What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His main research concerns Artificial intelligence, Artificial neural network, Wireless sensor network, Anomaly detection and Heart rate variability.
Marimuthu Palaniswami has researched Artificial intelligence in several fields, including Computational complexity theory, Machine learning and Pattern recognition.
His Artificial neural network study combines topics from a wide range of disciplines, such as Robustness, Control theory, Robust control and Adaptive control.
The various areas that Marimuthu Palaniswami examines in his Wireless sensor network study include Sensor node, Adaptation, Key and Base station.
The concepts of his Key study are interwoven with issues in Smart environment, Reduction, Energy, Statistical parameter and Web of Things.
His studies in Heart rate variability integrate themes in fields like Electrocardiography and Cardiology.
His most cited work include:
- Internet of Things (IoT): A vision, architectural elements, and future directions (6583 citations)
- An Information Framework for Creating a Smart City Through Internet of Things (777 citations)
- Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability (703 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Pattern recognition, Wireless sensor network, Support vector machine and Internal medicine.
His Artificial intelligence research incorporates elements of Machine learning, Computer vision and Signal processing.
His work in Pattern recognition addresses subjects such as Speech recognition, which are connected to disciplines such as Independent component analysis.
His Wireless sensor network research is included under the broader classification of Computer network.
His biological study spans a wide range of topics, including Fetus and Cardiology.
His work carried out in the field of Heart rate variability brings together such families of science as Entropy and Statistics.
He most often published in these fields:
- Artificial intelligence (29.90%)
- Pattern recognition (16.94%)
- Wireless sensor network (12.29%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (29.90%)
- Pattern recognition (16.94%)
- Cluster analysis (7.81%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Cluster analysis, Entropy and Signal processing.
His research integrates issues of Machine learning, Photoplethysmogram, Computer vision and Heart rate in his study of Artificial intelligence.
His Cluster analysis research includes themes of Visualization, Anomaly detection, Data mining and Random projection.
His research investigates the link between Anomaly detection and topics such as Real-time computing that cross with problems in Wireless sensor network.
His Wireless sensor network study is focused on Computer network in general.
His research in Entropy intersects with topics in Heart rate variability and Nonlinear system.
Between 2016 and 2021, his most popular works were:
- PPFA: Privacy Preserving Fog-Enabled Aggregation in Smart Grid (83 citations)
- Ensemble Empirical Mode Decomposition With Principal Component Analysis: A Novel Approach for Extracting Respiratory Rate and Heart Rate From Photoplethysmographic Signal (47 citations)
- Detection of epileptic seizure based on entropy analysis of short-term EEG. (34 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Marimuthu Palaniswami mostly deals with Artificial intelligence, Cluster analysis, Pattern recognition, Data mining and Random projection.
In his study, Hardware architecture, Variety and Load forecasting is inextricably linked to Machine learning, which falls within the broad field of Artificial intelligence.
His Cluster analysis research is multidisciplinary, relying on both Anomaly detection, Computation, Partition, Minimum spanning tree and Big data.
In his research on the topic of Anomaly detection, Wireless sensor network is strongly related with Real-time computing.
His Pattern recognition study combines topics in areas such as Hilbert–Huang transform, Photoplethysmogram, Noise and Respiratory rate.
His study in Data mining is interdisciplinary in nature, drawing from both Smart meter, Scalability, Tree and Trajectory.
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