Convolutional neural networks (CNNs) have gained prominence in the research literature
on image classification over the last decade. One shortcoming of CNNs, however, is their …

Pest infestation is a major cause of crop damage and lost revenues worldwide. Automatic
identification of invasive insects would significantly speed up the recognition of pests and …

Abstract Background and Objective To achieve the full potential of deep learning (DL)
models, such as understanding the interplay between model (size), training strategy, and …

In this work, we present an ensemble of descriptors for the classification of virus images
acquired using transmission electron microscopy. We trained multiple support vector …

A problem with convolutional neural networks (CNNs) is that they require large datasets to
obtain adequate robustness; on small datasets, they are prone to overfitting. Many methods …

Convolutional Neural Networks (CNNs) are used in many domains but the requirement of
large datasets for robust training sessions and no overfitting makes them hard to apply in …

Background and objective Convolutional neural networks (CNNs) offer human experts-like
performance and in the same time they are faster and more consistent in their prediction …

Features play a crucial role in computer vision. Initially designed to detect salient elements
by means of handcrafted algorithms, features now are often learned using different layers in …

The present work illustrates the promising intervention of smart diagnostics devices through
artificial intelligence (AI) and mechanobiological approaches in health care practices. The …

鉴于单一特征在瓷砖种类较多的情况下, 存在对瓷砖表面缺陷内容表达不明显,
导致复杂瓷砖识别率较低. 针对这个问题, 在词袋模型(BoF) 框架的基础上 …