Editor Profile

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Dr. Ibrahim Gad, Ph.D.

Assistant Professor

Present

Faculty of Science, Tanta University

Tanta

Egypt

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Dr. Ibrahim Gad received his Ph.D. in Computer Science on September 22, 2020, from the Department of Postgraduate Studies and Research in Computer Science at Mangalore University, India. He also holds an M.Sc. in Computer Science from Ain Shams University, Egypt (2014), and a B.Sc. in Statistics and Computer Science from Tanta University, Egypt (2006). He is currently an Assistant Professor in the Department of Computer Science, Faculty of Science, Tanta University, Egypt. His research interests include Data Science, Artificial Intelligence, Big Data, Machine Learning, and Deep Learning.

  • Data Science
  • Artificial Intelligence
  • Big Data
  • Machine Learning
  • Deep Learning
  • Ibrahim Gad, ” TOCA-IoT: Threshold Optimization and Causal Analysis for IoT Network Anomaly Detection Based on Explainable Random Forest”, MDPI, Algorithms, Volume 18, Issue 2, February 2025.
  • Elsayed Atlam, M. Rokaya, M. Masud, H. Meshref, Rakan Alotaibi, Abdulqader M. Almars, Mohammed Assiri, and Ibrahim Gad, ”Explainable artificial intelligence systems for predicting mental health problems in autistics”, Elsevier, Alexandria Engineering Journal, Volume 117, April 2025.
  • Majed M Alwateer, Ibrahim Gad, Mahmoud Elmarhomy, Ghada Elmarhomy, Hasan Hashim, Malik Almaliki, and Elsayed Atlam,” Interpretable Arabic Hate Speech Detection using Large Language Model”, IEEE, 2nd International Conference on Advanced Innovations in Smart Cities (ICAISC), Jeddah, Saudi Arabia, February 2025.
  • Elsaid Md Abdelrahim, Hasan Hashim, El-Sayed Atlam, Radwa Ahmed Osman, and Ibrahim Gad, ”TMS: Ensemble Deep Learning Model for Accurate Classification of Monkeypox Lesions Based on Transformer Models with SVM”, MDPI, Diagnostics, Volume 14, Issue 23, November 2024.
  • Majed Alwateer, El-Sayed Atlam, Mahmoud Abd El-Raouf, Osama Ghoneim, and Ibrahim Gad, ”Missing Data Imputation: A Comprehensive Review”, Scientific Research, Journal of Computer and Communications, Volume 12, Issue 11, November 2024.
  • Mona Elattar, Ahmed Younes, Ibrahim Gad, and Islam Elkabani,” Explainable AI model for PDFMal detection based on gradient boosting model”, Springer, Neural Computing and Applications, Volume 36, September 2024.
  • Elsayed Atlam, Malik Almaliki, Abdullah Alfahaid, Ibrahim Gad, Ghada Elmarhomy, Majed Alwateer, and Ali Ahmed, ” SLM-AIE: A Systematic Literature Map of Artificial Intelligence Ethics”, preprint, Artificial Intelligence Review, 2024.
  • Mohammad Kazim Hooshmand, Manjaiah Doddaghatta Huchaiah, Ahmad Reda Alzighaibi, Hasan Hashim, El-Sayed Atlam, and Ibrahim Gad,” Robust network anomaly detection using ensemble learning approach and explainable artificial intelligence (XAI)”, Elsevier, Alexandria Engineering Journal, Volume 94, Issue 1, May 2024.
  • Ibrahim Gad, Mohamed Torky, Yaseen Elshaier, Ashraf Darwish, and Aboul Ella Hassanien,” COVID-19 drug repurposing model based on pigeon-inspired optimizer and rough sets theory”, Springer, Neural Computing and Applications, Volume 36, Issue 9, February 2024.
  • El-Sayed Atlam, Mehedi Masud, Mahmoud Rokaya, Hossam Meshref, Ibrahim Gad, and Abdulqader M Almars, “EASDM: Explainable Autism Spectrum Disorder Model Based on Deep Learning”, Journal of Disability Research, Volume 3, Issue 1, February 2024.
  • Mehedi Masud, Mahmoud Rokaya, Hossam Meshref, Abdulqader M Almars, Ibrahim Gad, and El-Sayed Atlam, “A Novel Light-Weight Convolutional Neural Network Model to Predict Alzheimer’s Disease Applying Weighted Loss Function”, Journal of Disability Research, Volume 3, Issue 4, April 2024.
  • K. M. Yogesh, Arpitha S, and Ibrahim Gad, “One-D Convolution Neural Network Models for Human Activity Recognition using mHealth Datasets”, FAIML 2023: 2023 International Conference on Frontiers of Artificial Intelligence and Machine Learning, Beijing, China, April 14-16, 2023. ACM, New York, NY, USA, 13 Pages. https://doi.org/10.1145/3616901.3616933
  • K. M. Yogesh, T. Stephan, M. B. Bharath, Ibrahim Gad, S. Arpitha, and M. Prakash, “Characterization of darknet traffic detection using time domain features”, IET Digital Library, International Conference on Computer Vision and Internet of Things 2023 (ICCVIoT'23), Coimbatore, India, January 2024.
  • Mohamed Torky, Ibrahim Gad, and Aboul Ella Hassanien,” Explainable AI Model for Recognizing Financial Crisis Roots Based on Pigeon Optimization and Gradient Boosting Model”, Springer, International Journal of Computational Intelligence Systems, Volume 16, Issue 50, April 2023.
  • Nadiah A. Baghdadi, Sally Mohammed Farghaly Abdelaliem, Amer Malki, Ibrahim Gad, Ashraf Ewis and Elsayed Atlam, “Advanced machine learning techniques for cardiovascular disease early detection and diagnosis”, Springer, Journal of Big Data Volume 10, Issue 144, September 2023.
  • Amaal Farag Elessawy, Ibrahim Gad, Hatem Abdul-Kader, Asmaa Elsaid,” Double Spending Attacks in Decentralized Digital Currencies: Challenges and Countermeasures”, International Journal for Computers and Information, IJCI, Volume 10, Issue 3, October 2023.
  • Hasan Hashim, Ahmad Reda Alzighaibi, Amaal Farag Elessawy, Ibrahim Gad, Hatem Abdul-Kader, Asmaa Elsaid,” Securing Financial Transactions with a Robust Algorithm: Preventing Double-Spending Attacks”, MDPI, Computers, Volume 12, Issue 9, August 2023.
  • Osama Sweef, Elsayed Zaabout, Ahmed Bakheet, Mohamed Halawa, Ibrahim Gad, Mohamed Akela, Ehab Tousson, Ashraf Abdelghany, and Saori Furuta,” Unraveling Therapeutic Opportunities and the Diagnostic Potential of microRNAs for Human Lung Cancer”, MDPI, Pharmaceutics, Volume 15, Issue 8, July 2023.
  • Mohamed Torky, Ibrahim Gad, Ashraf Darwish, and Aboul Ella Hassanien,” Artificial Intelligence for Predicting Floods: A Climatic Change Phenomenon”, Springer, The Power of Data: Driving Climate Change with Data Science and Artificial Intelligence Innovations, Studies in Big Data, Volume 118, March 2023.
  • El-Sayed Atlam Malik Almaliki, Abdulqader M. Almars, and Ibrahim Gad,” ABMM: Arabic BERT-Mini Model for Hate-Speech Detection on Social Media”, MDPI, Electronics, Volume 12, Issue 4: 1048, February 2023.
  • Ibrahim Gad, Mahmoud Elmezain, Majed M Alwateer, Malik Almaliki, Ghada Elmarhomy, and Elsayed Atlam,” Breast Cancer Diagnosis Using a Machine Learning Model and Swarm Intelligence Approach”, IEEE, 1st International Conference on Advanced Innovations in Smart Cities (ICAISC), Jeddah, Saudi Arabia, April 2023.
  • M. Badr, Radwan Abu-Gdairi, A.I.EL–Maghrabi, and Ibrahim Gad, ”A Recent View of Generalized closed sets”, JOKULL, Volume 73, Issue 7, July 2023.
  • Talal H. Noor, Abdulqader Almars, Majed Alwateer, Malik Almaliki, Ibrahim Gad, and El-Sayed Atlam, “SARIMA: A Seasonal Autoregressive Integrated Moving Average Model for Crime Analysis in Saudi Arabia”, MDPI, Electronics, Volume 11, Issue 23: 3986. December 2022.
  • John Kasubi, Manjaiah Huchaiah, Mohammad Hooshmand, and Ibrahim Gad, “Ensemble-Based Human Activity Recognition For Multi Residents In Smart Home Environment”, Towards Excellence Journal, Volume 14, Issue 2, June 2022.
  • Amer Malki, El-Sayed Atlam, Aboul Ella Hassanien, Guesh Dagnew, Mostafa A Elhosseini, and Ibrahim Gad, “SARIMA Model-based Forecasting Required Number of COVID-19 Vaccines Globally and Empirical Analysis of Peoples’ View Towards the Vaccines”, Elsevier, Alexandria Engineering Journal, Volume 61, Issue 12, Pages 12091-12110, December 2022.
  • Talal H. Noor, Abdulqader Almars, Ibrahim Gad, El-Sayed Atlam, and Mahmoud Elmezain, “Spatial Impressions Monitoring during COVID-19 Pandemic Using Machine Learning Techniques”, MDPI, Computers 2022, Volume 11, Issue 4, March 2022.
  • Mahmoud Elmezain, Amer Malki, Ibrahim Gad, and El-Sayed Atlam, “Hybrid Deep Learning Model-Based Prediction of Images Towards Cyberbullying”, International Journal of Applied Mathematics and Computer Science, Volume 32, Issue 2, Pages 323–334, 2022.
  • Amer Malki, El-Sayed Atlam, and Ibrahim Gad, “Machine learning approach of detecting anomalies and forecasting time-series of IoT devices”, Elsevier, Alexandria Engineering Journal, Volume 61, Issue 11, Pages 8973-8986,November 2022.
  • Abdulqader M. Almars, El-Sayed Atlam, Talal H. Noor, Ghada ELmarhomy, Rasha Alagamy, and Ibrahim Gad, “Users opinion and emotion nderstanding in social media regarding COVID-19 vaccine”, Springer, Computing Journal, Volume 104, pages1481–1496, February 2022.
  • Abdulqader M. Almars, Ibrahim Gad, and El-Sayed Atlam, “Applications of AI and IoT in COVID-19 Vaccine and Its Impact on Social Life”, Springer, Medical Informatics and Bioimaging Using Artificial Intelligence, volume 1005, Pages 115-127, 2022.
  • El-Sayed Atlam, Ashraf Ewis, MM Abd El-Raouf, Osama Ghoneim, and Ibrahim Gad, “A new approach in identifying the psychological impact of COVID-19 on university student’s academic performance”, Elsevier, Alexandria Engineering Journal, Volume 61, Issue 7, Pages 5223-5233, July 2022.
  • Ibrahim Gad, Ashraf Darwish, Aboul Ella Hassanien, and Mincong Tang, “A Hybrid Quantum Deep Learning Approach Based on Intelligent ptimization to Predict the Broiler Energies”, Springer, 11th International Conference on Logistics, Informatics and Service Sciences, LISS 2021, Pages 693–704, 2022.
  • Ibrahim Gad, and Aboul Ella Hassanien, “A wind turbine fault identification using machine learning approach based on pigeon inspired optimizer”, IEEE, Tenth International Conference on Intelligent Computing and Information Systems ICICIS 2021, Pages 231-235, 2022.
  • Zohair Malki, El-Sayed Atlam, Ashraf Ewis, Guesh Dagnew, Osama A. Ghoneim, Abdallah A. Mohamed, Mohamed M. Abdel-Daim, and Ibrahim Gad, “The COVID-19 pandemic: prediction study based on machine learning models”, Springer, Environmental Factors and the Epidemics of COVID-19, 10 April 2021, DOI: 10.1007/s11356-021-13824-7
  • Mohammed Farsi, Doreswamy Hosahalli, B.R. Manjunatha, Ibrahim Gad, El- Sayed Atlam, Althobaiti Ahmed, Ghada Elmarhomy, Mahmoud Elmarhoumy, and Osama A. Ghoneim, “Parallel genetic algorithms for optimizing the SARIMA model for better forecasting of the NCDC weather data”, Alexandria Engineering Journal, volume 60, issue 1, pages 1299-1316, February 2021.
  • Doreswamy, Mohammad Kazim Hooshmand, and Ibrahim Gad, “Feature selection approach using ensemble learning for network anomaly detection”, CAAI Transactions on Intelligence Technology, volume 5, issue 4, pages 283 -293, 05 November 2020.
  • Hasan Hashim, Malik Almaliki, Rasha El-Agamy, MM El-Sharkasy, Guesh Dagnew, Ibrahim Gad, and Osama Ghoneim, “Integrating data warehouse and machine learning to predict on COVID-19 pandemic empirical data”, Journal of Theoretical and Applied Information Technology, Volume 99, Issue 1, 15 Jan 2021.
  • Zohair Malki, El-Sayed Atlam, Aboul Ella Hassanien, Guesh Dagnew, Mostafa A Elhosseini, and Ibrahim Gad,”Association between weather data and COVID- 19 pandemic predicting mortality rate: Machine learning approaches", Springer, Chaos, Solitons & Fractals, 2020.
  • Ibrahim Gad, Doreswamy, B.R. Manjunatha, and Osama A. Ghoneim, “A robust deep learning model for missing value imputation in big NCDC dataset”, Springer, Iran Journal of Computer Science, 2020.
  • Zohair Malki, El-Sayed Atlam, Ashraf Ewis, Guesh Dagnew, Ahmad Reda Alzighaibi, ELmarhomy Ghada,Mostafa A. Elhosseini, Aboul Ella Hassanien, and Ibrahim Gad, “ARIMA Models for Predicting the End of COVID-19 Pandemic and the Risk of a Second Rebound”, Springer, Neural Computing and Applications, 2020.
  • Zuhair Malki, El-Sayed Atlam, Guesh Dagnew, Ahmad Reda Alzighaibi, Elmarhomy Ghada, and Ibrahim Gad, “Bidirectional Residual LSTM-based Human Activity Recognition”, Canadian Center of Science and Education (CCSE), Journal of Computer and Information Science, vol. 13(3), pages 1-40, August 2020.
  • Ibrahim Gad, and Doreswamy, “A comparative study of prediction and classification models on NCDC weather data”, International Journal of Computers and Applications, May 2020.
  • Doreswamy, Harish kumar K.S., Yogesh K.M., and Ibrahim Gad, “Forecasting Air Pollution Particulate Matter (PM 2.5 ) Using Machine Learning Regression Models”, Journal of Procedia Computer science, Volume 171, 2020.
  • Yogesh K.M., Doreswamy, and Ibrahim Gad, “One-D Convolution Neural Network Models for Human Activity Recognition using mHealth Datasets”, ACM Woodstock conference (WOODSTOCK'18), ACM, NEWYORK, NY, USA, 2019.
  • Yogesh K.M., Doreswamy, and Ibrahim Gad, “Feature Selection Based Framework for Human Activity Recognition using mHealth Data Sets”, International Conference of Computational Intelligence and Applications (ICCIA- 2019), 2019.
  • Doreswamy, Harish Kumar K.S., and Ibrahim Gad, “Time series analysis for prediction of PM 2.5 using SARIMA model on TAQMN data”, Journal of Computational and Theoretical Nanoscience, 2019.
  • Doreswamy, Ibrahim Gad, and B.R. Manjunatha, "Multi-label Classification of Big NCDC Weather Data Using Deep Learning Model", International Conference on Soft Computing Systems, Springer, 2018.
  • Ibrahim Gad, and Doreswamy, “A Generic Approach of Filling Missing Values in NCDC Weather Stations Data”, International Conference on Advances in Computing, Communications, and Informatics (ICACCI 2018). Bangalore, 19th & 22nd of September, IEEE, 2018.
  • Doreswamy, Ibrahim Gad, and B.R. Manjunatha, "Performance evaluation of predictive models for missing data imputation in weather data”, International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE , 2017.
  • Doreswamy, Ibrahim Gad, and B.R. Manjunatha, " Hybrid data warehouse model for climate big data analysis”, International Conference on Circuit, Power and Computing Technologies (ICCPCT) ), IEEE , 2017.
  • Doreswamy, Ibrahim Gad, and B.R. Manjunatha, "Big Data Aggregation using Hadoop and MapReduce technique for Weather Forecasting", International Journal of Latest Trends in Engineering and Technology (Ijltet), Special Issue - Sacaim, November 2016.
  • Ibrahim Gad, and S. Daoud, "A parallel line sieve for the GNFS Algorithm", International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 5, No. 7, July 2014.
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