Despite the availability and ease of collecting a large amount of free, unlabeled data, the
expensive and time-consuming labeling process is still an obstacle to labeling a sufficient …

Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

The inception and rapid growth of the Web, social media, and other online forums have
resulted in the continuous and rapid generation of opinionated textual data. Several real …

The science of solving clinical problems by analyzing images generated in clinical practice
is known as medical image analysis. The aim is to extract information in an affective and …

Deep learning (DL) has been intensively exploited for remaining useful life (RUL) prediction
in the recent decade. Although with high precision and flexibility, DL methods need sufficient …

Abstract Machine learning (ML) is continuously unleashing its power in a wide range of
applications. It has been pushed to the forefront in recent years partly owing to the advent of …

Generally, data is available abundantly in unlabeled form, and its annotation requires some
cost. The labeling, as well as learning cost, can be minimized by learning with the minimum …

With a widespread use of digital imaging data in hospitals, the size of medical image
repositories is increasing rapidly. This causes difficulty in managing and querying these …

Nowadays online reviews play a significant role in influencing the decision of consumers.
Consumers show their experience and information about product quality in their reviews …

Sentiment analysis or opinion mining is one of the major tasks of NLP (Natural Language
Processing). Sentiment analysis has gain much attention in recent years. In this paper, we …