With the rapid advancement of global development, there is an increasing demand for health monitoring technologies. Human activity recognition and monitoring systems offer a powerful means of identifying daily movement patterns, which helps in understanding human behaviors and provides valuable insights for life management. This paper explores the issue of human motion state recognition using accelerometers and gyroscopes, proposing a human activity recognition system based on a majority decision model that integrates multiple machine learning algorithms. In this study, the majority decision model was compared with an integer programming model, and the accuracy was assessed through a confusion matrix and cross-validation based on a dataset generated from 10 volunteers performing 12 different human activities. The average activity recognition accuracy of the majority decision model can be as high as 91.92%. The results underscore the superior accuracy and efficiency of the majority decision model in human activity state recognition, highlighting its potential for practical applications in health monitoring systems.
Land Surface Temperature (LST), as a core variable in the coupling of land–atmosphere energy transfers and ecological responses, relies heavily on the global coverage capacity of thermal infrared remote sensing (TIR-LST) for dynamic monitoring. Currently, the time reconstruction…
Alzheimer’s disease (AD) is a neurodegenerative disease that results in a loss of cognitive functions. The early discovery of it can potentially stop or decrease the severity of AD. Extensive research has been conducted to find AD biomarkers. In recent years, due to the developme…
Software defect prediction (SDP) has emerged as a crucial task in ensuring software quality and reliability. The early and accurate identification of defect-prone modules significantly reduces maintenance costs and improves system performance. In this study, we introduce a novel…
The aim of this article is to develop a machine learning (ML)-based predictive model for industrial accidents in the energy sector. The dataset used in this study was obtained from the Kaggle platform and consists of summaries derived from reports of occupational incidents result…
This study aims to analyze online job postings using machine learning-based, semantic approaches and to identify the expertise roles and competencies required for big data professions. The methodology of this study employs latent Dirichlet allocation (LDA), a probabilistic topic…
In this paper, the application of machine learning and deep learning algorithms for fault and failure detection in maritime systems is examined, specifically focusing on the detection of pipe ruptures in a ship’s saltwater firefighting (FiFi) system using pressure sensor data. Ne…