In the past (few) years a lot of research and development projects were proposed concerning the implementation of smart home environment, sometimes even called an intelligent environment, comforting people who use various adaptations of internet of things and services. This paper advocates an opinion that a set of electronically controlled smart things must be intellectualized using human-type reasoning. A novel approach and new algorithms for the hierarchical fuzzy training, retraining, and selftraining for intellectualized home environments are proposed in this paper. Training algorithms based on fuzzy logic use top-down hierarchical analysis of home situations under consideration to conquer the curse of increasing number of rules. A successful combination of crisp algorithms for the identification of presence/absence of users in the environment with fuzzy logic-based algorithms for corresponding rules subsets development and processing enables the number of necessary rules to decrease significantly. In the paper a case is presented with the starting number of 2500 rules which later diminished approximately 5 times. For the first time, the changes in users' wishes are taken into account during the retraining process. An entirely new ability of the system was investigated, and a fuzzy logic-based algorithm for initiating a self-training process without any a priori information is developed as well. The vitality and efficiency of the proposed methodology was tested and simulated on a specialized virtual software/hardware modeling system. The proposed and simulated algorithms are delivered for use in two industrial projects.
The use of artificial intelligence in geriatrics is very promising and relevant, as the diagnosis of a geriatric patient is a complex, experience-based, and time-consuming process that involves a variety of questionnaires and subjective and inaccurate patient responses. This paper proposes the explainable artificial intelligence-based (XAI) clinical decision support system (CDSS) to assess nutrition-related factors (symptoms) and to determine the likelihood of geriatric patient health risks associated with four syndromes: malnutrition, oropharyngeal dysphagia, dehydration, and eating disorders in dementia. The proposed system’s prototype was tested under real conditions at the geriatric department of Lithuanian University of Health Sciences Kaunas Hospital. The subjects of this study were 83 geriatric patients with various health conditions. The assessments of the nutritional status and syndromes of the patients provided by the CDSS were compared with the diagnoses of the physicians obtained using standard assessment methods. The results show that proposed CDSS can efficiently diagnose nutrition-related geriatric syndromes with high accuracy: 87.95% for malnutrition, 87.95% for oropharyngeal dysphagia, 90.36% for eating disorders in dementia, and 86.75% for dehydration. The research confirms that the proposed XAI-based CDSS is an effective tool, able to assess nutrition-related health risk factors and their dependencies and, in some cases, makes even a more accurate decision than a less experienced physician.
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