Omid Tarkhaneh

TL;DR: This paper proposes a DE solution that achieves a good balance between exploration and exploitation through a new adaptive approach and new mutation strategies, and experimentally compares the proposed DE algorithm, referred to as Adaptive Differential Evolution with Levy Distribution (ALDE), against three DE benchmark algorithms on T2 weighted MRI brain images.

TL;DR: A Modified Differential Evolution approach to Feature Selection (MDEFS) is proposed by utilizing two new mutation strategies to create a feasible balance between exploration and exploitation and maintain the classification performance in an acceptable range concerning both the number of features and accuracy.

TL;DR: A new evolutionary training algorithm referred to as LPSONS is proposed, which combines the velocity operators in Particle Swarm Optimization (PSO) with Mantegna Lévy distribution to produce more diverse solutions by dividing the population and generation between different sections of the algorithm.

TL;DR: This paper proposes hybrid approach for data clustering using cuckoo search and differential evolution algorithms and shows that the proposed algorithm has ability in obtaining better results in terms of Convergence Speed, Accuracy, and also reducing number of functional evaluation.

TL;DR: In this article, the authors used the transfer learning approach employing DenseNet121 architecture to classify various species of oxford-102 flowers dataset and achieved an accuracy of 98.6% for 50 epochs.