Individual flexibility and workplace conflict: cloud-based data collection and fusion of neural networks

Using a novel hybrid-fusion neural network technique and cloud computing environment, this paper examines the relationship between individual flexibility, workplace interpersonal problems, and construction worker safety. The population is made up of construction firms based in Mashhad, Iran. This is an applied study, and the data collection approach used is a descriptive survey. The data was obtained using cloud-based storage, data analysis techniques, and data processing tools. The primary objective is to investigate the critical nature of personal safety on construction sites and techniques for minimizing construction accidents. The statistical population consisted of Mashhad, Iran-based construction firms. Due to the impossibility of studying the entire research population, a sample of n = 200 participants was chosen for this study. Following questionnaire distribution, 151 valid questionnaires were gathered. The primary study instrument was a 28-item questionnaire devised by the researcher based on the Questionnaire on the Experience and Evaluation of Work and occupational health. On a Likert scale, the things were rated. Cronbach's alpha coefficient was also used to determine the questionnaire's validity. SPSS-26 software was used to analyze the data in a MATLAB environment. The findings indicate that interpersonal conflict, workplace stress, supervisor involvement, physical injuries, dangerous incidents, and coworker participation are all connected with worker safety at a significant level of p 0.05. One of the research's advances is the use of new hybrid-fusion neural networks. Cloud computing has performed admirably in this study, taking into account the massive volume of data for speedy analysis and the development of a mechanism as an appropriate application. Additionally, the study was motivated by a dearth of comprehensive research on individual flexibility and interpersonal conflicts among construction workers.