What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Data mining, Machine learning and Convolutional neural network.
His research in Artificial intelligence intersects with topics in Field, Myocardial infarction and Atrial fibrillation.
While the research belongs to areas of Pattern recognition, Hamido Fujita spends his time largely on the problem of Approximate entropy, intersecting his research to questions surrounding Infarction.
His Data mining study which covers Information system that intersects with Transformation, Transaction data and Soft set.
His Machine learning research includes themes of Data flow diagram and Internet users, The Internet, Email spam.
His Convolutional neural network study combines topics in areas such as Ecg signal and Internal medicine, Ventricular fibrillation, Cardiology.
His most cited work include:
- Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals (340 citations)
- Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network (287 citations)
- An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems (240 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Data mining, Machine learning, Pattern recognition and Fuzzy logic are his primary areas of study.
The Artificial intelligence study combines topics in areas such as Computer vision and Natural language processing.
The concepts of his Data mining study are interwoven with issues in Structure, Set, Information system and Data set.
His Fuzzy logic research incorporates themes from Mathematical optimization, Group decision-making and Medical diagnosis.
His Rough set research is multidisciplinary, incorporating perspectives in Relation and Reduction.
His studies in Sentiment analysis integrate themes in fields like Sentence and Naive Bayes classifier.
He most often published in these fields:
- Artificial intelligence (59.25%)
- Data mining (23.35%)
- Machine learning (20.48%)
What were the highlights of his more recent work (between 2020-2021)?
- Artificial intelligence (59.25%)
- Fuzzy logic (17.62%)
- Degree (6.61%)
In recent papers he was focusing on the following fields of study:
Hamido Fujita mostly deals with Artificial intelligence, Fuzzy logic, Degree, Computer vision and Natural language processing.
He has included themes like Field and Machine learning in his Artificial intelligence study.
His Multivariate statistics, Cluster analysis and Decision tree study, which is part of a larger body of work in Machine learning, is frequently linked to Distress, bridging the gap between disciplines.
His research integrates issues of Mathematical optimization and Multicriteria decision in his study of Fuzzy logic.
His Degree study incorporates themes from Ranking, Pythagorean fuzzy sets and Group decision-making.
His work on Pose as part of general Computer vision research is frequently linked to Recoil, bridging the gap between disciplines.
Between 2020 and 2021, his most popular works were:
- CMC: A consensus multi-view clustering model for predicting Alzheimer's disease progression. (6 citations)
- A novel fuzzy rough set model with fuzzy neighborhood operators (6 citations)
- A novel fuzzy rough set model with fuzzy neighborhood operators (6 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Hamido Fujita mainly focuses on Artificial intelligence, Deep learning, Ranking, Image and Class.
His Artificial intelligence research focuses on Machine learning and how it relates to Representation.
The various areas that he examines in his Deep learning study include Language model, Conditional random field, Natural language processing and Word.
Hamido Fujita combines subjects such as Advice, Social network, Degree, Relation and Rule-based machine translation with his study of Ranking.
His Image research is multidisciplinary, incorporating elements of Training set, Pattern recognition, MNIST database, Source code and Entropy.
His biological study spans a wide range of topics, including Process, State and One-class classification.
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