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An Improved Belief Rule-Based Expert System with an Enhanced Learning Mechanism
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0002-3090-7645
2020 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Ett förbättrat BRB-baserat expertsystem med en utvecklad inlärningsmekanism (Swedish)
Abstract [en]

Belief rule-based expert systems (BRBESs) are widely used in various domains which provide an integrated framework to handle qualitative and quantitative data by addressing several kinds of uncertainty. The correctness of the data significantly affects the accuracy of the BRBESs. Learning plays an important role in BRBESs to upgrade their knowledge base and parameters values, necessary to improve the accuracy of prediction. In addition, comparatively larger datasets hinder the accuracy of BRBESs.

Therefore, this doctoral thesis focuses on four different aspects of BRBESs, namely, the accuracy of data, multi-level complex problem, learning of BRBES, and accuracy of prediction for comparatively large dataset.

First, the accuracy of data acquisition plays an important role, necessary to ensure accurate prediction in BRBESs. Therefore, the data coming from sensors contain anomaly due to various types of uncertainty, which hampers the accuracy of prediction. Hence, anomalous data needs to be filtered out. A novel algorithm based on belief rule base for detecting the anomaly from sensor data has been proposed in this thesis.

Second, BRBESs can be considered to handle the multi-level complex problem like the prediction of a flood as they address different types of uncertainty. A web based BRBES was developed for predicting flood which provides better usability, allows handling of larger numbers of rule bases, and facilitates scalability. In addition, a learning mechanism for multi-level BRBESs has been developed by taking account of flooding, considered as an example of a complex problem. This learning mechanism for multi-level BRBES demonstrates promising results in comparison to other machine learning techniques including, Long Short-term Memory (LSTM), Artificial neural network (ANN), Support Vector Machine (SVM), and Linear regression.

Third, different optimal training procedures used to support learning in BRBESs. Among these, Differential Evolution (DE) appears performing better in comparison to other evolution algorithms, including Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA). However, DE's performance depends considerably in assigning near-optimal values to its control parameters. Therefore, an enhanced belief rule-based adaptive differential evolution (eBRBaDE) proposed in this thesis with the capability of ensuring balanced exploitation and exploration in the search space by providing near-optimal values to the DE's control parameters. The capability of accurate prediction of eBRBaDE has been demonstrated by taking account of power usage effectiveness (PUE) of datacentre in comparison to other evolutionary algorithms used in BRBESs optimal training procedures.

Fourth, the recent advancement of sensor technologies enabled acquiring of a huge amount of data. In this context, deep learning appears as an effective method to process this huge amount of data. However, this high volume of data contains various types of uncertainties, including vagueness, imprecision, randomness, ignorance and incompleteness. Hence, an enhanced deep learning approach, named BRB-DL, has been developed by integrating BRBES, allowing the improvement of prediction accuracy, especially in case of a large dataset. The applicability of this BRB-DL has been carried out by considering a large amount of air pollution data to predict the air quality index (AQI) of different Chinese cities.

In the light of the above, it can be argued that the novel anomaly detection algorithm proposed in this thesis enables the removing of anomalous data. The proposed learning mechanism for multi-level BRBES allows handling of the multi-level complex problem. The optimal training procedure, named eBRBaDE, enabling determination of optimal learning parameters of BRBESs and finally, the integration of deep learning with BRBES allows to handle large data set.

Place, publisher, year, edition, pages
Luleå University of Technology, 2020.
Series
Doctoral thesis / Luleå University of Technologyy… → 31 dec 1996, ISSN 0348-8373
National Category
Computer and Information Sciences
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-78762ISBN: 978-91-7790-599-8 (print)ISBN: 978-91-7790-600-1 (electronic)OAI: oai:DiVA.org:ltu-78762DiVA, id: diva2:1427982
Public defence
2020-06-10, Hörsal A, Campus Skellefteå, Forskargatan 1, 931 62, Skellefteå, 10:00 (English)
Opponent
Supervisors
Available from: 2020-05-04 Created: 2020-05-04 Last updated: 2020-05-25Bibliographically approved
List of papers
1. A novel anomaly detection algorithm for sensor data under uncertainty
Open this publication in new window or tab >>A novel anomaly detection algorithm for sensor data under uncertainty
2018 (English)In: Soft Computing - A Fusion of Foundations, Methodologies and Applications, ISSN 1432-7643, E-ISSN 1433-7479, Vol. 22, no 5, p. 1623-1639Article in journal (Refereed) Published
Abstract [en]

It is an era of Internet of Things, where various types of sensors, especially wireless, are widely used to collect huge amount of data to feed various systems such as surveillance, environmental monitoring, and disaster management. In these systems, wireless sensors are deployed to make decisions or to predict an event in a real-time basis. However, the accuracy of such decisions or predictions depends upon the reliability of the sensor data. Unfortunately, erroneous data are received from the sensors. Consequently, it hampers the appropriate operations of the mentioned systems, especially in making decisions and prediction. Therefore, the detection of anomaly that exists with the sensor data drew significant attention and hence, it needs to be filtered before feeding a system to increase its reliability in making decisions or prediction. There exists various sensor anomaly detection algorithms, but few of them are able to address the uncertain phenomenon, associated with the sensor data. If these uncertain phenomena cannot be addressed by the algorithms, the filtered data into the system will not be able to increase the reliability of the decision-making process. These uncertainties may be due to the incompleteness, ignorance, vagueness, imprecision and ambiguity. Therefore, in this paper we propose a new belief-rule-based association rule (BRBAR) with the ability to handle the various types of uncertainties as mentioned.The reliability of this novel algorithm has been compared with other existing anomaly detection algorithms such as Gaussian, binary association rule and fuzzy association rule by using sensor data from various domains such as rainfall, temperature and cancer cell data. Receiver operating characteristic curves are used for comparing the performance of our proposed BRBAR with the aforementioned algorithms. The comparisons demonstrate that BRBAR is more accurate and reliable in detecting anomalies from sensor data under uncertainty. Hence, the use of such algorithm to feed the decision-making systems could be beneficial. Therefore, we have used this algorithm to feed appropriate sensor data to our recently developed belief-rule-based expert system to predict flooding in an area. Consequently, the reliability and the accuracy of the flood prediction system increase significantly. Such novel algorithm (BRBAR) can be used in other areas of applications. 

Place, publisher, year, edition, pages
Springer, 2018
Keywords
Internet of Things, Wireless sensor networks, Anomaly detection, Flood prediction, Belief-rule-based expert systems
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-60360 (URN)10.1007/s00500-016-2425-2 (DOI)000426566400022 ()
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Funder
Swedish Research Council, 2014-4251
Note

Validerad;2018;Nivå 2;2018-03-05 (andbra)

Available from: 2016-11-12 Created: 2016-11-12 Last updated: 2020-05-04Bibliographically approved
2. A Web Based Belief Rule Based Expert System to Predict Flood
Open this publication in new window or tab >>A Web Based Belief Rule Based Expert System to Predict Flood
2015 (English)In: Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services (iiWAS2015) / [ed] Maria Indrawan-Santiago; Matthias Steinbauer; Ismail Khalil; Gabriele Anderst-Kotsis, New York: Association for Computing Machinery (ACM), 2015, p. 19-26, article id 3Conference paper, Published paper (Refereed)
Abstract [en]

Natural calamity disrupts our daily life and brings many sufferings in our life. Among the natural calamities, flood is one of the most catastrophic. Predicting flood helps us to take necessary precautions and save human lives. Several types of data (meteorological condition, topography, river characteristics, and human activities) are used to predict flood water level in an area. In our previous works, we proposed a belief rule based flood prediction system in a desktop environment. In this paper, we propose a web-service based flood prediction expert system by incorporating belief rule base with the capability of reading sensor data such as rainfall, river flow on real time basis. This will facilitate the monitoring of the various flood-intensifying factors, contributing in increasing the flood water level in an area. Eventually, the decision makers would able to take measures to control those factors and to reduce the intensity of flooding in an area.

Place, publisher, year, edition, pages
New York: Association for Computing Machinery (ACM), 2015
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing; Enabling ICT (AERI)
Identifiers
urn:nbn:se:ltu:diva-27246 (URN)10.1145/2837185.2837212 (DOI)2-s2.0-84967203069 (Scopus ID)09c47bac-5b82-43aa-a855-c478efcdbf60 (Local ID)978-1-4503-3491-4 (ISBN)09c47bac-5b82-43aa-a855-c478efcdbf60 (Archive number)09c47bac-5b82-43aa-a855-c478efcdbf60 (OAI)
Conference
International Conference on Information Integration and Web-based Applications & Services : 11/12/2015 - 13/12/2015
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Note

Godkänd; 2015; 20151014 (karand)

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2020-05-04Bibliographically approved
3. Capacity Management of Hyperscale Data Centers Using Predictive Modelling
Open this publication in new window or tab >>Capacity Management of Hyperscale Data Centers Using Predictive Modelling
Show others...
2019 (English)In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 12, no 18, article id 3438Article in journal (Refereed) Published
Abstract [en]

Big Data applications have become increasingly popular with the emergence of cloud computing and the explosion of artificial intelligence. The increasing adoption of data-intensive machines and services is driving the need for more power to keep the data centers of the world running. It has become crucial for large IT companies to monitor the energy efficiency of their data-center facilities and to take actions on the optimization of these heavy electricity consumers. This paper proposes a Belief Rule-Based Expert System (BRBES)-based predictive model to predict the Power Usage Effectiveness (PUE) of a data center. The uniqueness of this model consists of the integration of a novel learning mechanism consisting of parameter and structure optimization by using BRBES-based adaptive Differential Evolution (BRBaDE), significantly improving the accuracy of PUE prediction. This model has been evaluated by using real-world data collected from a Facebook data center located in Luleå, Sweden. In addition, to prove the robustness of the predictive model, it has been compared with other machine learning techniques, such as an Artificial Neural Network (ANN) and an Adaptive Neuro Fuzzy Inference System (ANFIS), where it showed a better result. Further, due to the flexibility of the BRBES-based predictive model, it can be used to capture the nonlinear dependencies of many variables of a data center, allowing the prediction of PUE with much accuracy. Consequently, this plays an important role to make data centers more energy-efficient.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
learning, differential evolution, belief rule-based expert systems, predictive modelling, data center
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-75875 (URN)10.3390/en12183438 (DOI)000489101200034 ()2-s2.0-85071916245 (Scopus ID)
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networksPERCCOM
Funder
Swedish Research Council, 2014-4251
Note

Validerad;2019;Nivå 2;2019-09-09 (johcin)

Available from: 2019-09-06 Created: 2019-09-06 Last updated: 2020-05-04Bibliographically approved
4. An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution
Open this publication in new window or tab >>An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution
2020 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 20, no 7, p. 1-25, article id 1956Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI, 2020
Keywords
BRBES, Deep Learning, integration, sensor data, predict
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-78258 (URN)10.3390/s20071956 (DOI)
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networksPERvasive Computing and COMmunications for sustainable development
Funder
Swedish Research Council, 2014-4251
Note

Validerad;2020;Nivå 2;2020-04-01 (cisjan)

Available from: 2020-03-31 Created: 2020-03-31 Last updated: 2020-05-04Bibliographically approved
5. A learning mechanism for BRBES using enhanced Belief Rule-Based Adaptive Differential Evolution
Open this publication in new window or tab >>A learning mechanism for BRBES using enhanced Belief Rule-Based Adaptive Differential Evolution
2020 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Nowadays, Belief rule-based expert systems (BRBESs) are widely used in various domains which provides a framework to handle qualitative and quantitative data by addressing several kinds of uncertainty. Learning plays an important role in BRBES to upgrade its knowledge base and parameters values, necessary for the improvement of  the prediction accuracy. Different optimal training procedures such as Particle Swarm Optimisation (PSO), Differential Evolution (DE), and Genetic Algorithm (GA) have been used as learning mechanisms. Among these procedures, DE performs comparatively better than others. However, DE's performance depends significantly in assigning near optimal values to its control parameters including cross over and mutation factors. Therefore, the objective of this article is to present a novel optimal training procedure by integrating DE with BRBES. This is named as enhanced belief rule-based adaptive differential evolution (eBRBaDE) algorithm because it has the ability to determine the near-optimal values of both the control parameters while ensuring the balanced exploitation and exploration in the search space.  In addition, a new joint optimization learning mechanism by using eBRBaDE is presented where both parameter and structure of BRBES are considered.  The reliability of the eBRBaDE has been compared with evolutionary optimization algorithms such as GA, PSO, BAT, DE and L-SHADE. This comparison has been carried out by taking account of both conjunctive and disjunctive BRBESs while predicting the Power Usage Effectiveness (PUE) of a datacentre. The comparison demonstrates that the eBRBaDE provides higher prediction accuracy of PUE than from other evolutionary optimization algorithms.

Keywords
Optimization, Learning, Evolutionary Algorithm, Differential evolution, Belief Rule-based Expert Systems.
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-78912 (URN)
Conference
9th International Conference on Informatics, Electronics & Vision (ICIEV)
Available from: 2020-05-20 Created: 2020-05-20 Last updated: 2020-05-25
6. Inference and Multi-level Learning in a Belief Rule-Based Expert System to Predict Flooding
Open this publication in new window or tab >>Inference and Multi-level Learning in a Belief Rule-Based Expert System to Predict Flooding
2020 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Floods are one of the most dangerous catastrophic events. By the year 2050 flooding due to rise of ocean level  may  cost  one  trillion USD to coastal cities. Since flooding involves multi-dimensional elements, its accurate prediction is difficult. In addition, the elements cannot be measured with 100% accuracy. Belief rule-based expert systems (BRBESs) can be considered as an appropriate approach to handle  this  type  of  problem  because they are capable of addressing  uncertainty. However, BRBESs need to be equipped with the capacity to handle multi- level learning and inference to improve its accuracy of flood prediction. Therefore, this paper proposes a new learning and inference mechanism, named joint optimization using belief rule- based adaptive differential evolution (BRBaDE) for multi-level BRBES, which has the capability to handle multi-level learning and inference. Various machine learning methods, including Artificial Neural Networks (ANN), Support Vector Machine (SVM), Linear Regression and Long Short Term Memory have been compared with BRBaDE. The result exhibits that our proposed learning mechanism performs betters than learning techniques as mentioned above in terms of accuracy in flood prediction.

National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-78958 (URN)
Conference
9th International Conference on Informatics, Electronics & Vision (ICIEV)
Available from: 2020-05-20 Created: 2020-05-20 Last updated: 2020-05-25

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