What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer network
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Big data, Data mining, Scalability and Support vector machine.
He has researched Artificial intelligence in several fields, including Computer vision and Pattern recognition.
The study incorporates disciplines such as Variety and Analytics, Data science in addition to Big data.
The Data mining study combines topics in areas such as Spatial analysis and Geographic information system.
The concepts of his Support vector machine study are interwoven with issues in Alzheimer's disease, Statistical classification, Linear discriminant analysis and Principal component analysis.
His study in Principal component analysis is interdisciplinary in nature, drawing from both Deep learning and Adaptive neuro fuzzy inference system.
His most cited work include:
- A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system (202 citations)
- Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm (144 citations)
- A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem (137 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Pattern recognition, Big data, Deep learning and Machine learning.
While working on this project, Gunasekaran Manogaran studies both Artificial intelligence and Process.
In the subject of general Pattern recognition, his work in Convolutional neural network and Scale-invariant feature transform is often linked to Diabetic retinopathy, thereby combining diverse domains of study.
Gunasekaran Manogaran has included themes like Analytics and Data science in his Big data study.
Deep learning and Support vector machine are commonly linked in his work.
His study in the field of Decision tree also crosses realms of Event.
He most often published in these fields:
- Artificial intelligence (45.77%)
- Pattern recognition (21.83%)
- Big data (14.08%)
What were the highlights of his more recent work (between 2020-2021)?
- Artificial intelligence (45.77%)
- Pattern recognition (21.83%)
- Process (8.45%)
In recent papers he was focusing on the following fields of study:
Gunasekaran Manogaran mainly focuses on Artificial intelligence, Pattern recognition, Process, Distributed computing and Machine learning.
His research links Natural language processing with Artificial intelligence.
His work on Feature selection is typically connected to Diabetic retinopathy, Control and Action recognition as part of general Pattern recognition study, connecting several disciplines of science.
His Distributed computing research includes themes of Edge computing, The Internet, Reliability and Interoperability.
His Edge computing study integrates concerns from other disciplines, such as Server, Mobile edge computing and Distributed management.
His biological study spans a wide range of topics, including Feature extraction, Deep learning and Face.
Between 2020 and 2021, his most popular works were:
- A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic (49 citations)
- Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection. (14 citations)
- Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection. (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- Machine learning
His main research concerns Artificial intelligence, Process, Transfer of learning, Face and Feature extraction.
His Artificial intelligence research integrates issues from Machine learning and Contrast.
His work in Machine learning covers topics such as Forwarding plane which are related to areas like Data management.
Process combines with fields such as Pattern recognition, Feature selection, Optical flow, Fusion and Action recognition in his investigation.
His Transfer of learning research is multidisciplinary, relying on both Detector, Object, Object detection, Computer vision and Deep learning.
The various areas that Gunasekaran Manogaran examines in his Face study include Decision tree and Support vector machine.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
