What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer network
- Statistics
His scientific interests lie mostly in Control theory, Digital filter, Artificial intelligence, Infinite impulse response and Filter design.
In the field of Control theory, his study on Adaptive filter, Finite impulse response and Network synthesis filters overlaps with subjects such as Linear phase.
His studies in Digital filter integrate themes in fields like Low-pass filter and Algorithm.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Natural language processing, Computer vision and Pattern recognition.
His work deals with themes such as Particle filter, Kernel and Object detection, which intersect with Pattern recognition.
His studies examine the connections between Infinite impulse response and genetics, as well as such issues in Filter bank, with regards to Image segmentation.
His most cited work include:
- A new class of two-channel biorthogonal filter banks and wavelet bases (376 citations)
- Multimodal human discourse: gesture and speech (224 citations)
- Kernel particle filter for visual tracking (166 citations)
What are the main themes of his work throughout his whole career to date?
Rashid Ansari mainly focuses on Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Control theory.
His Artificial intelligence study often links to related topics such as Filter.
His research integrates issues of Mathematical optimization and Signal processing in his study of Algorithm.
His Control theory study combines topics from a wide range of disciplines, such as Digital filter, MIMO and Filter design.
His work on Infinite impulse response as part of his general Digital filter study is frequently connected to All-pass filter, thereby bridging the divide between different branches of science.
His Infinite impulse response research incorporates themes from Filter bank and Finite impulse response.
He most often published in these fields:
- Artificial intelligence (31.48%)
- Computer vision (19.44%)
- Algorithm (18.98%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (31.48%)
- Pattern recognition (11.57%)
- Computer vision (19.44%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Computer vision, Mobile device and Convolutional neural network.
By researching both Artificial intelligence and Facial muscles, Rashid Ansari produces research that crosses academic boundaries.
His work on Feature extraction, Level set method and Level set as part of general Pattern recognition research is frequently linked to Set, bridging the gap between disciplines.
His Iterative reconstruction, Segmentation and Match moving study in the realm of Computer vision connects with subjects such as Conjunctiva.
His study looks at the relationship between Convolutional neural network and topics such as Hidden Markov model, which overlap with Signal and Motion.
His Algorithm research is multidisciplinary, relying on both Artificial neural network and Nonlinear system.
Between 2013 and 2021, his most popular works were:
- Hierarchical Data Aggregation Using Compressive Sensing (HDACS) in WSNs (109 citations)
- Accurate localization in dense urban area using Google street view images (15 citations)
- A vision based system for traffic lights recognition (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- Statistics
His main research concerns Artificial intelligence, Nursing, Electronic health record, Big data and Linear network coding.
His work in Artificial intelligence addresses subjects such as Computer vision, which are connected to disciplines such as Reduction.
He focuses mostly in the field of Nursing, narrowing it down to topics relating to Nursing Outcomes Classification and, in certain cases, Nursing research, Knowledge extraction, Lagging and Knowledge management.
His Linear network coding research includes elements of Throughput and Packet loss.
The concepts of his Computer network study are interwoven with issues in Context and Wireless network.
The Facial Action Coding System study combines topics in areas such as Affective computing, Deep learning and Natural language processing.
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