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

Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT). Reasoning is applied on such sensor data in order to compute prediction. Generating a health warning that is based on prediction of atmospheric pollution, planning timely evacuation of people from vulnerable areas with respect to prediction of natural disasters, etc., are the use cases of sensor data stream where prediction is vital to protect people and assets. Thus, prediction accuracy is of paramount importance to take preventive steps and avert any untoward situation. Uncertainties of sensor data is a severe factor which hampers prediction accuracy. Belief Rule Based Expert System (BRBES), a knowledge-driven approach, is a widely employed prediction algorithm to deal with such uncertainties based on knowledge base and inference engine. In connection with handling uncertainties, it offers higher accuracy than other such knowledge-driven techniques, e.g., fuzzy logic and Bayesian probability theory. Contrarily, Deep Learning is a data-driven technique, which constitutes a part of Artificial Intelligence (AI). By applying analytics on huge amount of data, Deep Learning learns the hidden representation of data. Thus, Deep Learning can infer prediction by reasoning over available data, such as historical data and sensor data streams. Combined application of BRBES and Deep Learning can compute prediction with improved accuracy by addressing sensor data uncertainties while utilizing its discovered data pattern. Hence, this paper proposes a novel predictive model that is based on the integrated approach of BRBES and Deep Learning. The uniqueness of this model lies in the development of a mathematical model to combine Deep Learning with BRBES and capture the nonlinear dependencies among the relevant variables. We optimized BRBES further by applying parameter and structure optimization on it. Air pollution prediction has been taken as use case of our proposed combined approach. This model has been evaluated against two different datasets. One dataset contains synthetic images with a corresponding label of PM2.5 concentrations. The other one contains real images, PM2.5 concentrations, and numerical weather data of Shanghai, China. We also distinguished a hazy image between polluted air and fog through our proposed model. Our approach has outperformed only BRBES and only Deep Learning in terms of prediction accuracy.

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A Total Crop-Diagnosis Platform Based on Deep Learning Models in a Natural Nutrient Environment

Applied Sciences ◽

10.3390/app8101992 ◽

2018 ◽

Vol 8 (10) ◽

pp. 1992 ◽

Cited By ~ 2

Author(s):

YiNa Jeong ◽

SuRak Son ◽

SangSik Lee ◽

ByungKwan Lee

Keyword(s):

Deep Learning ◽

Data Storage ◽

Good Condition ◽

Sensor Data ◽

Weather Data ◽

Learning Models ◽

Weather Information ◽

Nutrient Environment ◽

Total Crop ◽

Crop Diagnosis

This paper proposes a total crop-diagnosis platform (TCP) based on deep learning models in a natural nutrient environment, which collects the weather information based on a farm’s location information, diagnoses the collected weather information and the crop soil sensor data with a deep learning technique, and notifies a farm manager of the diagnosed result. The proposed TCP is composed of 1 gateway and 2 modules as follows. First, the optimized farm sensor gateway (OFSG) collects data by internetworking sensor nodes which use Zigbee, Wi-Fi and Bluetooth protocol and reduces the number of sensor data fragmentation times through the compression of a fragment header. Second, the data storage module (DSM) stores the collected farm data and weather data in a farm central server. Third, the crop self-diagnosis module (CSM) works in the cloud server and diagnoses by deep learning whether or not the status of a farm is in good condition for growing crops according to current weather and soil information. The TCP performance shows that the data processing rate of the OFSG is increased by about 7% compared with existing sensor gateways. The learning time of the CSM is shorter than that of the long short-term memory models (LSTM) by 0.43 s, and the success rate of the CSM is higher than that of the LSTM by about 7%. Therefore, the TCP based on deep learning interconnects the communication protocols of various sensors, solves the maximum data size that sensor can transfer, predicts in advance crop disease occurrence in an external environment, and helps to make an optimized environment in which to grow crops.

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Air Pollution Prediction with Multi-Modal Data and Deep Neural Networks

Remote Sensing ◽

10.3390/rs12244142 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4142

Author(s):

Jovan Kalajdjieski ◽

Eftim Zdravevski ◽

Roberto Corizzo ◽

Petre Lameski ◽

Slobodan Kalajdziski ◽

...

Keyword(s):

Air Pollution ◽

Urban Areas ◽

Data Augmentation ◽

Class Imbalance ◽

Pollution Monitoring ◽

Sensor Data ◽

Weather Data ◽

Generative Adversarial Networks ◽

Class Imbalance Problem ◽

Air Pollution Monitoring

Air pollution is becoming a rising and serious environmental problem, especially in urban areas affected by an increasing migration rate. The large availability of sensor data enables the adoption of analytical tools to provide decision support capabilities. Employing sensors facilitates air pollution monitoring, but the lack of predictive capability limits such systems’ potential in practical scenarios. On the other hand, forecasting methods offer the opportunity to predict the future pollution in specific areas, potentially suggesting useful preventive measures. To date, many works tackled the problem of air pollution forecasting, most of which are based on sequence models. These models are trained with raw pollution data and are subsequently utilized to make predictions. This paper proposes a novel approach evaluating four different architectures that utilize camera images to estimate the air pollution in those areas. These images are further enhanced with weather data to boost the classification accuracy. The proposed approach exploits generative adversarial networks combined with data augmentation techniques to mitigate the class imbalance problem. The experiments show that the proposed method achieves robust accuracy of up to 0.88, which is comparable to sequence models and conventional models that utilize air pollution data. This is a remarkable result considering that the historic air pollution data is directly related to the output—future air pollution data, whereas the proposed architecture uses camera images to recognize the air pollution—which is an inherently much more difficult problem.

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

Algorithms ◽

10.3390/a14070213 ◽

2021 ◽

Vol 14 (7) ◽

pp. 213

Author(s):

Sharif Noor Zisad ◽

Etu Chowdhury ◽

Mohammad Shahadat Hossain ◽

Raihan Ul Islam ◽

Karl Andersson

Keyword(s):

Deep Learning ◽

Expert System ◽

Sentiment Analysis ◽

State Of The Art ◽

Integrated System ◽

Data Driven ◽

Psychological State ◽

Rule Base ◽

Belief Rule Base ◽

Analysis System

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.

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Exoskeleton Follow-Up Control Based on Parameter Optimization of Predictive Algorithm

Applied Bionics and Biomechanics ◽

10.1155/2021/8850348 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Shijia Zha ◽

Tianyi Li ◽

Lidan Cheng ◽

Jihua Gu ◽

Wei Wei ◽

...

Keyword(s):

Prediction Accuracy ◽

Target Position ◽

Interaction Force ◽

Human Motion ◽

Sensor Data ◽

Prediction Algorithm ◽

Human Machine Interaction ◽

Predictive Algorithm ◽

Machine Interaction

The prediction of sensor data can help the exoskeleton control system to get the human motion intention and target position in advance, so as to reduce the human-machine interaction force. In this paper, an improved method for the prediction algorithm of exoskeleton sensor data is proposed. Through an algorithm simulation test and two-link simulation experiment, the algorithm improves the prediction accuracy by 14.23 ± 0.5%, and the sensor data is smooth. Input the predicted signal into the two-link model, and use the calculated torque method to verify the prediction accuracy data and smoothness. The simulation results showed that the algorithm can predict the joint angle of the human body and can be used for the follow-up control of the swinging legs of the exoskeleton.

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An Integrated Real-Time Deep Learning and Belief Rule Base Intelligent System to Assess Facial Expression Under Uncertainty

2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR) ◽

10.1109/icievicivpr48672.2020.9306622 ◽

2020 ◽

Author(s):

Tawsin Uddin Ahmed ◽

Mohammad Newaj Jamil ◽

Mohammad Shahadat Hossain ◽

Karl Andersson ◽

Mohammed Sazzad Hossain

Keyword(s):

Deep Learning ◽

Facial Expression ◽

Real Time ◽

Intelligent System ◽

Rule Base ◽

Belief Rule Base

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Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327908666180727123657 ◽

2019 ◽

Vol 9 (1) ◽

pp. 16-31

Author(s):

Kyungkoo Jun

Keyword(s):

Fourier Transform ◽

Deep Learning ◽

Short Term Memory ◽

Window Size ◽

Sensor Data ◽

Data Sets ◽

Data Set ◽

Proposed Model ◽

Testing Data ◽

Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

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A Fusion Prognostic Method for Remaining Useful Life Prediction Based on an Extended Belief Rule Base and Particle Filters

IEEE Access ◽

10.1109/access.2021.3079301 ◽

2021 ◽

pp. 1-1

Author(s):

Bincheng Wen ◽

Mingqing Xiao ◽

Guanghao Wang ◽

Zhao Yang ◽

Jianfeng Li ◽

...

Keyword(s):

Life Prediction ◽

Particle Filters ◽

Remaining Useful Life ◽

Rule Base ◽

Belief Rule Base ◽

Useful Life

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A new approximate belief rule base expert system for complex system modelling

Decision Support Systems ◽

10.1016/j.dss.2021.113558 ◽

2021 ◽

pp. 113558

Author(s):

You Cao ◽

Zhijie Zhou ◽

Changhua Hu ◽

Shuaiwen Tang ◽

Jie Wang

Keyword(s):

Complex System ◽

Expert System ◽

Rule Base ◽

System Modelling ◽

Belief Rule Base

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Representation Learning for Fine-Grained Change Detection

Sensors ◽

10.3390/s21134486 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4486

Author(s):

Niall O’Mahony ◽

Sean Campbell ◽

Lenka Krpalkova ◽

Anderson Carvalho ◽

Joseph Walsh ◽

...

Keyword(s):

Deep Learning ◽

Change Detection ◽

Model Calibration ◽

State Of The Art ◽

Representation Learning ◽

Machine Intelligence ◽

The State ◽

Sensor Data ◽

Fine Grained ◽

Learning Techniques

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

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