An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

A fault diagnosis method for wireless sensor network nodes based on a belief rule base with adaptive attribute weights

Due to the harsh operating environment and ultralong operating hours of wireless sensor networks (WSNs), node failures are inevitable. Ensuring the reliability of the data collected by the WSN necessitates the utmost importance of diagnosing faults in nodes within the WSN. Typically, the initial step in the fault diagnosis of WSN nodes involves extracting numerical features from neighboring nodes. A solitary data feature is often assigned a high weight, resulting in the failure to effectively distinguish between all types of faults. Therefore, this study introduces an enhanced variant of the traditional belief rule base (BRB), called the belief rule base with adaptive attribute weights (BRB-AAW). First, the data features are extracted as input attributes for the model. Second, a fault diagnosis model for WSN nodes, incorporating BRB-AAW, is established by integrating parameters initialized by expert knowledge with the extracted data features. Third, to optimize the model's initial parameters, the projection covariance matrix adaptive evolution strategy (P-CMA-ES) algorithm is employed. Finally, a comprehensive case study is designed to verify the accuracy and effectiveness of the proposed method. The results of the case study indicate that compared with the traditional BRB method, the accuracy of the proposed model in WSN node fault diagnosis is significantly improved.

Air pollution prediction system using XRSTH-LSTM algorithm

Globally, there are significant worries about the rise in air pollution (AP) from substances that are harmful to human health, different living forms, and unfavorable environmental imbalances. To overcome the problem, AI-based prediction model is the need of the hour. Therefore, an attempt was made to develop a novel AP prediction system based on Xavier Reptile Switan-h-based Long-Short Term Memory (XRSTH-LSTM), which undergoes fine-tuning at various steps such as pre-processing, attribute extraction, and air-quality index prediction, in order to reduce computational cost and also to increase accuracy as well as precision. The dataset used to train the proposed methodology is Air Quality Data in India (2015-2020), taken from publically available sources Kaggle. The dataset includes information on the AQI and air quality at different stations in numerous Indian cities at hourly and daily intervals. The accuracy has been calculated using MSE, MAPE, RMSE, precision, recall, and F-measure. The robustness of the proposed model is tested using parameters such as negative predicted value and Mathew correlation coefficient. The proposed model is found to efficiently process air quality with an improved accuracy of 98.52% and precision of 99.79%, which is 0.74% higher than the existing state-of-the-art model. The testing findings showed that the proposed approach worked better than the current models and offered a higher rate of accuracy in predicting air pollution.

An Empirical Mode Decomposition Fuzzy Forecast Model for Air Quality

Air quality has a significant influence on people's health. Severe air pollution can cause respiratory diseases, while good air quality is beneficial to physical and mental health. Therefore, the prediction of air quality is very important. Since the concentration data of air pollutants are time series, their time characteristics should be considered in their prediction. However, the traditional neural network for time series prediction is limited by its own structure, which makes it very easy for it to fall into a local optimum during the training process. The empirical mode decomposition fuzzy forecast model for air quality, which is based on the extreme learning machine, is proposed in this paper. Empirical mode decomposition can analyze the changing trend of air quality well and obtain the changing trend of air quality under different time scales. According to the changing trend under different time scales, the extreme learning machine is used for fast training, and the corresponding prediction value is obtained. The adaptive fuzzy inference system is used for fitting to obtain the final air quality prediction result. The experimental results show that our model improves the accuracy of both short-term and long-term prediction by about 30% compared to other models, which indicates the remarkable efficacy of our approach. The research of this paper can provide the government with accurate future air quality information, which can take corresponding control measures in a targeted manner.

Evolutionary Optimization for the Belief-Rule-Based System: Method and Applications

Evolutionary optimization (EO) has been proven to be highly effective computation means in solving asymmetry problems in engineering practices. In this study, a novel evolutionary optimization approach for the belief rule base (BRB) system is proposed, namely EO-BRB, by constructing an optimization model and employing the Differential Evolutionary (DE) algorithm as its optimization engine due to its ability to locate an optimal solution for problems with nonlinear complexity. In the EO-BRB approach, the most representative referenced values of the attributes which are pre-determined in traditional learning approaches are to be optimized. In the optimization model, the mean squared error (MSE) between the actual and observed data is taken as the objective, while the initial weights of all the rules, the beliefs of the scales in the conclusion part, and the referenced values of the attributes are taken as the restraints. Compared with the traditional learning approaches for the BRB system, the EO-BRB approach (1) does not require transforming the numerical referenced values of the attributes into linguistic terms; (2) does not require identifying any initial solution; (3) does not require any mathematical deduction and/or case-specific information which verifies it as a general approach; and (4) can help downsize the BRB system while producing superior performances. Thus, the proposed EO-BRB approach can make the best use of the nonlinear modeling ability of BRB and the optimization superiority of the EO algorithms. Three asymmetry numerical and practical cases are studied to validate the efficiency of the proposed EO-BRB approach.

Self-Powered Wireless Sensor Matrix for Air Pollution Detection with a Neural Predictor

Predicting the status of particulate air pollution is extremely important in terms of preventing possible vascular and lung diseases, improving people’s quality of life and, of course, actively counteracting pollution magnification. Hence, there is great interest in developing methods for pollution prediction. In recent years, the importance of methods based on classical and more advanced neural networks is increasing. However, it is not so simple to determine a good and universal method due to the complexity and multiplicity of measurement data. This paper presents an approach based on Deep Learning networks, which does not use Bayesian sub-predictors. These sub-predictors are used to marginalize the importance of some data part from multisensory platforms. In other words—to filter out noise and mismeasurements before the actual processing with neural networks. The presented results shows the applied data feature extraction method, which is embedded in the proposed algorithm, allows for such feature clustering. It allows for more effective prediction of future air pollution levels (accuracy—92.13%). The prediction results shows that, besides using standard measurements of temperature, humidity, wind parameters and illumination, it is possible to improve the performance of the predictor by including the measurement of traffic noise (Accuracy—94.61%).

AutoBRB: An automated belief rule base model for pathologic complete response prediction in gastric cancer

Gastric cancer is one of the most severe malignant lesions. Neoadjuvant chemotherapy (NAC) has proven to be an effective method in gastric cancer treatment, and patients who achieved the pathologic complete response (pCR) after NAC can improve survival time further. To accurately predict pCR in an interpretable way, a new automated belief rule base (AutoBRB) model is developed with careful data analysis in this paper. In AutoBRB, to determine the referential values that are important for the rule building, both the information gain ratio and expert knowledge are used, while a table-based strategy is designed to initialize the belief degrees for each rule. Then, the differential evolution (DE) algorithm is employed and modified for model optimization to improve the model's performance. Finally, with the help of training data, an adaptive searching strategy is designed to set the confidence threshold for the final prediction. The experimental results demonstrate that AutoBRB shows a more reasonable performance on the prediction of pCR.

An Integrated Deep Learning and Belief Rule-Based Expert System for Visual Sentiment Analysis under Uncertainty

Visual sentiment analysis has become more popular than textual ones in various domains for decision-making purposes. On account of this, we develop a visual sentiment analysis system, which can classify image expression. The system classifies images by taking into account six different expressions such as anger, joy, love, surprise, fear, and sadness. In our study, we propose an expert system by integrating a Deep Learning method with a Belief Rule Base (known as the BRB-DL approach) to assess an image’s overall sentiment under uncertainty. This BRB-DL approach includes both the data-driven and knowledge-driven techniques to determine the overall sentiment. Our integrated expert system outperforms the state-of-the-art methods of visual sentiment analysis with promising results. The integrated system can classify images with 86% accuracy. The system can be beneficial to understand the emotional tendency and psychological state of an individual.

Fusion of GF and MODIS Data for Regional-Scale Grassland Community Classification with EVI2 Time-Series and Phenological Features

Satellite-borne multispectral data are suitable for regional-scale grassland community classification owing to comprehensive coverage. However, the spectral similarity of different communities makes it challenging to distinguish them based on a single multispectral data. To address this issue, we proposed a support vector machine (SVM)–based method integrating multispectral data, two-band enhanced vegetation index (EVI2) time-series, and phenological features extracted from Chinese GaoFen (GF)-1/6 satellite with (16 m) spatial and (2 d) temporal resolution. To obtain cloud-free images, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) algorithm was employed in this study. By using the algorithm on the coarse cloudless images at the same or similar time as the fine images with cloud cover, the cloudless fine images were obtained, and the cloudless EVI2 time-series and phenological features were generated. The developed method was applied to identify grassland communities in Ordos, China. The results show that the Caragana pumila Pojark, Caragana davazamcii Sanchir and Salix schwerinii E. L. Wolf grassland, the Potaninia mongolica Maxim, Ammopiptanthus mongolicus S. H. Cheng and Tetraena mongolica Maxim grassland, the Caryopteris mongholica Bunge and Artemisia ordosica Krasch grassland, the Calligonum mongolicum Turcz grassland, and the Stipa breviflora Griseb and Stipa bungeana Trin grassland are distinguished with an overall accuracy of 87.25%. The results highlight that, compared to multispectral data only, the addition of EVI2 time-series and phenological features improves the classification accuracy by 9.63% and 14.7%, respectively, and even by 27.36% when these two features are combined together, and indicate the advantage of the fine images in this study, compared to 500 m moderate-resolution imaging spectroradiometer (MODIS) data, which are commonly used for grassland classification at regional scale, while using 16 m GF data suggests a 23.96% increase in classification accuracy with the same extracted features. This study indicates that the proposed method is suitable for regional-scale grassland community classification.

A comparative study of two methods to predict the incidence of hepatitis B in Guangxi, China

In recent years, the incidence of hepatitis B (HB) in Guangxi is higher than that of the national level; it has been increasing, so it is urgent to do a good predictive research of HB incidence, which can help analyze the early warning of hepatitis B in Guangxi, China. In the study, the feasibility of predicting HB incidence in Guangxi by autoregressive integrated moving average (ARIMA) model method and Elman neural network (ElmanNN) method was discussed respectively, and the prediction accuracy of the two models was compared. Finally, we established the ARIMA (0, 1, 1) model and ElmanNN with 8 neurons. Both ARIMA (0, 1, 1) model and ElmanNN model had good performance, and their prediction accuracy were high. The fitting and prediction root-mean-square error (RMSE) and mean absolute error (MAE) of ElmanNN were smaller than those of ARIMA (0, 1, 1) model, which indicated that ElmanNN was superior to ARIMA (0, 1, 1) model in predicting the incidence of hepatitis B in Guangxi. Based on the ElmanNN, the HB incidence from September 2019 to December 2020 in Guangxi was predicted, the predicted results showed that the incidence of HB in 2020 was slightly higher than that in 2019 and the change trend was similar to that in 2019, for 2021 and beyond, the ElmanNN model could be used to continue the predictive analysis.