Mahmood Khalsan

University of Northampton

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Michael Opoku Agyeman

University of Northampton

Lee R. Machado

University of Northampton

Suraj Ajit

University of Northampton

Eman S. Al-Shamery

University of Babylon

Mu Mu

University of Northampton

Min Yang

Institute of High Energy Physics

Kin Teo

Universiti of Malaysia Sabah

Kenneth Tze Kin Teo

Universiti of Malaysia Sabah

Juvy Amor

Universiti of Malaysia Sabah

Mohd Hafiz Zakaria

Technical University of Malaysia Malacca

Cooperative Institutions

Universiti of Malaysia Sabah

281

Weatherford College

132

Conference Board

Vietnam National University Ho Chi Minh City

Hanoi University of Science and Technology

National University of Malaysia

Ho Chi Minh City University of Technology

Information Technology University

Vietnam National University, Hanoi

ORCID

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Authors Index

Aghil Jafari An Le Anh Dong Phi Le Nguyen Anh Huy Quyen

10.1109/icsse58758.2023.10227171

Cite

Citations (0)

Developing a Multidimensional Fuzzy Deep Learning for Cancer Classification using Gene Expression Data

Mahmood Khalsan Mu Mu Eman S. Al-Shamery Suraj Ajit Lee R. Machado

In the realm of cancer research, the identification of biomarker genes plays a pivotal role in accurate classification and diagnosis. This study delves into the intersection of machine learning and gene selection to enhance the precision of biomarker identification for cancer classification. Leveraging advanced computational techniques. In the quest for improved cancer classification, studies face challenges due to high-dimensional gene expression data and limited gene relevance. To address these challenges, we developed a novel multidimensional fuzzy deep learning (MFDL) to select subset of significant genes and using those genes to train the model for better accuracy. MFDL is exploring the integration of fuzzy concepts within filter and wrapper methods to select significant genes and applying a fuzzy classifier to improve cancer classification accuracy. Through rigorous experimentation and validation, six gene expression data used, the findings demonstrated the efficacy of our methodology on diverse cancer datasets. The results underscore the importance of integrative computational methods in deciphering the intricate genomic landscape of cancer and spotlight the potential for improved diagnostic accuracy. The developed model showcased outstanding performance across the six employed datasets, demonstrating an average accuracy of 98%, precision of 98.3%, recall of 97.6%, and an f1-score of 97.8%.

Identification

Biomarker Discovery

10.5121/csit.2023.132304

Cite

Citations (1)

Table of Contents

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) (2023)

Chenyang Hao M Hemel Md. Arifur Rahman Bhuiyan T Reaz K Almohamad

Table (database)

10.1109/iicaiet59451.2023.10291650

Cite

Citations (0)

Enhancing Cancer Classification Through the Development of a Fuzzy Gene Selection-Wrapper Plus Method

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET) (2023)

Mahmood Khalsan Mu Mu Eman S. Al-Shamery Lee R. Machado Michael Opoku Agyeman

Deep Learning (DL) approaches have made sub-stantial advances in developing classification models in most disciplines, in terms of both accuracy and speed. However, analyzing gene expression data for cancer classification remains challenging due to the nature of the complexity of available datasets, which are characterized by high dimensionality (a small number of samples and a large number of features). We developed a new fuzzy gene selection wrapper plus (FGSWP) to select the most significant genes with the goal of reducing the complexity of cancer classification and the dimensionality of the datasets. FGSWP has shown its efficacy by lowering the number of genes and enhancing classifier model performance. Wrapper techniques are not suitable for use with high-dimensional data, hence fuzzy gene selection is used as the initial phase for reducing the number of genes before utilizing wrapper techniques. The accuracy attained in all datasets used ranged from ${85.3{\%}}$ to 99.3% when FGSWP and Multilayer Perceptron (MLP) were employed together.

Gene selection

Multilayer perceptron

10.1109/iicaiet59451.2023.10291820

Cite

Citations (1)

A Novel Fuzzy Classifier Model for Cancer Classification Using Gene Expression Data

IEEE Access (2023)

Mahmood Khalsan Mu Mu Eman S. Al-Shamery Suraj Ajit Lee R. Machado

In the pursuit of better cancer classification, many studies have been conducted to identify the genes associated with cancer. However, the high dimensionality of gene expression data and the limited relevance of a few genes pose significant challenges to this endeavor. Existing gene selection methods yield divergent gene lists, further complicating the classification process. To overcome this issue, we developed a novel approach called Fuzzy Gene Selection (FGS), which combines the strengths of various gene selection methods in the field. FGS was developed using three feature selection techniques (Mutual Information, F-ClassIf, and Chi-squared) to rank genes based on their importance. These methods generated scores and rankings for each gene. Fuzzification and Defuzzification techniques were then applied to combine these scores into a single best score for each gene. This approach aids in identifying genes of significance in cancer classification, especially in multi-class scenarios. Classifiers often produce convergent decisions in such cases, where the predicted probabilities for different classes do not always correspond to the correct predicted class with the highest probability. To address this, we developed a novel Fuzzy classifier that leverages the contributions from each node's traditional deep classifier. This novel approach combines the strengths of traditional deep classifiers at individual nodes, enabling the Fuzzy classifier to make more robust and informed predictions. The Fuzzy classifier (FC) has demonstrated improvements in accuracy and the generalization of the proposed algorithm to accurately classify different cancer types.

Gene selection

10.1109/access.2023.3325381

Cite

Citations (7)

Intersection Three Feature Selection and Machine Learning Approaches for Cancer Classification

Mahmood Khalsan Mu Mu Eman S. Al-Shamery Lee R. Machado Michael Opoku Agyeman

Machine learning (ML) methods have a plaid an important role in classification and prediction in most fields. However, analyzing gene expression is remain complex in cancer classification because of the high dimensionality of the provided dataset in gene expression. Consequentially, intersection-based three feature selection methods (ITFS) was developed to select optimal features (genes) that would be used as identifiers for classification and reduce the dimensionality of the available data in gene expression. ITFS has employed three feature selection methods (Mutual Information (MI), F-ClassIf, and Minimum Redundancy Maximum Relevance (mRMR)). Therefore, employing intersection concept that leads to select only the genes that have been selected by the three feature selection techniques. These selected genes would be used as identifiers for the training classifier model. Our study applied the proposed ITFS to six gene expression datasets downloaded from (Microarray and RNAseq tools) for validating the effectiveness of ITFS on classifier methods. The highest average accuracy improvement in the six datasets was when Multilayer Perceptron (MLP) and ITFS employed together compared to employing MLP individually. The proposed ITFS-MLP model has produced classification accuracy between (92% to 100%) for the six datasets and the average accuracy is 96%.

Gene selection

Perceptron

Multilayer perceptron

10.1109/icsse58758.2023.10227163

Cite

Citations (4)

A Survey of Machine Learning Approaches Applied to Gene Expression Analysis for Cancer Prediction

IEEE Access (2022)

Mahmood Khalsan Lee R. Machado Eman S. Al-Shamery Suraj Ajit Karen Anthony

Machine learning approaches are powerful techniques commonly employed for developing cancer prediction models using associated gene expression and mutation data. This manuscript provides a comprehensive review of recent cancer studies that have employed gene expression data from several cancer types (breast, lung, kidney, ovarian, liver, central nervous system and gallbladder) for survival prediction, tumor identification and stratification. We also provide an overview of biomarker studies that are associated with these cancer types. The survey captures multiple aspects of machine learning associated cancer studies, including cancer classification, cancer prediction, identification of biomarker genes, microarray, and RNA-Seq data. We discuss the technical issues with current cancer prediction models and the corresponding measurement tools for determining the activity levels of gene expression between cancerous tissues and noncancerous tissues. Additionally, we investigate how identifying putative biomarker gene expression patterns can aid in predicting future risk of cancer and inform the provision of personalized treatment.

Biomarker Discovery

Identification

Cancer Biomarkers

Kidney cancer

10.1109/access.2022.3146312

Cite

Citations (43)

An Overview of Prevention/Mitigation against Memory Corruption Attack

Mahmood Khalsan Michael Opoku Agyeman

One of the most prevalent, ancient and devastating vulnerabilities which is increasing rapidly is Memory corruption. It is a vulnerability where a memory location contents of a computer system are altered because of programming errors allowing execution of arbitrary codes. It particularly happens in low-level programming languages such as C, C++ because of their lack of memory safety. Many defend techniques against this kind of attacks have been presented and implemented to prevent it. However, an advanced version of the attack can bypass some of these techniques and harm the system. In this work, we present an overview of the Memory corruption attacks and the existing mitigation techniques for both compilers and operating systems. We hope that this survey will provide sufficient details that can be useful for researchers and system designer.

Memory Protection

Vulnerability

Language Change

10.1145/3284557.3284564

Cite

Citations (3)

Fuzzy Gene Selection and Cancer Classification Based on Deep Learning Model

arXiv (Cornell University) (2023)

Mahmood Khalsan Mu Mu Eman S. Al-Shamery Lee R. Machado Suraj Ajit

Machine learning (ML) approaches have been used to develop highly accurate and efficient applications in many fields including bio-medical science. However, even with advanced ML techniques, cancer classification using gene expression data is still complicated because of the high dimensionality of the datasets employed. We developed a new fuzzy gene selection technique (FGS) to identify informative genes to facilitate cancer classification and reduce the dimensionality of the available gene expression data. Three feature selection methods (Mutual Information, F-ClassIf, and Chi-squared) were evaluated and employed to obtain the score and rank for each gene. Then, using Fuzzification and Defuzzification methods to obtain the best single score for each gene, which aids in the identification of significant genes. Our study applied the fuzzy measures to six gene expression datasets including four Microarray and two RNA-seq datasets for evaluating the proposed algorithm. With our FGS-enhanced method, the cancer classification model achieved 96.5%,96.2%,96%, and 95.9% for accuracy, precision, recall, and f1-score respectively, which is significantly higher than 69.2% accuracy, 57.8% precision, 66% recall, and 58.2% f1-score when the standard MLP method was used. In examining the six datasets that were used, the proposed model demonstrates it's capacity to classify cancer effectively.

Identification

10.48550/arxiv.2305.04883

Cite

Citations (3)