Artificial intelligence-driven ensemble deep learning models for smart monitoring of indoor activities in IoT environment for people with disabilities

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 5, 2025

Disabled persons demanding healthcare is a developing global occurrence. The support in longer-term care includes nursing, intricate medical, recovery, and social help services. price large, but advanced technologies can aid decreasing expenditure by certifying effective health services enhancing the superiority of life. transformative latent Internet Things (IoT) prolongs existence nearly one billion worldwide with disabilities. By incorporating smart devices technologies, IoT provides solutions to tackle numerous tasks challenged individuals disabilities promote equality. Human activity detection methods are technical area which studies classification actions or movements an individual achieves over recognition signals directed smartphones wearable sensors images video frames. They efficient functions actions, observing crucial functions, tracking. Conventional machine learning deep approaches effectively detect human activity. This study develops designs metaheuristic optimization-driven ensemble model for monitoring indoor activities disabled (MOEM-SMIADP) model. proposed MOEM-SMIADP concentrates on detecting classifying using applications physically people. First, data preprocessing performed min-max normalization convert input into useful format. Furthermore, marine predator algorithm employed feature selection. For activities, utilizes three classifiers, namely graph convolutional network model, long short-term memory sequence-to-sequence (LSTM-seq2seq) method, autoencoder. Eventually, hyperparameter tuning accomplished improved coati optimization enhance outcomes models. A wide range experiments was accompanied endorse performance technique. validation technique portrayed superior accracy value 99.07% existing methods.

Language: Английский

Structural health monitoring of arch dams with deep learning: a comparative study of recurrent neural networks in daily, bi-monthly, and monthly predictions

Journal of Civil Structural Health Monitoring, Journal Year: 2025, Volume and Issue: unknown

Published: April 13, 2025

Language: Английский

Multivariate probabilistic prediction of dam displacement behaviour using extended Seq2Seq learning and adaptive kernel density estimation

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103343 - 103343

Published: April 18, 2025

Language: Английский