Enhancing hotel efficiency and environmental health with biosensors and big data analytics

被引：0
|
作者：
Shu, Boyang [1 ,2 ]
Ruan, Xianbing [1 ]
Li, Pan [3 ]
机构：
[1] School of Business Administration, Zhongnan University of Economics and Law, Wuhan,430073, China
[2] Wuhan Branch of China Tourism Academy, Wuhan,430079, China
[3] School of Economics and Business Administration, Central China Normal University, Wuhan,430079, China
来源：
MCB Molecular and Cellular Biomechanics | 2024年 / 21卷 / 01期
关键词：
The hospitality industry embraces digital technologies to enhance efficiency; guest satisfaction; and environmental sustainability. Integrating biosensors and big data analytics allows hotels to monitor operational processes while maintaining high ecological health standards. However; the impact on environmental health is not fully understood; leading to suboptimal decisions and missed opportunities. This research proposes a novel Tabu Search Drove-Extended Multi-Layer Perceptron (TSD-EMLP) to evaluate the effectiveness of digital operations in hotels through the use of biosensors and big data for predicting hotel environmental conditions. Initially; smart biosensors are deployed in key areas and heating; ventilation; and air conditioning denoted as HVAC systems in the hotel; then the water quality data; noise levels; lighting quality; waste management data; operational data; financial and operational effectiveness data are collected and transmitted to the Internet of Things (IoT) cloud for further process. Interquartile Range (IQR) utilizes the IoT cloud data to remove sensor errors and anomalous events to frame outlier data for the Wavelet Packet Transform (WPT) feature extraction process to decompose sensor data into detailed frequency bands; allowing for precise analysis of complex environmental signals; TSD-EMLP model predicts the environmental health in hotels using the decomposed data. The results demonstrate reducing energy consumption; ventilation systems; indoor environment control; and guest satisfaction; improving air quality; and adjusting environmental settings based on real-time environmental conditions through TSD-EMLP optimized settings. TSD-EMLP classification model achieved high accuracy in predicting hotel environmental health; with a low improvement in guest satisfaction metrics. Copyright © 2024 by author(s);
D O I：
10.62617/mcb.v21i1.336
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