What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of investigation include Artificial intelligence, Pattern recognition, Algorithm, Feature selection and Particle swarm optimization.
The study incorporates disciplines such as Machine learning and Computer vision in addition to Artificial intelligence.
As a part of the same scientific family, Aboul Ella Hassanien mostly works in the field of Pattern recognition, focusing on Thresholding and, on occasion, Image segmentation.
His Algorithm research includes themes of Mathematical optimization, Binary number, Fitness function and Benchmark.
His Feature selection study incorporates themes from Data mining, Feature, Curse of dimensionality, Selection and Optimization problem.
His research investigates the connection with Particle swarm optimization and areas like Genetic algorithm which intersect with concerns in Multi-swarm optimization, Energy, Real-time computing and Wireless sensor network.
His most cited work include:
- Binary grey wolf optimization approaches for feature selection (445 citations)
- Wearable and Implantable Wireless Sensor Network Solutions for Healthcare Monitoring (336 citations)
- Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation (244 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Support vector machine.
His research on Artificial intelligence frequently connects to adjacent areas such as Data mining.
Aboul Ella Hassanien is involved in the study of Data mining that focuses on Rough set in particular.
His work focuses on many connections between Pattern recognition and other disciplines, such as Particle swarm optimization, that overlap with his field of interest in Genetic algorithm.
He works in the field of Computer vision, focusing on Image in particular.
Feature selection is closely attributed to Feature in his study.
He most often published in these fields:
- Artificial intelligence (59.74%)
- Pattern recognition (33.30%)
- Machine learning (15.64%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (59.74%)
- Pattern recognition (33.30%)
- Pandemic (2.42%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Pattern recognition, Pandemic, Deep learning and Convolutional neural network.
His Artificial intelligence study frequently draws connections to adjacent fields such as Machine learning.
His Pattern recognition research includes elements of Transfer of learning, Image and Cluster analysis.
License, Data science, China and Social media are fields of study that overlap with his Pandemic research.
His Deep learning study frequently draws connections between adjacent fields such as Test set.
Aboul Ella Hassanien has researched Convolutional neural network in several fields, including Overfitting and Hyperparameter.
Between 2019 and 2021, his most popular works were:
- Forecasting Models for Coronavirus Disease (COVID-19): A Survey of the State-of-the-Art (67 citations)
- A machine learning forecasting model for COVID-19 pandemic in India. (61 citations)
- Detection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Dataset. (47 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Artificial intelligence, Pattern recognition, Deep learning, Pandemic and Machine learning.
In general Artificial intelligence study, his work on Feature selection, Support vector machine, Feature and Segmentation often relates to the realm of Process, thereby connecting several areas of interest.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Quantitative structure–activity relationship, Genetic algorithm, Extreme learning machine, Image and Transfer of learning.
He has included themes like Visualization, Analytics, Convolutional neural network and Automatic summarization in his Deep learning study.
His study in the fields of Rough set, Artificial neural network, Multilayer perceptron and Linear regression under the domain of Machine learning overlaps with other disciplines such as Time duration.
His study explores the link between Artificial neural network and topics such as Search algorithm that cross with problems in Data mining.
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