Publications

@article{el-kenawy_ocotillo_2025,
	title = {Ocotillo {Optimization} {Algorithm} ({OcOA}): {A} {Desert}-{Inspired} {Metaheuristic} for {Adaptive} {Optimization}},
	shorttitle = {Ocotillo {Optimization} {Algorithm} ({OcOA})},
	url = {https://americaspg.com/public/articleinfo/28/show/3253},
	doi = {10.54216/JAIM.080104},
	abstract = {american scientific publishing group},
	number = {Issue 1},
	urldate = {2024-10-26},
	journal = {Journal of Artificial Intelligence and Metaheuristics},
	author = {El-Kenawy, El-Sayed M. and Rizk, Faris H. and Zaki, Ahmed Mohamed and Mohamed, Mahmoud Elshabrawy and Ibrahim, Abdelhameed and Abdelhamid, Abdelaziz A. and Khodadadi, Nima and Almetwally, Ehab M. and Eid, Marwa M.},
	month = jan,
	year = {2025},
	note = {Publisher: American Scientific Publishing Group (ASPG)},
	pages = {39--59},
}

american scientific publishing group

@article{zaki_k-nearest_2025,
	title = {K-{Nearest} {Neighbors} {Approach} to {Analyze} and {Predict} {Air} {Quality} in {Delhi}},
	url = {https://americaspg.com/articleinfo/28/show/3519},
	doi = {10.54216/JAIM.090104},
	abstract = {american scientific publishing group},
	number = {Issue 1},
	urldate = {2025-04-26},
	journal = {Journal of Artificial Intelligence and Metaheuristics},
	author = {Zaki, Ahmed Mohamed and Mahmood, Shahid},
	month = jan,
	year = {2025},
	note = {Publisher: American Scientific Publishing Group (ASPG)},
	pages = {34--43},
}

american scientific publishing group

@article{zaki_securing_2024,
	title = {Securing the {Skies}: {A} {Study} of {Cybersecurity} {Measures} in {Unmanned} {Aerial} {Vehicles}},
	volume = {Volume 8},
	copyright = {All rights reserved},
	shorttitle = {Securing the {Skies}},
	url = {https://www.americaspg.com/articleinfo/20/show/2440},
	doi = {10.54216/IJWAC.080106},
	abstract = {american scientific publishing group},
	language = {en},
	number = {Issue 1},
	urldate = {2024-01-26},
	journal = {International Journal of Wireless and Ad Hoc Communication},
	author = {Zaki, Ahmed Mohamed and Abdelhamid, Abdelaziz A. and Ibrahim, Abdelhameed and Eid, Marwa M. and El-Kenawy, El-Sayed M.},
	month = jan,
	year = {2024},
	note = {Publisher: American Scientific Publishing Group (ASPG)},
	pages = {51--55},
}

american scientific publishing group

@article{abed_applications_2024,
	title = {The {Applications} of {Digital} {Transformation} {Towards} {Achieving} {Sustainable} {Development} {Goals}: {Practical} {Case} {Studies} in {Different} {Countries} of the {World}},
	volume = {Volume 7},
	shorttitle = {The {Applications} of {Digital} {Transformation} {Towards} {Achieving} {Sustainable} {Development} {Goals}},
	url = {https://www.americaspg.com/articleinfo/28/show/2502},
	doi = {10.54216/JAIM.070104},
	abstract = {american scientific publishing group},
	language = {en},
	number = {Issue 1},
	urldate = {2024-02-14},
	journal = {Journal of Artificial Intelligence and Metaheuristics},
	author = {Abed, Amira Hassan and Rizk, Faris H. and Zaki, Ahmed Mohamed and Elshewey, Ahmed M.},
	month = feb,
	year = {2024},
	note = {Publisher: American Scientific Publishing Group (ASPG)},
	pages = {53--66},
}

american scientific publishing group

@article{rizk_exploring_2024,
	title = {Exploring {Predictive} {Models} for {Students}' {Performance} in {Exams}: {A} {Comparative} {Analysis} of {Regression} {Algorithms}},
	volume = {Volume 7},
	shorttitle = {Exploring {Predictive} {Models} for {Students}' {Performance} in {Exams}},
	url = {https://www.americaspg.com/articleinfo/28/show/2501},
	doi = {10.54216/JAIM.070103},
	abstract = {american scientific publishing group},
	language = {en},
	number = {Issue 1},
	urldate = {2024-02-14},
	journal = {Journal of Artificial Intelligence and Metaheuristics},
	author = {Rizk, Faris H. and Saleh, Ahmed and Elgaml, Abdulrhman and Elsakaan, Ahmed and Zaki, Ahmed Mohamed},
	month = feb,
	year = {2024},
	note = {Publisher: American Scientific Publishing Group (ASPG)},
	pages = {38--52},
}

american scientific publishing group

@article{rizk_optimizing_2024,
	title = {Optimizing {Student} {Performance} {Prediction} {Using} {Binary} {Waterwheel} {Plant} {Algorithm} for {Feature} {Selection} and {Machine} {Learning}},
	volume = {Volume 7},
	url = {https://www.americaspg.com/articleinfo/28/show/2500},
	doi = {10.54216/JAIM.070102},
	abstract = {american scientific publishing group},
	language = {en},
	number = {Issue 1},
	urldate = {2024-02-14},
	journal = {Journal of Artificial Intelligence and Metaheuristics},
	author = {Rizk, Faris H. and Elshabrawy, Mahmoud and Sameh, Basant and Mohamed, Karim and Zaki, Ahmed Mohamed},
	month = feb,
	year = {2024},
	note = {Publisher: American Scientific Publishing Group (ASPG)},
	pages = {19--37},
}

american scientific publishing group

@article{farag_exploring_2024,
	title = {Exploring {Optimization} {Algorithms}: {A} {Review} of {Methods} and {Applications}},
	volume = {Volume 7},
	copyright = {All rights reserved},
	shorttitle = {Exploring {Optimization} {Algorithms}},
	url = {https://www.americaspg.com/articleinfo/28/show/2547},
	doi = {10.54216/JAIM.070201},
	abstract = {american scientific publishing group},
	language = {en},
	number = {Issue 2},
	urldate = {2024-03-03},
	journal = {Journal of Artificial Intelligence and Metaheuristics},
	author = {Farag, Abdulrahman Abdullah and Ali, Ziad Mohammed and Zaki, Ahmed Mohamed and H.Rizk, Faris and Eid, Marwa M. and EL-Kenawy, EL-Sayed M.},
	month = mar,
	year = {2024},
	note = {Publisher: American Scientific Publishing Group (ASPG)},
	pages = {08--17},
}

american scientific publishing group

@article{ali_advances_2024,
	title = {Advances and {Challenges} in {Feature} {Selection} {Methods}: {A} {Comprehensive} {Review}},
	volume = {Volume 7},
	copyright = {All rights reserved},
	shorttitle = {Advances and {Challenges} in {Feature} {Selection} {Methods}},
	url = {https://www.americaspg.com/articleinfo/28/show/2546},
	doi = {10.54216/JAIM.070105},
	abstract = {american scientific publishing group},
	language = {en},
	number = {Issue 1},
	urldate = {2024-03-03},
	journal = {Journal of Artificial Intelligence and Metaheuristics},
	author = {Ali, Mohamed Ziad and Abdullah, Abdulrahman and Zaki, Ahmed Mohamed and Rizk, Faris H. and Eid, Marwa M. and El-Kenway, Elsayed M.},
	month = mar,
	year = {2024},
	note = {Publisher: American Scientific Publishing Group (ASPG)},
	pages = {67--77},
}

american scientific publishing group

@article{gaber_optimizing_2024,
	title = {Optimizing {Marketing} {Strategies}: {Integration} of {Al}-{Biruni} {Earth} {Radius} {Algorithm} for {Feature} {Selection} and {Pipeline} {Regression} {Model}},
	volume = {1},
	issn = {3009-7452},
	shorttitle = {Optimizing {Marketing} {Strategies}},
	url = {https://jaiep.journals.ekb.eg/article_354005.html},
	doi = {10.21608/jaiep.2024.354005},
	abstract = {With the current business environment becoming increasingly ferocious, the effectiveness of digital marketing strategies is no longer a matter of debate as many organizations have realized the need to gain an edge over competition and improve the ROI with their marketing efforts. This study looks into the specifics of digital marketing effectiveness by, in the process, analyzing true indicators and key metrics. Demonstrating an understanding of the complexity of online marketing operations and the diversity of the variables involved, econometric techniques provide feature choice that affects campaign outcomes the most. At first, the variety of performance between different algorithms from feature selection gave the average error ranging from 0.38264 to 0.44194. However, following the optimization provides the tendency to see a decrease in mean errors and an improving performance. Afterward, the step of predictive modeling is implemented, employing diverse machine learning algorithms including ExtraTreesRegressor, GradientBoostingRegressor, SVR, and CatBoost to assess the effectiveness of foreshowing marketing outcomes. Before the optimization, the recommendations made by the predictive modeling are not too accurate and uniform for each algorithm. That being said, however, once the optimization is done, enhancement in prediction accuracy to the tune of substantial improvement is observed with metrics indicating the same as less MSE, RMSE, and R2. Contributing to a more thorough comprehension of the issue of selecting features and models for predicting as well as efficiency of digital marketing, the study also offers an understanding of the opportunities and obstacles that are present in the process of building digital marketing strategies. A thorough evaluation of top metrics and KPIs gives decision-makers data-driven tools to define their marketing activities, deliver tangible results, and stay relevant in the fast-paced digital environment of today.},
	number = {1},
	urldate = {2024-05-24},
	journal = {Journal of Artificial Intelligence in Engineering Practice},
	author = {Gaber, Khaled Sh and Zaki, Ahmed Mohamed and Eid, Marwa M. and Khafaga, Doaa Sami and Alhussan, Amel Ali and Mohamed, Mahmoud Elshabrawy},
	month = apr,
	year = {2024},
	note = {Publisher: The Scientific Association for Studies and Applied Research (SASAR).},
	pages = {18--33},
	file = {Full Text PDF:C\:\\Users\\Ahmed\\Zotero\\storage\\H8TLJJBC\\Gaber et al. - 2024 - Optimizing Marketing Strategies Integration of Al.pdf:application/pdf},
}

With the current business environment becoming increasingly ferocious, the effectiveness of digital marketing strategies is no longer a matter of debate as many organizations have realized the need to gain an edge over competition and improve the ROI with their marketing efforts. This study looks into the specifics of digital marketing effectiveness by, in the process, analyzing true indicators and key metrics. Demonstrating an understanding of the complexity of online marketing operations and the diversity of the variables involved, econometric techniques provide feature choice that affects campaign outcomes the most. At first, the variety of performance between different algorithms from feature selection gave the average error ranging from 0.38264 to 0.44194. However, following the optimization provides the tendency to see a decrease in mean errors and an improving performance. Afterward, the step of predictive modeling is implemented, employing diverse machine learning algorithms including ExtraTreesRegressor, GradientBoostingRegressor, SVR, and CatBoost to assess the effectiveness of foreshowing marketing outcomes. Before the optimization, the recommendations made by the predictive modeling are not too accurate and uniform for each algorithm. That being said, however, once the optimization is done, enhancement in prediction accuracy to the tune of substantial improvement is observed with metrics indicating the same as less MSE, RMSE, and R2. Contributing to a more thorough comprehension of the issue of selecting features and models for predicting as well as efficiency of digital marketing, the study also offers an understanding of the opportunities and obstacles that are present in the process of building digital marketing strategies. A thorough evaluation of top metrics and KPIs gives decision-makers data-driven tools to define their marketing activities, deliver tangible results, and stay relevant in the fast-paced digital environment of today.

@inproceedings{rizk_predictive_2024,
	title = {Predictive {Modeling} of {Portuguese} {Student} {Performance}: {Comparative} {Machine} {Learning} {Analysis}},
	shorttitle = {Predictive {Modeling} of {Portuguese} {Student} {Performance}},
	url = {https://ieeexplore.ieee.org/document/10620557},
	doi = {10.1109/ITC-Egypt61547.2024.10620557},
	abstract = {Such an analysis of different machine learning methods for predicting the achievement levels of students in Portuguese secondary education makes this essay. The research highlights the importance of accurate expectations of learners' results for education system administrations and respective policymakers. The current study makes use of the “Student Performance in Portuguese Secondary education” dataset and employs machine learning algorithms, namely MLPRegressor, XGBoost, DecisionTreeRegressor, CatBoost, and KNeighborsRe-gressor, to the corpus. Performance metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), etc., are used to judge every model's performance. The conclusion that can be drawn from the data is that the MLPRegressor model leads among the competitors, having an MSE equivalent of 0.0103, which is superior to others. The findings of this study are of great significance for educational institutions and policymakers as they work to make appropriate contact with students' performance prediction.},
	urldate = {2024-08-17},
	booktitle = {2024 {International} {Telecommunications} {Conference} ({ITC}-{Egypt})},
	author = {Rizk, Faris H. and Mohamed, Mahmoud Elshabrawy and Sameh, Basant and Zaki, Ahmed Mohamed and Eid, Marwa M. and El-kenawy, El-Sayed M.},
	month = jul,
	year = {2024},
	keywords = {Machine learning, Student Performance, machine learning, Machine learning algorithms, Data models, Predictive models, Measurement, Education, Market research, Portuguese secondary education, predicting model},
	pages = {26--31},
	file = {IEEE Xplore Abstract Record:C\:\\Users\\Ahmed\\Zotero\\storage\\36YZ5GND\\10620557.html:text/html},
}

Such an analysis of different machine learning methods for predicting the achievement levels of students in Portuguese secondary education makes this essay. The research highlights the importance of accurate expectations of learners' results for education system administrations and respective policymakers. The current study makes use of the “Student Performance in Portuguese Secondary education” dataset and employs machine learning algorithms, namely MLPRegressor, XGBoost, DecisionTreeRegressor, CatBoost, and KNeighborsRe-gressor, to the corpus. Performance metrics such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), etc., are used to judge every model's performance. The conclusion that can be drawn from the data is that the MLPRegressor model leads among the competitors, having an MSE equivalent of 0.0103, which is superior to others. The findings of this study are of great significance for educational institutions and policymakers as they work to make appropriate contact with students' performance prediction.

@inproceedings{rizk_enhancing_2024,
	title = {Enhancing {Student} {Performance} {Prediction} with {Greylag} {Goose} {Optimization} {Algorithm}},
	url = {https://ieeexplore.ieee.org/document/10620568},
	doi = {10.1109/ITC-Egypt61547.2024.10620568},
	abstract = {The current paper addresses the central role of hyperparameter optimization in improving the predictive power of the MLP Regressor for forecasting student performance in Portuguese secondary schools. The uniqueness of this research lies in its exploration of metaheuristic optimization algorithms, specifically highlighting GGO (Greylag Goose Optimization) for enhancement. The study utilized a dataset crucial for understanding and predicting student performance, with a special focus on its distinctive features. By comprehensively tuning the MLP Regressor, the paper demonstrates remarkable improvements in various performance measures, as evident in the enclosed tables. Specifically, the MSE values calculated for the MLP Regressor both before and after GGO optimization are compared. Without optimization, the MLP Regressor had an MSE of 0.0103. After GGO optimization, the MSE significantly improved to 0.0060, indicating enhanced accuracy with GGO in the model. These findings emphasize that hyperparameter optimization, particularly the GGO technique, is crucial for refining the MLP Regressor in predicting student performance. The paper not only delves into the technical aspects but also concludes by highlighting the broader consequences of these outcomes. The potential educational applications are substantial, as improved models can provide more accurate predictions, empowering educators and policymakers to make informed decisions in education. This paper establishes a foundation for future research directions, contributing to the pool of ideas for educational predictive modeling.},
	urldate = {2024-08-17},
	booktitle = {2024 {International} {Telecommunications} {Conference} ({ITC}-{Egypt})},
	author = {Rizk, Faris H. and Mohamed, Mahmoud Elshabrawy and Sameh, Basant and Zaki, Ahmed Mohamed and Eid, Marwa M. and El-kenawy, El-Sayed M.},
	month = jul,
	year = {2024},
	keywords = {Metaheuristics, machine learning, Accuracy, Prediction algorithms, Predictive models, Education, education, Greylag Goose Optimization, hyperparameter optimization, Hyperparameter optimization, MLP Regressor, Refining, Student Performance prediction},
	pages = {32--37},
	file = {IEEE Xplore Abstract Record:C\:\\Users\\Ahmed\\Zotero\\storage\\EAAFFH85\\10620568.html:text/html},
}

The current paper addresses the central role of hyperparameter optimization in improving the predictive power of the MLP Regressor for forecasting student performance in Portuguese secondary schools. The uniqueness of this research lies in its exploration of metaheuristic optimization algorithms, specifically highlighting GGO (Greylag Goose Optimization) for enhancement. The study utilized a dataset crucial for understanding and predicting student performance, with a special focus on its distinctive features. By comprehensively tuning the MLP Regressor, the paper demonstrates remarkable improvements in various performance measures, as evident in the enclosed tables. Specifically, the MSE values calculated for the MLP Regressor both before and after GGO optimization are compared. Without optimization, the MLP Regressor had an MSE of 0.0103. After GGO optimization, the MSE significantly improved to 0.0060, indicating enhanced accuracy with GGO in the model. These findings emphasize that hyperparameter optimization, particularly the GGO technique, is crucial for refining the MLP Regressor in predicting student performance. The paper not only delves into the technical aspects but also concludes by highlighting the broader consequences of these outcomes. The potential educational applications are substantial, as improved models can provide more accurate predictions, empowering educators and policymakers to make informed decisions in education. This paper establishes a foundation for future research directions, contributing to the pool of ideas for educational predictive modeling.

@inproceedings{sherif_revolutionizing_2024,
	title = {Revolutionizing {Oil} {Spill} {Detection}: {A} {Machine} {Learning} {Approach} for {Satellite} {Image} {Classification}},
	shorttitle = {Revolutionizing {Oil} {Spill} {Detection}},
	url = {https://ieeexplore.ieee.org/document/10620599},
	doi = {10.1109/ITC-Egypt61547.2024.10620599},
	abstract = {Identifying and labeling oil spills in satellite imagery is an essential activity of both environmental monitoring and disaster response actions. This work is dedicated to applying an Artificial Neural Network (ANN) model for gathering oil spill data by using a dataset that is specially curated for this reason. Our dataset was developed from satellite pictures of the ocean, some of which depict oil spills and some that do not. The features were extracted from each picture using computer vision algorithms. Our ANN model is trained to distinguish between two classes: The metrics that are looked at consist of accuracy, sensitivity, specificity, PPV, NPV, and statistical significance, and they illustrate how the model performs. As a result, the ANN model gets an accuracy of 96.88\% and a sensitivity of 92.86\% at the same time, while the specificity is 99.88\%. The sensitivity of this diagnostic test is 96.30\%, and the specificity is 94.74\%. A p-value of 0.985997 means that the reported finding reaches a statistical significance, which is enough to support our hypothesis. This can be concluded from the results of ANN, showing the potential of this model to successfully classify the image patches into two sets, namely the ones covered by oil spills and the oil spill-free ones. The research work is a great contribution to the development of the area of environmental monitoring through the machine learning methods used for quick and appropriate detection of environmental hazards.},
	urldate = {2024-08-17},
	booktitle = {2024 {International} {Telecommunications} {Conference} ({ITC}-{Egypt})},
	author = {Sherif, Khaled and Rizk, Faris H. and Zaki, Ahmed Mohamed and Eid, Marwa M. and Khodadadi, Nima and Ibrahim, Abdelhameed and Abdelhamid, Abdelaziz A. and Abualigah, Laith and El-Kenawy, El-Sayed M.},
	month = jul,
	year = {2024},
	keywords = {Machine learning, Machine Learning, Computational modeling, Artificial neural networks, Predictive models, Oils, Analysis, Class Balancing, Data, Disasters, Environmental Monitoring, Feature Standardization, Model Evaluation, Oil Spill Classification, Sensitivity},
	pages = {245--250},
	file = {IEEE Xplore Abstract Record:C\:\\Users\\Ahmed\\Zotero\\storage\\QAYHZFIG\\10620599.html:text/html},
}

Identifying and labeling oil spills in satellite imagery is an essential activity of both environmental monitoring and disaster response actions. This work is dedicated to applying an Artificial Neural Network (ANN) model for gathering oil spill data by using a dataset that is specially curated for this reason. Our dataset was developed from satellite pictures of the ocean, some of which depict oil spills and some that do not. The features were extracted from each picture using computer vision algorithms. Our ANN model is trained to distinguish between two classes: The metrics that are looked at consist of accuracy, sensitivity, specificity, PPV, NPV, and statistical significance, and they illustrate how the model performs. As a result, the ANN model gets an accuracy of 96.88% and a sensitivity of 92.86% at the same time, while the specificity is 99.88%. The sensitivity of this diagnostic test is 96.30%, and the specificity is 94.74%. A p-value of 0.985997 means that the reported finding reaches a statistical significance, which is enough to support our hypothesis. This can be concluded from the results of ANN, showing the potential of this model to successfully classify the image patches into two sets, namely the ones covered by oil spills and the oil spill-free ones. The research work is a great contribution to the development of the area of environmental monitoring through the machine learning methods used for quick and appropriate detection of environmental hazards.

@inproceedings{abdelmalak_pothole_2024,
	title = {Pothole {Detection} in {Asphalt} {Roads}: {A} {Comprehensive} {Approach} for {Enhanced} {Road} {Maintenance} and {Safety} with {AlexNet} {Model}},
	shorttitle = {Pothole {Detection} in {Asphalt} {Roads}},
	url = {https://ieeexplore.ieee.org/document/10620566},
	doi = {10.1109/ITC-Egypt61547.2024.10620566},
	abstract = {The research article described in this paper puts forward a novel method of using an integrated software approach and high-end hardware devices for adaptive and intelligent detection of potholes on asphalt roads. The Pothole Detection Dataset is used for the dataset analysis, and we put VGG19Net, ResNet-50, GoogLeNet, and AlexNet among the computer vision models to analyze the applicability of these models. Different types of networks were compared, and AlexNet showed the best results as it achieved 92.15\% accuracy, 91.38 \% sensitivity (TPR), and a surprisingly high F-score, which reached 96.52\%. Furthermore, by using its time of 279.35 seconds, which might be considered very fast, AlexNet shows many strengths in helping to do this, as well as identifying road anomalies, making it a perfect candidate for real-world utilization. This research demonstrates the emergence of sophisticated integrated pothole repair solutions, emphasizing the importance of both software and hardware in developing sophisticated pothole detection. Practices and this research could be an example for further surveying road inspection technologies.},
	urldate = {2024-08-17},
	booktitle = {2024 {International} {Telecommunications} {Conference} ({ITC}-{Egypt})},
	author = {Abdelmalak, Mark Emad Sobhi and Khodadadi, Nima and Zaki, Ahmed Mohamed and Eid, Marwa M. and Rizk, Faris H. and Ibrahim, Abdelhameed and Abdelhamid, Abdelaziz A. and Abualigah, Laith and El-kenawy, El-Sayed M.},
	month = jul,
	year = {2024},
	keywords = {Pothole detection, Computer vision, Computational modeling, Analytical models, Adaptation models, AlexNet, Asphalt, Asphalt road safety, Hardware, Road maintenance, Roads},
	pages = {269--274},
	file = {IEEE Xplore Abstract Record:C\:\\Users\\Ahmed\\Zotero\\storage\\CG6D6EXW\\10620566.html:text/html},
}

The research article described in this paper puts forward a novel method of using an integrated software approach and high-end hardware devices for adaptive and intelligent detection of potholes on asphalt roads. The Pothole Detection Dataset is used for the dataset analysis, and we put VGG19Net, ResNet-50, GoogLeNet, and AlexNet among the computer vision models to analyze the applicability of these models. Different types of networks were compared, and AlexNet showed the best results as it achieved 92.15% accuracy, 91.38 % sensitivity (TPR), and a surprisingly high F-score, which reached 96.52%. Furthermore, by using its time of 279.35 seconds, which might be considered very fast, AlexNet shows many strengths in helping to do this, as well as identifying road anomalies, making it a perfect candidate for real-world utilization. This research demonstrates the emergence of sophisticated integrated pothole repair solutions, emphasizing the importance of both software and hardware in developing sophisticated pothole detection. Practices and this research could be an example for further surveying road inspection technologies.

@article{alharbi_forecasting_2024,
	title = {Forecasting of energy efficiency in buildings using multilayer perceptron regressor with waterwheel plant algorithm hyperparameter},
	volume = {12},
	issn = {2296-598X},
	url = {https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2024.1393794/full},
	doi = {10.3389/fenrg.2024.1393794},
	abstract = {{\textless}p{\textgreater}Energy consumption in buildings is gradually increasing and accounts for around forty percent of the total energy consumption. Forecasting the heating and cooling loads of a building during the initial phase of the design process in order to identify optimal solutions among various designs is of utmost importance. This is also true during the operation phase of the structure after it has been completed in order to ensure that energy efficiency is maintained. The aim of this paper is to create and develop a Multilayer Perceptron Regressor (MLPRegressor) model for the purpose of forecasting the heating and cooling loads of a building. The proposed model is based on automated hyperparameter optimization using Waterwheel Plant Algorithm The model was based on a dataset that described the energy performance of the structure. There are a number of important characteristics that are considered to be input variables. These include relative compactness, roof area, overall height, surface area, glazing area, wall area, glazing area distribution of a structure, and orientation. On the other hand, the variables that are considered to be output variables are the heating and cooling loads of the building. A total of 768 residential buildings were included in the dataset that was utilized for training purposes. Following the training and regression of the model, the most significant parameters that influence heating load and cooling load have been identified, and the WWPA-MLPRegressor performed well in terms of different metrices variables and fitted time.{\textless}/p{\textgreater}},
	language = {English},
	urldate = {2024-08-17},
	journal = {Frontiers in Energy Research},
	author = {Alharbi, Amal H. and Khafaga, Doaa Sami and Zaki, Ahmed Mohamed and El-Kenawy, El-Sayed M. and Ibrahim, Abdelhameed and Abdelhamid, Abdelaziz A. and Eid, Marwa M. and El-Said, M. and Khodadadi, Nima and Abualigah, Laith and Saeed, Mohammed A.},
	month = may,
	year = {2024},
	note = {Publisher: Frontiers},
	keywords = {machine learning, energy efficiency, Waterwheel Plant Algorithm, Cooling/ Heating Loads, Grey Wolf optimization, Hyperparameter Tunning, Multilayer Perceptron},
	file = {Full Text:C\:\\Users\\Ahmed\\Zotero\\storage\\KLHHAGEF\\Alharbi et al. - 2024 - Forecasting of energy efficiency in buildings usin.pdf:application/pdf},
}

\textlessp\textgreaterEnergy consumption in buildings is gradually increasing and accounts for around forty percent of the total energy consumption. Forecasting the heating and cooling loads of a building during the initial phase of the design process in order to identify optimal solutions among various designs is of utmost importance. This is also true during the operation phase of the structure after it has been completed in order to ensure that energy efficiency is maintained. The aim of this paper is to create and develop a Multilayer Perceptron Regressor (MLPRegressor) model for the purpose of forecasting the heating and cooling loads of a building. The proposed model is based on automated hyperparameter optimization using Waterwheel Plant Algorithm The model was based on a dataset that described the energy performance of the structure. There are a number of important characteristics that are considered to be input variables. These include relative compactness, roof area, overall height, surface area, glazing area, wall area, glazing area distribution of a structure, and orientation. On the other hand, the variables that are considered to be output variables are the heating and cooling loads of the building. A total of 768 residential buildings were included in the dataset that was utilized for training purposes. Following the training and regression of the model, the most significant parameters that influence heating load and cooling load have been identified, and the WWPA-MLPRegressor performed well in terms of different metrices variables and fitted time.\textless/p\textgreater

@article{alkanhel_enhancing_2024,
	title = {Enhancing {Wireless} {Sensor} {Network} {Efficiency} through {Al}-{Biruni} {Earth} {Radius} {Optimization}},
	volume = {79},
	copyright = {All rights reserved},
	issn = {1546-2218, 1546-2226},
	url = {https://www.techscience.com/cmc/v79n3/57108},
	doi = {10.32604/cmc.2024.049582},
	abstract = {The networks of wireless sensors provide the ground for a range of applications, including environmental monitoring and industrial operations. Ensuring the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network optimization. Network infrastructure planning should be focused on increasing performance, and it should be affected by the detailed data about node distribution. This work recommends the creation of each sensor’s specs and radius of influence based on a particular geographical location, which will contribute to better network planning and design. By using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization, our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and exploitation. Through implementing adaptive protocols according to varying environments and sensor constraints, our study aspires to improve overall network operation. We compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization, Particle Swarm Optimization, Genetic Algorithms, and Whale Optimization about performance on real-world problems. Being the most efficient in the optimization process, Biruni displays the lowest error rate at 0.00032. The two other statistical techniques, like ANOVA, are also useful in discovering the factors influencing the nature of sensor data and network-specific problems. Due to the multi-faceted support the comprehensive approach promotes, there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be made. Through delivering better performance and reliability for various \textit{in-situ} applications, this research leads to a fusion of time series forecasters and a customized optimizer algorithm.},
	language = {en},
	number = {3},
	urldate = {2024-08-17},
	journal = {Computers, Materials \& Continua},
	author = {Alkanhel, Reem and Khafaga, Doaa and Zaki, Ahmed and Eid, Marwa and Al-Mooneam, Abdyalaziz and Ibrahim, Abdelhameed and Towfek, S.},
	year = {2024},
	note = {Publisher: Tech Science Press},
	pages = {3549--3568},
	file = {Full Text PDF:C\:\\Users\\Ahmed\\Zotero\\storage\\I9W4JVFH\\Alkanhel et al. - 2024 - Enhancing Wireless Sensor Network Efficiency throu.pdf:application/pdf},
}

The networks of wireless sensors provide the ground for a range of applications, including environmental monitoring and industrial operations. Ensuring the networks can overcome obstacles like power and communication reliability and sensor coverage is the crux of network optimization. Network infrastructure planning should be focused on increasing performance, and it should be affected by the detailed data about node distribution. This work recommends the creation of each sensor’s specs and radius of influence based on a particular geographical location, which will contribute to better network planning and design. By using the ARIMA model for time series forecasting and the Al-Biruni Earth Radius algorithm for optimization, our approach bridges the gap between successive terrains while seeking the equilibrium between exploration and exploitation. Through implementing adaptive protocols according to varying environments and sensor constraints, our study aspires to improve overall network operation. We compare the Al-Biruni Earth Radius algorithm along with Gray Wolf Optimization, Particle Swarm Optimization, Genetic Algorithms, and Whale Optimization about performance on real-world problems. Being the most efficient in the optimization process, Biruni displays the lowest error rate at 0.00032. The two other statistical techniques, like ANOVA, are also useful in discovering the factors influencing the nature of sensor data and network-specific problems. Due to the multi-faceted support the comprehensive approach promotes, there is a chance to understand the dynamics that affect the optimization outcomes better so decisions about network design can be made. Through delivering better performance and reliability for various in-situ applications, this research leads to a fusion of time series forecasters and a customized optimizer algorithm.

@article{khodadadi_predicting_2024,
	title = {Predicting normalized difference vegetation index using a deep attention network with bidirectional {GRU}: a hybrid parametric optimization approach},
	issn = {2364-4168},
	shorttitle = {Predicting normalized difference vegetation index using a deep attention network with bidirectional {GRU}},
	url = {https://doi.org/10.1007/s41060-024-00640-8},
	doi = {10.1007/s41060-024-00640-8},
	abstract = {Scalable and accurate normalized difference vegetation index (NDVI) prediction is necessary to track the status of vegetation and the environment and to support proper ecological management. Herein, we present an innovative deep-learning approach to improve NDVI prediction performances by considering enhanced temporal modeling and hybrid optimization processes. The analysis is based on a core model that integrates a Bidirectional Gated Recurrent Unit (BiGRU) with the profound attention feature since the primary research incorporates the capability of complex temporal in addition to NDVI-time series value. The model performs better through a dual algorithm combining the waterwheel plant algorithm (WWPA) and statistical fractal search (SFS) named WWPASFS-BiGRU. The proposed approach is evaluated using real-world NDVI datasets, demonstrating its capability to outperform traditional models and state-of-the-art deep learning methods. Key performance metrics highlight the model’s accuracy, with a root mean square error (RMSE) as low as 0.00011, reflecting its superior predictive ability. Comparative experiments showcase the robustness of our model across different environmental conditions and geographical settings, affirming its applicability in diverse ecological forecasting scenarios. Additionally, extensive statistical validation, including ANOVA and Wilcoxon tests, confirms the model’s consistency and reliability. The effectiveness of the WWPASFS-BiGRU model is illustrated through applications in predicting NDVI trends across regions in Saudi Arabia, providing critical insights for ecosystem management and sustainable development planning.},
	language = {en},
	urldate = {2024-10-26},
	journal = {International Journal of Data Science and Analytics},
	author = {Khodadadi, Nima and Towfek, S. K. and Zaki, Ahmed Mohamed and Alharbi, Amal H. and Khodadadi, Ehsan and Khafaga, Doaa Sami and Abualigah, Laith and Ibrahim, Abdelhameed and Abdelhamid, Abdelaziz A. and Eid, Marwa M.},
	month = oct,
	year = {2024},
	keywords = {Artificial Intelligence, Hybrid optimization, Attention model, Bidirectional gated recurrent unit (BiGRU), Forecasting model, Statistical fractal search, Waterwheel plant algorithm},
	file = {Full Text PDF:C\:\\Users\\Ahmed\\Zotero\\storage\\LRUYPJY9\\Khodadadi et al. - 2024 - Predicting normalized difference vegetation index .pdf:application/pdf},
}

Scalable and accurate normalized difference vegetation index (NDVI) prediction is necessary to track the status of vegetation and the environment and to support proper ecological management. Herein, we present an innovative deep-learning approach to improve NDVI prediction performances by considering enhanced temporal modeling and hybrid optimization processes. The analysis is based on a core model that integrates a Bidirectional Gated Recurrent Unit (BiGRU) with the profound attention feature since the primary research incorporates the capability of complex temporal in addition to NDVI-time series value. The model performs better through a dual algorithm combining the waterwheel plant algorithm (WWPA) and statistical fractal search (SFS) named WWPASFS-BiGRU. The proposed approach is evaluated using real-world NDVI datasets, demonstrating its capability to outperform traditional models and state-of-the-art deep learning methods. Key performance metrics highlight the model’s accuracy, with a root mean square error (RMSE) as low as 0.00011, reflecting its superior predictive ability. Comparative experiments showcase the robustness of our model across different environmental conditions and geographical settings, affirming its applicability in diverse ecological forecasting scenarios. Additionally, extensive statistical validation, including ANOVA and Wilcoxon tests, confirms the model’s consistency and reliability. The effectiveness of the WWPASFS-BiGRU model is illustrated through applications in predicting NDVI trends across regions in Saudi Arabia, providing critical insights for ecosystem management and sustainable development planning.

@article{el-kenawy_ihow_2024,
	title = {{iHow} {Optimization} {Algorithm}: {A} {Human}-{Inspired} {Metaheuristic} {Approach} for {Complex} {Problem} {Solving} and {Feature} {Selection}},
	volume = {1},
	issn = {3009-7452},
	shorttitle = {{iHow} {Optimization} {Algorithm}},
	url = {https://jaiep.journals.ekb.eg/article_386694.html},
	doi = {10.21608/jaiep.2024.386694},
	abstract = {In this paper, we propose the iHow Optimization Algorithm (iHowOA), a novel metaheuristic algorithm inspired by human-like cognitive processes such as learning, knowledge acquisition, and experience-based decision-making. The iHowOA aims to enhance the exploration-exploitation balance inherent to solving complex optimization problems by mimicking how humans gather data, learn, and improve over time. We tested the algorithm on standard benchmark functions, including those from the CEC 2005 suite, to evaluate its performance in terms of convergence, computational efficiency, and solution accuracy. Furthermore, the Binary iHowOA (biHowOA) was employed for feature selection tasks, and its performance was compared with other popular optimization algorithms. The results show that iHowOA achieves superior performance, consistently finding optimal solutions while maintaining computational efficiency. The biHowOA also demonstrated strong capability in feature selection, providing reduced feature sets with minimal classification error. Our experiments confirm that iHowOA offers an effective solution for both continuous optimization and feature selectionchallenges.},
	number = {2},
	urldate = {2024-10-26},
	journal = {Journal of Artificial Intelligence in Engineering Practice},
	author = {El-Kenawy, El-Sayed M. and Rizk, Faris H. and Zaki, Ahmed Mohamed and Elshabrawy, Mahmoud and Ibrahim, Abdelhameed and Abdelhamid, Abdelaziz A. and Khodadadi, Nima and ALmetwally, Ehab M. and Eid, Marwa M.},
	month = nov,
	year = {2024},
	note = {Publisher: The Scientific Association for Studies and Applied Research (SASAR).},
	pages = {36--53},
	file = {Full Text PDF:C\:\\Users\\Ahmed\\Zotero\\storage\\CQQIVKLM\\El-Kenawy et al. - 2024 - iHow Optimization Algorithm A Human-Inspired Meta.pdf:application/pdf},
}

In this paper, we propose the iHow Optimization Algorithm (iHowOA), a novel metaheuristic algorithm inspired by human-like cognitive processes such as learning, knowledge acquisition, and experience-based decision-making. The iHowOA aims to enhance the exploration-exploitation balance inherent to solving complex optimization problems by mimicking how humans gather data, learn, and improve over time. We tested the algorithm on standard benchmark functions, including those from the CEC 2005 suite, to evaluate its performance in terms of convergence, computational efficiency, and solution accuracy. Furthermore, the Binary iHowOA (biHowOA) was employed for feature selection tasks, and its performance was compared with other popular optimization algorithms. The results show that iHowOA achieves superior performance, consistently finding optimal solutions while maintaining computational efficiency. The biHowOA also demonstrated strong capability in feature selection, providing reduced feature sets with minimal classification error. Our experiments confirm that iHowOA offers an effective solution for both continuous optimization and feature selectionchallenges.

@article{el-kenawy_nioa_2024,
	title = {{NiOA}: {A} {Novel} {Metaheuristic} {Algorithm} {Modeled} on the {Stealth} and {Precision} of {Japanese} {Ninjas}},
	volume = {1},
	issn = {3009-7452},
	shorttitle = {{NiOA}},
	url = {https://jaiep.journals.ekb.eg/article_386693.html},
	doi = {10.21608/jaiep.2024.386693},
	abstract = {This paper presents a new metaheuristic optimization algorithm called the Ninja Optimization Algorithm (NiOA) owing to its characteristics such as stealth, precision, and adaptability of the ninjas of Japan. NiOA is proposed to avoid high exploration and exploitation costs within such complex search spaces and to avoid the problem of getting trapped in local optima. The algorithm imitates ninja searching techniques because it has a scanning phase, adapted to search large areas to look for answers, while the more specific phase is used to refine the answers found. The performance of NiOA is compared with other benchmark optimization functions and some of the frequently used CEC 2005 benchmarks. These benchmarks are well suited to test unimodal and multimodal optimization problems of good quality. Experimental results prove that NiOA can significantly provide better optimization results regarding solution quality, convergence rate, and time complexity, suggesting that NiOA is a robust algorithm for solving high-dimensional large-scale optimization problems. Furthermore, it reveals that NiOA is applicable to solve different kinds of problem spaces, signifying that NiOA can be used in practice on scientific and engineering problems.},
	number = {2},
	urldate = {2024-10-26},
	journal = {Journal of Artificial Intelligence in Engineering Practice},
	author = {El-Kenawy, El-Sayed M. and Rizk, Faris H. and Zaki, Ahmed Mohamed and Elshabrawy, Mahmoud and Ibrahim, Abdelhameed and Abdelhamid, Abdelaziz A. and Khodadadi, Nima and ALmetwally, Ehab M. and Eid, Marwa M.},
	month = oct,
	year = {2024},
	note = {Publisher: The Scientific Association for Studies and Applied Research (SASAR).},
	pages = {17--35},
	file = {Full Text PDF:C\:\\Users\\Ahmed\\Zotero\\storage\\ADXWFZB3\\El-Kenawy et al. - 2024 - NiOA A Novel Metaheuristic Algorithm Modeled on t.pdf:application/pdf},
}

This paper presents a new metaheuristic optimization algorithm called the Ninja Optimization Algorithm (NiOA) owing to its characteristics such as stealth, precision, and adaptability of the ninjas of Japan. NiOA is proposed to avoid high exploration and exploitation costs within such complex search spaces and to avoid the problem of getting trapped in local optima. The algorithm imitates ninja searching techniques because it has a scanning phase, adapted to search large areas to look for answers, while the more specific phase is used to refine the answers found. The performance of NiOA is compared with other benchmark optimization functions and some of the frequently used CEC 2005 benchmarks. These benchmarks are well suited to test unimodal and multimodal optimization problems of good quality. Experimental results prove that NiOA can significantly provide better optimization results regarding solution quality, convergence rate, and time complexity, suggesting that NiOA is a robust algorithm for solving high-dimensional large-scale optimization problems. Furthermore, it reveals that NiOA is applicable to solve different kinds of problem spaces, signifying that NiOA can be used in practice on scientific and engineering problems.

@article{el-kenawy_football_2024,
	title = {Football {Optimization} {Algorithm} ({FbOA}): {A} {Novel} {Metaheuristic} {Inspired} by {Team} {Strategy} {Dynamics}},
	copyright = {All rights reserved},
	shorttitle = {Football {Optimization} {Algorithm} ({FbOA})},
	url = {https://americaspg.com/articleinfo/28/show/3236},
	doi = {10.54216/JAIM.080103},
	abstract = {american scientific publishing group},
	number = {Issue 1},
	urldate = {2024-10-26},
	journal = {Journal of Artificial Intelligence and Metaheuristics},
	author = {El-Kenawy, El-Sayed M. and Rizk, Faris H. and Zaki, Ahmed Mohamed and Mohamed, Mahmoud Elshabrawy and Ibrahim, Abdelhameed and Abdelhamid, Abdelaziz A. and Khodadadi, Nima and Almetwally, Ehab M. and Eid, Marwa M.},
	year = {2024},
	note = {Publisher: American Scientific Publishing Group (ASPG)},
	pages = {21--38},
}

american scientific publishing group

@article{zaki_machine_2024,
	title = {Machine {Learning} {Approaches} for {Malaria} {Risk} {Prediction} and {Detection}: {Trends} and {Insights}},
	shorttitle = {Machine {Learning} {Approaches} for {Malaria} {Risk} {Prediction} and {Detection}},
	url = {https://www.americaspg.com/articleinfo/41/show/3367},
	doi = {10.54216/MOR.010105},
	abstract = {american scientific publishing group},
	number = {Issue 1},
	urldate = {2024-12-07},
	journal = {Metaheuristic Optimization Review},
	author = {Zaki, Ahmed Mohamed and Gaber, Khaled Sh and Rizk, Faris H. and Mohamed, Mahmoud Elshabrawy},
	month = jan,
	year = {2024},
	note = {Publisher: American Scientific Publishing Group (ASPG)},
	pages = {55--65},
}

american scientific publishing group

@article{fouad_optimized_2023,
	title = {An {Optimized} {Architecture} for {COVID}‑19 {Prediction} {Using} {Chest} {X}‑{Ray} {Images}},
	volume = {Volume 6},
	url = {https://americaspg.com/articleinfo/28/show/2261},
	doi = {10.54216/JAIM.060104},
	abstract = {american scientific publishing group},
	language = {en},
	number = {Issue 1},
	urldate = {2023-11-29},
	journal = {Journal of Artificial Intelligence and Metaheuristics},
	author = {Fouad, Yasser and Osman, Ahmed M. and Abdelmaged, Ibrahim E. and Zaki, Ahmed Mohamed and Elshewey, Ahmed M.},
	month = nov,
	year = {2023},
	note = {Publisher: American Scientific Publishing Group (ASPG)},
	pages = {35--47},
}

american scientific publishing group

@inproceedings{marie_tech-care_2023,
	title = {Tech-{Care}: {A} {High}-{Tech} {Eye}-{Controlled} {Wheelchair} for {Paralyzed} {Patients}},
	shorttitle = {Tech-{Care}},
	url = {https://ieeexplore.ieee.org/document/10206404},
	doi = {10.1109/ITC-Egypt58155.2023.10206404},
	abstract = {Quadriplegia is a devastating condition that can have a profound impact on an individual's quality of life. It is a result of spinal cord injury that affects all four limbs, severely limiting mobility and independence. However, with the rapid advancement in technology, there is hope for people with quadriplegia to regain some degree of independence.This paper proposes a revolutionary eye-controlled wheelchair that can provide independent mobility for people with disabilities. The system utilizes cutting-edge face landmark recognition technology to track eye movements and control the motorized movement of the wheelchair. The Python Dlib library was used to locate facial landmarks using a group of regression trees using Kazemi and Sullivan's face alignment in one millisecond. In this approach, face and eye recognition are accomplished using facial landmarks. It also features an ultrasonic sensor that can detect obstacles and prevent accidents. The whole system is controlled by Raspberry Pi, which is cost-effective and user-friendly.The proposed technology is a game-changer for people with disabilities. It eliminates the need for physical support and allows for greater independence and freedom of movement. With its advanced eye-tracking technology and reliable safety features, this eye-controlled wheelchair represents a significant breakthrough in the field of assistive technology. The proposed system is not just a technological innovation but a source of hope and empowerment for people with quadriplegia.},
	urldate = {2023-12-02},
	booktitle = {2023 {International} {Telecommunications} {Conference} ({ITC}-{Egypt})},
	author = {Marie, Hanaa Salem and Al-Sabahi, Mohammed Alaa and El-Sayed, Ahmed Awad and Zaki, Ahmed Mohamed and Salah, Omar Mahmoud and Al-Sadani, Mahmoud Elsayed and Abdo, Abdallah Mohammed and Moawad, Muhammad Eyad},
	month = jul,
	year = {2023},
	pages = {413--418},
}

Quadriplegia is a devastating condition that can have a profound impact on an individual's quality of life. It is a result of spinal cord injury that affects all four limbs, severely limiting mobility and independence. However, with the rapid advancement in technology, there is hope for people with quadriplegia to regain some degree of independence.This paper proposes a revolutionary eye-controlled wheelchair that can provide independent mobility for people with disabilities. The system utilizes cutting-edge face landmark recognition technology to track eye movements and control the motorized movement of the wheelchair. The Python Dlib library was used to locate facial landmarks using a group of regression trees using Kazemi and Sullivan's face alignment in one millisecond. In this approach, face and eye recognition are accomplished using facial landmarks. It also features an ultrasonic sensor that can detect obstacles and prevent accidents. The whole system is controlled by Raspberry Pi, which is cost-effective and user-friendly.The proposed technology is a game-changer for people with disabilities. It eliminates the need for physical support and allows for greater independence and freedom of movement. With its advanced eye-tracking technology and reliable safety features, this eye-controlled wheelchair represents a significant breakthrough in the field of assistive technology. The proposed system is not just a technological innovation but a source of hope and empowerment for people with quadriplegia.

@article{zaki_comprehensive_2023,
	title = {A {Comprehensive} {Exploration} of {Machine} {Learning} {Models} for {Predicting} {Online} {Auction} {Prices}},
	volume = {Volume 2},
	copyright = {All rights reserved},
	url = {https://americaspg.com/articleinfo/37/show/2315},
	doi = {10.54216/FinTech-I.020203},
	abstract = {american scientific publishing group},
	language = {en},
	number = {Issue 2},
	urldate = {2023-12-17},
	journal = {Financial Technology and Innovation},
	author = {Zaki, Ahmed Mohamed and Abdelhamid, Abdelaziz A. and Ibrahim, Abdelhameed and Eid, Marwa M. and El-Kenawy, El-Sayed M.},
	month = dec,
	year = {2023},
	note = {Publisher: American Scientific Publishing Group (ASPG)},
	pages = {27--36},
}

american scientific publishing group

@article{el-kenawy_time_2023,
	title = {Time {Series} {Forecasting} of {Cryptocurrency} {Prices} with {Long} {Short}-{Term} {Memory} {Networks}},
	volume = {Volume 2},
	copyright = {All rights reserved},
	url = {https://americaspg.com/articleinfo/37/show/2314},
	doi = {10.54216/JSDGT.030205},
	abstract = {american scientific publishing group},
	language = {en},
	number = {Issue 2},
	urldate = {2023-12-17},
	journal = {Financial Technology and Innovation},
	author = {El-Kenawy, El-Sayed M. and Abdelhamid, Abdelaziz A. and Ibrahim, Abdelhameed and Eid, Marwa M. and Rizk, Faris H. and Zaki, Ahmed Mohamed},
	month = dec,
	year = {2023},
	note = {Publisher: American Scientific Publishing Group (ASPG)},
	pages = {18--26},
}

american scientific publishing group

@article{zaki_predictive_2023,
	title = {Predictive {Analytics} and {Machine} {Learning} in {Direct} {Marketing} for {Anticipating} {Bank} {Term} {Deposit} {Subscriptions}},
	volume = {Volume 11},
	copyright = {All rights reserved},
	url = {https://americaspg.com/articleinfo/1/show/2312},
	doi = {10.54216/AJBOR.110110},
	abstract = {american scientific publishing group},
	language = {en},
	number = {Issue 1},
	urldate = {2023-12-17},
	journal = {American Journal of Business and Operations Research},
	author = {Zaki, Ahmed Mohamed and Khodadadi, Nima and Lim, Wei Hong and Towfek, S. K.},
	month = dec,
	year = {2023},
	note = {Publisher: American Scientific Publishing Group (ASPG)},
	pages = {79--88},
}

american scientific publishing group

@article{abdelmgeed_evaluation_2023,
	title = {An {Evaluation} of {ARIMA} and {Persistence} {Models} in {IoT}-{Driven} {Smart} {Homes}},
	volume = {Volume 6},
	copyright = {All rights reserved},
	url = {https://americaspg.com/articleinfo/28/show/2313},
	doi = {10.54216/JAIM.060201},
	abstract = {american scientific publishing group},
	language = {en},
	number = {Issue 2},
	urldate = {2023-12-17},
	journal = {Journal of Artificial Intelligence and Metaheuristics},
	author = {Abdelmgeed, Ahmed and Zaki, Ahmed Mohamed and Soliman, Marwa Adel},
	month = dec,
	year = {2023},
	note = {Publisher: American Scientific Publishing Group (ASPG)},
	pages = {08--15},
}

american scientific publishing group

@article{rizk_integrated_2023,
	title = {Integrated {CNN} and {Waterwheel} {Plant} {Algorithm} for {Enhanced} {Global} {Traffic} {Detection}},
	volume = {Volume 6},
	copyright = {All rights reserved},
	url = {https://americaspg.com/articleinfo/28/show/2341},
	doi = {10.54216/JAIM.060204},
	abstract = {american scientific publishing group},
	language = {en},
	number = {Issue 2},
	urldate = {2023-12-24},
	journal = {Journal of Artificial Intelligence and Metaheuristics},
	author = {Rizk, Faris H. and Arkhstan, Sofia and Zaki, Ahmed Mohamed and Kandel, Mohamed Ahmed and Towfek, S. K.},
	month = dec,
	year = {2023},
	note = {Publisher: American Scientific Publishing Group (ASPG)},
	pages = {36--45},
}

american scientific publishing group

@article{zaki_advancing_2023,
	title = {Advancing {Parking} {Space} {Surveillance} using {A} {Neural} {Network} {Approach} with {Feature} {Extraction} and {Dipper} {Throated} {Optimization} {Integration}},
	volume = {Volume 6},
	copyright = {All rights reserved},
	url = {https://americaspg.com/articleinfo/28/show/2339},
	doi = {10.54216/JAIM.060202},
	abstract = {american scientific publishing group},
	language = {en},
	number = {Issue 2},
	urldate = {2023-12-24},
	journal = {Journal of Artificial Intelligence and Metaheuristics},
	author = {Zaki, Ahmed Mohamed and Towfek, S. K. and Gee, Weiguo and Zhang, Wang and Soliman, Marwa Adel},
	month = dec,
	year = {2023},
	note = {Publisher: American Scientific Publishing Group (ASPG)},
	pages = {16--25},
}

american scientific publishing group

@article{kandel_evaluating_2023,
	title = {Evaluating the {Efficacy} of {Deep} {Learning} {Architectures} in {Predicting} {Traffic} {Patterns} for {Smart} {City} {Development}},
	volume = {Volume 6},
	copyright = {All rights reserved},
	url = {https://americaspg.com/articleinfo/28/show/2340},
	doi = {10.54216/JAIM.060203},
	abstract = {american scientific publishing group},
	language = {en},
	number = {Issue 2},
	urldate = {2023-12-24},
	journal = {Journal of Artificial Intelligence and Metaheuristics},
	author = {Kandel, Mohamed Ahmed and Rizk, Faris H. and Hongou, Lima and Zaki, Ahmed Mohamed and Khan, Hakan and El-Kenawy, El-Sayed M.},
	month = dec,
	year = {2023},
	note = {Publisher: American Scientific Publishing Group (ASPG)},
	pages = {26--35},
}

american scientific publishing group

@article{abdelmalak_ber-xgboost_2023,
	title = {{BER}-{XGBoost}: {Pothole} {Detection} based on {Feature} {Extraction} and {Optimized} {XGBoost} using {BER} {Metaheuristic} {Algorithm}},
	volume = {Volume 6},
	copyright = {All rights reserved},
	shorttitle = {{BER}-{XGBoost}},
	url = {https://americaspg.com/articleinfo/28/show/2342},
	doi = {10.54216/JAIM.060205},
	abstract = {american scientific publishing group},
	language = {en},
	number = {Issue 2},
	urldate = {2023-12-24},
	journal = {Journal of Artificial Intelligence and Metaheuristics},
	author = {Abdelmalak, Mark Emad S. and Gaber, Khaled Sh and Ahmed, Mariam Abdallah and OubeBlika, Najaad and Zaki, Ahmed Mohamed and Eid, Marwa M.},
	month = dec,
	year = {2023},
	note = {Publisher: American Scientific Publishing Group (ASPG)},
	pages = {46--55},
}

american scientific publishing group

@article{zaki_enhancing_2023,
	title = {Enhancing {K}-{Nearest} {Neighbors} {Algorithm} in {Wireless} {Sensor} {Networks} through {Stochastic} {Fractal} {Search} and {Particle} {Swarm} {Optimization}},
	volume = {Volume 13},
	copyright = {All rights reserved},
	url = {https://www.americaspg.com/articleinfo/2/show/2391},
	doi = {10.54216/JCIM.130108},
	abstract = {american scientific publishing group},
	language = {en},
	number = {Issue 1},
	urldate = {2024-01-06},
	journal = {Journal of Cybersecurity and Information Management},
	author = {Zaki, Ahmed Mohamed and Abdelhamid, Abdelaziz A. and Ibrahim, Abdelhameed and Eid, Marwa M. and El-Kenawy, El-Sayed M.},
	year = {2023},
	note = {Publisher: American Scientific Publishing Group (ASPG)},
	pages = {76--84},
}

american scientific publishing group

@article{zaki_metaheuristic_2023,
	title = {Metaheuristic {Optimization} for {Enhancing} {Cyber} {Security} {Index} {Prediction}: {A} {DTO}+{FGW} {Approach} with {MLP} {Integration}},
	volume = {Volume 4},
	copyright = {All rights reserved},
	shorttitle = {Metaheuristic {Optimization} for {Enhancing} {Cyber} {Security} {Index} {Prediction}},
	url = {https://www.americaspg.com/articleinfo/31/show/2392},
	doi = {10.54216/IJAACI.040202},
	abstract = {american scientific publishing group},
	language = {en},
	number = {Issue 2},
	urldate = {2024-01-06},
	journal = {International Journal of Advances in Applied Computational Intelligence},
	author = {Zaki, Ahmed Mohamed and Abdelhamid, Abdelaziz A. and Ibrahim, Abdelhameed and Eid, Marwa M. and El-Kenawy, El-Sayed M.},
	year = {2023},
	note = {Publisher: American Scientific Publishing Group (ASPG)},
	pages = {15--25},
}

american scientific publishing group

@article{zaki_enhancing_2023-1,
	title = {Enhancing {Cyber} {Security} {Attack} {Prediction}: {A} {Weighted} {Optimized} {Ensemble} {Approach} {Using} {DTO}+{DE} {Algorithm}},
	volume = {Volume 7},
	copyright = {All rights reserved},
	shorttitle = {Enhancing {Cyber} {Security} {Attack} {Prediction}},
	url = {https://www.americaspg.com/articleinfo/20/show/2385},
	doi = {10.54216/IJWAC.070205},
	abstract = {american scientific publishing group},
	language = {en},
	number = {Issue 2},
	urldate = {2024-01-06},
	journal = {International Journal of Wireless and Ad Hoc Communication},
	author = {Zaki, Ahmed Mohamed and Abdelhamid, Abdelaziz A. and Ibrahim, Abdelhameed and Eid, Marwa M. and El-Kenawy, El-Sayed M.},
	year = {2023},
	note = {Publisher: American Scientific Publishing Group (ASPG)},
	pages = {64--73},
}

american scientific publishing group