What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
Vijay Kumar mainly focuses on Benchmark, Artificial intelligence, Pattern recognition, Mathematical optimization and Computational complexity theory.
Vijay Kumar has included themes like Optimization problem, Metaheuristic algorithms and Constrained optimization in his Benchmark study.
His Artificial intelligence study integrates concerns from other disciplines, such as Cover and Theoretical computer science.
His study focuses on the intersection of Pattern recognition and fields such as Deep learning with connections in the field of Transfer of learning and Chest ct.
In general Mathematical optimization, his work in Metaheuristic and Optimization algorithm is often linked to Hyena and Scale linking many areas of study.
Computational complexity theory is a subfield of Algorithm that he investigates.
His most cited work include:
- Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications (231 citations)
- Emperor penguin optimizer: A bio-inspired algorithm for engineering problems (200 citations)
- Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks. (132 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Metaheuristic, Benchmark, Algorithm and Pattern recognition.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Computer vision.
Vijay Kumar interconnects Pareto principle, Harmony search, Selection and Cluster analysis in the investigation of issues within Metaheuristic.
His study in Benchmark is interdisciplinary in nature, drawing from both Computational complexity theory, Convergence and Multi-objective optimization, Optimization problem, Mathematical optimization.
His Algorithm study which covers Encryption that intersects with Chaotic, Differential evolution and Pixel.
His Pattern recognition study combines topics from a wide range of disciplines, such as Correlation clustering, Fitness function and Transfer of learning.
He most often published in these fields:
- Artificial intelligence (38.21%)
- Metaheuristic (18.70%)
- Benchmark (17.89%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (38.21%)
- Pattern recognition (17.07%)
- Deep learning (7.32%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Pattern recognition, Deep learning, Metaheuristic and Algorithm.
His study connects Machine learning and Artificial intelligence.
While the research belongs to areas of Pattern recognition, Vijay Kumar spends his time largely on the problem of Transfer of learning, intersecting his research to questions surrounding Radiological weapon and Computed tomography.
The study incorporates disciplines such as Convolutional neural network, Differential evolution and Sensitivity in addition to Deep learning.
The concepts of his Algorithm study are interwoven with issues in Transfer function, Computational intelligence, Encryption and Benchmark.
His research integrates issues of Particle swarm optimization, Mathematical optimization, Binary number and Engineering design process in his study of Benchmark.
Between 2019 and 2021, his most popular works were:
- Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks. (132 citations)
- Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning. (75 citations)
- Automated Deep Transfer Learning-Based Approach for Detection of COVID-19 Infection in Chest X-rays. (44 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
Vijay Kumar mainly investigates Artificial intelligence, Pattern recognition, Severe acute respiratory syndrome coronavirus 2, 2019-20 coronavirus outbreak and Algorithm.
Vijay Kumar conducted interdisciplinary study in his works that combined Artificial intelligence and Transmission.
His Pattern recognition research includes elements of Transfer of learning and Deep learning.
His work deals with themes such as Ensemble forecasting and Radiography, which intersect with Transfer of learning.
In his research, Convolutional neural network is intimately related to Chest ct, which falls under the overarching field of Deep learning.
His Algorithm research incorporates elements of Swarm behaviour and Computational intelligence.
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