What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Artificial intelligence, Pattern recognition, Segmentation, Computer vision and Support vector machine are his primary areas of study.
His study brings together the fields of Field and Artificial intelligence.
His Field research incorporates themes from Domain, Image and Data mining.
The concepts of his Pattern recognition study are interwoven with issues in Brain tumor and Gynecology.
His study in Segmentation is interdisciplinary in nature, drawing from both Silhouette, Digital mammography, Pectoral muscle and Medical imaging.
Tanzila Saba interconnects Transfer of learning, Artificial neural network and Convolutional neural network in the investigation of issues within Deep learning.
His most cited work include:
- Medical Image Segmentation Methods, Algorithms, and Applications (138 citations)
- Energy Efficient Multipath Routing Protocol for Mobile Ad-Hoc Network Using the Fitness Function (73 citations)
- Neural networks for document image preprocessing: state of the art (65 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, Segmentation, Computer vision and Feature extraction.
His work on Machine learning expands to the thematically related Artificial intelligence.
The various areas that Tanzila Saba examines in his Pattern recognition study include Histogram, Brain tumor, Preprocessor and Feature.
His work carried out in the field of Segmentation brings together such families of science as Cursive, Natural language processing and Medical imaging.
His works in RGB color model, Discrete cosine transform and Pixel are all subjects of inquiry into Computer vision.
His Segmentation-based object categorization research is under the purview of Scale-space segmentation.
He most often published in these fields:
- Artificial intelligence (62.87%)
- Pattern recognition (37.50%)
- Segmentation (20.22%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (62.87%)
- Pattern recognition (37.50%)
- Deep learning (8.82%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Deep learning, Feature extraction and Feature selection.
Tanzila Saba combines subjects such as Brain tumor and Machine learning with his study of Artificial intelligence.
His work deals with themes such as Feature, Cluster analysis, Image, Local binary patterns and Entropy, which intersect with Pattern recognition.
His Feature extraction study incorporates themes from Image segmentation, Medical imaging, Image processing, Early detection and RGB color model.
The study incorporates disciplines such as Artificial neural network, Feature fusion and Texture in addition to Feature selection.
His Segmentation study combines topics in areas such as Preprocessor, Selection and Melanoma detection.
Between 2019 and 2021, his most popular works were:
- Brain tumor detection using fusion of hand crafted and deep learning features (37 citations)
- Hand-crafted and deep convolutional neural network features fusion and selection strategy: An application to intelligent human action recognition (32 citations)
- Detecting Pneumonia using Convolutions and Dynamic Capsule Routing for Chest X-ray Images. (30 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Tanzila Saba mainly focuses on Artificial intelligence, Pattern recognition, Deep learning, Segmentation and Feature extraction.
His research on Artificial intelligence frequently connects to adjacent areas such as Machine learning.
His Pattern recognition research incorporates elements of Artificial neural network, Entropy and Brain tumor.
His biological study spans a wide range of topics, including Routing, Pneumonia, Contextual image classification, Transfer of learning and Supervised learning.
His studies deal with areas such as Medical physics, Early detection and Medical imaging as well as Segmentation.
His Feature extraction research includes elements of Active contour model, Endoscopy and Capsule endoscopy.
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