What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Algorithm
- Mathematical optimization
Millie Pant spends much of her time researching Mathematical optimization, Algorithm, Particle swarm optimization, Differential evolution and Multi-swarm optimization.
Her research is interdisciplinary, bridging the disciplines of Benchmark and Mathematical optimization.
The Algorithm study combines topics in areas such as Watermark and Digital watermarking.
Her work deals with themes such as Control theory, Inertia, Settling time, Position and Fuzzy logic, which intersect with Particle swarm optimization.
Her Differential evolution research includes themes of Overcurrent, Variety, Balanced histogram thresholding, Rate of convergence and Optimization problem.
Her studies in Multi-swarm optimization integrate themes in fields like Swarm behaviour and Metaheuristic.
Her most cited work include:
- An efficient Differential Evolution based algorithm for solving multi-objective optimization problems (191 citations)
- Particle swarm optimization: Hybridization perspectives and experimental illustrations (189 citations)
- A robust image watermarking technique using SVD and differential evolution in DCT domain (109 citations)
What are the main themes of her work throughout her whole career to date?
Millie Pant mainly focuses on Mathematical optimization, Differential evolution, Artificial intelligence, Particle swarm optimization and Algorithm.
In her work, Convergence and Algorithm design is strongly intertwined with Benchmark, which is a subfield of Mathematical optimization.
Her research in Differential evolution tackles topics such as Rate of convergence which are related to areas like Premature convergence.
Her biological study spans a wide range of topics, including Swarm intelligence, Computer vision and Pattern recognition.
Her Particle swarm optimization research incorporates themes from Genetic algorithm and Swarm behaviour.
She has researched Algorithm in several fields, including Watermark, Stochastic optimization and Digital watermarking.
She most often published in these fields:
- Mathematical optimization (43.14%)
- Differential evolution (36.27%)
- Artificial intelligence (23.53%)
What were the highlights of her more recent work (between 2017-2021)?
- Artificial intelligence (23.53%)
- Differential evolution (36.27%)
- Mathematical optimization (43.14%)
In recent papers she was focusing on the following fields of study:
Artificial intelligence, Differential evolution, Mathematical optimization, Optimization problem and Particle swarm optimization are her primary areas of study.
Her Artificial intelligence research is multidisciplinary, incorporating elements of Computer vision and Pattern recognition.
Differential evolution is a subfield of Algorithm that Millie Pant explores.
As a part of the same scientific study, Millie Pant usually deals with the Mathematical optimization, concentrating on Distribution networks and frequently concerns with Swarm behaviour, Engineering design process and Optimization algorithm.
She has included themes like Metaheuristic, Global optimization, Backup, Nonlinear system and Evolutionary algorithm in her Optimization problem study.
Her Particle swarm optimization research includes elements of Genetic algorithm, Dynamic programming, Rate of convergence and Benchmark.
Between 2017 and 2021, her most popular works were:
- Differential Evolution: A review of more than two decades of research (38 citations)
- Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019 (26 citations)
- Colour image segmentation with histogram and homogeneity histogram difference using evolutionary algorithms (19 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Machine learning
Her main research concerns Artificial intelligence, Particle swarm optimization, Pattern recognition, Differential evolution and Computer vision.
In general Artificial intelligence study, her work on Motion estimation, Matching, Harmony search and Segmentation-based object categorization often relates to the realm of Block, thereby connecting several areas of interest.
Her Particle swarm optimization study results in a more complete grasp of Mathematical optimization.
Her research in Mathematical optimization intersects with topics in Convergence, Recommender system and Benchmark.
Her study in the field of Image segmentation is also linked to topics like Block-matching algorithm.
Her Differential evolution research is multidisciplinary, relying on both Variety, Variation, Key and Crossover.
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