World's Best Scientists 2026 revealed!
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Computer Science
Malaysia
2026
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Computer Science
Japan
2022

D-Index & Metrics

Computer Science

D-Index
96
Citations
28956
World Ranking
448
National Ranking
1

Research.com Recognitions

  • 2026 - Research.com Computer Science in Malaysia Leader Award
  • 2025 - Research.com Computer Science in Malaysia Leader Award
  • 2022 - Research.com Computer Science in Japan Leader Award
  • 2009 - ACM Senior Member

Overview

Hamido Fujita is affiliated with the University of Technology Malaysia. Their research focuses primarily on computer science, with a considerable body of work spanning multiple subfields including artificial intelligence, computer vision and pattern recognition, information systems, computational theory and mathematics, and management science and operations research.

The scientist's main research topics include rough sets and fuzzy logic, anomaly detection techniques and applications, multi-criteria decision making, data mining algorithms and applications, topic modeling, imbalanced data classification techniques, and video surveillance and tracking methods.

They have published extensively, with frequent contributions to the following publication venues:

  • Information Sciences
  • Applied Soft Computing
  • Knowledge-Based Systems
  • Applied Intelligence
  • Expert Systems with Applications

Among recent papers authored or coauthored by Hamido Fujita are:

  • Adaptive stock trading strategies with deep reinforcement learning methods (2020, Information Sciences)
  • Forecasting of COVID19 per regions using ARIMA models and polynomial functions (2020, Applied Soft Computing)
  • Object Detection Binary Classifiers methodology based on deep learning to identify small objects handled similarly: Application in video surveillance (2020, Knowledge-Based Systems)
  • Multiclass Prediction Model for Student Grade Prediction Using Machine Learning (2021, IEEE Access)
  • Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions (2022, IEEE Access)

Collaborations have been significant in their career, with frequent coauthors including Ali Selamat, Toshitaka Hayashi, Ondřej Krejcar, Massimo Esposito, and Yonghua Zhou.

Hamido Fujita has contributed to multiple book publications mainly through Springer Science+Business Media. Titles include:

  • Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices (2021)
  • Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence (2022)
  • Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices (2020)
  • Advances and Trends in Artificial Intelligence. From Theory to Practice (2021)
  • Advances and Trends in Artificial Intelligence. Theory and Applications (2023)
  • Rough Sets (2022)
  • Intelligent Information and Database Systems (2023)

Their contributions in research have been recognized by the ACM Senior Member award, received in 2009.

Best Publications

  • Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals

    U. Rajendra Acharya;U. Rajendra Acharya;U. Rajendra Acharya;Hamido Fujita;Shu Lih Oh;Yuki Hagiwara

  • Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network

    U. Rajendra Acharya;Hamido Fujita;Oh Shu Lih;Yuki Hagiwara

  • An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems

    Hossam Faris;Majdi M. Mafarja;Ali Asghar Heidari;Ibrahim Aljarah

  • 25years at Knowledge-Based Systems

    M.J. Cobo;M.A. Martínez;M. Gutiérrez-Salcedo;H. Fujita

  • Deep Learning Fault Diagnosis Method Based on Global Optimization GAN for Unbalanced Data

    Funa Zhou;Funa Zhou;Shuai Yang;Hamido Fujita;Danmin Chen

  • Consensus Reaching in Social Network Group Decision Making: Research Paradigms and Challenges

    Yucheng Dong;Quanbo Zha;Hengjie Zhang;Gang Kou

  • A visual interaction consensus model for social network group decision making with trust propagation

    Jian Wu;Francisco Chiclana;Hamido Fujita;Enrique Herrera-Viedma

  • Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images

    U Raghavendra;Hamido Fujita;Sulatha V Bhandary;Anjan Gudigar

  • Application of entropies for automated diagnosis of epilepsy using EEG signals

    U. Rajendra Acharya;H. Fujita;Vidya K. Sudarshan;Shreya Bhat

  • Imbalanced enterprise credit evaluation with DTE-SBD

    Jie Sun;Jie Lang;Hamido Fujita;Hui Li

  • Automated detection of coronary artery disease using different durations of ECG segments with convolutional neural network

    U. Rajendra Acharya;U. Rajendra Acharya;U. Rajendra Acharya;Hamido Fujita;Oh Shu Lih;Muhammad Adam

  • A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust

    Jian Wu;Lifang Dai;Francisco Chiclana;Hamido Fujita

  • Emergency decision making for natural disasters: An overview

    Lei Zhou;Xianhua Wu;Zeshui Xu;Hamido Fujita

  • A study of graph-based system for multi-view clustering

    Hao Wang;Hao Wang;Yan Yang;Bing Liu;Hamido Fujita

  • Class-imbalanced dynamic financial distress prediction based on Adaboost-SVM ensemble combined with SMOTE and time weighting

    Jie Sun;Hui Li;Hamido Fujita;Binbin Fu

  • Fuzzy Group Decision Making With Incomplete Information Guided by Social Influence

    Nicola Capuano;Francisco Chiclana;Hamido Fujita;Enrique Herrera-Viedma

  • Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals

    U. Rajendra Acharya;Hamido Fujita;Shu Lih Oh;Yuki Hagiwara

  • Automated detection of atrial fibrillation using long short-term memory network with RR interval signals

    Oliver Faust;Alex Shenfield;Murtadha Kareem;Tan Ru San

  • Towards felicitous decision making

    Hai Wang;Zeshui Xu;Hamido Fujita;Shousheng Liu

  • Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network

    U. Rajendra Acharya;U. Rajendra Acharya;U. Rajendra Acharya;Hamido Fujita;Shu Lih Oh;U. Raghavendra

Frequent Co-Authors

U. Rajendra Acharya
U. Rajendra Acharya University of Southern Queensland
Tianrui Li
Tianrui Li Southwest Jiaotong University
Francisco Chiclana
Francisco Chiclana De Montfort University
Enrique Herrera-Viedma
Enrique Herrera-Viedma University of Granada
Ali Selamat
Ali Selamat University of Technology Malaysia
Jen Hong Tan
Jen Hong Tan Singapore General Hospital
Zeshui Xu
Zeshui Xu Sichuan University
Philippe Fournier-Viger
Philippe Fournier-Viger Shenzhen University
Vincenzo Loia
Vincenzo Loia University of Salerno
Shu Lih Oh
Shu Lih Oh Ngee Ann Polytechnic

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