What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Pattern recognition
- Statistics
Ram Bilas Pachori spends much of his time researching Pattern recognition, Artificial intelligence, Speech recognition, Electroencephalography and Support vector machine.
His research on Pattern recognition often connects related topics like Time–frequency analysis.
His Artificial intelligence research is multidisciplinary, relying on both Multivariate statistics, Filter and Signal processing.
Ram Bilas Pachori interconnects Segmentation, Feature, Feature vector, Biorthogonal system and Kernel adaptive filter in the investigation of issues within Speech recognition.
The study incorporates disciplines such as Hilbert–Huang transform and Histogram in addition to Electroencephalography.
The Support vector machine study combines topics in areas such as Time–frequency representation, Feature selection and Radial basis function.
His most cited work include:
- Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition (338 citations)
- Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions (240 citations)
- Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals (209 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Pattern recognition, Electroencephalography, Wavelet transform and Speech recognition.
The various areas that Ram Bilas Pachori examines in his Artificial intelligence study include Hilbert–Huang transform, Time–frequency analysis and Signal processing.
His Pattern recognition study incorporates themes from Brain–computer interface and Radial basis function.
His Electroencephalography study combines topics from a wide range of disciplines, such as Energy, Rhythm and Epilepsy.
In his study, which falls under the umbrella issue of Wavelet transform, Fourier–Bessel series is strongly linked to Series expansion.
His Speech recognition research focuses on subjects like Algorithm, which are linked to Time–frequency representation and Representation.
He most often published in these fields:
- Artificial intelligence (61.88%)
- Pattern recognition (58.91%)
- Electroencephalography (35.15%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (61.88%)
- Pattern recognition (58.91%)
- Electroencephalography (35.15%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Pattern recognition, Electroencephalography, Support vector machine and Wavelet transform are his primary areas of study.
His work deals with themes such as Fourier series, Autoencoder and Entropy, which intersect with Pattern recognition.
His Electroencephalography research includes elements of Classifier, Higher-order statistics, Rhythm and Epilepsy.
His biological study spans a wide range of topics, including Radial basis function, Sleep apnea, Multivariate statistics, Fourier transform and Signal processing.
The concepts of his Wavelet transform study are interwoven with issues in Time–frequency analysis, Fourier–Bessel series, Series expansion, Algorithm and Filter bank.
His study in Algorithm is interdisciplinary in nature, drawing from both Time–frequency representation, Distribution, Fractional calculus and Rényi entropy.
Between 2019 and 2021, his most popular works were:
- An efficient removal of power-line interference and baseline wander from ECG signals by employing Fourier decomposition technique (23 citations)
- Time-Frequency Domain Deep Convolutional Neural Network for the Classification of Focal and Non-Focal EEG Signals (18 citations)
- Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study. (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Artificial intelligence, Pattern recognition, Electroencephalography, Wavelet transform and Filter bank are his primary areas of study.
His is doing research in Deep learning and Convolutional neural network, both of which are found in Artificial intelligence.
His Pattern recognition study integrates concerns from other disciplines, such as Artificial neural network and Autoencoder.
His work deals with themes such as Higher-order statistics and Epilepsy, which intersect with Electroencephalography.
The Wavelet transform study combines topics in areas such as Radial basis function, k-nearest neighbors algorithm, Series expansion, Entropy and Feature selection.
His Filter bank research is multidisciplinary, relying on both Energy, Fourier transform and Bandwidth.
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