Shuihua Wang

TL;DR: This survey presented a comprehensive investigation of PSO, including its modifications, extensions, and applications to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology.

TL;DR: A novel spam detection method that focused on reducing the false positive error of mislabeling nonspam as spam, which demonstrated the MBPSO is superior to GA, RSA, PSO, and BPSO in terms of classification performance and wrappers are more effective than filters with regard to classification performance indexes.

TL;DR: This paper presents a neural network (NN) based method to classify a given MR brain image as normal or abnormal, which first employs wavelet transform to extract features from images, and then applies the technique of principle component analysis (PCA) to reduce the dimensions of features.

TL;DR: This study designed and validated a 13-layer convolutional neural network (CNN) that is effective in image-based fruit classification and observed using data augmentation can increase the overall accuracy.

TL;DR: A hybrid classification method based on fitness-scaled chaotic artificial bee colony (FSCABC) algorithm and feedforward neural network (FNN) was seen to be effective in classifying fruits.