Refurbishing ANN with the aid of adaptive crow search optimisation for effectively diagnosing railway wheel condition

The research intends to diagnosis the railway wheel condition with the aid of artificial neural network (ANN). In diagnosing, ANN has been proven its convenience over manual computation in various applications. The research utilises optimisation techniques for identifying appropriate hidden layers and their associated neurons to enhance the performance of ANN techniques. This configuration process includes optimisation techniques like evolutionary algorithm (EA), genetic algorithm (GA), particle swarm optimisation (PSO), and crow search optimisation (CSO). Also, this research includes modified and improved conventional strategy in CSO, which urge incorporating novel strategy called adaptive crow search optimisation (ACSO) to enhance the performance. The proposed strategy unveils proficient performance of 99.2% accuracy, which is 1.7% greater than the conventional ANN model and an average of 0.9% greater than other contest techniques consider for configuration. The credibility of the ANN model gets increased while employs the optimisation techniques in diagnosing the railway wheel condition.