulation in a two-area connected power system show the best performance of the proposed MDL-NFC model.It has a quicker frequency recovery time of 2.8 seconds, a lower overshoot of 0.02 Hz and zero steady-state error.Comparatively, the controller implemented using Q-learning restarts after 5.6 seconds and has 0.06 Hz overshoot, whereas classical controllers do so after 12.8 seconds.Also, the MDL-NFC compensates the rate of communication by 35 %t, which is indicative of its flexibility and bandwidth efficiency in the hybrid cyber-attacks.
Investigating feature extraction by SIFT methods for prostate cancer early detection Shadan Mohammed Jihad, Ali A. Alsaud, Firas H. Almukhtar, Shahab Kareem, Raghad Zuhair Yousif Egyptian Informatics Journal, 2025 Globally, for this leading type of cancer among males, early detection is indispensable for increasing treatment success rates and prognoses of the patients. This research study, therefore, seeks to explore the effectiveness of the SIFT method in improving feature extraction toward the accurate detection of incipient prostate cancer. The robust SIFT relates to tasks of object recognition within computer vision, in the recognition of prostatic regions where grey-level distributions differ remarkably between benign and malignant tissues. The adopted methodology was based on the comparative analysis and benchmarking of the performance of feature extraction based on SIFT against traditional image processing techniques with a generic representation on a number of metrics: sensitivity, specificity, and overall diagnostic accuracy. A dataset consisting of annotated prostate MRI images was utilized to train and validate the model. According to the results so far revealed, the SIFT model can isolate and recognize key features across different scales and angles far better than the cue given by any of the conventional methods currently in use, therefore indicating a much more accurate and reliable cue to early-stage prostate cancer. Besides, the model developed on SIFT was found to have significantly improved the rate of detection for early-stage prostate tumors, which usually go undetected in conventional methods of imaging. This study, therefore, highlights the potential for use in the early detection of prostate cancer with advanced feature extraction methods, such as SIFT, and points toward a very promising direction of further research on applying computer vision techniques to problems in medical diagnostic applications. It would, therefore, suggest further experimentations to optimize these methodologies in clinical settings, otherwise which may revolutionize clinical diagnostics for prostate cancer and early intervention strategies.
Lung cancer diagnosis through CT images using principal component analysis (PCA) and error correcting output codes (ECOC) Firas H. Almukhtar Journal of Control and Decision, 2024 Lung cancer has been a leading cause of cancer-related mortality in recent years, and early detection can increase patients’ chances of recovery. Machine learning and image processing may be used to analyze Computed Tomography (CT) scans for signs of lung cancer; by integrating several machine learning models, the accuracy of lung cancer diagnoses can be increased. In this paper, we propose a method that introduces a segmentation algorithm based on Social Spider Optimization (SSO) to detect suspicious regions in the CT image. The proposed method uses a combination of Error Correcting Output Codes (ECOC) and Support Vector Machine (SVM) to classify suspicious regions and diagnose lung cancer. The efficiency has been evaluated and compared with previous works. The results show that the proposed method can diagnose lung cancer with an average accuracy of 96.67% and can be used as an efficient tool for assisting specialists in diagnosing lung cancer.
Facial emotions recognition using local monotonic pattern and grey level co-occurrence matrices images aided development Firas H. Almukhtar Expert Systems, 2023 Abstract In this article, local monotonic pattern (LMP) paired with grey level co‐occurrence matrix (GLCM) methods are suggested to identify facial emotions with a high identification rate even when the face pictures are rotated. The proposed method extracts image features using the properties of the LMP algorithm and features extracted from the GLCM, which are then fed into the Support Vector Machine (SVM) process that reduces the dimensionality of the features vector and classifies the output into different facial expressions or emotions. The SVM performance rate is then compared to the K‐nearest neighbour approach (KNN) to see which method produces the best facial emotion identification and categorization. The study identified facial emotions in the images using advanced algorithms of GLCM and LMP models to be compared. As a result, the accuracy of SVM and KNN was utilized to determine the method's usefulness in classification using the application of MATLAB. A result of more than 93% was achieved using the SVM method compared with 89.4% using the KNN for the recognition process. The study also demonstrated that this approach would lead to more classification outcomes if the LMP and GLCM are combined with an edge‐based technique yielding a new method that is more efficient and more effective.
A computer vision-based lane detection technique using gradient threshold and hue-lightness-saturation value for an autonomous vehicle Md. Abdullah Al Noman, Zhai LI, Firas Husham Almukhtar, Md. Faishal Rahaman, Batyrkhan Omarov, Samrat Ray, Shahajan Miah, Chengping Wang International Journal of Electrical and Computer Engineering, 2023 <span lang="EN-US">Automatic lane detection for driver assistance is a significant component in developing advanced driver assistance systems and high-level application frameworks since it contributes to driver and pedestrian safety on roads and highways. However, due to several limitations that lane detection systems must rectify, such as the uncertainties of lane patterns, perspective consequences, limited visibility of lane lines, dark spots, complex background, illuminance, and light reflections, it remains a challenging task. The proposed method employs vision-based technologies to determine the lane boundary lines. We devised a system for correctly identifying lane lines on a homogeneous road surface. Lane line detection relies heavily on the gradient and hue lightness saturation (HLS) thresholding which detects the lane line in binary images. The lanes are shown, and a sliding window searching method is used to estimate the color lane. The proposed system achieved 96% accuracy in detecting lane lines on the different roads, and its performance was assessed using data from several road image databases under various illumination circumstances.</span>
UTILIZING FOURIER SERIES TO RECREATE COMPLEX TRIGONOMETRIC PATTERNS Journal of the Balkan Tribological Association, 2023
Usability Evaluation of Tablet-Based Electronic Medical Record Interface in Supporting Elderly Medical Doctors Hasanin Mohammed Salman, Firas Husham Almukhtar International Journal of Online and Biomedical Engineering, 2023 Recently, tablet-based devices have become significantly more utilized platforms for electronic medical record (EMR) systems. EMR is the digital counterpart of the medical doctor’s office paper charts. EMR systems contain the medical and treatment histories of the patients in a unified practice. Nevertheless, statistics indicate that a considerable percentage of medical doctors are elderly, aged 60 and above. As using mobile handheld devices (including tablets) poses a well-recognized usability challenge for elderly users, the user interface (UI) usability of tablet-based EMR systems must be thoroughly assessed, considering the needs of elderly medical doctors. Accordingly, our objective is to address this need. Three expert evaluators implemented the heuristic evaluation (HE) approach to evaluate the UI usability of a commercial EMR system that is a tablet-based platform. Applying the HE approach helped identify usability problems that elderly medical doctors might encounter when utilizing a tablet-based EMR UI. In total, eight usability problems contributed to the seven heuristic violations discovered.
Medical Image Categorization Combining Image Segmentation and Machine Learning Shahab Wahhab Kareem, Firas Husham Almukhtar, Ari Taha Guron, Hasanin Mohammed Salman 9th International Engineering Conference on Sustainable Technology and Development Iec 2023, 2023 The mortality rate has grown in recent years because of an increase in the frequency of encephalon tumors in each age group. In medical imaging, tumors are hard to see because of their complicated structure and noise, which makes it complex and time-consuming for specialists to find them. It is essential to find and pinpoint the tumor’s location at an early stage, so this is very important. Medical scans can be used to look for and predict cancerous spots at different levels. These scans can be combined with segmentation and relegation methods to help doctors make an early diagnosis, which can save a lot of time. Physical tumor identification has become a challenging and time-consuming process for medical practitioners due to the intricate structure of tumors and the involution of noise in magnetic resonance (MR) imaging data. As a result, detecting and pinpointing the site of cancer at an early stage is critical. Medical scans can be used with segmentation and relegation procedures to deliver an accurate diagnosis at an early stage in cancer tumor locations at various levels. This research offers a system based on machine learning for segmenting and classifying MRI images for brain tumor identification. As a side note, several machine learning algorithms, such as Naïve Bayes, Decision Tree, Nearest Neighbours, Random Forest, and SVM, have been explicitly used for automatically segmenting and labelling MRI scans of the brain to help in the detection of malignant growths, image segmentation, feature extraction, and classification.
Design Development of Machine Learning Secure Image Transmission Based Cooperative Communication and Gaussian Elimination International Journal of Intelligent Systems and Applications in Engineering, 2022
Meta-reinforced Dual-layer Non-fragile Control for Resilient Load Frequency Regulation Under Hybrid Cyber-physical Attacks. SA Rasool, SW Kareem, FH Almukhtar, AS Mohammed International Journal of Intelligent Engineering & Systems 19 (3), 523 , 2026 2026
Investigating feature extraction by SIFT methods for prostate cancer early detection SM Jihad, A Aalsaud, FH Almukhtar, S Kareem, RZ Yousif Egyptian Informatics Journal 29, 100607 , 2025 2025 Citations: 7
An optimized up to 16-user and 160 Gbps dual cascaded optical modulators PON-based power combined array fiber Bragg grating and pre-distortion device for 5th G system SK Jalal, RZ Yousif, FH Al-Mukhtar, SW Kareem Photonic Network Communications 49 (1), 1 , 2025 2025 Citations: 8
Multimodal Deep Learning for Video Classification RI Kadhim, F Al-Mukhtar, AT Guron, AK Shwayaa, TA Al-Sharify, B Al-Attar, ... 2024 8th International Symposium on Innovative Approaches in Smart … , 2024 2024 Citations: 13
Lung cancer diagnosis through CT images using principal component analysis (PCA) and error correcting output codes (ECOC) FH Almukhtar Journal of Control and Decision 11 (3), 472-482 , 2024 2024 Citations: 4
Enhanced passive optical network system-based VCSEL and PSK electrical modulator for 5th generation RZ Yousif, SK Jalal, FH Al-Mukhtar Wireless Networks 30 (4), 2203-2217 , 2024 2024 Citations: 13
Seamless Integration: Advanced Deep Learning Techniques for Image Stitching. SMJ Abdalwahd, A Aalsaud, FH Almukhtar, SW Kareem, RZ Yousif, ... Frontiers in Health Informatics 13 (3) , 2024 2024 Citations: 1
Identifying Smart Strategies for Effective Agriculture Solution Using Data Mining Techniques (Retraction of Vol 2022, art no 6600049, 2022) AJB Suarez, B Singh, FH Almukhtar, R Kler, S Vyas, K Kaliyaperumal JOURNAL OF FOOD QUALITY 2024 , 2024 2024
Design and development of an effective classifier for medical images based on machine learning and image segmentation FSK FH Almukhtar, SW Kareem Egyptian Informatics Journal 25 (100545) , 2024 2024 Citations: 10
Usability Evaluation of Tablet-Based Electronic Medical Record Interface in Supporting Elderly Medical Doctors. HM Salman, FH Almukhtar International Journal of Online & Biomedical Engineering 19 (13) , 2023 2023 Citations: 1
Thyroid Nodule Image Joint Segmentation and Classification Based on Deep Learning FH Al-Mukhtar, DS Ismael, RZ Yousif, SO Haji, BN Mohammed Zanco Journal of Pure and Applied Sciences 35 (5), 60-71 , 2023 2023 Citations: 3
Usability Evaluation of Tablet-Based Electronic Medical Record Interface in Supporting Elderly Medical Doctors FHA Hasanin Mohammed Salman International Journal of Online and Biomedical Engineering (iJOE) 19 (13 … , 2023 2023
Medical image categorization combining image segmentation and machine learning SW Kareem, FH Almukhtar, AT Guron, HM Salman 2023 9th International Engineering Conference on Sustainable Technology and … , 2023 2023 Citations: 5
Deep Learning for Breast Cancer Diagnosis Using Histopathological Images FFHR Mohammad Gouse Galety , Firas Husham Almukhtar, Rebaz Jamal Maaroof Intelligent Computing and Applications Proceedings of ICDIC 2020 315, 447-454 , 2023 2023
Deep Learning Techniques for Pattern Recognition in EEG Audio Signal-Processing-Based Eye-Closed and Eye-Open Cases F Husham Almukhtar, A Abbas Ajwad, AS Kamil, RA Jaleel, R Adil Kamil, ... Electronics 11 (23), 4029 , 2022 2022 Citations: 5
Facial emotions recognition using local monotonic pattern and gray level co-occurrence matrices plant leaf images aided agriculture development FH Almukhtar Optik 271, 170161 , 2022 2022 Citations: 4
Facial emotions recognition using local monotonic pattern and grey-level co-occurrence matrices images aided development FH Almukhtar Expert Systems Journal , 2022 2022 Citations: 1
Optimised Internet of Thing framework based hybrid meta‐heuristic algorithms for E‐healthcare monitoring RAJ Muhaned Al‐Hashimi, Shymaa Mohammed Jameel, Firas Husham Almukhtar ... IET Networks 2022, 1-13 , 2022 2022 Citations: 21
Supervised Approach to Identify Autism Spectrum Neurological Disorder via Label Distribution Learning SK N. V. L. M Krishna Munagala, V. Saravanan, Firas Husham Almukhtar, Naveed ... Computational Intelligence and Neuroscience 2022, 1-10 , 2022 2022 Citations: 11
Electroencephalography Image Classification Using Convolutional Neural Networks AVSRM Mohammad Gouse Galety, Firas Al-Mukhtar, Fanar Rofoo The International Conference on Innovations in Computing Research ICR 2022 … , 2022 2022 Citations: 15
MOST CITED SCHOLAR PUBLICATIONS
Search engine optimization: a review F Almukhtar, N Mahmoodd, S Kareem Applied computer science 17 (1), 70-80 , 2021 2021.0 Citations: 137
Optimized video internet of things using elliptic curve cryptography based encryption and decryption FHA Bilal S.A.Alhayani, NaghamHamid Computers and Electrical Engineering 101, 1-10 , 2022 2022.0 Citations: 91
Face recognition system based on kernel discriminant analysis, k-nearest neighbor and support vector machine MZN Al-Dabagh, MH Mohammed Alhabib, FH AL-Mukhtar International Journal of Research and Engineering 5 (3), 335-338 , 2018 2018.0 Citations: 71
& Wang, C.(2023). A computer vision-based lane detection technique using gradient threshold and hue-lightness-saturation value for an autonomous vehicle MA Al Noman, L Zhai, FH Almukhtar, MF Rahaman, B Omarov, S Ray International Journal of Electrical and Computer Engineering 13 (1), 347 , 0 Citations: 64
Medical image classification using different machine learning algorithms S Ismael, S Kareem, F Almukhtar AL-Rafidain Journal of Computer Sciences and Mathematics 14 (1), 133-145 , 2020 2020.0 Citations: 50
Deep Neural Network Concepts for Classification using Convolutional Neural Network: A Systematic Review and Evaluation. MG Galety, A Mukthar, F Husham, RJ Maaroof, F Rofoo Technium 3 (8) , 2021 2021.0 Citations: 33
Optimised Internet of Thing framework based hybrid meta‐heuristic algorithms for E‐healthcare monitoring RAJ Muhaned Al‐Hashimi, Shymaa Mohammed Jameel, Firas Husham Almukhtar ... IET Networks 2022, 1-13 , 2022 2022.0 Citations: 21
Automatic Classification of Covid-19 Chest X-Ray Images Using Local Binary Pattern and Binary Particle Swarm Optimization for Feature Selection DRZYYSA Bazhdar N. Mohammed, Dr. Firas Husham Almukhtar Cihan Univeristy-Erbil Scientific Journal 5 (2), 46-51 , 2021 2021.0 Citations: 18
Marking attendance using modern face recognition (fr): Deep learning using the opencv method MG Galety, FH Almukhtar, RJ Maaroof, F Rofoo, S Arun 2022 8th International Conference on Smart Structures and Systems (ICSSS), 1-6 , 2022 2022.0 Citations: 17
Exploiting Wavelet Transform, Principal Component Analysis, Support Vector Machine, and K-Nearest Neighbors for Partial Face Recognition HIH Mustafa Zuhaer Nayef Al-Dabagh, Mustafa H. Mohammed Alhabib, Firas H. AL ... Cihan University-Erbil Scientific Journal (CUESJ) 3 (2), 80-84 , 2019 2019.0 Citations: 17
Electroencephalography Image Classification Using Convolutional Neural Networks AVSRM Mohammad Gouse Galety, Firas Al-Mukhtar, Fanar Rofoo The International Conference on Innovations in Computing Research ICR 2022 … , 2022 2022.0 Citations: 15
[Retracted] Identifying Smart Strategies for Effective Agriculture Solution Using Data Mining Techniques AJB Suarez, B Singh, FH Almukhtar, R Kler, S Vyas, K Kaliyaperumal Journal of Food Quality 2022 (1), 6600049 , 2022 2022.0 Citations: 15
Breast Cancer Diagnostic System Based on MR images Using KPCA-Wavelet Transform and Support Vector Machine ALD Dr. Firas H. AL-Mukhtar, Mustafa Zuhaer International Journal of Advanced Engineering Research and Science (ISSN … , 2017 2017.0 Citations: 15
A comparative evaluation of cancer classification via TP53 gene mutations using Machin learning DY Mikhail, FH Al-Mukhtar, SW Kareem Asian Pacific journal of cancer prevention: APJCP 23 (7), 2459 , 2022 2022.0 Citations: 14
Multimodal Deep Learning for Video Classification RI Kadhim, F Al-Mukhtar, AT Guron, AK Shwayaa, TA Al-Sharify, B Al-Attar, ... 2024 8th International Symposium on Innovative Approaches in Smart … , 2024 2024.0 Citations: 13
Enhanced passive optical network system-based VCSEL and PSK electrical modulator for 5th generation RZ Yousif, SK Jalal, FH Al-Mukhtar Wireless Networks 30 (4), 2203-2217 , 2024 2024.0 Citations: 13
Real-Time Face Recognition System Using KPCA, LBP and Support Vector Machine AM Firas, MZN AL-Dabagh International Journal of Advanced Engineering Research and Science 4 (2), 237062 , 2017 2017.0 Citations: 13
Supervised Approach to Identify Autism Spectrum Neurological Disorder via Label Distribution Learning SK N. V. L. M Krishna Munagala, V. Saravanan, Firas Husham Almukhtar, Naveed ... Computational Intelligence and Neuroscience 2022, 1-10 , 2022 2022.0 Citations: 11
Design and development of an effective classifier for medical images based on machine learning and image segmentation FSK FH Almukhtar, SW Kareem Egyptian Informatics Journal 25 (100545) , 2024 2024.0 Citations: 10
Parallel Generation of non linear curves with computer aided application FH Al-Mukhtar A thesis of doctorate, Iraqi commission for computer and information , 2003 2003.0 Citations: 10
