Maytham N Meqdad

@uomus.edu.iq

Computer Techniques Engineering Department
Al-Mustaqbal University College

Maytham N Meqdad


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https://researchid.co/maytham.meqdad

RESEARCH INTERESTS

Cloud Computing
Computer Networking
Parallel Computing
IOT
46

Scopus Publications

854

Scholar Citations

15

Scholar h-index

18

Scholar i10-index

Scopus Publications

  • Enhancing single-lead ECG arrhythmia classification via multi-teacher decomposed feature distillation
    Majid Sepahvand, Maytham N. Meqdad, Fardin Abdali-Mohammadi
    Computer Methods in Biomechanics and Biomedical Engineering, 2025
    When an arrhythmia occurs in the heart, all electrocardiogram (ECG) leads show evidence of it, but it is more prominent in some leads. This medical fact serves as the foundation for the knowledge distillation (KD) model proposed in this paper, which aims to enhance weak leads by leveraging information from stronger ones. The model employs single-lead signals for the student network and twelve-lead signals for the teacher network. Tucker decomposition is used in this KD model to decompose the teacher's feature maps. According to evaluations, the student model achieves an accuracy of 96.48% on the Chapman ECG dataset classification task.
  • State-of-the-art in human activity recognition based on inertial measurement unit sensors: survey and applications
    Majid Sepahvand, Maytham N. Meqdad, Fardin Abdali-Mohammadi
    International Journal of Computers and Applications, 2025
  • Human Identification Through Iris Recognition Based on Genetic Algorithm and Machine Learning
    Maytham N. Meqdad, Fardin Abdali-Mohammadi, Seifedine Kadry
    International Journal on Engineering Applications, 2025
    The unique structure of the iris has established this biometric trait as an effective method for developing robust and reliable identification systems. However, it is crucial to extract features that provide a thorough description of the individual's iris, as identity recognition is a significant security concern that many businesses must implement correctly. In this study, a combined method is developed for iris segmentation, and a genetic algorithm-based approach is presented to compute optimal features in terms of separability. This approach encompasses three tasks: feature selection, feature weighting through a genetic algorithm, and learning new features through feature combination. The goal of this combined method is to extract features related to iris morphology and texture. Thus, three feature extraction methods, including local binary patterns and Gabor filters, were applied. Subsequently, the weighted genetic algorithm is employed to minimize the dimensions of the features while improving their discrimination ability. In the final detection stage, a single classification algorithm, the support vector machine, is used to implement lightweight classification, facilitating the method's implementation on devices with hardware limitations. Numerical evaluations of this classification demonstrate its acceptable accuracy compared to neural network-based methods. Experiments conducted on two datasets, IITD and CASIA Interval, resulted in detection rates of 99.55% and 93.50%, respectively. In comparison with state-of-the-art approaches, there is a meaningful difference in the outcomes of the proposed method.
  • Explainable Ensemble Learning Model for Anti-Money Laundering Using SHAP Interpretations
    Mrunal Salwadkar, Ramy Riad Al-Fatlawy, Vedadri Yoganand Bharadwaj, Anita Sofia Liz.D.R., K. Ranjith Singh, Maytham N. Meqdad, Mustafa Ali Alwash
    Iccr 2025 3rd International Conference on Cyber Resilience, 2025
    The practice of money laundering poses a significant challenge for financial institutions, such as banks, as it harms the economy and facilitates the activities of criminals. Utilizing traditional methods of detection is challenging because they are difficult to comprehend and require specialized expertise. An Anti-Money Laundering (AML) explainable ensemble learning model is discussed in the article. This model utilizes SHAP (SHapley Additive exPlanations) to simplify the model and make it more accessible to viewers. Several different classifiers, including Random Forest, XGBoost, and LightGBM, are used to leverage their predictive capabilities. Through the examination of actual bank transaction data, the system acquires the ability to recognize suspicious behavior. The SHAP values might be of assistance in comprehending significant aspects such as the length of time and frequency with which this account is used. Additionally, they offer both local and global explanations for the events that occur within the model. It is clear from the findings that the models perform more effectively than a single model. It is estimated that they have a recall rate of 91% and an accuracy rate of 94%. SHAP might be used by those who have a great deal of knowledge about the matter to determine the reason for the presence of warnings and the significance of those warnings. Clear expectations are associated with dependable models, thanks to the straightforward design, which ultimately leads to more responsible anti-money laundering measures.
  • Hybrid Clustering and Classification Algorithm for Medical Image Segmentation and Disease Prediction
    D. Karthika, C. Radhika, S. Kiruthika, K. S. Mohanasathiya, Maytham N. Meqdad, Doha Ismail Abd, Abdulkareem A. Jasim Abushraida, Sulaiman M. Karim
    Iccr 2025 3rd International Conference on Cyber Resilience, 2025
    In computer-aided diagnosis systems, the segmentation of medical images and the Prediction of diseases are essential processes that must be carried out.These tasks need an effective use of computer resources and a great degree of accuracy. This work aims to develop a novel Hybrid Clustering and Classification Algorithm (HCCA) that combines unsupervised and supervised learning techniques in a complementary way to improve medical picture analysis. A pre-segmentation approach using fuzzy C-means (FCM) clustering helps to consistently identify and isolate regions of interest (ROIs) in several medical imaging modalities, including computed tomography (CT) scans and magnetic resonance imaging (MRI) scans. This is done to accurately and dependably identify and isolate ROIs. It is then included in a deep neural network (DNN) classifier that is improved using convolutional layers for disease categorization. This follows the segmentation of these traits having been finished. Among these issues are noise, unevenness in intensity, and overlapping borders of blood vessels. Benchmark medical datasets like BraTS and LIDC-IDRI are used to implement experimental evaluation techniques. During the assessment process, special focus is given to situations including lung nodules and brain tumors. Compared to conventional approaches, the results show notable increases in segmentation accuracy (as measured by Dice and Jaccard coefficients) and classification performance (in terms of precision, recall, and area under the curve). Particularly in the area of segmentation, these changes are rather clear. The HCCA architecture that has been suggested seems to have real-time clinical deployment as a promising use. Automatic and accurate segmentation and Prediction provide a solution for medical diagnostics that is both scalable, strong, and interpretable.
  • Swin Transformer Architecture for Accurate Brain Tumor Classification and Localization in MRI-Based Medical Diagnosis
    S. Senthil Kumar, Khushbu Kriplani, Rame Riadhusin, Nigama Prasan Sahoo, Varanasi Srinivas, Zahraa N. Salaman, Maytham N. Meqdad, Abdulkareem A. Jasim Abushraida
    Iccr 2025 3rd International Conference on Cyber Resilience, 2025
    Medical imaging is fundamental for early illness diagnosis and tumor identification, providing essential visual information on anatomical and pathological features. The increasing need for automated, precise, and scalable diagnostic tools has propelled the development of advanced deep-learning (DL) models for image classification and object recognition. Though proficient in spatial feature extraction, traditional Convolutional Neural Networks (CNNs) are fundamentally constrained in their capability to simulate long-range relationships and multi-scale contextual information. These limitations result in inadequate performance in intricate medical situations characterized by nuanced textural differences, uneven tumor margins, and diverse imaging techniques. Moreover, current models often have elevated false positive rates and restricted generalization across various datasets. This study introduces a novel design utilizing the Swin Transformer-Based Medical Image Diagnosis and Detection Network (Swin-MedNet), a hierarchical Vision Transformer architecture that employs self-attention mechanisms for detecting and classifying brain tumors. This architecture effectively integrates local and global features with linear computing complexity. The model has a multi-stage encoder that gradually merges patches, facilitating scalable representation learning. The design incorporates a Feature Pyramid Network (FPN) and a Region Proposal Network (RPN) to improve semantic localization and tumor segmentation precision for item recognition. Experimental validation on benchmark datasets, including BraTS (brain tumor), showed enhanced performance, elevated classification accuracy, mean Average Precision (mAP), and reduced false detection rates compared to existing methodologies.
  • Stable Diffusion Model for Image Restoration to Enhance Low-Quality Satellite Images
    Vazira Akhatova, Dildora Nosirova, Parvina Zakiryayeva, Baxriddin Normamatov, G’Olib Kuvandikov, Eman M, Maytham N. Meqdad, Sajad Ali Zearah
    Iccr 2025 3rd International Conference on Cyber Resilience, 2025
    Satellite images are important for studying the environment, managing disasters, and analyzing climate change. However, many of these images have low resolution, noise, and distortions, making them hard to interpret accurately. Traditional methods, like interpolation and deep learning-based super-resolution, often fail to recover fine details or keep image quality consistent. This study investigates low-quality satellite image improvement and restoration using Stable Diffusion Model (SDM). This model produces better, more detailed images by progressively eliminating noise in several stages. This research trained the model on a range of satellite images and evaluated its Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Fréchet Inception Distance (FID). By maintaining minor details and lowering mistakes, Stable diffusion model is revealed to be better than conventional approaches. These top-notch pictures benefit other remote sensing projects, enhance disaster response planning, and enable more precise research of climate patterns by experts. This study shows that one can effectively improve satellite images using a stable diffusion model.
  • A new gas lift allocation method in the IoT environment using a hybrid optimization algorithm
    Mehdi Darbandi, Maytham N. Meqdad, Ahmad Hammoud, Habibeh Nazif
    Scientific Reports, 2024
    In the realm of petroleum extraction, well productivity declines as reservoirs deplete, eventually reaching a point where continued extraction becomes economically unfeasible. To counteract this, artificial lift techniques are employed, with gas injection being a prevalent method. Ideally, unrestricted gas injection could maximize oil output. However, gas scarcity necessitates judicious resource management to optimize oil production while minimizing gas usage. Gas injection serves to alleviate hydrostatic pressure within wells, thereby enhancing oil recovery. Conventional gas allocation strategies often prove inadequate when confronted with the complex, non-linear constraints of real-world scenarios, particularly under gas supply limitations. This research introduces an innovative approach to gas allocation optimization, leveraging Internet of Things (IoT) technology in conjunction with advanced computational methods. The study melds two optimization algorithms: Particle Swarm Optimization (PSO) and Atom Search Optimization (ASO). This hybrid technique harnesses IoT capabilities for real-time data acquisition and processing, enabling more precise and adaptive optimization. The proposed methodology incorporates PSO's individual and collective learning mechanisms into the ASO framework, accelerating the solution refinement process. Additionally, it introduces dynamic parameters to balance broad exploration with focused exploitation of the solution space. The algorithm's efficacy is further enhanced by implementing an adaptive force constant for each "atom" (solution candidate), which evolves based on the atom's performance over successive iterations. Empirical evaluation of this novel approach demonstrated significant improvements in both energy efficiency and gas utilization. Specifically, the hybrid method achieved average reductions of 12.12% in energy consumption and 18.05% in gas injection volume compared to existing techniques. Also, the results showed that battery life and cost are better than the other methods and have been improved by an average of 7.67% and 9.48%, respectively.
  • Using Neural Networks to Forecast the Configuration of Proteins
    Maytham N. Meqdad, Zainab N. Al-Qudsy, Seifedine Kadry, Ali S. Haleem
    Ingenierie Des Systemes D Information, 2024
    Predicting the secondary structure of proteins continues to be a significant hurdle in the field of bioinformatics.This anticipation plays a crucial role as an intermediary stage in addressing the challenge of predicting the tertiary structure of proteins, which is instrumental in determining their functions.This prediction holds the potential to facilitate drug development and contribute to the identification of viral diseases.One can forecast the secondary structure of a protein by examining its primary components, including the amino acid sequence and various additional factors.Through the examination of established sequences and recognized protein types, it becomes feasible to anticipate unfamiliar sequences.The objective of this article is to enhance the forecast accuracy of protein secondary structure by adjusting the current code, aiming to reach an 80% accuracy rate.
  • A machine learning study on a municipal solid waste-to-energy system for environmental sustainability in a multi-generation energy system for hydrogen production
    Yulan Zhang, Abdulrahman Jaffar Aldosky, Vishal Goyal, Maytham N. Meqdad, Tirumala Uday Kumar Nutakki, Theyab R. Alsenani, Van Nhanh Nguyen, Mahidzal Dahari, Phuoc Quy Phong Nguyen, H. Elhosiny Ali
    Process Safety and Environmental Protection, 2024
  • Fault tolerance challenges in wearable computing for vital applications: a survey
    Majid Sepahvand, Maytham N. Meqdad, Fardin Abdali-Mohammadi
    Journal of Medical Engineering and Technology, 2024
  • Leveraging bone age assessment via a novel joint decomposition teacher–student learning paradigm from X-ray images
    Majid Sepahvand, Fardin Abdali-Mohammadi, Maytham N. Meqdad
    Computer Methods in Biomechanics and Biomedical Engineering Imaging and Visualization, 2024
  • Synthetic photoplethysmogram (PPG) signal generation using a genetic programming-based generative model
    Fatemeh Ghasemi, Majid Sepahvand, Maytham N. Meqdad, Fardin Abdali Mohammadi
    Journal of Medical Engineering and Technology, 2024
  • Evaluating the Effectiveness of Heart Disease Prediction
    International Journal of Intelligent Systems and Applications in Engineering, 2024
  • A systematic and comprehensive review and investigation of intelligent IoT-based healthcare systems in rural societies and governments
    Yisu Ge, Guodao Zhang, Maytham N. Meqdad, Shuzheng Chen
    Artificial Intelligence in Medicine, 2023
  • Numerical simulation of thermal behavior of cerebral blood vessels using computational hemodynamic method
    Yutao Li, Shahab Naghdi Sedeh, As'ad Alizadeh, Maytham N. Meqdad, Ahmed Hussien Alawadi, Navid Nasajpour-Esfahani, Davood Toghraie, Maboud Hekmatifar
    Ain Shams Engineering Journal, 2023
  • Human activity recognition method using joint deep learning and acceleration signal
    Maytham N. Meqdad, Abdullah Hasan Hussein, Saif O. Husain, Alyaa Mohammed Jawad, Seifedine Kadry
    Iaes International Journal of Artificial Intelligence, 2023
  • Classification of electroencephalography using cooperative learning based on participating client balancing
    Maytham N. Meqdad, Saif O. Husain, Alyaa Mohammed Jawad, Seifedine Kadry, Ahlam R. Khekan
    International Journal of Electrical and Computer Engineering, 2023
  • Classification of electrocardiogram signals based on federated learning and a gaussian multivariate aggregation module
    Maytham N. Meqdad, Abdullah Hasan Hussein, Saif O. Husain, Alyaa Mohammed Jawad
    Indonesian Journal of Electrical Engineering and Computer Science, 2023
  • A Fire Evacuation and Control System in Smart Buildings Based on the Internet of Things and a Hybrid Intelligent Algorithm
    Ali Mohammadiounotikandi, Hassan Falah Fakhruldeen, Maytham N. Meqdad, Banar Fareed Ibrahim, Nima Jafari Navimipour, Mehmet Unal
    Fire, 2023
  • Bone Anomaly Detection by Extracting Regions of Interest and Convolutional Neural Networks
    Maytham N. Meqdad, Hafiz Tayyab Rauf, Seifedine Kadry
    Applied System Innovation, 2023
  • Gated Capsule Networks for Intrusion Detection Systems to Improve the Security of WSN-IoT
    Ad Hoc and Sensor Wireless Networks, 2023
  • Apple Spots and Defects Detection Based on Machine Vision, Fuzzy Systems, and Improved Gray Wolf Optimization Algorithm
    Gabriel Gomes de Oliveira, Elham Moghadamnia, Reza Radfar, Mohammad Worya Khordehbinan, Mohammad Hosein Sabzalian, Maytham N. Meqdad
    Lecture Notes in Electrical Engineering, 2023
  • Improved Dragonfly Optimization Algorithm for Detecting IoT Outlier Sensors
    Maytham N. Meqdad, Seifedine Kadry, Hafiz Tayyab Rauf
    Future Internet, 2022
  • CNN supported framework for automatic extraction and evaluation of dermoscopy images
    Xiaochun Cheng, Seifedine Kadry, Maytham N. Meqdad, Rubén González Crespo
    Journal of Supercomputing, 2022
  • A New 12-Lead ECG Signals Fusion Method Using Evolutionary CNN Trees for Arrhythmia Detection
    Maytham N. Meqdad, Fardin Abdali-Mohammadi, Seifedine Kadry
    Mathematics, 2022
  • Meta Structural Learning Algorithm With Interpretable Convolutional Neural Networks for Arrhythmia Detection of Multisession ECG
    Maytham N. Meqdad, Fardin Abdali-Mohammadi, Seifedine Kadry
    IEEE Access, 2022
  • Automatic detection of ischemic-stroke-lesion with CNN segmentation: A study
    FerasNadhimHasoon Al Attar, Seifedine Kadry, K. Suresh Manic, Maytham N. Meqdad
    Journal of Physics Conference Series, 2022
  • Automatic classification of histology images into normal/cancer class with pre-trained CNN
    Geetha Achuthan, Seifedine Kadry, K. Suresh Manic, Maytham N. Meqdad
    Journal of Physics Conference Series, 2022
  • Mayfly-Algorithm Selected Features for Classification of Breast Histology Images into Benign/Malignant Class
    Seifedine Kadry, Venkatesan Rajinikanth, Gautam Srivastava, Maytham N. Meqdad
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2022
  • Assessment of Brain Tumor in Flair MRI Slice with Joint Thresholding and Segmentation
    Seifedine Kadry, David Taniar, Maytham N. Meqdad, Gautam Srivastava, Venkatesan Rajinikanth
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2022
  • Intelligent Deep Learning Based Multi-Retinal Disease Diagnosis and Classification Framework
    Thavavel Vaiyapuri, S. Srinivasan, Mohamed Yacin Sikkandar, T. S. Balaji, Seifedine Kadry, Maytham N. Meqdad, Yunyoung Nam
    Computers Materials and Continua, 2022
  • Evolutionary algorithm based task scheduling in iot enabled cloud environment
    Omar A. Saraereh, Ashraf Ali
    Computers Materials and Continua, 2022
  • Two-tier clustering with routing protocol for iot assisted wsn
    A. Arokiaraj Jovith, Mahantesh Mathapati, M. Sundarrajan, N. Gnanasankaran, Seifedine Kadry, Maytham N. Meqdad, Shabnam Mohamed Aslam
    Computers Materials and Continua, 2022
  • Cyber physical systems: A smart city perspective
    Firoz Khan, R. Lakshmana Kumar, Seifedine Kadry, Yunyoung Nam, Maytham N. Meqdad
    International Journal of Electrical and Computer Engineering, 2021
  • Autonomous vehicles: A study of implementation and security
    Firoz Khan, R. Lakshmana Kumar, Seifedine Kadry, Yunyoung Nam, Maytham N. Meqdad
    International Journal of Electrical and Computer Engineering, 2021
  • Performance analysis of sentiments in Twitter dataset using SVM models
    Lakshmana Kumar Ramasamy, Seifedine Kadry, Yunyoung Nam, Maytham N. Meqdad
    International Journal of Electrical and Computer Engineering, 2021
  • Bilateral control system of the space robot with large delays
    G. Alferov, P. Efimova, D. Shymanchuk, S. Kadry, M. N. Meqdad
    Telkomnika Telecommunication Computing Electronics and Control, 2021
  • Recognizing emotional state of user based on learning method and conceptual memories
    Telkomnika Telecommunication Computing Electronics and Control, 2020
  • New prediction method for data spreading in social networks based on machine learning algorithm
    Maytham N. Meqdad, Rawya Al-Akam, Seifedine Kadry
    Telkomnika Telecommunication Computing Electronics and Control, 2020
  • Image processing based eye detection methods a theoretical review
    B. Vijayalaxmi, Chavali Anuradha, Kaushik Sekaran, Maytham N. Meqdad, Seifedine Kadry
    Bulletin of Electrical Engineering and Informatics, 2020
  • Implementation of face and eye detection on DM6437 board using simulink model
    B. Vijayalaxmi, Kaushik Sekaran, N. Neelima, P. Chandana, Maytham N. Meqdad, Seifedine Kadry
    Bulletin of Electrical Engineering and Informatics, 2020
  • Development of an IoT-based and cloud-based disease prediction and diagnosis system for healthcare using machine learning algorithms
    Fardin Abdali-Mohammadi, Maytham N. Meqdad, Seifedine Kadry
    Iaes International Journal of Artificial Intelligence, 2020
  • Design of optimal search engine using text summarization through artificial intelligence techniques
    Kaushik Sekaran, P. Chandana, J. Rethna Virgil Jeny, Maytham N. Meqdad, Seifedine Kadry
    Telkomnika Telecommunication Computing Electronics and Control, 2020
  • Smart agriculture management system using internet of things
    Kaushik Sekaran, Maytham N. Meqdad, Pardeep Kumar, Soundar Rajan, Seifedine Kadry
    Telkomnika Telecommunication Computing Electronics and Control, 2020
  • Enabling Techniques for 10 Gbps Long-Haul Transmission in Non-Coherent OCDMA Systems
    Maytham N. Meqdad, Hasan Sh. Majdi
    9th International Symposium on Telecommunication with Emphasis on Information and Communication Technology Ist 2018, 2018

RECENT SCHOLAR PUBLICATIONS

  • Enhancing single-lead ECG arrhythmia classification via multi-teacher decomposed feature distillation
    M Sepahvand, M N. Meqdad, F Abdali-Mohammadi
    Computer Methods in Biomechanics and Biomedical Engineering, 1-17 , 2025
    2025
  • Multi-Teacher Knowledge Distillation via Tucker-Guided Representation Alignment and Adaptive Feature Mapping
    M Sepahvand, MN Meqdad, FA Mohammadi
    2025
  • Explainable Ensemble Learning Model for Anti-Money Laundering Using SHAP Interpretations
    M Salwadkar, RR Al-Fatlawy, VY Bharadwaj, ASL DR, KR Singh, ...
    2025 3rd International Conference on Cyber Resilience (ICCR), 1-8 , 2025
    2025
  • Swin Transformer Architecture for Accurate Brain Tumor Classification and Localization in MRI-Based Medical Diagnosis
    SS Kumar, K Kriplani, R Riadhusin, NP Sahoo, V Srinivas, ZN Salaman, ...
    2025 3rd International Conference on Cyber Resilience (ICCR), 1-7 , 2025
    2025
    Citations: 2
  • Stable Diffusion Model for Image Restoration to Enhance Low-Quality Satellite Images
    V Akhatova, D Nosirova, P Zakiryayeva, B Normamatov, GO Kuvandikov, ...
    2025 3rd International Conference on Cyber Resilience (ICCR), 1-7 , 2025
    2025
    Citations: 1
  • Hybrid Clustering and Classification Algorithm for Medical Image Segmentation and Disease Prediction
    D Karthika, C Radhika, S Kiruthika, KS Mohanasathiya, MN Meqdad, ...
    2025 3rd International Conference on Cyber Resilience (ICCR), 1-7 , 2025
    2025
  • Human Identification Through Iris Recognition Based on Genetic Algorithm and Machine Learning.
    MN Meqdad, F Abdali-Mohammadi, S Kadry
    International Journal on Engineering Applications 13 (2) , 2025
    2025
    Citations: 1
  • State-of-the-art in human activity recognition based on inertial measurement unit sensors: survey and applications
    M Sepahvand, MN Meqdad, F Abdali-Mohammadi
    International Journal of Computers and Applications 47 (1), 1-16 , 2025
    2025
    Citations: 7
  • A new gas lift allocation method in the IoT environment using a hybrid optimization algorithm
    M Darbandi, MN Meqdad, A Hammoud, H Nazif
    Scientific Reports 14 (1), 30657 , 2024
    2024
  • Synthetic photoplethysmogram (PPG) signal generation using a genetic programming-based generative model
    F Ghasemi, M Sepahvand, M N. Meqdad, F Abdali Mohammadi
    Journal of Medical Engineering & Technology 48 (6), 223-235 , 2024
    2024
    Citations: 2
  • Using Neural Networks to Forecast the Configuration of Proteins
    MN Meqdad, ZN Al-Qudsy, S Kadry, AS Haleem
    Ingénierie des Systèmes d’Information 29 (4), 1461-1468 , 2024
    2024
  • Leveraging bone age assessment via a novel joint decomposition teacher–student learning paradigm from X-ray images
    M Sepahvand, F Abdali-Mohammadi, MN Meqdad
    Computer Methods in Biomechanics and Biomedical Engineering: Imaging … , 2024
    2024
    Citations: 3
  • Processing Biomedical Signals by Neural Networks Using Hardware-Constrained System
    MN Meqdad, RT Ahmed, ALH Mustafa
    Revue d'Intelligence Artificielle 38 (2), 739-745 , 2024
    2024
    Citations: 1
  • Algorithmic Insights into Predicting Hypertension Using Health Data in Cloud-Based Environments
    AS S. V. N. Sreenivasu, Maytham N. Meqdad, M. Ravi Kishore, Harendra Singh ...
    International Journal of Intelligent Systems and Applications in Engineering … , 2024
    2024
  • Scalable Machine Learning Framework For Patient Outcome Prediction With Cloud-Based Healthcare Data
    DAS Neha Jain ,Maytham N. Meqdad , Vibhav Krashan Chaurasiya, Diwakar ...
    International Journal of Intelligent Systems and Applications in Engineering … , 2024
    2024
  • Fault tolerance challenges in wearable computing for vital applications: a survey
    M Sepahvand, MN Meqdad, F Abdali-Mohammadi
    Journal of Medical Engineering & Technology 48 (2), 48-63 , 2024
    2024
    Citations: 1
  • A machine learning study on a municipal solid waste-to-energy system for environmental sustainability in a multi-generation energy system for hydrogen production
    Y Zhang, AJ Aldosky, V Goyal, MN Meqdad, TUK Nutakki, TR Alsenani, ...
    Process Safety and Environmental Protection 182, 1171-1184 , 2024
    2024
    Citations: 22
  • Utilizing Collaborating Biomedical Deep Model for Diagnosis of Ultrasonography Tumor Images
    MN Meqdad, S Kadry
    Al-Mustaqbal Journal of Sustainability in Engineering Sciences 2 (1), 1 , 2024
    2024
  • IOT BASED SMART CHOLESTEROL MONITORING DEVICE
    AS Dr. M. Duraipandian ,Dr. Maytham N. Meqdad, Dr. Manish Pundlik ,Kavita ...
    IN Patent 152,964 , 2024
    2024
  • Evaluating the effectiveness of heart disease prediction
    JS Mishra, MN Meqdad, A Sharma, A Deepak, NK Gupta, R Bajaj, ...
    Int. J. Intell. Syst. Appl. Eng 12, 163-173 , 2024
    2024
    Citations: 5

MOST CITED SCHOLAR PUBLICATIONS

  • Smart Agriculture Management System Using Internet of Things
    K Sekaran, MN Meqdad, SK Kumar, Pardeep, SoundarRajan
    Telkomnika 18 (3), 1275-84 , 2020
    2020
    Citations: 156
  • Development of an IoT-based and cloud-based disease prediction and diagnosis system for healthcare using machine learning algorithms
    F Abdali-Mohammadi, MN Meqdad, S Kadry
    IAES. December , 2020
    2020
    Citations: 86
  • Performance analysis of sentiments in Twitter dataset using SVM models
    LK Ramasamy, S Kadry, Y Nam, MN Meqdad
    International Journal of Electrical and Computer Engineering (IJECE) 11 (3 … , 2021
    2021
    Citations: 73
  • Autonomous vehicles: A study of implementation and security.
    F Khan, RL Kumar, S Kadry, MN Meqdad
    International Journal of Electrical & Computer Engineering (2088-8708) 11 (4 … , 2021
    2021
    Citations: 59
  • Cyber physical systems: A smart city perspective
    F Khan, RL Kumar, S Kadry, Y Nam, MN Meqdad
    International Journal of Electrical and Computer Engineering 11 (4), 3609 , 2021
    2021
    Citations: 52
  • Design of optimal search engine using text summarization through artificial intelligence techniques
    K Sekaran, P Chandana, JRV Jeny, MN Meqdad, S Kadry
    TELKOMNIKA (Telecommunication Computing Electronics and Control) 18 (3 … , 2020
    2020
    Citations: 52
  • A fire evacuation and control system in smart buildings based on the internet of things and a hybrid intelligent algorithm
    A Mohammadiounotikandi, HF Fakhruldeen, MN Meqdad, BF Ibrahim, ...
    Fire 6 (4), 171 , 2023
    2023
    Citations: 44
  • A systematic and comprehensive review and investigation of intelligent IoT-based healthcare systems in rural societies and governments
    Y Ge, G Zhang, MN Meqdad, S Chen
    Artificial Intelligence in Medicine 146, 102702 , 2023
    2023
    Citations: 42
  • Two-Tier Clustering with Routing Protocol for IoT Assisted WSN.
    AA Jovith, M Mathapati, M Sundarrajan, N Gnanasankaran, S Kadry, ...
    Computers, Materials & Continua 71 (2) , 2022
    2022
    Citations: 31
  • Intelligent Deep Learning Based Multi-Retinal Disease Diagnosis and Classification Framework.
    T Vaiyapuri, S Srinivasan, MY Sikkandar, TS Balaji, S Kadry, MN Meqdad
    Computers, Materials & Continua 73 (3) , 2022
    2022
    Citations: 27
  • Meta structural learning algorithm with interpretable convolutional neural networks for arrhythmia detection of multisession ECG
    MN Meqdad, F Abdali-Mohammadi, S Kadry
    IEEE Access 10, 61410-61425 , 2022
    2022
    Citations: 23
  • Image processing based eye detection methods a theoretical review
    B Vijayalaxmi, C Anuradha, K Sekaran, MN Meqdad, S Kadry
    Bulletin of Electrical Engineering and Informatics 9 (3), 1189-1197 , 2020
    2020
    Citations: 23
  • A machine learning study on a municipal solid waste-to-energy system for environmental sustainability in a multi-generation energy system for hydrogen production
    Y Zhang, AJ Aldosky, V Goyal, MN Meqdad, TUK Nutakki, TR Alsenani, ...
    Process Safety and Environmental Protection 182, 1171-1184 , 2024
    2024
    Citations: 22
  • CNN supported framework for automatic extraction and evaluation of dermoscopy images: X. Cheng et al.
    X Cheng, S Kadry, MN Meqdad, RG Crespo
    The Journal of Supercomputing 78 (15), 17114-17131 , 2022
    2022
    Citations: 22
  • A new 12-lead ECG signals fusion method using evolutionary CNN trees for arrhythmia detection
    MN Meqdad, F Abdali-Mohammadi, S Kadry
    Mathematics 10 (11), 1911 , 2022
    2022
    Citations: 17
  • New Prediction Method for Data Spreading in Social Networks Based on Machine Learning Algorithm
    S Maytham N Meqdad, Rawya Al-Akam
    TELKOMNIKA Telecommunication, Computing, Electronics and Control 18 (6 … , 2020
    2020
    Citations: 15
  • Gated Capsule Networks for Intrusion Detection Systems to Improve the Security of WSN-IoT.
    UV Arivazhagu, P Ilanchezhian, MN Meqdad, V Prithivirajan
    Adhoc & Sensor Wireless Networks 56 , 2023
    2023
    Citations: 10
  • Implementation of face and eye detection on DM6437 board using simulink model
    B Vijayalaxmi, K Sekaran, N Neelima, P Chandana, MN Meqdad, S Kadry
    Bulletin of Electrical Engineering and Informatics 9 (2), 785-791 , 2020
    2020
    Citations: 10
  • Recognizing emotional state of user based on learning method and conceptual memories
    MN Meqdad, F Abdali-Mohammadi, S Kadry
    TELKOMNIKA (Telecommunication Computing Electronics and Control) 18 (6 … , 2020
    2020
    Citations: 8
  • Enabling Techniques for 10 Gbps Long-Haul Transmission in Non-Coherent OCDMA Systems.
    MN Meqdad, HS Majdi
    2018 9th International Symposium on Telecommunications (IST), 457-459 , 2018
    2018
    Citations: 8