What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Particle physics, Nuclear physics and Large Hadron Collider.
His work deals with themes such as Machine learning, Condition monitoring and Signal processing, which intersect with Artificial intelligence.
Asoke K. Nandi has researched Signal processing in several fields, including Speech recognition, Blind signal separation, Modulation and Estimator.
Asoke K. Nandi is involved in the study of Pattern recognition that focuses on Feature extraction in particular.
His Particle physics research includes themes of Antiproton and Proton.
His Large Hadron Collider study combines topics from a wide range of disciplines, such as Photino and Massless particle.
His most cited work include:
- Experimental observation of isolated large transverse energy electrons with associated missing energy at $\sqrt s$ = 540 GeV (657 citations)
- Algorithms for automatic modulation recognition of communication signals (493 citations)
- Experimental observation of lepton pairs of invariant mass around 95 GeV/c2 at the Cern SPS collider (477 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Pattern recognition, Algorithm, Cluster analysis and Control theory.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Computer vision.
His study looks at the relationship between Pattern recognition and topics such as Condition monitoring, which overlap with Artificial neural network and Compressed sensing.
His research integrates issues of Estimator, Blind signal separation and Signal processing in his study of Algorithm.
His Blind signal separation research is multidisciplinary, relying on both Speech recognition, Source separation, Higher-order statistics, Mathematical optimization and Applied mathematics.
Asoke K. Nandi has included themes like MIMO, Communication channel and Blind equalization in his Control theory study.
He most often published in these fields:
- Artificial intelligence (43.21%)
- Pattern recognition (29.64%)
- Algorithm (15.11%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (43.21%)
- Pattern recognition (29.64%)
- Cluster analysis (16.25%)
In recent papers he was focusing on the following fields of study:
Asoke K. Nandi mostly deals with Artificial intelligence, Pattern recognition, Cluster analysis, Feature extraction and Image segmentation.
Artificial intelligence is often connected to Machine learning in his work.
His studies deal with areas such as Artificial neural network, Autoencoder, Change detection and Compressed sensing as well as Pattern recognition.
The study incorporates disciplines such as Data mining and Event-related potential in addition to Cluster analysis.
Asoke K. Nandi combines subjects such as Statistical classification and Feature selection with his study of Feature extraction.
Fuzzy logic is closely connected to Pixel in his research, which is encompassed under the umbrella topic of Image segmentation.
Between 2014 and 2021, his most popular works were:
- Applications of machine learning to machine fault diagnosis: A review and roadmap (168 citations)
- Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering (145 citations)
- Central Dogma of Molecular Biology (85 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Asoke K. Nandi spends much of his time researching Artificial intelligence, Pattern recognition, Cluster analysis, Computational biology and Feature extraction.
His Artificial intelligence research incorporates themes from Machine learning and Disease.
Asoke K. Nandi interconnects Real image, Fuzzy clustering and Compressed sensing in the investigation of issues within Pattern recognition.
His biological study spans a wide range of topics, including Artificial neural network, Feature learning and Condition monitoring.
His Cluster analysis study incorporates themes from Datasets as Topic and Bioinformatics.
His research integrates issues of Feature, Support vector machine, Statistical classification, Principal component analysis and Feature selection in his study of Feature extraction.
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