In this paper, an overview of the artificial neural networks is presented. Their main and
popular types such as the multilayer feedforward neural network (MLFFNN), the recurrent …

The primary aim of this study is to investigate suitable Statistical Neural Network (SNN)
models and their hybrid version for COVID-19 mortality prediction in Indian populations and …

Prediction and correct voting is critical task in imbalance data multi-class classification.
Accuracy and performance of multi-class depends on voting and prediction of new class …

This paper proposes a method, called autoencoder with probabilistic random forest (AE-
PRF), for detecting credit card frauds. The proposed AE-PRF method first utilizes the …

In this study, linear and nonlinear modeling was performed to predict the urban air quality of
the Lucknow city (India). Partial least squares regression (PLSR), multivariate polynomial …

In this paper, we introduce a deep learning model to classify children as either healthy or
potentially having autism with 94.6% accuracy using Deep Learning. Patients with autism …

In the absence of medical diagnosis evidences, it is difficult for the experts to opine about the
grade of disease with affirmation. Generally many tests are done that involve clustering or …

Structural health monitoring (SHM) technique is increasingly used in civil engineering
structures, with which the authentic environmental and structural response data can be …

In the highly corrosive environment where marine structures operate, the current industry
practice is to overcompensate for corrosion induced thickness loss (CITL) and replace any …

The field of VR-EEG affective computing is rapidly progressing. However, it faces challenges
such as lacking a solid psychological theory foundation, limited classification accuracy, and …