Now-a-days we are virtually residing in the digital networking era, and constantly sharing
thoughts, opinions about things that are associated with us. As a result, social networking …

The overall purpose of this paper is to demonstrate how data preprocessing, training size
variation, and subsampling can dynamically change the performance metrics of imbalanced …

News headlines can be a good data source for detecting the barriers to the spreading of
news in news media, which can be useful in many real-world applications. In this study, we …

Within the e-commerce sphere, optimizing the product classification process assumes
pivotal importance, owing to its direct influence on operational efficiency and profitability. In …

Nowadays, deep learning techniques are increasingly growing in machine vision for object
recognition, segmentation, classification, and so on, in a wide variety of applications. In this …

Training data for user behavior models that predict subjective dimensions of visual
perception are often too scarce for deep learning methods to be applicable. With the typical …

Abstract The International Statistical Classification of Diseases and Related Health
Problems (ICD) is one of the widely used classification system for diagnoses and …

Deep learning is a powerful multi-layer architecture that has important applications in image
processing and text classification. This paper first introduces the development of deep …

Deep neural networks have proven to be effective in various domains, especially in natural
language processing and image processing. However, one of the challenges associated …

Background: The publicly available large dataset plays an important role in the development
of the natural language processing/computational linguistic research field. However, up to …