Mumtazimah Mohamad

@unisza.edu.my

Faculty of Informatics and Computing
Universiti Sultan Zainal Abidin

Mumtazimah Mohamad


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https://researchid.co/ummurifqi
Mumtazimah Mohamad was born in Terengganu, Malaysia. She received the bachelor’s degree in information technology from Universiti Kebangsaan Malaysia, in 2000, the M.Sc. degree in computer science from Universiti Putra Malaysia, and the Ph.D. degree in computer science from Universiti Malaysia Terengganu, in 2014. She was a Junior Lecturer, in 2000. Currently, she is an Associate Professor with the Department of Computer Science, Faculty of Informatics and Computing (FIK), Universiti Sultan Zainal Abidin, Terengganu, Malaysia. She has published over 50 research articles in peer-reviewed journals, book chapters, and proceeding. She has appointed a reviewer and technical committee for many conferences and journals and worked as a researcher in several national funded Research and Development projects. Her research interests include pattern recognition, machine learning, artificial intelligence, and parallel processing.

EDUCATION

B. Sc ( Information Technology ) , Universiti Kebangsaan Malaysia, 2000
Master of Science ( Computer Science- Software Engineering), Universiti Putra Malaysia, 2015
Ph.D ( Computer Science), Universiti Malaysia Terengganu, 2014

RESEARCH INTERESTS

Data Science, Machine Learning, Pattern Recognition, Artificial Intelligence
47

Scopus Publications

563

Scholar Citations

11

Scholar h-index

15

Scholar i10-index

Scopus Publications

  • An Intelligent Botnet Detection System for IoT Using Neural Networks and an Enhanced Moth Search Optimize
    Sanaa A. A. Ghaleb, Mumtazimah Mohamad, Waheed A. H. M Ghanem, Abdullah B. Nasser
    Journal of Soft Computing and Data Mining, 2025
  • Classification of stunting for early childhood in indramayu using machine learning methods
    International Journal of Basic and Applied Science, 2025
  • A SYSTEMATIC LITERATURE REVIEW ON BIBLIOMETRIC PROFILING ANALYSIS USING MACHINE LEARNING
    Journal of Theoretical and Applied Information Technology, 2025
  • Improved moth search algorithm with mutation operator for numerical optimization problems
    Sanaa A. A. Ghaleb, Mumtazimah Mohamad, Waheed Ali Hussein Mohammed Ghanem, Arifah Che Alhadi, Abdullah B. Nasser, Hanan Aldowah
    Indonesian Journal of Electrical Engineering and Computer Science, 2024
    The moth search algorithm (MSA) is a meta-heuristic optimization technique inspired by moth behavior, has shown remarkable efficacy in solving optimization challenges. However, its poor exploration capability results in an imbalance between exploitation and exploration. To address this issue, this research introduces a new mutation operator to enhance exploration by increasing population diversity. The proposed enhanced moth search algorithm (EMSA) aims to expedite convergence and improve overall robustness by exploring new solutions more effectively. Evaluation on ten benchmark functions demonstrates EMSA's superior exploration capabilities, efficiently tackling optimization problems and yielding more optimal solutions within the search space. Compared to conventional MSA and other established algorithms, EMSA delivers well-balanced results, showcasing its effectiveness in optimizing the search space. In the future, the EMSA could potentially find applications in addressing real-world engineering optimization challenges.
  • Global impact on human obesity – A robust non-linear panel data analysis
    Mubbasher Munir, Zahrahtul Amani Zakaria, Atif Amin Baig, Mumtazimah Binti Mohamad, Noman Arshed, Reda Alhajj
    Nutrition and Health, 2024
    Purpose: Recent studies in economics showed that humans are bounded rational. This being consumers, they are not perfect judges of what matters for the standard of living. While with a marked increase in economic and social wellbeing, there is a consistent rise in obesity levels, especially in the developed world. Thus, this study intends to explore the empirical and socio-economic antecedents of human obesity across countries using six global indexes. Methods: This study used the data of 40 countries between 1975 to 2018 and used the Panel FGLS Regression with the quadratic specification. Findings: The results showed that health and food indicators increase global human obesity, environment and education indicators decrease global human obesity, and economic and social indicators follow an inverted U-shaped pattern in affecting global human obesity. Originality: Previous studies have used infant mortality and life expectancy as the major health indicator in determining the standard of living while overlooking global human obesity as a major deterrent to welfare. This study has provided a holistic assessment of the causes of obesity in global contexts.
  • Global human obesity and political globalization; asymmetric relationship through world human development levels
    Mubbasher Munir, Zahrahtul Amani Zakaria, Reda Alhajj, Mumtazimah Binti Mohamad, Atif Amin Baig, Noman Arshed
    Nutrition and Health, 2024
    Purpose - Political globalization is a crucial and distinct component of strengthening global organizations. Obesity is a global epidemic in a few nations, and it is on the verge of becoming a pandemic that would bring plenty of diseases. This research aims to see how the political globalization index affects worldwide human obesity concerning global human development levels. Methods- To assess any cross-sectional dependence among observed 109 nations, the yearly period from 1990 to 2017 is analyzed using second generation panel data methods. KAO panel cointegration test and Fully Modified Least Square model were used to meet our objectives. Finding- Low level of political globalization tends to increase global human obesity because countries cannot sway international decisions and resources towards them. While the high level of political globalization tends to reduce obesity because it can control and amends international decisions. For the regression model, a fully modified Least Square model was utilized. The study observed that the R squared values for all models are healthy, with a minimum of 87 percent variables explaining differences in global obesity at the country level. Originality: There is very important to tackle the globalization issue to reduce global human obesity. With the simplicity of dietary options and the amount of physical labour they undergo in their agricultural duties, an increase in rural population percentage tends to lower the average national obesity value.
  • Hamming Distance Approach to Reduce Role Mining Scalability
    Nazirah Abd Hamid, Siti Rahayu Selamat, Rabiah Ahmad, Mumtazimah Mohamad
    International Journal of Advanced Computer Science and Applications, 2023
    Role-based Access Control has become the standard of practice for many organizations for restricting control on limited resources in complicated infrastructures or systems. The main objective of the role mining development is to define appropriate roles that can be applied to the specified security access policies. However, the mining scales in this kind of setting are extensive and can cause a huge load on the management of the systems. To resolve the above mentioned problems, this paper proposes a model that implements Hamming Distance approach by rearranging the existing matrix as the input data to overcome the scalability problem. The findings of the model show that the generated file size of all datasets substantially have been reduced compared to the original datasets It has also shown that Hamming Distance technique can successfully reduce the mining scale of datasets ranging between 30% and 47% and produce better candidate roles. Keywords—Role-based Access Control; role mining; hamming distance; data mining
  • A Novel Hybrid DL Model for Printed Arabic Word Recognition based on GAN
    Yazan M. Alwaqfi, Mumtazimah Mohamad, Ahmad T. Al-Taani, Nazirah Abd Hamid
    International Journal of Advanced Computer Science and Applications, 2023
    The recognition of printed Arabic words remains an open area for research since Arabic is among the most complex languages. Prior research has shown that few efforts have been made to develop models of accurate Arabic recognition, as most of these models have faced the increasing complexity of the performance and lack of benchmark Arabic datasets. Meanwhile, Deep learning models, such as Convolutional Neural Networks (CNNs), have been shown to be beneficial in reducing the error rate and enhancing accuracy in Arabic character recognition systems. The reliability of these models increases with the depth of layers. Still, the essential condition for more layers is an extensive amount of data. Since CNN generates features by analysing large amounts of data, its performance is directly proportional to the volume of data, as DL models are considered data-hungry algorithms. Nevertheless, this technique suffers from poor generalisation ability and overfitting issues, which affect the Arabic recognition models' accuracy. These issues are due to the limited availability of Arabic databases in terms of accessibility and size, which led to a central problem facing the Arabic language nowadays. Therefore, the Arabic character recognition models still have gaps that need to be bridged. The Deep Learning techniques are also to be improved to increase the accuracy by manipulating the strength of technique in a neural network for handling the lack of datasets and the generalisation ability of the neural network in model building. To solve these problems, this study proposes a hybrid model for Arabic word recognition by adapting a deep convolutional neural network (DCNN) to work as a classifier based on a generative adversarial network (GAN) work as a data augmentation technique to develop a robust hybrid model for improving the accuracy and generalisation ability. Each proposed model is separately evaluated and compared with other state-of-the-art models. These models are tested on the Arabic printed text image dataset (APTI). The proposed hybrid deep learning model shows excellent performance regarding the accuracy, with a score of 99.76% compared to 94.81% for the proposed DCNN model on the APTI dataset. The proposed model indicates highly competitive performance and enhanced accuracy compared to the existing state-of-the-art Arabic printed word recognition models. The results demonstrate that the generalisation of networks and the handling of overfitting have also improved. This study output is comparable to other competitive models and contributes an enhanced Arabic recognition model to the body of knowledge.
  • Grasshopper Optimization Algorithm Based Spam Detection System Using Multi-Objective Wrapper Feature Selection and Neural Network Classification
    Sanaa A. A. Ghaleb, Mumtazimah Mohamad, Waheed A. H. M. Ghanem, Akibu Mahmoud Abdullahi, Abdullah B. Nasser, Sami Abdulla Mohsen Saleh, Humaira Arshad, Abiodun Esther Omolara, Oludare Isaac Abiodun, Mohamed Ghetas
    Lecture Notes in Networks and Systems, 2023
  • Generative Adversarial Network for an Improved Arabic Handwritten Characters Recognition
    Yazan Alwaqfi, Mumtazimah Mohamad, Ahmad Al-Taani
    International Journal of Advances in Soft Computing and Its Applications, 2022
    Currently, Arabic character recognition remains one of the most complicated challenges in image processing and character identification. Many algorithms exist in neural networks, and one of the most interesting algorithms is called generative adversarial networks (GANs), where 2 neural networks fight against one another. A generative adversarial network has been successfully implemented in unsupervised learning and it led to outstanding achievements. Furthermore, this discriminator is used as a classifier in most generative adversarial networks by employing the binary sigmoid cross-entropy loss function. This research proposes employing sigmoid cross-entropy to recognize Arabic handwritten characters using multi-class GANs training algorithms. The proposed approach is evaluated on a dataset of 16800 Arabic handwritten characters. When compared to other approaches, the experimental results indicate that the multi-class GANs approach performed well in terms of recognizing Arabic handwritten characters as it is 99.7% accurate. Keywords: Generative Adversarial Networks (GANs), Arabic Characters, Optical Character Recognition, Convolutional Neural Networks (CNNs).
  • E-mail Spam Classification Using Grasshopper Optimization Algorithm and Neural Networks
    Sanaa A. A. Ghaleb, Mumtazimah Mohamad, Syed Abdullah Fadzli, Waheed A.H.M. Ghanem
    Computers Materials and Continua, 2022
  • Feature Selection by Multiobjective Optimization: Application to Spam Detection System by Neural Networks and Grasshopper Optimization Algorithm
    Sanaa A. A. Ghaleb, Mumtazimah Mohamad, Waheed Ali H. M. Ghanem, Abdullah B. Nasser, Mohamed Ghetas, Akibu Mahmoud Abdullahi, Sami Abdulla Mohsen Saleh, Humaira Arshad, Abiodun Esther Omolara, Oludare Isaac Abiodun
    IEEE Access, 2022
  • Integrating mutation operator into grasshopper optimization algorithm for global optimization
    Sanaa A. A. Ghaleb, Mumtazimah Mohamad, Engku Fadzli Hasan Syed Abdullah, Waheed A. H. M. Ghanem
    Soft Computing, 2021
  • Sentiment Analysis Technique and Neutrosophic Set Theory for Mining and Ranking Big Data from Online Reviews
    Ibrahim Awajan, Mumtazimah Mohamad, Ashraf Al-Quran
    IEEE Access, 2021
  • An Integrated Model to Email Spam Classification Using an Enhanced Grasshopper Optimization Algorithm to Train a Multilayer Perceptron Neural Network
    Sanaa A. A. Ghaleb, Mumtazimah Mohamad, Engku Fadzli Hasan Syed Abdullah, Waheed A. H. M. Ghanem
    Communications in Computer and Information Science, 2021
  • Training Neural Networks by Enhance Grasshopper Optimization Algorithm for Spam Detection System
    Sanaa A. A. Ghaleb, Mumtazimah Mohamad, Syed Abdullah Fadzli, Waheed Ali H. M. Ghanem
    IEEE Access, 2021
  • Spam Classification Based on Supervised Learning Using Grasshopper Optimization Algorithm and Artificial Neural Network
    Sanaa A. A. Ghaleb, Mumtazimah Mohamad, Engku Fadzli Hasan Syed Abdullah, Waheed A. H. M. Ghanem
    Communications in Computer and Information Science, 2021
  • A review of Arabic optical character recognition techniques & performance
    Yazan M Alwaqfi, Mumtazimah Mohamad
    International Journal of Engineering Trends and Technology, 2020
  • A multi-classifier method based deep learning approach for breast cancer
    Mokhairi Makhtar, Rosaida Rosly, Mohd Khalid Awang, Mumtazimah Mohamad, Aznida Hayati Zakaria
    International Journal of Engineering Trends and Technology, 2020
  • Web service oriented architecture solution for accounting information system for SMEs legal firm
    Mumtazimah Mohamad, Zuhairah Ariff Abd Ghadas, Abd Ghaddas, Wan Syahida, Wan Nur Syahida Wan Ismail, et al.
    International Journal of Recent Technology and Engineering, 2019
  • Analysis of oral cancer prediction with Pairwise preprocessing techniques using hybrid feature selection and ensemble classification
    International Journal of Recent Technology and Engineering, 2019
  • Lactation mobile application in islam perspective
    International Journal of Engineering and Advanced Technology, 2019
  • Complexity Approximation of Classification Task for Large Dataset Ensemble Artificial Neural Networks
    Mumtazimah Mohamad, Md Yazid Mohd Saman, Nazirah Abd Hamid
    Lecture Notes in Electrical Engineering, 2019
  • Concept Based Lattice Mining (CBLM) Using Formal Concept Analysis (FCA) for Text Mining
    Hasni Hassan, Md. Yazid Mohd Saman, Zailani Abdullah, Mumtazimah Mohamad
    Lecture Notes in Electrical Engineering, 2019
  • Improving Accuracy of Imbalanced Clinical Data Classification Using Synthetic Minority Over-Sampling Technique
    Fatihah Mohd, Masita Abdul Jalil, Noor Maizura Mohamad Noora, Suryani Ismail, Wan Fatin Fatihah Yahya, Mumtazimah Mohamad
    Communications in Computer and Information Science, 2019
  • Using smartphone application to notify muslim travelers the Jama’ Qasar Pray, Azan times and other facilities
    International Journal of Engineering and Advanced Technology, 2019
  • A review on sentiment analysis in Arabic using document level
    International Journal of Engineering and Technology Uae, 2018
  • Rainfall frequency analysis using LH-moments approach: A case of Kemaman Station, Malaysia
    Zahrahtul Amani Zakaria, Jarah Moath Ali Suleiman, Mumtazimah Mohamad
    International Journal of Engineering and Technology Uae, 2018
  • Current issues in Ciphertext Policy-Attribute based scheme for cloud computing: A survey
    Norhidayah Muhammad, Jasni Mohamad Zain, Mumtazimah Mohamad
    International Journal of Engineering and Technology Uae, 2018
  • Wireless network traffic analysis and troubleshooting using Raspberry Pi
    Mohamad Nur Haziq Mohd Safri, Wan Nor Shuhadah Wan Nik, Zarina Mohamad, Mumtazimah Mohamad
    International Journal of Engineering and Technology Uae, 2018
  • Noise removal using statistical operators for efficient leaf identification
    Mingoo Kang
    International Journal of Computer Aided Engineering and Technology, 2018
  • An analysis of large data classification using ensemble Neural Network
    Mumtazimah Mohamad, Wan Nor Shuhadah Wan Nik, Zahrahtul Amani Zakaria, Arifah Che Alhadi
    International Journal of Engineering and Technology Uae, 2018
  • Detection and feature extraction for images signatures
    International Journal of Engineering and Technology Uae, 2018
  • The reconstructed heterogeneity to enhance ensemble neural network for large data
    Mumtazimah Mohamad, Mokhairi Makhtar, Mohd Nordin Abd Rahman
    Advances in Intelligent Systems and Computing, 2017
  • Performance comparison of neural network parallelization techniques and its application for large data classification
    Mumtazimah Mohamad, Mokhairi Makhtar, Mohd Nordin Abd Rahman, Roslinda Muda
    Advanced Science Letters, 2017
  • Implementation of Apriori algorithm for a new flood area prediction system
    Nur Ashikin Harun, Mokhairi Makhtar, Azwa Abd Aziz, Mumtazimah Mohamad, Zahrahtul Amani Zakaria
    Advanced Science Letters, 2017
  • Mouse movement behavioral biometric for static user authentication
    Roslinda Muda, Nazirah Abd Hamid, Siti Dhalila Mohd Satar, Mumtazimah Mohamad, Nurul Afnan Mahadi, Fatimah Ghazali
    Advanced Science Letters, 2017
  • Optimizing sensitivity and specificity of ensemble classifiers for diabetic patients
    Journal of Theoretical and Applied Information Technology, 2015
  • Indoor global path planning based on critical cells using Dijkstra algorithm
    Journal of Theoretical and Applied Information Technology, 2015
  • Indoor global path planning based on critical cells using dijkstra algorithm
    Journal of Theoretical and Applied Information Technology, 2015
  • Comparison of diverse ensemble neural network for large data classification
    International Journal of Advances in Soft Computing and Its Applications, 2015
  • The contribution of feature selection and morphological operation for on-line business system’s image classification
    Mokhairi Makhtar, Nur Shazwani Kamarudin, Syed Abdullah Fadzli, Mumtazimah Mohamad, Fatma Susilawati Mohamad, Mohd Fadzil Abdul Kadir
    International Journal of Multimedia and Ubiquitous Engineering, 2015
  • Comparison of image classification techniques using caltech 101 dataset
    Journal of Theoretical and Applied Information Technology, 2015
  • The use of output combiners in enhancing the performance of large data for ANNs
    Iaeng International Journal of Computer Science, 2014
  • Divide and conquer approach in reducing ANN training time for small and large data
    Mumtazimah Mohamad, Md Yazid Mohd Saman, Muhammad Suzuri Hitam
    Journal of Applied Sciences, 2013
  • Integrating an e-learning model using IRT, Felder-Silverman and neural network approach
    Fatma Susilawati Mohamad, Mohamad Mumtazimah, Syed Abdullah Fadzli
    2013 2nd International Conference on Informatics and Applications Icia 2013, 2013
  • A framework for multiprocessor neural networks systems
    Mumtazimah Mohamad, Md Yazid Mohamad Saman, Muhammad Suzuri Hitam
    International Conference on ICT Convergence, 2012

RECENT SCHOLAR PUBLICATIONS

  • An Intelligent Botnet Detection System for IoT Using Neural Networks and an Enhanced Moth Search Optimize
    S Ghaleb, WAHM Ghanem, AB Nasser, M Mohamad
    Journal of Soft Computing and Data Mining 6 (3), 33-45 , 2025
    2025
  • Comparative Analysis of Machine Learning and Deep learning Techniques for Early Prediction of Breast Cancer
    M Al-Duais, AAG Al-Khulaidi, FS Mohamad, W Yousef, B Al-Fuhaidi, ...
    Journal of Future Artificial Intelligence and Technologies 2 (2), 242-254 , 2025
    2025
    Citations: 5
  • Machine learning techniques for early detection and diagnosis of breast cancer prediction
    M Al-Duais, AAG AL-Khulaidi, FS Mohamad, W Yousef, B AL-Futhaidi, ...
    The Indonesian Journal of Computer Science 14 (2) , 2025
    2025
    Citations: 1
  • A systematic literature review on bibliometric profiling analysis using machine learning
    AIA RIDZUAN, WMAFW HAMZAH, M MAKHTAR, M MOHAMAD, I ISMAIL, ...
    Journal of Theoretical and Applied Information Technology 103 (5), 1982-1998 , 2025
    2025
    Citations: 1
  • Global impact on human obesity–A robust non-linear panel data analysis
    M Munir, ZA Zakaria, AA Baig, MB Mohamad, N Arshed, R Alhajj
    Nutrition and Health 30 (3), 531-548 , 2024
    2024
    Citations: 4
  • Global human obesity and political globalization; asymmetric relationship through world human development levels
    M Munir, ZA Zakaria, R Alhajj, MB Mohamad, AA Baig, N Arshed
    Nutrition and Health 30 (3), 489-497 , 2024
    2024
    Citations: 6
  • Improved moth search algorithm with mutation operator for numerical optimization problems
    SAA Ghaleb, M Mohamad, WAHM Ghanem, AC Alhadi, AB Nasser, ...
    Institute of Advanced Engineering and Science , 2024
    2024
    Citations: 1
  • A Novel Hybrid DL Model for Printed Arabic Word Recognition based on GAN
    NAH Yazan M. Alwaqfi, Mumtazimah Mohamad, Ahmad T. Taani
    International Journal of Advanced Computer Science and Applications(IJACSA … , 2023
    2023
    Citations: 6
  • A Comparison Between The Existing Unisza's Mobile Learning And The Proposed Design According To A New Conceptual Framework.
    OJ Alkfaween, YA El-Ebiary, MB Mohamad
    Journal of Pharmaceutical Negative Results 14 , 2023
    2023
    Citations: 4
  • Hamming Distance Approach to Reduce Role Mining Scalability
    N Abd Hamid, SR Selamat, R Ahmad, M Mohamad
    International Journal of Advanced Computer Science and Applications 14 (6) , 2023
    2023
  • Feature selection by multiobjective optimization: application to spam detection system by neural networks and grasshopper optimization algorithm
    SAA Ghaleb, M Mohamad, WAHM Ghanem, AB Nasser, M Ghetas, ...
    IEEE Access 10, 98475-98489 , 2022
    2022
    Citations: 28
  • Grasshopper optimization algorithm based spam detection system using multi-objective wrapper feature selection and neural network classification
    SAA Ghaleb, M Mohamad, WAHM Ghanem, AM Abdullahi, AB Nasser, ...
    International Conference on Emerging Technologies and Intelligent Systems … , 2022
    2022
    Citations: 1
  • E-mail Spam Classification Using Grasshopper Optimization Algorithm and Neural Networks.
    SAA Ghaleb, M Mohamad, SA Fadzli, WAHM Ghanem
    Computers, Materials & Continua 71 (3) , 2022
    2022
    Citations: 10
  • Generative Adversarial Network for an Improved Arabic Handwritten Characters Recognition.
    YM Alwaqfi, M Mohamad, AT Al-Taani
    International Journal of Advances in Soft Computing & Its Applications 14 (1) , 2022
    2022
    Citations: 21
  • Development of global education index and establish relationship with human obesity through human development levels clustering
    M Munir, ZA Zakaria, AA Baig, MB Mohamad
    J Int J Spec Educ 37 (02601060221125146) , 2022
    2022
    Citations: 5
  • Training neural networks by enhance grasshopper optimization algorithm for spam detection system
    SAA Ghaleb, M Mohamad, SA Fadzli, WAHM Ghanem
    IEEE Access 9, 116768-116813 , 2021
    2021
    Citations: 26
  • Integrating mutation operator into grasshopper optimization algorithm for global optimization: SAA Ghaleb et al.
    SAA Ghaleb, M Mohamad, EFH Syed Abdullah, WAHM Ghanem
    Soft Computing 25 (13), 8281-8324 , 2021
    2021
    Citations: 18
  • Sentiment analysis technique and neutrosophic set theory for mining and ranking big data from online reviews
    I Awajan, M Mohamad, A Al-Quran
    IEEE Access 9, 47338-47353 , 2021
    2021
    Citations: 114
  • Spam classification based on supervised learning using grasshopper optimization algorithm and artificial neural network
    M Mumtazimah, SA Engku Fadzli Hasan, SAA Ghaleb, W Ghanem
    2021
  • An integrated model to email spam classification using an enhanced grasshopper optimization algorithm to train a multilayer perceptron neural network
    M Mohamad, E Abdullah, SAA Ghaleb, W Ghanem
    2021

MOST CITED SCHOLAR PUBLICATIONS

  • Sentiment analysis technique and neutrosophic set theory for mining and ranking big data from online reviews
    I Awajan, M Mohamad, A Al-Quran
    IEEE Access 9, 47338-47353 , 2021
    2021
    Citations: 114
  • Modelling for extraction of major phytochemical components from Eurycoma longifolia
    M Mohamad, MW Ali, A Ahmad
    Journal of Applied Sciences 10 (21), 2572-2577 , 2010
    2010
    Citations: 51
  • Academic social network sites: Opportunities and challenges
    M Mohamad, YM Lazim, S Rosle
    International Journal of Engineering and Technology(UAE) 7 (13.3), 133-136 , 2018
    2018
    Citations: 29
  • Feature selection by multiobjective optimization: application to spam detection system by neural networks and grasshopper optimization algorithm
    SAA Ghaleb, M Mohamad, WAHM Ghanem, AB Nasser, M Ghetas, ...
    IEEE Access 10, 98475-98489 , 2022
    2022
    Citations: 28
  • Comparison of Image Classification Techniques Using Caltech 101 Dataset
    NS Kamarudin, M Makhtar, SA Fadzli, M Mohamad, FS Mohamad, ...
    Journal of Theoretical and Applied Information Technology 71 (1), 79-86 , 2015
    2015
    Citations: 28
  • Training neural networks by enhance grasshopper optimization algorithm for spam detection system
    SAA Ghaleb, M Mohamad, SA Fadzli, WAHM Ghanem
    IEEE Access 9, 116768-116813 , 2021
    2021
    Citations: 26
  • A review on OpenCV
    M Mohamad, MYM Saman, MS Hitam, M Telipot
    Terengganu: Universitas Malaysia Terengganu 3 (1) , 2015
    2015
    Citations: 23
  • Generative Adversarial Network for an Improved Arabic Handwritten Characters Recognition.
    YM Alwaqfi, M Mohamad, AT Al-Taani
    International Journal of Advances in Soft Computing & Its Applications 14 (1) , 2022
    2022
    Citations: 21
  • Integrating mutation operator into grasshopper optimization algorithm for global optimization: SAA Ghaleb et al.
    SAA Ghaleb, M Mohamad, EFH Syed Abdullah, WAHM Ghanem
    Soft Computing 25 (13), 8281-8324 , 2021
    2021
    Citations: 18
  • Rainfall frequency analysis using LH-moments approach: A case of Kemaman Station, Malaysia
    ZA Zakaria, JMA Suleiman, M Mohamad
    Int. J. Eng. Technol 7 (2), 107-110 , 2018
    2018
    Citations: 15
  • Spam classification based on supervised learning using grasshopper optimization algorithm and artificial neural network
    SAA Ghaleb, M Mohamad, EFHS Abdullah, WAHM Ghanem
    International Conference on Advances in Cyber Security, 420-434 , 2020
    2020
    Citations: 13
  • Recent advances on soft computing and data mining
    T Herawan, R Ghazali, MM Deris
    Sl: Springer , 2017
    2017
    Citations: 11
  • Divide and conquer approach in reducing ann training time for small and large data
    M Mohamad, MYM Saman, MS Hitam
    J. Appl. Sci 13 (1), 133-139 , 2013
    2013
    Citations: 11
  • E-mail Spam Classification Using Grasshopper Optimization Algorithm and Neural Networks.
    SAA Ghaleb, M Mohamad, SA Fadzli, WAHM Ghanem
    Computers, Materials & Continua 71 (3) , 2022
    2022
    Citations: 10
  • Improving accuracy of imbalanced clinical data classification using synthetic minority over-sampling technique
    F Mohd, M Abdul Jalil, NMM Noora, S Ismail, WFF Yahya, M Mohamad
    International Conference on Computing, 99-110 , 2019
    2019
    Citations: 10
  • An integrated model to email spam classification using an enhanced grasshopper optimization algorithm to train a multilayer perceptron neural network
    SAA Ghaleb, M Mohamad, EFHS Abdullah, WAHM Ghanem
    International Conference on Advances in Cyber Security, 402-419 , 2020
    2020
    Citations: 8
  • A Review of Arabic Optical Character Recognition Techniques & Performance
    YM Alwaqfi, M Mohamad
    International Journal of Engineering Trends and Technology (IJETT) –, 44-51 , 2020
    2020
    Citations: 8
  • Detection and feature extraction for images signatures
    FS Mohamad, FM Alsuhimat, MA Mohamed, M Mohamad, AA Jamal
    International Journal of Engineering & Technology 7 (3), 44-48 , 2018
    2018
    Citations: 8
  • A review on sentiment analysis in Arabic using document level
    I Awajan, M Mohamad
    International Journal of Engineering and Technology(UAE) 7 (13.3), 128-132 , 2018
    2018
    Citations: 8
  • Enhancement Processing Time and Accuracy Training via Significant Parameters in the Batch BP Algorithm
    MS Al _ Duais, FS Mohamad, M Mohamad, MN Husen
    Inernational Journal of Intelligent Systems and Applications 12 (1), 43-54 , 2020
    2020
    Citations: 7