By learning the relevant features of clinical images along with the relationships between them, the neural network can outperform more traditional methods.

Synthetic cannabinoids, a class of new psychoactive substances, have emerged as a significant public health and social stability threat due to their structural diversity, rapid iteration, and stronger ...

To address the shortcomings of classical chaotic time series in image encryption algorithms in terms of low complexity, fewer control parameters, and limited range of value domains, this paper ...

Personnel safety is particularly important in public places where security checks are required, such as airports, train stations and subway stations. In view of the features of disorderly placement of ...

In this paper, we introduce an azimuth estimation algorithm that utilizes deep learning and deconvolution. The algorithm combines traditional azimuth estimation techniques for feature extraction, ...

This study addresses an energy-efficient multiobjective distributed assembly permutation flowshop scheduling problem with sequence dependent setup time. The objectives are to minimize the maximum ...

These findings underscore the efficacy of advanced image processing and deep learning in the early and accurate detection of Alzheimer’s disease.

Motor symptoms such as tremor and bradykinesia can develop concurrently in Parkinson’s disease; thus, the ideal home monitoring system should be capable of tracking symptoms continuously despite ...

Over the past few years, stroke has been among the top ten causes of death in Taiwan. Stroke symptoms belong to an emergency condition, the sooner the patient is treated, the more chance the patient ...

An Efficient Encoding Spectral Information in Hyperspectral Images for Transfer Learning of Mask R-CNN for Instance Segmentation of Tomato Sepals ...

This work tackles an integrated order batching, picker assignment, batch sequencing, and picker routing problem in warehouse environments. A Learning-Aided Iterated Local Search (LILS) is proposed to ...

This research presents a comprehensive comparative analysis of various pre-trained backbone models and machine learning techniques for output layers in convolutional neural networks (CNNs) applied to ...