Using new artificial bee colony as probabilistic neural network for breast cancer data classification

Abstract

PurposeBreast cancer is an important medical disorder, which is not a single disease but a cluster more than 200 different serious medical complications.Design/methodology/approachThe new artificial bee colony (ABC) implementation has been applied to probabilistic neural network (PNN) for training and testing purpose to classify the breast cancer data set.FindingsThe new ABC algorithm along with PNN has been successfully applied to breast cancers data set for prediction purpose with minimum iteration consuming.Originality/valueThe new implementation of ABC along PNN can be easily applied to times series problems for accurate prediction or classification.

Using the AraBERT Model for Customer Satisfaction Classification of Telecom Sectors in Saudi Arabia

“…Convolutional Neural Network (CNN), which was initially developed in the Neural Network Image Processing Community (NNIPC), is a famous kind of Feed-forward Neural Network (FNN) with a profound structure and has shown outstanding simulation results in various tasks, particularly in NLP tasks, such as sentence analysis of various languages in different applications. Multiple types of CNN, including the typical model, have been an important focus of research as they can be applied to complex problems involving time-varying patterns [49][50][51]. The standard CNN involves two operations, which feature extractors, convolution and pooling; the obtained output is then associated with the following [23].…”