Smart environment is an efficient and cost-effective way to afford intelligent supports for the elderly people. Human activity recognition is a crucial aspect of the research field of smart environments, and it has attracted widespread attention lately. The goal of this study is to develop an effective sensor-based human activity recognition model based on the belief-rule-based system (BRBS), which is one of representative rule-based expert systems. Specially, a new belief rule base (BRB) modeling approach is proposed by taking into account the self- organizing rule generation method and the multi-temporal rule representation scheme, in order to address the problem of combination explosion that existed in the traditional BRB modelling procedure and the time correlation found in continuous sensor data in chronological order. The new BRB modeling approach is so called self-organizing and multi-temporal BRB (SOMT-BRB) modeling procedure. A case study is further deducted to validate the effectiveness of the SOMT-BRB modeling procedure. By comparing with some conventional BRBSs and classical activity recognition models, the results show a significant improvement of the BRBS in terms of the number of belief rules, modelling efficiency, and activity recognition accuracy.

Cumulative belief rule-based system (CBRBS) is a recent representative of explainable artificial intelligence (XAI). However, the use of CBRBS as XAI still faces many challenges, e.g., over-reliance on expert experience and applying unreasonable rule synthesis in the existing modeling process. Hence, a novel modeling approach is proposed for constructing CBRBS in the aim of providing a better XAI, in which a joint optimization model is proposed first to describe the mathematical model of parameter and structure optimization, and the corresponding algorithm is further designed to automatically achieve the joint optimization of CBRBS. Afterward, a domain-based calculation method of synthesis factor is proposed to develop a new rule synthesis method for CBRBS, which not only achieves the reduction of inefficient and inconsistent rules but also takes into account interpretability and generalization ability. In experimental analysis, the proposed modeling approach is employed to construct CBRBS for handling rice taste assessment and benchmark classification problems. The comparison results show that the proposed approach makes it possible for CBRBS to achieve a good balance between model complexity and inference accuracy. More importantly, the resulting CBRBS has better accuracy and lower complexity than some existing rule-based systems and classical classifiers.

Big data classification problems have drawn great attention from diverse fields, and many classifiers have been developed. Among those classifiers, the extended belief rule-based system (EBRBS) has shown its potential in both big data and multiclass situations, while the time complexity and computing efficiency are two challenging issues to be handled in EBRBS. As such, three improvements of EBRBS are proposed first in this paper to decrease the time complexity and computing efficiency of EBRBS for multiclass classification under the assumption of large amount of data, including the strategy to skip rule weight calculation, a simplified evidential reasoning algorithm, and the domain division-based rule reduction method. This turns out to be a micro version of the EBRBS, called Micro-EBRBS. Moreover, one of commonly used cluster computing, named Apache Spark, is then applied to implement the parallel rule generation and inference schemes of the Micro-EBRBS for big data multiclass classification problems. The comparative analyses of experimental studies demonstrate that the Micro-EBRBS not only can obtain a desired accuracy but also has the comparatively better time complexity and computing efficiency than some popular classifiers, especially for multiclass classification problems.