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Amir F. Atiya

Amir F. Atiya

D-Index & Metrics

Computer Science

D-Index
48
Citations
13427
World Ranking
6055
National Ranking
8

Overview

Amir F. Atiya is affiliated with Cairo University in Egypt and has contributed extensively to the field of computer science, with a focus on artificial intelligence and related subfields. Their research spans multiple domains, including computer vision and pattern recognition, cognitive neuroscience, management science and operations research, and electrical and electronic engineering.

Atiya's recent scholarly output includes these publications:

  • Epileptic Seizures Detection Using Deep Learning Techniques: A Review, 2021, International Journal of Environmental Research and Public Health
  • A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning, 2023, Machine Learning
  • SpinalNet: Deep Neural Network With Gradual Input, 2022, IEEE Transactions on Artificial Intelligence
  • SpinalNet: Deep Neural Network with Gradual Input, 2020, arXiv (Cornell University)
  • Decision boundary clustering for efficient local SVM, 2021, Applied Soft Computing

Their frequent co-authors include:

  • Samir I. Shaheen
  • Abbas Khosravi
  • Saeid Nahavandi
  • Dina Elreedy
  • Sandra Rizkallah

Atiya has published multiple papers in key venues such as:

  • arXiv (Cornell University)
  • Applied Sciences
  • International Journal of Environmental Research and Public Health
  • Machine Learning
  • IEEE Transactions on Artificial Intelligence

The main field of study for Amir F. Atiya is computer science, particularly with a strong focus on artificial intelligence. Subfields of study further specifying the research interests include artificial intelligence with 18 publications, computer vision and pattern recognition with 4 publications, cognitive neuroscience with 3 publications, management science and operations research with 3 publications, and electrical and electronic engineering with 2 publications.

The core topics explored in their work encompass:

  • EEG and Brain-Computer Interfaces
  • Imbalanced Data Classification Techniques
  • Advanced Text Analysis Techniques
  • Electricity Theft Detection Techniques
  • Text and Document Classification Technologies
  • Advanced Bandit Algorithms Research
  • Consumer Market Behavior and Pricing

This profile provides an overview of Amir F. Atiya's research contributions reflecting a diverse interest in various areas of computer science and artificial intelligence, demonstrated through both collaboration and publication patterns.

Best Publications

  • Bankruptcy prediction for credit risk using neural networks: A survey and new results

    A.F. Atiya

  • An empirical comparison of machine learning models for time series forecasting

    Nesreen K. Ahmed;Amir F. Atiya;Neamat El Gayar;Hisham El-Shishiny

  • Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals

    A Khosravi;S Nahavandi;D Creighton;A F Atiya

  • A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition

    Souhaib Ben Taieb;Gianluca Bontempi;Amir F. Atiya;Antti Sorjamaa

  • Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances

    A. Khosravi;S. Nahavandi;D. Creighton;A. F. Atiya

  • A Comprehensive Analysis of Synthetic Minority Oversampling TEchnique (SMOTE) for Handling Class Imbalance

    Dina Elreedy;Amir F. Atiya

  • New results on recurrent network training: unifying the algorithms and accelerating convergence

    A.F. Atiya;A.G. Parlos

  • Introduction to financial forecasting

    Yaser S. Abu-Mostafa;Amir F. Atiya

  • How delays affect neural dynamics and learning

    P. Baldi;A.F. Atiya

  • A comparison between neural-network forecasting techniques-case study: river flow forecasting

    A.F. Atiya;S.M. El-Shoura;S.I. Shaheen;M.S. El-Sherif

  • ASTD: Arabic Sentiment Tweets Dataset

    Mahmoud Nabil;Mohamed Aly;Amir Atiya

  • Epileptic Seizures Detection Using Deep Learning Techniques: A Review.

    Afshin Shoeibi;Marjane Khodatars;Navid Ghassemi;Navid Ghassemi;Mahboobeh Jafari

  • Application of the recurrent multilayer perceptron in modeling complex process dynamics

    A.G. Parlos;K.T. Chong;A.F. Atiya

  • LABR: A Large Scale Arabic Book Reviews Dataset

    Mohamed Aly;Amir Atiya

  • Multi-step-ahead prediction using dynamic recurrent neural networks

    A. G. Parlos;O. T. Rais;A. F. Atiya

  • Combination of Long Term and Short Term Forecasts, with Application to Tourism Demand Forecasting

    Robert R. Andrawis;Amir F. Atiya;Hisham El-Shishiny

  • Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition

    Robert R. Andrawis;Amir F. Atiya;Hisham El-Shishiny

  • A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting

    Souhaib Ben Taieb;Amir F. Atiya

  • Speed up grid-search for parameter selection of support vector machines

    Hatem A. Fayed;Amir F. Atiya

  • An accelerated learning algorithm for multilayer perceptron networks

    A.G. Parlos;B. Fernandez;A.F. Atiya;J. Muthusami

  • A Novel Template Reduction Approach for the $K$ -Nearest Neighbor Method

    H.A. Fayed;A.F. Atiya

  • LABR: A Large Scale Arabic Book Reviews Dataset

    Mahmoud Nabil;Mohamed Aly;Amir Atiya

Frequent Co-Authors

Malik Magdon-Ismail
Malik Magdon-Ismail Rensselaer Polytechnic Institute
U. Rajendra Acharya
U. Rajendra Acharya University of Southern Queensland
Kil To Chong
Kil To Chong Jeonbuk National University
Abbas Khosravi
Abbas Khosravi Deakin University
Saeid Nahavandi
Saeid Nahavandi Swinburne University of Technology
Carlo S. Regazzoni
Carlo S. Regazzoni University of Genoa
Roohallah Alizadehsani
Roohallah Alizadehsani Deakin University
Dipti Srinivasan
Dipti Srinivasan National University of Singapore
Moloud Abdar
Moloud Abdar Deakin University
Gianluca Bontempi
Gianluca Bontempi Université Libre de Bruxelles

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