What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer network
- Operating system
His scientific interests lie mostly in Artificial intelligence, Wireless sensor network, Computer network, Computer security and Routing.
His studies in Artificial intelligence integrate themes in fields like Machine learning, Computer vision and Pattern recognition.
The Wireless sensor network study combines topics in areas such as Network performance, Algorithm, Particle swarm optimization, Cluster analysis and Real-time computing.
His Computer network research is multidisciplinary, relying on both Wireless, Mobile sink, Key distribution in wireless sensor networks and Sensor fusion.
In his work, False positive rate, Language model, Android and Android malware is strongly intertwined with Mobile device, which is a subfield of Computer security.
The various areas that Arun Kumar Sangaiah examines in his Routing study include Simulated annealing, Heuristic and Heuristic.
His most cited work include:
- Anonymous mutual authentication and key agreement scheme for wearable sensors in wireless body area networks (148 citations)
- An integrated decision support system based on ANN and Fuzzy_AHP for heart failure risk prediction (148 citations)
- A three-factor anonymous authentication scheme for wireless sensor networks in internet of things environments (147 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Computer network, Cloud computing, Computer security and Wireless sensor network.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning, Computer vision and Pattern recognition.
His Computer network study incorporates themes from Wireless, Wireless network and The Internet.
His study focuses on the intersection of Cloud computing and fields such as Distributed computing with connections in the field of Scheduling.
Particularly relevant to Authentication is his body of work in Computer security.
Arun Kumar Sangaiah has researched Wireless sensor network in several fields, including Real-time computing and Cluster analysis.
He most often published in these fields:
- Artificial intelligence (28.47%)
- Computer network (13.37%)
- Cloud computing (11.88%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (28.47%)
- Cloud computing (11.88%)
- Machine learning (8.91%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Cloud computing, Machine learning, Computer network and Pattern recognition are his primary areas of study.
Deep learning, Artificial neural network, Feature extraction, Support vector machine and Convolutional neural network are the subjects of his Artificial intelligence studies.
His Cloud computing research incorporates themes from Computer security, Scalability, Index and Text segmentation.
His Computer network research includes themes of Wireless, Key, The Internet, Vehicular ad hoc network and Edge computing.
His biological study spans a wide range of topics, including Node and Transmission.
His research integrates issues of Image, Filter and Benchmark in his study of Pattern recognition.
Between 2019 and 2021, his most popular works were:
- Raspberry Pi assisted face recognition framework for enhanced law-enforcement services in smart cities (68 citations)
- Alcoholism identification via convolutional neural network based on parametric ReLU, dropout, and batch normalization (65 citations)
- Spatial and semantic convolutional features for robust visual object tracking (63 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- Operating system
His main research concerns Artificial intelligence, Convolutional neural network, Support vector machine, Cloud computing and Wireless sensor network.
His Artificial intelligence research integrates issues from Machine learning and Pattern recognition.
In his study, Medical history, Overfitting, Chronic alcoholism and Speech recognition is strongly linked to Normalization, which falls under the umbrella field of Convolutional neural network.
His research in Support vector machine intersects with topics in Bag-of-words model, Local binary patterns, Deep learning, Computer vision and Image scaling.
He interconnects Distributed computing, Computer security, Real-time computing, Online machine learning and Scheduling in the investigation of issues within Cloud computing.
He has included themes like Local area network, Edge computing, Enhanced Data Rates for GSM Evolution, Green computing and Risk analysis in his Wireless sensor network study.
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