RT editorial
T1 Preface to JAISE 15(2)
A1 Augusto, Juan Carlos
A1 Aghajan, Hamid
A1 Muñoz Ortega, Andrés
A2 Ingeniería Informática
AB This regular issue of JAISE is composed of four articles:A systematic literature review of Smart Home Technology Acceptance, by N. Daruwala and U. Oberst, ap-plied the Technological Acceptance Model to analyse acceptance of Smart Home Technology (SHT). Perceivedusefulness was the most prominent influencing factor on a positive view of SHT. Other subsidiary models alsohighlighted the influence of compatibility, attitude, and cost/price. The literature analysed in the article showeda predominance in the so-called “western” world; however, a recent increase and shift in publications has beenobserved from the Asia/Africa regions.A model-based simulator for smart homes: Enabling reproducibility and standardization, by S. Venerusoet al., tackles the need for data in training certain algorithms by providing a model-based simulator capable of gen-erating synthetic datasets that emulate the characteristics of the vast majority of real datasets, using the eXtensibleEvent Stream – XES international standard commonly used for representing events and validating the results withtwo real scenarios from the literature.Effects of environmental control before sleeping on autonomic nervous activity and sleep: A pilot study,by Y. Matsuhisa et al., discusses the results of influencing the environment through temperature, light, air flow,music, and aroma, before sleeping, and assesses the data collected in an experiment involving sixteen participants.By measuring the level of sleep achieved in relation to the participant’s level of relaxation or stress before sleeping,the authors determined that the mentioned environmental improvements can offer beneficial effects in the depth ofsleep.Computational methods for predicting human behaviour in smart environments, by R. Dunne et al., providesa literature review focused on computational methods of human behaviour prediction. It covers a range of differentmodes to represent behaviour (e.g., time-series event tables, graph data structures, and probabilistic models), andnotes the predominance of machine learning, probabilistic graphical models, and statistical and trend analysis inpredicting events in a data sequence
PB IOS Press BV
SN 1876-1364
YR 2023
FD 2023
LK http://hdl.handle.net/10498/31475
UL http://hdl.handle.net/10498/31475
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 11-nov-2026