What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
Artificial intelligence, Machine learning, Classifier, Particle swarm optimization and Mathematical optimization are his primary areas of study.
His Artificial intelligence research incorporates elements of Big data and Pattern recognition.
His work deals with themes such as Ant colony optimization algorithms and Data mining, which intersect with Machine learning.
Vasile Palade works mostly in the field of Classifier, limiting it down to concerns involving Artificial neural network and, occasionally, Control engineering and Genetic algorithm.
While the research belongs to areas of Particle swarm optimization, Vasile Palade spends his time largely on the problem of Convergence, intersecting his research to questions surrounding Optimization problem, Nonlinear system and Robustness.
As a part of the same scientific family, Vasile Palade mostly works in the field of Imbalanced data, focusing on Contrast and, on occasion, Training set.
His most cited work include:
- An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics (809 citations)
- FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning (251 citations)
- Quantum-behaved particle swarm optimization: Analysis of individual particle behavior and parameter selection (212 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Artificial neural network, Machine learning, Pattern recognition and Fuzzy logic.
His study brings together the fields of Data mining and Artificial intelligence.
His Artificial neural network study incorporates themes from Control engineering and Mathematical optimization, Gradient method.
As part of one scientific family, he deals mainly with the area of Mathematical optimization, narrowing it down to issues related to the Benchmark, and often Particle swarm optimization.
His study in Machine learning is interdisciplinary in nature, drawing from both Inference and Ant colony optimization algorithms.
Vasile Palade interconnects Fuzzy classification and Adaptive neuro fuzzy inference system in the investigation of issues within Neuro-fuzzy.
He most often published in these fields:
- Artificial intelligence (60.00%)
- Artificial neural network (23.67%)
- Machine learning (23.67%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (60.00%)
- Deep learning (9.39%)
- Artificial neural network (23.67%)
In recent papers he was focusing on the following fields of study:
Vasile Palade mainly focuses on Artificial intelligence, Deep learning, Artificial neural network, Real-time computing and Machine learning.
Artificial intelligence is often connected to Pattern recognition in his work.
Vasile Palade works mostly in the field of Deep learning, limiting it down to topics relating to Fuzzy logic and, in certain cases, Operations research.
His Artificial neural network research integrates issues from Adversarial system, Applied mathematics, Sensor fusion and Nonlinear system.
The various areas that Vasile Palade examines in his Machine learning study include Ant colony optimization algorithms, Pedestrian and Big data.
The Convolutional neural network study which covers Benchmark that intersects with Particle swarm optimization and Enhanced Data Rates for GSM Evolution.
Between 2018 and 2021, his most popular works were:
- Interactive machine learning: experimental evidence for the human in the algorithmic loop: A case study on Ant Colony Optimization (67 citations)
- Edge Intelligence-Assisted Smoke Detection in Foggy Surveillance Environments (21 citations)
- An Effective Method of Weld Defect Detection and Classification Based on Machine Vision (21 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
The scientist’s investigation covers issues in Artificial intelligence, Real-time computing, Deep learning, Benchmark and Inertial navigation system.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Development, Pheromone and Pattern recognition.
His Pattern recognition research is multidisciplinary, incorporating elements of Task and Machine vision.
His Deep learning research is multidisciplinary, relying on both Redundancy and Automatic summarization.
His Artificial neural network research incorporates themes from Gyroscope and Estimation.
Vasile Palade has researched Feature extraction in several fields, including F1 score, Data mining and Feature.
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