In recent years, the exponential growth of digital documents has been met by rapid progress
in text classification techniques. Newly proposed machine learning algorithms leverage the …

In recent years, there has been an exponential growth in the number of complex documents
and texts that require a deeper understanding of machine learning methods to be able to …

Deep convolutional neural networks, assisted by architectural design strategies, make
extensive use of data augmentation techniques and layers with a high number of feature …

Deep neural networks (DNNs) have achieved the state-of-the-art (SOTA) performance in
numerous fields. However, DNNs need high computation times, and people always expect …

Recently, offensive content has become increasingly popular for harassing and criticizing
people on numerous social media platforms. This paper proposes an offensive text …

Automatic text categorization is the operation of sorting out the text documents into pre-
defined text categories using some machine learning algorithms. Normally, it defines the …

The wide implementation of electronic health record (EHR) systems facilitates the collection
of large-scale health data from real clinical settings. Despite the significant increase in …

We report that a very high accuracy on the MNIST test set can be achieved by using simple
convolutional neural network (CNN) models. We use three different models with 3x3, 5x5 …

Most capsule network designs rely on traditional matrix multiplication between capsule
layers and computationally expensive routing mechanisms to deal with the capsule …

We present a convolutional neural network design with additional branches after certain
convolutions so that we can extract features with differing effective receptive fields and levels …