What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Internal medicine
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Electroencephalography, Convolutional neural network and Support vector machine.
The study incorporates disciplines such as Speech recognition, Computer vision and Sensitivity in addition to Artificial intelligence.
The concepts of his Pattern recognition study are interwoven with issues in Artificial neural network and Approximate entropy.
His work in Electroencephalography covers topics such as Epilepsy which are related to areas like Continuous wavelet transform.
U. Rajendra Acharya interconnects Ecg signal and Internal medicine, Myocardial infarction, Cardiology in the investigation of issues within Convolutional neural network.
His biological study spans a wide range of topics, including Kernel, Sample entropy, Linear discriminant analysis and Cross-validation.
His most cited work include:
- Heart rate variability: a review (1555 citations)
- Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. (568 citations)
- Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. (568 citations)
What are the main themes of his work throughout his whole career to date?
U. Rajendra Acharya mostly deals with Artificial intelligence, Pattern recognition, Support vector machine, Electroencephalography and Deep learning.
His studies deal with areas such as Machine learning and Computer vision as well as Artificial intelligence.
His studies in Pattern recognition integrate themes in fields like Ecg signal, Speech recognition and Sensitivity.
His Naive Bayes classifier study, which is part of a larger body of work in Support vector machine, is frequently linked to Probabilistic neural network, bridging the gap between disciplines.
U. Rajendra Acharya combines subjects such as Sleep Stages and Epilepsy with his study of Electroencephalography.
As part of his studies on Deep learning, U. Rajendra Acharya often connects relevant subjects like Artificial neural network.
He most often published in these fields:
- Artificial intelligence (156.85%)
- Pattern recognition (113.47%)
- Support vector machine (51.70%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (156.85%)
- Pattern recognition (113.47%)
- Deep learning (38.34%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Deep learning, Electroencephalography and Machine learning.
In his research, Signal processing is intimately related to Sensitivity, which falls under the overarching field of Artificial intelligence.
His Pattern recognition research incorporates elements of Ecg signal, Filter and Sleep Stages.
He has included themes like Segmentation, Digital pathology, Field, Ensemble learning and Atrial fibrillation in his Deep learning study.
His Electroencephalography research is multidisciplinary, relying on both Sleep disorder, Parkinson's disease, Channel, Energy and Polysomnogram.
When carried out as part of a general Machine learning research project, his work on Genetic algorithm is frequently linked to work in Noise, therefore connecting diverse disciplines of study.
Between 2019 and 2021, his most popular works were:
- Automated detection of COVID-19 cases using deep neural networks with X-ray images. (433 citations)
- Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. (147 citations)
- A deep learning approach for Parkinson’s disease diagnosis from EEG signals (126 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Internal medicine
- Machine learning
U. Rajendra Acharya mainly focuses on Artificial intelligence, Pattern recognition, Deep learning, Electroencephalography and Machine learning.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Autism spectrum disorder and Time–frequency analysis.
His Pattern recognition study focuses on Orthogonal wavelet in particular.
His Deep learning study combines topics from a wide range of disciplines, such as Artificial neural network, Field, Coronary artery disease, Ensemble learning and Feature extraction.
His Field research incorporates themes from Channel, Identification, Support vector machine and Word error rate.
In his study, Sensitivity, Parkinson's disease, Feature, Eeg recording and Central nervous system is inextricably linked to Abnormality, which falls within the broad field of Electroencephalography.
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