8 results on '"Elhosseini, Mostafa"'
Search Results
2. Silent no more: a comprehensive review of artificial intelligence, deep learning, and machine learning in facilitating deaf and mute communication
- Author
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ZainEldin, Hanaa, Gamel, Samah A., Talaat, Fatma M., Aljohani, Mansourah, Baghdadi, Nadiah A., Malki, Amer, Badawy, Mahmoud, and Elhosseini, Mostafa A.
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- 2024
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3. Mathematical Modeling and Analysis of Credit Scoring Using the LIME Explainer: A Comprehensive Approach.
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Aljadani, Abdussalam, Alharthi, Bshair, Farsi, Mohammed A., Balaha, Hossam Magdy, Badawy, Mahmoud, and Elhosseini, Mostafa A.
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CREDIT ratings ,CREDIT analysis ,PARTICLE swarm optimization ,MATHEMATICAL analysis ,DECISION trees ,CLASSIFICATION algorithms ,MATHEMATICAL models ,MACHINE learning - Abstract
Credit scoring models serve as pivotal instruments for lenders and financial institutions, facilitating the assessment of creditworthiness. Traditional models, while instrumental, grapple with challenges related to efficiency and subjectivity. The advent of machine learning heralds a transformative era, offering data-driven solutions that transcend these limitations. This research delves into a comprehensive analysis of various machine learning algorithms, emphasizing their mathematical underpinnings and their applicability in credit score classification. A comprehensive evaluation is conducted on a range of algorithms, including logistic regression, decision trees, support vector machines, and neural networks, using publicly available credit datasets. Within the research, a unified mathematical framework is introduced, which encompasses preprocessing techniques and critical algorithms such as Particle Swarm Optimization (PSO), the Light Gradient Boosting Model, and Extreme Gradient Boosting (XGB), among others. The focal point of the investigation is the LIME (Local Interpretable Model-agnostic Explanations) explainer. This study offers a comprehensive mathematical model using the LIME explainer, shedding light on its pivotal role in elucidating the intricacies of complex machine learning models. This study's empirical findings offer compelling evidence of the efficacy of these methodologies in credit scoring, with notable accuracies of 88.84%, 78.30%, and 77.80% for the Australian, German, and South German datasets, respectively. In summation, this research not only amplifies the significance of machine learning in credit scoring but also accentuates the importance of mathematical modeling and the LIME explainer, providing a roadmap for practitioners to navigate the evolving landscape of credit assessment. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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4. Enhancing feature selection with GMSMFO: A global optimization algorithm for machine learning with application to intrusion detection.
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Hussein, Nazar K., Qaraad, Mohammed, Amjad, Souad, Farag, M. A., Hassan, Saima, Mirjalili, Seyedali, and Elhosseini, Mostafa A.
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OPTIMIZATION algorithms ,METAHEURISTIC algorithms ,INTRUSION detection systems (Computer security) ,MACHINE learning ,GLOBAL optimization ,FEATURE selection ,SWARM intelligence - Abstract
The paper addresses the limitations of the Moth-Flame Optimization (MFO) algorithm, a meta-heuristic used to solve optimization problems. The MFO algorithm, which employs moths’ transverse orientation navigation technique, has been used to generate solutions for such problems. However, the performance of MFO is dependent on the flame production and spiral search components, and the search mechanism could still be improved concerning the diversity of flames and the moths’ ability to find solutions. The authors propose a revised version called GMSMFO, which uses a Novel Gaussian mutation mechanism and shrink MFO to enhance population diversity and balance exploration and exploitation capabilities. The study evaluates the performance of GMSMFO using the CEC 2017 benchmark and 20 datasets, including a high-dimensional intrusion detection system dataset. The proposed algorithm is compared to other advanced metaheuristics, and its performance is evaluated using statistical tests such as Friedman and Wilcoxon rank-sum. The study shows that GMSMFO is highly competitive and frequently superior to other algorithms. It can identify the ideal feature subset, improving classification accuracy and reducing the number of features used. The main contribution of this research paper includes the improvement of the exploration/exploitation balance and the expansion of the local search. The ranging controller and Gaussian mutation enhance navigation and diversity. The research paper compares GMSMFO with traditional and advanced metaheuristic algorithms on 29 benchmarks and its application to binary feature selection on 20 benchmarks, including intrusion detection systems. The statistical tests (Wilcoxon rank-sum and Friedman) evaluate the performance of GMSMFO compared to other algorithms. The algorithm source code is available at https://github.com/MohammedQaraad/GMSMFO-algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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5. An Integrated Machine Learning-Based Brain Computer Interface to Classify Diverse Limb Motor Tasks: Explainable Model.
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Hashem, Hend A., Abdulazeem, Yousry, Labib, Labib M., Elhosseini, Mostafa A., and Shehata, Mohamed
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COMPUTER interfaces ,METAHEURISTIC algorithms ,MACHINE learning ,FEATURE selection ,SIGNAL processing ,MOTORS ,MOTOR imagery (Cognition) ,BRUSHLESS direct current electric motors - Abstract
Terminal neurological conditions can affect millions of people worldwide and hinder them from doing their daily tasks and movements normally. Brain computer interface (BCI) is the best hope for many individuals with motor deficiencies. It will help many patients interact with the outside world and handle their daily tasks without assistance. Therefore, machine learning-based BCI systems have emerged as non-invasive techniques for reading out signals from the brain and interpreting them into commands to help those people to perform diverse limb motor tasks. This paper proposes an innovative and improved machine learning-based BCI system that analyzes EEG signals obtained from motor imagery to distinguish among various limb motor tasks based on BCI competition III dataset IVa. The proposed framework pipeline for EEG signal processing performs the following major steps. The first step uses a meta-heuristic optimization technique, called the whale optimization algorithm (WOA), to select the optimal features for discriminating between neural activity patterns. The pipeline then uses machine learning models such as LDA, k-NN, DT, RF, and LR to analyze the chosen features to enhance the precision of EEG signal analysis. The proposed BCI system, which merges the WOA as a feature selection method and the optimized k-NN classification model, demonstrated an overall accuracy of 98.6%, outperforming other machine learning models and previous techniques on the BCI competition III dataset IVa. Additionally, the EEG feature contribution in the ML classification model is reported using Explainable AI (XAI) tools, which provide insights into the individual contributions of the features in the predictions made by the model. By incorporating XAI techniques, the results of this study offer greater transparency and understanding of the relationship between the EEG features and the model's predictions. The proposed method shows potential levels for better use in controlling diverse limb motor tasks to help people with limb impairments and support them while enhancing their quality of life. [ABSTRACT FROM AUTHOR]
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- 2023
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6. Addressing constrained engineering problems and feature selection with a time-based leadership salp-based algorithm with competitive learning.
- Author
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Qaraad, Mohammed, Amjad, Souad, Hussein, Nazar K., and Elhosseini, Mostafa A.
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MACHINE learning ,FEATURE selection ,METAHEURISTIC algorithms ,STATISTICS ,EVOLUTIONARY algorithms ,LEADERSHIP ,REINFORCEMENT learning - Abstract
Like most metaheuristic algorithms, salp swarm algorithm (SSA) suffers from slow convergence and stagnation in the local optima. The study develops a novel Time-Based Leadership Salp-Based Competitive Learning (TBLSBCL) to address the SSA's flaws. The TBLSBCL presents a novel search technique to address population diversity, an imbalance between exploitation and exploration, and the SSA algorithm's premature convergence. Hybridization consists of two stages: First, a time-varying dynamic structure represents the SSA hierarchy of leaders and followers. This approach increases the number of leaders while decreasing the number of salp's followers linearly. Utilizing the effective exploitation of the SSA, the position of the population's leader is updated. Second, the competitive learning strategy is used to update the status of the followers by teaching them from the leaders. The goal of adjusting the salp swarm optimizer algorithm is to help the basic approach avoid premature convergence and quickly steer the search to the most promising likely search space. The proposed TBLSBCL method is tested using the CEC 2017 benchmark, feature selection problems for 19 datasets (including three high-dimensional datasets). The TBLSBCL was then evaluated using a benchmark set of seven wellknown constrained design challenges in diverse engineering fields defined in the benchmark set of real-world problems presented at the CEC 2020 conference (CEC 2020). In each experiment, TBLSBCL is compared with seven other state-of-the-art metaheuristics and other advanced algorithms that include seven variants of the salp swarm. Friedman and Wilcoxon rank-sum statistical tests are also used to examine the results. According to the experimental data and statistical tests, the TBLSBCL algorithm is very competitive and often superior to the algorithms employed in the studies. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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7. MATLAB-based framework for data analytics applied to Hajj dataset: Hajj health meter.
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Farsi, Mohammed, Malki, Zohair, Elhosseini, Mostafa A., Badawy, Mahmoud, and Farouk, Ahmed
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BIG data ,PILGRIMAGE to Mecca ,FOOD transportation ,INTERNET of things ,MACHINE learning ,ENERGY consumption - Abstract
The total number of pilgrims for the Hajj Season of 1438H reached 2,352, 122 — according to the General Authority for statistics Kingdom of Saudi Arabia. Pilgrims data analysis and prediction help concerned entities of the country in the future planning programs for the purpose of ensuring the necessary services — social, health, security, food and transportation services to name a few. Predictive analytics is the process of using data along with analysis, statistics, and machine learning techniques to create a predictive model for forecasting future events. Predictive analytics is often discussed in the context of big data as businesses apply algorithms to derive insights from large datasets using a framework like Hadoop, HDFS, and Spark. Building MATLAB-based framework for data analytics applied to Hajj dataset is the main aim of this research paper. The proposed framework is mainly relying on four main concepts; namely the cloud-based Internet of things (IoT), fog, Edge-of-Things (EoT), and predictive analytics. This proposed framework helps in reducing the amount of data sent, lowering network traffic, increasing bandwidth, and reducing power energy consumption. On top that, the framework including regression has the potential to predict how likely Hajj is susceptible to illness or even death. [ABSTRACT FROM AUTHOR]
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- 2020
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8. Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches.
- Author
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Malki, Zohair, Atlam, El-Sayed, Hassanien, Aboul Ella, Dagnew, Guesh, Elhosseini, Mostafa A., and Gad, Ibrahim
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COVID-19 pandemic , *COVID-19 , *MACHINE learning , *PANDEMICS , *WEATHER , *SEASONAL variations of diseases - Abstract
Nowadays, a significant number of infectious diseases such as human coronavirus disease (COVID-19) are threatening the world by spreading at an alarming rate. Some of the literatures pointed out that the pandemic is exhibiting seasonal patterns in its spread, incidence and nature of the distribution. In connection to the spread and distribution of the infection, scientific analysis that answers the questions whether the next summer can save people from COVID-19 is required. Many researchers have been exclusively asked whether high temperature during summer can slow down the spread of the COVID-19 as it has with other seasonal flues. Since there are a lot of questions that are unanswered right now, and many mysteries aspects about the COVID-19 that is still unknown to us, in-depth study and analysis of associated weather features are required. Moreover, understanding the nature of COVID-19 and forecasting the spread of COVID-19 request more investigation of the real effect of weather variables on the transmission of the COVID-19 among people. In this work, various regressor machine learning models are proposed to extract the relationship between different factors and the spreading rate of COVID-19. The machine learning algorithms employed in this work estimate the impact of weather variables such as temperature and humidity on the transmission of COVID-19 by extracting the relationship between the number of confirmed cases and the weather variables on certain regions. To validate the proposed method, we have collected the required datasets related to weather and census features and necessary prepossessing is carried out. From the experimental results, it is shown that the weather variables are more relevant in predicting the mortality rate when compared to the other census variables such as population, age, and urbanization. Thus, from this result, we can conclude that temperature and humidity are important features for predicting COVID-19 mortality rate. Moreover, it is indicated that the higher the value of temperature the lower number of infection cases. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
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