This paper addresses the challenge of fault detection in Wireless Sensor Networks (WSNs), commonly used in fields like environmental monitoring and healthcare. WSNs, prone to various faults due to their deployment in unpredictable environments, require effective solutions for fault detection. Traditional machine learning approaches show limitations such as unsuitability for streaming data and the detection of a single fault type. We propose the use of deep neural networks, particularly Recurrent Neural Networks (RNNs), for fault detection in WSNs, focusing on temperature and humidity data. The paper emphasizes the importance of careful model selection, tuning, and thorough evaluation to enhance the accuracy and robustness of fault detection in real-world WSN applications.

Vietnam promotes the implementation of STEM education in the general education program to help students focus on experiential activities and apply knowledge to

Clinical outcome analysis using patient medical data facilitates clinical decision-making and increases prognostic accuracy. Recently, deep learning (DL) with learning big data features has shown expert-level accuracy in predicting clinical outcomes. Many of these sophisticated machine learning models, however, lack interpretability, creating significant trust-related healthcare issues. This necessarily requires the need for interpretable AI systems capable of explaining their decisions. In this respect, the paper proposes an interpretable classifier of the adaptive neuro-fuzzy inference method (iANFIS), which combines the fuzzy inference system with critical rule selection by attention mechanism. The rule-based processing of ANFIS helps the user to understand the behavior of the proposed model. The essential activated rule and the most important input features that predict the outcome are identified by the attention-based rule selector. We conduct two experiments with two cancer diagnostic datasets for verifying the performance of the proposed iANFIS. By using recursive rule elimination (RRE) to prune fuzzy rules, the model’s complexity is significantly reduced while preserving system efficiency that makes it more interpretable.