What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Computer vision
- Statistics
Her primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Image processing.
Her study in Artificial intelligence concentrates on Pixel, Sparse approximation, Iterative reconstruction, Compressed sensing and Deep learning.
Her study looks at the relationship between Computer vision and topics such as Interpolation, which overlap with Bilinear interpolation, Edge and Image scaling.
Rabab K. Ward interconnects Contextual image classification and Facial recognition system in the investigation of issues within Pattern recognition.
Rabab K. Ward has researched Algorithm in several fields, including Transformation, Common Scrambling Algorithm, Mathematical optimization and Filter.
Her Image processing research includes elements of Image quality, Tone mapping and Video image.
Her most cited work include:
- A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. (708 citations)
- Deep sentence embedding using long short-term memory networks: analysis and application to information retrieval (465 citations)
- Telomeres in the mouse have large inter-chromosomal variations in the number of t2ag3 repeats (452 citations)
What are the main themes of her work throughout her whole career to date?
Her primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Algorithm and Speech recognition.
Her study in Artificial intelligence focuses on Feature extraction, Wavelet, Data compression, Compressed sensing and Pixel.
The Pattern recognition study combines topics in areas such as Contextual image classification, Image, Facial recognition system and Artificial neural network.
Rabab K. Ward combines subjects such as Transform coding and Mathematical optimization with her study of Algorithm.
Her Speech recognition research integrates issues from Brain–computer interface and Electroencephalography.
The study incorporates disciplines such as Motion estimation and Block-matching algorithm in addition to Motion compensation.
She most often published in these fields:
- Artificial intelligence (58.00%)
- Computer vision (33.57%)
- Pattern recognition (24.43%)
What were the highlights of her more recent work (between 2014-2021)?
- Artificial intelligence (58.00%)
- Pattern recognition (24.43%)
- Computer vision (33.57%)
In recent papers she was focusing on the following fields of study:
Rabab K. Ward spends much of her time researching Artificial intelligence, Pattern recognition, Computer vision, Deep learning and Compressed sensing.
Rabab K. Ward usually deals with Artificial intelligence and limits it to topics linked to Machine learning and Field.
Her work deals with themes such as Epileptic seizure, Epilepsy, Electroencephalography and Signal, which intersect with Pattern recognition.
Her research is interdisciplinary, bridging the disciplines of Perception and Computer vision.
Her Compressed sensing study integrates concerns from other disciplines, such as Dynamic contrast-enhanced MRI, Solver, Data mining and Greedy algorithm.
Her Iterative reconstruction research includes elements of Algorithm and Computed tomography.
Between 2014 and 2021, her most popular works were:
- Deep sentence embedding using long short-term memory networks: analysis and application to information retrieval (465 citations)
- Image Fusion With Convolutional Sparse Representation (275 citations)
- Deep learning for pixel-level image fusion: Recent advances and future prospects (216 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Her scientific interests lie mostly in Artificial intelligence, Pattern recognition, Deep learning, Photoplethysmogram and Compressed sensing.
Her Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Natural language processing.
Her Pattern recognition research is multidisciplinary, incorporating elements of Epileptic seizure, Epilepsy, Electroencephalography and Image.
Her Electroencephalography research is multidisciplinary, relying on both Speech recognition and Scalp.
Her Compressed sensing research integrates issues from Optimization problem, Solver and Data mining.
Her Convolution study which covers Neural coding that intersects with Computer vision.
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