Mehdi Neshat

TL;DR: This paper is a review of AFSA algorithm and describes the evolution of this algorithm along with all improvements, its combination with various methods as well as its applications.

TL;DR: This study adopts a novel hybrid deep learning-based evolutionary approach in an attempt to improve the accuracy of wind speed prediction, consisting of a bidirectional long short-term memory neural network, an effective hierarchical evolutionary decomposition technique and an improved generalised normal distribution optimisation algorithm for hyper-parameter tuning.

TL;DR: Modeling swallow swarm movement and their other behavior, this optimization method represents a new optimization method that has proved high efficiency, such as fast move in flat areas, not getting stuck in local extremum points, high convergence speed, and intelligent participation in the different groups of particles.

TL;DR: A fuzzy system has been designed for learning, analysis and diagnosis of liver disorders that in comparison with other traditional diagnostic systems is faster, cheaper, and also more liable and more accurate.

TL;DR: A novel composite deep learning-based evolutionary approach for accurate forecasting of the power output in wind-turbine farms, developed in three stages, supported the superiority of the proposed hybrid model in terms of accurate forecasting and computational runtime compared with earlier published hybrid models.