What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Mechanical engineering
Robot, Genetic algorithm, Artificial neural network, Control theory and Algorithm are his primary areas of study.
Dilip Kumar Pratihar has included themes like Soft computing and Fuzzy logic in his Robot study.
Dilip Kumar Pratihar performs multidisciplinary study in Genetic algorithm and Electron beam welding in his work.
Dilip Kumar Pratihar interconnects Statistical parameter, Fluid mechanics, Cluster analysis and Maxima and minima in the investigation of issues within Artificial neural network.
His research in Control theory focuses on subjects like Fuzzy control system, which are connected to Fuzzy set.
Dilip Kumar Pratihar has researched Algorithm in several fields, including Casting, Molding, Experimental data and Grain size.
His most cited work include:
- A genetic-fuzzy approach for mobile robot navigation among moving obstacles (117 citations)
- Optimization of bead geometry in electron beam welding using a Genetic Algorithm (86 citations)
- Time-optimal, collision-free navigation of a car-like mobile robot using neuro-fuzzy approaches (84 citations)
What are the main themes of his work throughout his whole career to date?
Dilip Kumar Pratihar mainly investigates Genetic algorithm, Artificial intelligence, Control theory, Robot and Fuzzy logic.
His Genetic algorithm study combines topics from a wide range of disciplines, such as Artificial neural network and Algorithm, Particle swarm optimization.
His work deals with themes such as Finite element method and Welding, which intersect with Artificial neural network.
His Artificial intelligence research incorporates elements of Machine learning and Computer vision.
His research investigates the connection with Robot and areas like Simulation which intersect with concerns in Trajectory.
His Fuzzy logic study incorporates themes from Knowledge base, Motion planning and Mobile robot.
He most often published in these fields:
- Genetic algorithm (29.55%)
- Artificial intelligence (22.27%)
- Control theory (17.81%)
What were the highlights of his more recent work (between 2018-2021)?
- Welding (14.57%)
- Electron beam welding (9.72%)
- Genetic algorithm (29.55%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Welding, Electron beam welding, Genetic algorithm, Control theory and Algorithm.
Many of his research projects under Welding are closely connected to X-ray photoelectron spectroscopy with X-ray photoelectron spectroscopy, tying the diverse disciplines of science together.
Dilip Kumar Pratihar combines subjects such as Particle swarm optimization and Artificial intelligence, Benchmark with his study of Genetic algorithm.
His work in the fields of Control theory, such as Torque and Nonlinear system, overlaps with other areas such as Serial manipulator.
His biological study spans a wide range of topics, including Computational intelligence and Crossover.
His Cartesian coordinate system study deals with Robot intersecting with Mechanical system.
Between 2018 and 2021, his most popular works were:
- A survey on influence maximization in a social network (22 citations)
- An optimization-based decision tree approach for predicting slip-trip-fall accidents at work (19 citations)
- ComBIM: A community-based solution approach for the Budgeted Influence Maximization Problem (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Mechanical engineering
His primary areas of investigation include Mathematical optimization, Set, Welding, Maximization and Genetic algorithm.
His biological study focuses on Optimization problem.
When carried out as part of a general Welding research project, his work on Laser beam welding is frequently linked to work in Electron beam welding, therefore connecting diverse disciplines of study.
His Genetic algorithm study combines topics in areas such as Slip, Random forest and Decision rule.
In his study, Algorithm is strongly linked to Crossover, which falls under the umbrella field of Selection.
His Algorithm research is multidisciplinary, incorporating perspectives in Artificial neural network, Evolution strategy and Feed forward.
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