Interdisciplinary data fuel fisheries oceanography research and the ecosystem‐based
approaches to management and sustainable development it informs. Underlying this is a …

Deep Neural Networks trained in a fully supervised fashion are the dominant technology in
perception-based autonomous driving systems. While collecting large amounts of unlabeled …

Since the beginning of the 21st century, electronic monitoring (EM) has emerged as a cost‐
efficient supplement to existing catch monitoring programmes in fisheries. An EM system …

Due to the fact that it is expensive and time-consuming to annotate a large amount of data,
the available labeled data to train a deep neural network is usually scarce, resulting in the …

In the machine learning domain, active learning is an iterative data selection algorithm for
maximizing information acquisition and improving model performance with limited training …

State-of-the-art Active Learning (AL) methods often encounter challenges associated with a
hysteretic learning process and an expensive data sampling mechanism. The former implies …

Sample augmentation is effective for improving the supervised performance of land-cover
classification with hyperspectral remote sensed image (HRSI) when the training samples are …

Computational prediction of absolute essential genes using machine learning has gained
wide attention in recent years. However, essential genes are mostly conditional and not …

Agriculture is one of great economic growth in India by producing variety of peanuts for
various co products development. Peanut Crop and weed identification is an important step …

In object detection, the cost of labeling is much high because it needs not only to confirm the
categories of multiple objects in an image but also to accurately determine the bounding …