Accidents occur in the construction industry as a result of non-compliance with personal
protective equipment (PPE). As a result of diverse environments, it is difficult to detect PPE …

This paper presents a novel approach to sentiment analysis specifically customized for
predicting stock market movements, bypassing the need for external dictionaries that are …

This paper provides a comprehensive survey on recent advances in deep learning (DL)
techniques for the channel coding problems. Inspired by the recent successes of DL in a …

The development of 2D-to-3D approaches for 3D monocular single-frame human pose
estimation faces challenges related to noisy input and failure to capture long-range joint …

This study introduces a novel predictive methodology for diagnosing and predicting gear
problems in DC motors. Leveraging AdaBoost with weak classifiers and regressors, the …

Atmospheric aerosol significantly affects the climate environment and public health, and
Aerosol Optical Depth (AOD) is a fundamental optical characteristic parameter of aerosols …

Nowadays, cluster analyses are widely used in mental health research to categorize student
stress levels. However, conventional clustering methods experience challenges with large …

The purpose is to optimize emotion recognition capabilities in the tourism industry during
emergency events and enhance management efficiency. Hence, based on the principles of …

This paper proposes a resource-efficient keyword spotting (KWS) system based on a
convolutional neural network (CNN). The end-to-end KWS process is performed based …

Natural disasters pose significant challenges to disaster management agencies,
necessitating the development of efficient solutions to reduce risks and mitigate the impact …