What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Her primary scientific interests are in Artificial intelligence, Particle swarm optimization, Machine learning, Image segmentation and Artificial neural network.
Her Artificial intelligence research includes elements of Information processing, Computer vision and Pattern recognition.
Her study looks at the intersection of Pattern recognition and topics like Thresholding with Jaccard index and Active contour model.
Her research investigates the link between Particle swarm optimization and topics such as Genetic algorithm that cross with problems in Context.
Amira S. Ashour has included themes like Classifier, Fuzzy partitioning, Data mining and Sensitivity in her Machine learning study.
Her Artificial neural network study integrates concerns from other disciplines, such as Local optimum, Reduction and Fuzzy classifier.
Her most cited work include:
- Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings (174 citations)
- Multi-level image thresholding using Otsu and chaotic bat algorithm (132 citations)
- Internet of Things and Big Data Technologies for Next Generation Healthcare (98 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of study are Artificial intelligence, Pattern recognition, Computer vision, Segmentation and Cluster analysis.
Her Artificial intelligence research integrates issues from Machine learning and Particle swarm optimization.
In her research on the topic of Particle swarm optimization, Evolutionary algorithm is strongly related with Genetic algorithm.
Her Pattern recognition research incorporates elements of Contextual image classification, Image, Thresholding and Fuzzy logic.
Her study ties her expertise on Image processing together with the subject of Segmentation.
Her study of Multilayer perceptron is a part of Artificial neural network.
She most often published in these fields:
- Artificial intelligence (50.81%)
- Pattern recognition (30.08%)
- Computer vision (13.82%)
What were the highlights of her more recent work (between 2018-2021)?
- Artificial intelligence (50.81%)
- Pattern recognition (30.08%)
- Segmentation (12.20%)
In recent papers she was focusing on the following fields of study:
Amira S. Ashour mainly focuses on Artificial intelligence, Pattern recognition, Segmentation, Cluster analysis and Fuzzy logic.
Domain is closely connected to Machine learning in her research, which is encompassed under the umbrella topic of Artificial intelligence.
Her work deals with themes such as Contextual image classification and Artificial neural network, which intersect with Pattern recognition.
Her work carried out in the field of Segmentation brings together such families of science as Image processing, Genetic algorithm, Centroid and Histogram.
She interconnects Pixel, Silhouette, Filter and Jaccard index in the investigation of issues within Cluster analysis.
Her studies deal with areas such as Entropy, MATLAB, Cardiac pacemaker and Pattern recognition as well as Fuzzy logic.
Between 2018 and 2021, her most popular works were:
- Design of a proportional-integral-derivative controller for an automatic generation control of multi-area power thermal systems using firefly algorithm (55 citations)
- A survey of the state-of-the-arts on neutrosophic sets in biomedical diagnoses (50 citations)
- Morphological Segmentation Analysis and Texture-based Support Vector Machines Classification on Mice Liver Fibrosis Microscopic Images (33 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Amira S. Ashour mainly investigates Artificial intelligence, Pattern recognition, Segmentation, Cluster analysis and Convolutional neural network.
Her Artificial intelligence study frequently draws connections between adjacent fields such as Mathematical optimization.
Her research in Pattern recognition intersects with topics in Contextual image classification, Artificial neural network, Deep learning and Feature.
Her studies in Segmentation integrate themes in fields like Histogram, Centroid, Jaccard index and Genetic algorithm.
She focuses mostly in the field of Cluster analysis, narrowing it down to topics relating to Pixel and, in certain cases, Euclidean distance, Mixture model, Silhouette and Algorithm.
The concepts of her Convolutional neural network study are interwoven with issues in Classifier, Convolution and Cirrhosis.
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