Papers by Mohammad Mezher
Applied Artificial Intelligence
Frontiers in Artificial Intelligence
Cancer is defined as an abnormal growth of human cells classified into benign and malignant. The ... more Cancer is defined as an abnormal growth of human cells classified into benign and malignant. The site makes further classification of cancers of initiation and genomic underpinnings. Lung cancer displays extreme heterogeneity, making genomic classification vital for future targeted therapies. Especially considering lung cancers account for 1.76 million deaths worldwide annually. However, tumors do not always correlate to cancer as they can be benign, severely dysplastic (pre-cancerous), or malignant (cancerous). Lung cancer presents with ambiguous symptoms, thus is difficult to diagnose and is detected later compared to other cancers. Diagnosis relies heavily on radiology and invasive procedures. Different models developed employing Artificial Intelligence (AI), and Machine Learning (ML) have been used to classify various cancers. In this study, the authors propose a Genetic Folding Strategy (GFS) based model to predict lung cancer from a lung cancer dataset. We developed and implem...
International Journal of Advanced Computer Science and Applications
Now-a-days, social media sites and travel blogs have become one of the most vital expression sour... more Now-a-days, social media sites and travel blogs have become one of the most vital expression sources. Tourists express everything related to their experiences, reviews, and opinions about the place they visited. Moreover, the sentiment classification of tourist reviews on social media sites plays an increasingly important role in tourism growth and development. Accordingly, these reviews are valuable for both new tourists and officials to understand their needs and improve their services based on the assessment of tourists. The tourism industry anywhere also relies heavily on the opinions of former tourists. However, most tourists write their reviews in their local dialect, making sentiment classification more difficult because there are no specific rules to control the writing system. Moreover, there is a gap between Modern Standard Arabic (MSA) and local dialects. one of the most prominent issues in sentiment analysis is that the local dialect lexicon has not seen significant development. Although a few lexicons are available to the public, they are sparse and small. Thus, this paper aims to build a model capable of accurate sentiment classification in the Saudi dialect for Arabic in tourist place reviews using deep learning techniques. Machine learning techniques help classifying these reviews into (positive, negative, and neutral). In this paper, three machine learning algorithms were used, Support-Vector Machine (SVM), Long short-term memory (LSTM), and Recurrent Neural Network (RNN). These algorithms are classified using Google Map data set for tourist places in Saudi Arabia. Performance classification of these algorithms is done using various performance measures such as accuracy, precision, recall and Fscore. The results show that the SVM algorithm outperforms the deep learning techniques. The result of SVM was 98%, outperforming the LSTM, and RNN had the same performance of 96%.
Abstract—Breast cancer is considered the second most common cancer in women compared to all other... more Abstract—Breast cancer is considered the second most common cancer in women compared to all other cancers. It is fatal in less than half of all cases and is the main cause of mortality in women. It accounts for 16% of all cancer mortalities worldwide. Early diagnosis of breast cancer increases the chance of recovery. Data mining techniques can be utilized in the early diagnosis of breast cancer. In this paper, an academic experimental breast cancer dataset is used to perform a data mining practical experiment using the Waikato Environment for Knowledge Analysis (WEKA) tool. The WEKA Java application represents a rich resource for conducting performance metrics during the execution of experiments. Pre-processing and feature extraction are used to optimize the data. The classification process used in this study was summarized through thirteen experiments. Additionally, 10 experiments using various different classification algorithms were conducted. The introduced algorithms were: Naïv...
Abstract—Breast cancer is considered the second most common cancer in women compared to all other... more Abstract—Breast cancer is considered the second most common cancer in women compared to all other cancers. It is fatal in less than half of all cases and is the main cause of mortality in women. It accounts for 16% of all cancer mortalities worldwide. Early diagnosis of breast cancer increases the chance of recovery. Data mining techniques can be utilized in the early diagnosis of breast cancer. In this paper, an academic experimental breast cancer dataset is used to perform a data mining practical experiment using the Waikato Environment for Knowledge Analysis (WEKA) tool. The WEKA Java application represents a rich resource for conducting performance metrics during the execution of experiments. Pre-processing and feature extraction are used to optimize the data. The classification process used in this study was summarized through thirteen experiments. Additionally, 10 experiments using various different classification algorithms were conducted. The introduced algorithms were: Naïv...
PeerJ Computer Science
Cancer’s genomic complexity is gradually increasing as we learn more about it. Genomic classifica... more Cancer’s genomic complexity is gradually increasing as we learn more about it. Genomic classification of various cancers is crucial in providing oncologists with vital information for targeted therapy. Thus, it becomes more pertinent to address issues of patient genomic classification. Prostate cancer is a cancer subtype that exhibits extreme heterogeneity. Prostate cancer contributes to 7.3% of new cancer cases worldwide, with a high prevalence in males. Breast cancer is the most common type of cancer in women and the second most significant cause of death from cancer in women. Breast cancer is caused by abnormal cell growth in the breast tissue, generally referred to as a tumour. Tumours are not synonymous with cancer; they can be benign (noncancerous), pre-malignant (pre-cancerous), or malignant (cancerous). Fine-needle aspiration (FNA) tests are used to biopsy the breast to diagnose breast cancer. Artificial Intelligence (AI) and machine learning (ML) models are used to diagnose...
International Journal of Electronic Banking
Journal of Advanced Computational Intelligence and Intelligent Informatics, 2022
Genetic folding (GF) is a robust evolutionary optimization algorithm. For efficient hyper-scale G... more Genetic folding (GF) is a robust evolutionary optimization algorithm. For efficient hyper-scale GFs, a hybrid parallel approach based on CPU architecture Parallel GF (PGF) is proposed. It aids in resolving kernel tricks that are difficult to predict using conventional optimization approaches. The regression and classification problems are solved using PGF. Four concurrent CPUs are formed to parallelize the GF, and each executes eight threads. It is also easily scalable to multi-core CPUs. PGFLibPy is a Python-based machine learning framework for classification and regression problems. PGFLibPy was used to build a model of the UCI dataset that reliably predicts regression values. The toolbox activity is used for binary and multiclassification datasets to classify UCI. PGFLibPy’s has 25 Python files and 18 datasets. Dask parallel implementation is being considered in the toolbox. According to this study, this toolbox can categorize and predict models on any other dataset. The source c...
Journal of Advanced Computational Intelligence and Intelligent Informatics, 2022
Genetic folding (GF) is a robust evolutionary optimization algorithm. For efficient hyper-scale G... more Genetic folding (GF) is a robust evolutionary optimization algorithm. For efficient hyper-scale GFs, a hybrid parallel approach based on CPU architecture Parallel GF (PGF) is proposed. It aids in resolving kernel tricks that are difficult to predict using conventional optimization approaches. The regression and classification problems are solved using PGF. Four concurrent CPUs are formed to parallelize the GF, and each executes eight threads. It is also easily scalable to multi-core CPUs. PGFLibPy is a Python-based machine learning framework for classification and regression problems. PGFLibPy was used to build a model of the UCI dataset that reliably predicts regression values. The toolbox activity is used for binary and multiclassification datasets to classify UCI. PGFLibPy’s has 25 Python files and 18 datasets. Dask parallel implementation is being considered in the toolbox. According to this study, this toolbox can categorize and predict models on any other dataset. The source c...
2022 2nd International Conference on Computing and Information Technology (ICCIT)
In the software industry, testing operation represents a challenging task that is independent of ... more In the software industry, testing operation represents a challenging task that is independent of software product quality control. Traditional and common software testing methods are time and resource consuming, especially in manual and human-based methods. In this paper, an automated software test case data generation is proposed based on Genetic Algorithms (GA). Random population generation is used to produce new test data generations based on fitness selection and with different genetic operators by either crossover schemes, mutation schemes, or both of them. The proposed methodology is applied using Python programming language and is tested with four different programs, and by using Distributed Evolutionary Algorithms in Python (DEAP), in addition to unit testing and coverage libraries. The experiments' results prove the advantage of the proposed mechanism over manual testing while results variations are maintained with different crossover and mutation deployments and optimal deployment values are investigated in terms of code coverage percentage.
Applied Intelligence, 2014
Genetic Folding (GF) algorithm is a new class of evolutionary algorithms specialized for complica... more Genetic Folding (GF) algorithm is a new class of evolutionary algorithms specialized for complicated computer problems. GF algorithm uses a linear sequence of numbers of genes structurally organized in integer numbers, separated with dots. The encoded chromosomes in the population are evaluated using a fitness function. The fittest chromosome survives and is subjected to modification by genetic operators. The creation of these encoded chromosomes, with the fitness functions and the genetic operators, allows the algorithm to perform with high efficiency in the genetic folding life cycle. Multi-classification problems have been chosen to illustrate the power and versatility of GF. In classification problems, the kernel function is important to construct binary and multi classifier for support vector machines. Different types of standard kernel functions have been compared with our proposed algorithm. Promising results have been shown in comparison to other published works.
IJCSMC, 2020
Cancer is a disease that develops in the human body due to gene mutation. Because of various fact... more Cancer is a disease that develops in the human body due to gene mutation. Because of various factors, cells can become cancerous and grow rapidly, destroying normal cells at the same time. Support vector machines allow for accurate classification and detection of the classes. The advantage of kernel selection is to derive global learning rates for SVMs using the Genetic Folding algorithm. The developed GF algorithm outperforms traditional SVMs in the UCI Breast Cancer Wisconsin Diagnostic (BCWD) dataset under a certain comparative analysis, which is conducted under a set of conditions that describe the behavior of the compared algorithms. The observation that relates the GF performance appears to be comparable with SVM. The statistical analysis relies on a careful analysis of the ROC curve. Moreover, the GF algorithm shows that accuracy rates are obtained adaptively, that is, without knowing the parameters resulting from the margin conditions. The experimental results show that the ...
Research and Development in Intelligent Systems XXVII, 2010
ABSTRACT
Artificial Intelligence for Sustainable Finance and Sustainable Technology
Artificial Intelligence for Sustainable Finance and Sustainable Technology
This paper is a primarily attempt to design a toolbox for Genetic Folding algorithm using MATLAB.... more This paper is a primarily attempt to design a toolbox for Genetic Folding algorithm using MATLAB. The toolbox was designed for training ACO in solving Santa Fe Trail problem. However, GF algorithm can encode and decode any type of problem into a linearly folding scheme. For advance or even simple type of problems, a string scheme is encoding to represent a set of operators. GF is a novel algorithm in solving optimization problems as shown in the experiments. We will also illustrate the benefits of GF algorithm conducted into ACO in order finding the best feeding function in comparison with the literature. Keywords-Genetic Folding; Genetic Algorithm; Genetic Programming; Ant Colony Optimization; Evolutionary Algorithms; Santa Fe Trail Problem
2020 International Conference on Computing and Information Technology (ICCIT-1441)
Nowadays the amount of data is rapidly increasing. For example, in 2019, International Telecommun... more Nowadays the amount of data is rapidly increasing. For example, in 2019, International Telecommunication Union ITU states that the number of Internet users has become about 4.1 billion (53.6% of the global population). The big amount of data exceeds our ability to analyze and extract useful information without the help of computer techniques. Data mining is a common technique used in Machine Learning (ML) to extract useful knowledge from big data. Classification algorithms are also widely used for achieving accurate prediction. The classification techniques compared here were K-Nearest Nearest Neighbor (K-NN), Radial Basis Function Support Vector Machine (RBF SVM), Linear SVM, Sigmoid SVM, Logistic Regression (LR), Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), and Naive Bayes (NB). This study aims at comparing the accuracy of six classification techniques using the confusion matrix evaluation model. The UCI PIMA Indian Diabetes Dataset is considered and deployed on the Anaconda python platform. The results showed that the achieved accuracy by using K-NN is 0.7265, by RBF SVM is 0.612, by Linear SVM is 0.7721, by Sigmoid SVM is 0.6510, by LR is 0.7695, by LDA is 0.7734, by CART is 0.6952, and by NB 0.7551.
2021 National Computing Colleges Conference (NCCC), 2021
Artificial Intelligence (AI) combined with efficient image processing tools help doctors better p... more Artificial Intelligence (AI) combined with efficient image processing tools help doctors better predict disease progression. Alzheimer’s Disease (AD) early diagnosis is one of the most difficult challenges in medical imaging systems involving AD classification beyond AD detection. In this paper, a Convolutional Neural Network (CNN) is implemented for AD detection and stage classification for Magnetic Resonance Imaging (MRI). The implementation methodology begins with basic preprocessing techniques, such as image resizing and pixel normalization, and then extracted features are reconstructed into a 1-Dimensional vector to be fed to the CNN with accompanying labels. Four different labels are used according to the four different AD stages considered, which are (Non-Demented, Mild Demented, Moderate Demented, and Very Mild Demented). The prediction model’s evaluation shows an efficient result in accuracy and model loss for only ten epochs; the accuracy of the model recorded was 97%. The...
International Journal of Advanced Computer Science and Applications, 2020
Breast cancer is the leading cause of death among women with cancer. Computer-aided diagnosis is ... more Breast cancer is the leading cause of death among women with cancer. Computer-aided diagnosis is an efficient method for assisting medical experts in early diagnosis, improving the chance of recovery. Employing artificial intelligence (AI) in the medical area is very crucial due to the sensitivity of this field. This means that the low accuracy of the classification methods used for cancer detection is a critical issue. This problem is accentuated when it comes to blurry mammogram images. In this paper, convolutional neural networks (CNNs) are employed to present the traditional convolutional neural network (TCNN) and supported convolutional neural network (SCNN) approaches. The TCNN and SCNN approaches contribute by overcoming the shift and scaling problems included in blurry mammogram images. In addition, the flipped rotation-based approach (FRbA) is proposed to enhance the accuracy of the prediction process (classification of the type of cancerous mass) by taking into account the...
International Journal of Advanced Computer Science and Applications, 2020
Breast cancer is considered the second most common cancer in women compared to all other cancers.... more Breast cancer is considered the second most common cancer in women compared to all other cancers. It is fatal in less than half of all cases and is the main cause of mortality in women. It accounts for 16% of all cancer mortalities worldwide. Early diagnosis of breast cancer increases the chance of recovery. Data mining techniques can be utilized in the early diagnosis of breast cancer. In this paper, an academic experimental breast cancer dataset is used to perform a data mining practical experiment using the Waikato Environment for Knowledge Analysis (WEKA) tool. The WEKA Java application represents a rich resource for conducting performance metrics during the execution of experiments. Pre-processing and feature extraction are used to optimize the data. The classification process used in this study was summarized through thirteen experiments. Additionally, 10 experiments using various different classification algorithms were conducted. The introduced algorithms were: Naive Bayes, ...
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Papers by Mohammad Mezher