In the intricate network environment, the secure transmission of medical images faces
challenges such as information leakage and malicious tampering, significantly impacting the …

This study explores the potential of leveraging tourist-generated images to enhance
engagement and loyalty at intercity tourist sites through a photo challenge game. With over …

This research addresses the critical challenge of disaster waste management, a growing
concern exacerbated by the increasing frequency and intensity of natural disasters like …

The emergent of machine learning (ML) and deep learning (DL) methods have created a
substantial window of chance for their use in the industry. Given that both ML and DL …

Industrial defect detection plays a critical role in controlling product quality. Obtaining
industrial defects with diverse and balanced classes in natural environments is often …

Skin disease cases are becoming more common, and diagnosing these diseases in a clinic
is never an easy task. A deep learning (DL) based model was previously used to diagnose …

The convolutional neural network has been proven to be a robust recognition and diagnosis
model for modelling diseases. In general, a convolutional neural network consists of feature …

This paper presents an improved swarming algorithm that enhances low-illumination
images. The algorithm combines a hybrid Harris Eagle algorithm with double gamma (IHHO …

Simple Summary This study aimed to develop a deep learning model based on a
convolutional neural network (CNN) architecture to predict Invasive Ductal Carcinoma (IDC) …

Deep learning (DL) models play a crucial role in medical image analysis, with their
performance reliant on the scale and diversity of available training data. However, medical …