Mustafa Abdul Salam

Mustafa Abdul Salam
Benha University · Faculty of Computers and Information

Associate Professor of Artificial Intelligence, Faculty of Computers and Artificial Intelligence ; Benha University

About

46
Publications
171,896
Reads
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474
Citations
Citations since 2016
35 Research Items
456 Citations
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2016201720182019202020212022020406080100120
2016201720182019202020212022020406080100120
Introduction
Mustafa Abdul Salam currently works at the Faculty of Computers and Information, Benha University. Mustafa does research in Artificial Neural Network, Artificial Intelligence and Information Systems (Business Informatics). Their current project is 'Hybrid Computational Intelligence models for prediction and feature selection'.

Publications

Publications (46)
Article
Full-text available
Brain tumor is a fatal disease and one of the major causes of rising death rates in adults. Predicting methylation of the O6-Methylguanine-DNA Methyltransferase (MGMT) gene status utilizing Magnetic resonance imaging (MRI) imaging is highly important since it is a predictor of brain tumor responses to chemotherapy, which reduces the number of neede...
Article
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Auto-grading of short answer questions is considered a challenging problem in the processing of natural language. It requires a system to comprehend the free text answers to automatically assign a grade for a student answer compared to one or more model answers. This paper suggests an optimized deep learning model for grading short-answer questions...
Article
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With the privacy concern of mental patients' records as it is protected with federal privacy legislation, this paper proposes an optimized federated learning model for schizophrenia detection from functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) outputs. As the diagnosis of schizophrenia has no biologica...
Article
Full-text available
Crime is a common social problem that affects the quality of life. As the number of crimes increases, it is necessary to build a model to predict the number of crimes that may occur in a given period, identify the characteristics of a person who may commit a particular crime, and identify places where a particular crime may occur. Data privacy is t...
Article
Finding, locating, and resolving software defects takes a lot of time and effort on the part of software engineers. Humans are required to search and analyses data in traditional testing. Humans are prone to making incorrect assumptions, resulting in distorted results, which leads to defects being undetected. Machine learning enables systems to lea...
Article
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Extreme Learning Machine (ELM) is popular in batch learning, sequential learning, and progressive learning, due to its speed, easy integration , and generalization ability. While, Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence, high time and space complexity. In ELM, the hidden layer typically necessit...
Article
Full-text available
Breast cancer is one of the most common types of cancer worldwide, it was found that breast cancer contributes 11.7% of the total number of cases recently diagnosed with cancer for both sexes and 24.5% for females only. Early detection of cancer increases the probability of recovery. This work has three contributions. The first contribution is impr...
Article
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This research proposes two earthquake prediction models using seismic indicators and hybrid machine learning techniques in the region of southern California. Seven seismic indicators were mathematically and statistically calculated depending on pervious recorded seismic events in the earthquake catalogue of that region. These indicators are namely,...
Article
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The current COVID-19 pandemic threatens human life, health, and productivity. AI plays an essential role in COVID-19 case classification as we can apply machine learning models on COVID-19 case data to predict infectious cases and recovery rates using chest x-ray. Accessing patient’s private data violates patient privacy and traditional machine lea...
Article
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In recent years, cloud computing research, specifically data replication techniques and their applications, has been growing. If the replicas number is raised and put in multiple positions, it will be expensive to maintain the data usability, performance and stability of the application systems. In this paper, two bio- inspired algorithms were prop...
Article
Full-text available
In recent years, cloud computing research, specifically data replication techniques and their applications, has been growing. If the replicas number is raised and put in multiple positions, it will be expensive to maintain the data usability, performance and stability of the application systems. In this paper, two bio-inspired algorithms were propo...
Article
Full-text available
In recent years, there has been increasing interest in cloud computing research, especially replication strategies and their applications. When the number of replicas is increased and placed in different places, maintaining the system’s data availability, performance and reliability will increase the cost. In this paper, two multi-objectives swarm...
Article
Full-text available
The objective of this work is to propose ten efficient scaling techniques for the Wisconsin Diagnosis Breast Cancer (WDBC) dataset using the support vector machine (SVM). These scaling techniques are efficient for the linear programming approach. SVM with proposed scaling techniques was applied on the WDBC dataset. The scaling techniques are, namel...
Article
Full-text available
Today's palm trees diseases which cause a huge loss in production are extremely hard to detect either because these diseases are hidden inside the texture of the palm itself and cannot be seen by naked eyes or because it appears on its leaves which are hardly examined due to how far they really are from the ground. In this paper we're interested in...
Article
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Sentiment analysis on social media is one of the most popular text mining application and many researchers have devoted more efforts in this interesting field. Sentiment analysis is a method for analyzing data and extracting the feeling it represents. Twitter is considered one of the most common social media forums used by people on various occasio...
Article
Full-text available
Sentiment analysis on social media is one of the most popular text mining application and many researchers have devoted more efforts in this interesting field. Sentiment analysis is a method for analyzing data and extracting the feeling it represents. Twitter is considered one of the most common social media forums used by people on various occasio...
Article
Full-text available
The state of the weather became a point of attraction for researchers in recent days. It control in many fields as agriculture, the country determines the types of crops depend on state of the atmosphere. It is therefore important to know the weather in the coming days to take precautions. Forecasting the weather in future especially rainfall won t...
Article
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Cloud computing is a modern technology for dealing with large-scale data. The Cloud has been used to process the selection and placement of replications on a large scale. Most previous studies concerning replication used mathematical models, and few studies focused on artificial intelligence (AI). The Artificial Bee Colony (ABC) is a member of the...
Article
Full-text available
Sentiment analysis is very useful for getting the overall attitude of people towards a specific topic. It can be thought of as an ability for the machine to have a common sense to judge people's opinions. This analysis becomes even more useful and efficient when the machine has access to large quantities of opinions and reviews towards the subject...
Article
Full-text available
Breast cancer is cancer that forms in the cells of the breasts. Breast cancer is the most diagnosed cancer in women. Breast cancer can occur in both men and women, but it's far more common in women. Detection of disease in its advanced stages and treatment can greatly improve the survival rate of patients. In this paper, we use new hybrid methods n...
Chapter
Full-text available
.The neutrosophic primal simplex algorithm moves from a neutrosophic basic feasible solution. If there is no such a solution, we cannot apply the neutrosophic primal simplex method for solving the neutrosophic linear programming problem. This work contributes other new neutrosophic linear programming models and it proposes a ranking function for bo...
Article
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Recently, there are emergence and advent of data Inter-personal interaction web sites, micro blogs, wikis, in addition to Web applications and data, e.g. tweets and web-postings express views and opinions on different topics, issues and events in many applications, in addition to, different domains that includes business, economy, politics, sociolo...
Conference Paper
Full-text available
Recently, there are emergence and advent of data Inter-personal interaction web sites, micro blogs, wikis, in addition to Web applications and data, e.g. tweets and web-postings express views and opinions on different topics, issues and events in many applications, in addition to, different domains that includes business, economy, politics, sociolo...
Conference Paper
In this paper, a variant of the recently introduced whale optimization algorithm (WOA) was proposed based on adaptive switching of random walk per individual search agent. WOA is recently proposed bio-inspired optimizers that employ two different random walks. The original optimizer stochastically switches between the two random walk at each iterat...
Article
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The main target of this paper is to forecast the cloud computing load in the google trace, it presents the use of Kalman filter with a Neuro-fuzzy system composed of an Adaptive Neuro-Fuzzy Inference System (ANFIS) optimized by Quantum Differential Evolution Algorithm. The algorithm was evaluated with actual google cluster trace data and proved the...
Conference Paper
In this work, a proposed hybrid dragonfly algorithm (DA) with extreme learning machine (ELM) system for prediction problem is presented. ELM model is considered a promising method for data regression and classification problems. It has fast training advantage, but it always requires a huge number of nodes in the hidden layer. The usage of a large n...
Article
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In this Paper, five recent natural inspired algorithms are proposed to optimize and train Least Square- Support Vector Machine (LS-SVM). These algorithms are namely, Flower Pollination Algorithm (FPA), Bat algorithm (BA), Modified Cuckoo Search (MCS), Artificial Bee Colony (ABC), and Particle Swarm Optimization (PSO). These algorithms are proposed...
Article
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Extreme learning machine (ELM), which is recent learning method for single hidden layer feedforward neural network has been at focus since last few years. ELM overcomes slow training speed problem of gradient-based learning algorithms. In ELM hidden nodes can be set without knowing training data. But ELM may need more hidden nodes than gradient-bas...
Article
Full-text available
In this Paper, Modified Cuckoo Search algorithm (MCS), which is improved version of Cuckoo Search (CS) algorithm, has been used. MCS algorithm modifies CS algorithm which is inspired from the reproduction strategy of cuckoo birds. MCS algorithm exchange information between the top eggs, or best solutions which not found in standard CS algorithm. Th...
Article
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In this Paper, Bat algorithm (BA), which is considered one of the most recently bio-inspired algorithms, has been used as optimization tool. BA based on the echolocation features of microbats. Bats can differentiate between prey and obstacles in a spectacular way. BA uses automatic zooming to balance exploration and exploitation during the search p...
Article
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In this paper, Artificial Bee Colony (ABC) algorithm which inspired from the behavior of honey bees swarm is presented. ABC is a stochastic population-based evolutionary algorithm for problem solving. ABC algorithm, which is considered one of the most recently swarm intelligent techniques, is proposed to optimize least square support vector machine...
Article
Full-text available
Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange. The successful prediction of a stock's future price will maximize investor's gains. This paper proposes a machine learning model to predict stock market price. The proposed algorithm integrates...
Article
Full-text available
Comparing Differential Evolution (DE), and Particle Swarm Optimization (PSO) at neural netwotk training for stock prediction
Conference Paper
Full-text available
This paper presents a comparison between two stochastic, population based and real-valued algorithms. These algorithms are namely Differential Evolution (DE) and Particle Swarm Optimization (PSO). These algorithms are used in the training of feed-forward neural network to be used in the prediction of the daily stock market prices. Stock market pred...
Conference Paper
Full-text available
In this paper, Particle swarm optimization (PSO), a stochastic population-based evolutionary algorithm for problem solving is presented. PSO algorithm has been successfully applied in many research and application areas. PSO algorithm is proposed to train feed-forward neural network to predict the daily stock prices. Stock market prediction is the...
Conference Paper
Full-text available
In this paper, Differential Evolution (DE) algorithm which can be considered one of global evolutionary optimization method over continuous search spaces is used for feedforward neural network training. DE is capable of handling nondifferentiable, nonlinear and multimodal objective functions. This evolutionary algorithm has recently been successful...

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Projects

Projects (5)
Project
Solve NP-complete problems like: Graph labeling metric dimension of a graph Knapsack problem TSP
Project
The main goal of this project is how to differ among three approaches neutrosophic set, intiutionstic fuzzy set and fuzzy set.