Mumtazimah Mohamad
@unisza.edu.my
Faculty of Informatics and Computing
Universiti Sultan Zainal Abidin
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
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Nazirah Abd Hamid, Siti Rahayu Selamat, Rabiah Ahmad, and Mumtazimah Mohamad
The Science and Information Organization
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
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, and Mohamed Ghetas
Springer International Publishing
Yazan M. Alwaqfi, Mumtazimah Mohamad, Ahmad T. Al-Taani, and Nazirah Abd Hamid
The Science and Information Organization
Mubbasher Munir, Zahrahtul Amani Zakaria, Atif Amin Baig, Mumtazimah Binti Mohamad, Noman Arshed, and Reda Alhajj
SAGE Publications
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.
Mubbasher Munir, Zahrahtul Amani Zakaria, Reda Alhajj, Mumtazimah Binti Mohamad, Atif Amin Baig, and Noman Arshed
SAGE Publications
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.
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, and Oludare Isaac Abiodun
Institute of Electrical and Electronics Engineers (IEEE)
Networks are strained by spam, which also overloads email servers and blocks mailboxes with unwanted messages and files. Setting the protective level for spam filtering might become even more crucial for email users when malicious steps are taken since they must deal with an increase in the number of valid communications being marked as spam. By finding patterns in email communications, spam detection systems (SDS) have been developed to keep track of spammers and filter email activity. SDS has also enhanced the tool for detecting spam by reducing the rate of false positives and increasing the accuracy of detection. The difficulty with spam classifiers is the abundance of features. The importance of feature selection (FS) comes from its role in directing the feature selection algorithm’s search for ways to improve the SDS’s classification performance and accuracy. As a means of enhancing the performance of the SDS, we use a wrapper technique in this study that is based on the multi-objective grasshopper optimization algorithm (MOGOA) for feature extraction and the recently revised EGOA algorithm for multilayer perceptron (MLP) training. The suggested system’s performance was verified using the SpamBase, SpamAssassin, and UK-2011 datasets. Our research showed that our novel approach outperformed a variety of established practices in the literature by as much as 97.5%, 98.3%, and 96.4% respectively.
Yazan Alwaqfi, Mumtazimah Mohamad, and Ahmad Al-Taani
Alzaytoonah University of Jordan
Abstract 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).
Sanaa A. A. Ghaleb, Mumtazimah Mohamad, Syed Abdullah Fadzli, and Waheed A.H.M. Ghanem
Computers, Materials and Continua (Tech Science Press)
Sanaa A. A. Ghaleb, Mumtazimah Mohamad, Engku Fadzli Hasan Syed Abdullah, and Waheed A. H. M. Ghanem
Springer Science and Business Media LLC
Sanaa A. A. Ghaleb, Mumtazimah Mohamad, Syed Abdullah Fadzli, and Waheed Ali H. M. Ghanem
Institute of Electrical and Electronics Engineers (IEEE)
A significant negative impact of spam e-mail is not limited only to the serious waste of resources, time, and efforts, but also increases communications overload and cybercrime. Perhaps the most damaging aspect of spam email is that it has become such a major tool for attacks of cross-site scripting, malware infection, phishing, and cross-site request forgery, etc. Due to the nature of the adaptive unsolicited spam, it has been weakening the effect of the previous discovery techniques. This article proposes a new Spam Detection System (SDS) framework, by using a series of six different variants of enhanced Grasshopper Optimization Algorithm (EGOAs), which are investigated and combined with a Multilayer Perceptron (MLP) for the purpose of advanced spam email detection. In this context, the combination of MLP and EGOAs produces Neural Network (NN) models, referred to (EGOAMLPs). The main idea of this research is to use EGOAs to train the MLP to classify the emails as spam and non-spam. These models are applied to SpamBase, SpamAssassin, and UK-2011 Webspam benchmark datasets. In this way, the effectiveness of our models on detecting diverse forms of spam email is evidenced. The results showed that the proposed MLP model trained by EGOAs achieves a higher performance compared to other optimization methods in terms of accuracy, detection rate, and false alarm rate.
Ibrahim Awajan, Mumtazimah Mohamad, and Ashraf Al-Quran
Institute of Electrical and Electronics Engineers (IEEE)
Recently, a huge amount of online consumer reviews (OCRs) is being generated through social media, web contents, and microblogs. This scale of big data cannot be handled by traditional methods. Sentiment analysis (SA) or opinion mining is emerging as a powerful and efficient tool in big data analytics and improving decision making. This research paper introduces a novel method that integrates neutrosophic set (NS) theory into the SA technique and multi-attribute decision making (MADM) to rank the different products based on numerous online reviews. The method consists of two parts: Determining sentiment scores of the online reviews based on the SA technique and ranking alternative products via NS theory. In the first part, the online reviews of the alternative products concerning multiple features are crawled and pre-processed. A neutral lexicon consists of 228 neutral words and phrases is compiled and the Valence Aware Dictionary and sEntiment Reasoner (VADER) for sentiment reasoning is adapted to handle the neutral data. The compiled neutral lexicon, as well as the adapted VADER, are utilized to build a novel adaptation called Neutro-VADER. The Neutro-VADER assigns positive, neutral, and negative sentiment scores to each review concerning the product feature. In this stage, the novel idea is to point out the positive, neutral, and negative sentiment scores as the truth, indeterminacy, and falsity memberships degrees of the neutrosophic number. The overall performance of each alternative concerning each feature based on a neutrosophic number is measured. In the second part, the ranking of alternatives is being evaluated through the simplified neutrosophic number weighted averaging (SNNWA) operator and cosine similarity measure methods. A case study with real datasets (Twitter datasets) is provided to illustrate the application of the proposed method. The results show good performance in handling the neutral data on the SA stage as well as the ranking stage. In the SA stage, findings show that the Neutro-VADER in the proposed method can deal successfully with all types of uncertainties including indeterminacy comparable with the traditional VADER in the other methods. In the ranking stage, the results show a great similarity and consistency while using other ranking methods such as PROMETHEE II, TOPSIS, and TODIM methods.
Sanaa A. A. Ghaleb, Mumtazimah Mohamad, Engku Fadzli Hasan Syed Abdullah, and Waheed A. H. M. Ghanem
Springer Singapore
Sanaa A. A. Ghaleb, Mumtazimah Mohamad, Engku Fadzli Hasan Syed Abdullah, and Waheed A. H. M. Ghanem
Springer Singapore
Mokhairi Makhtar, Rosaida Rosly, Mohd Khalid Awang, Mumtazimah Mohamad, and Aznida Hayati Zakaria
Seventh Sense Research Group Journals
Yazan M Alwaqfi and Mumtazimah Mohamad
Seventh Sense Research Group Journals
The need for efficient account management is critical because it gives an additional advantage to strategic information management. Accounting systems for legal firms involving distinct business processes and practices with client records and a reasonably detailed account of its own. Although currently there is a lot of accounting software locally and internationally, however, it is not appropriate to the needs of world-class firms, small and medium enterprises (SMEs), especially in terms of size, scope and cost. Majority of them manage the account by themselves such as manually record keeping or non-integrate system. Therefore, it is not surprising when the legal practitioner SMEs have difficulties in managing their account. Computerized Accounting Information Systems (CAIS @ Law) is proposed as a web service to help SMEs firms in managing financial transactions, performance monitoring firm, statements of cash flows and inflows out customer's account and the auditing process. Business process requirements have been identified, namely using web services oriented architecture through the virtual server. It requires a basic operating system, easy and reduced cost and customizable and easy to operate. This system can provide sufficient material for submission to the Bar Council as proof of the account as well as the use of a variety of clients and SME sector scale. The developed prototype successfully employed by local SMEs and shows competitive features with dynamics customization ability based on the capacity of the firm.
Fatihah Mohd, Masita Abdul Jalil, Noor Maizura Mohamad Noora, Suryani Ismail, Wan Fatin Fatihah Yahya, and Mumtazimah Mohamad
Springer International Publishing
Hasni Hassan, Md. Yazid Mohd Saman, Zailani Abdullah, and Mumtazimah Mohamad
Springer Singapore
Mumtazimah Mohamad, Md Yazid Mohd Saman, and Nazirah Abd Hamid
Springer Singapore
Mingoo Kang
Inderscience Publishers