Belief Rule Base

Bangladesh is one of the most flood-prone countries in the world due to its geographical location and climatic conditions. Floods cause significant human, economic, and environmental losses every year. According to a World Bank assessment, the August 2024 floods alone resulted in approximately USD 1.67 billion in direct damages. This study proposes a machine learning-based approach to predicting flood severity using rainfall and river water level data. A Decision Tree model was trained on nine years of simulated historical records (2015-2023). The model achieved an accuracy of 33%, reflecting the dataset's limitations, but it successfully classified severe flood events and highlighted the relative importance of rainfall and river levels. These preliminary findings demonstrate the feasibility of AIdriven flood forecasting. With larger datasets and advanced algorithms such as Random Forests and Neural Networks, such methods could improve disaster preparedness, strengthen early warning systems, and reduce human suffering in Bangladesh.

Acute coronary syndrome (ACS) is responsible for the obstruction of coronary arteries, resulting in the loss of lives. The onset of ACS can be determined by looking at the various signs and symptoms of a patient. However, the accuracy of ACS determination is often put into question since there exist different types of uncertainties with the signs and symptoms. Belief rule-based expert systems (BRBESs) are widely used to capture uncertain knowledge and to accomplish the task of reasoning under uncertainty by employing belief rule base and evidential reasoning. This article presents the process of developing a BRBES to determine ACS predictability. The BRBES has been validated against the data of 250 patients suffering from chest pain. It is noticed that the outputs created from the BRBES are more dependable than that of the opinion of cardiologists as well as other two expert system tools, namely artificial neural networks and support Communicated by V. Loia.

A critical issue in the clinical decision support system (CDSS) research area is how to represent and reason with both uncertain medical domain knowledge and clinical symptoms to arrive at accurate conclusions. Although a number of methods and tools have been developed in the past two decades for modelling clinical guidelines, few of those modelling methods have capabilities of handling the uncertainties that exist in almost every stage of a clinical decision-making process. This paper describes how to apply a recently developed generic rule-base inference methodology using the evidential reasoning approach (RIMER) to model clinical guidelines and the clinical inference process in a CDSS. In RIMER, a rule base is designed with belief degrees embedded in all possible consequents of a rule. Such a rule base is capable of capturing vagueness, incompleteness and nonlinear causal relationships, while traditional IF-THEN rules can be represented as a special case. Inference in such a rule base is implemented using the evidential reasoning approach which has the capability of handling different types and degrees of uncertainty in both medical domain knowledge and clinical symptoms. A case study demonstrates that employing RIMER in developing a guideline-based CDSS is a valid novel approach.

This paper describes a prototype clinical decision support system (CDSS) for risk stratification of patients with cardiac chest pain. A newly developed belief rule-based inference methodology-RIMER was employed for developing the prototype. Based on the belief rule-based inference methodology, the prototype CDSS can deal with uncertainties in both clinical domain knowledge and clinical data. Moreover, the prototype can automatically update its knowledge base via a belief rule base (BRB) learning module which can adjust BRB through accumulated historical clinical cases. The domain specific knowledge used to construct the knowledge base of the prototype was learned from real patient data. We simulated a set of 1000 patients in cardiac chest pain to validate the prototype. The belief rule-based prototype CDSS has been found to perform extremely well. Firstly, the system can provide more reliable and informative diagnosis recommendations than manual diagnosis using traditional rules when there are clinical uncertainties. Secondly, the diagnostic performance of the system can be significantly improved after training the BRB through accumulated clinical cases.

Motivations: Ischemic Heart Disease (IHD) is one of the most common causes of the death in many countries [1]. The disease is unpredictable in onset and hence, requires rigorous diagnosis in regards to multiple signs and symptoms such as cardiac pain, breathlessness, sweating, palpitation, nausea vomiting and hypertension [2]. However, these signs and

Capability approach is hard to operationalize and capabilities are sometimes difficult to reduce into the individual level. We describe our approach to solve these problems paired with an adequate ICT infrastructure and believe that the impact of concrete eGovernment services will become more visible and available. Citizens can inspect it, professionals can target their re-engineering on failure cases, learn from best cases, and researchers can compare different types of eGovernment. The solving of operationalisation will also strengthen the capability approach, by extending its scope from macro theory towards more easy application on the institutional and organizational levels.

E-government development is often complex, with multiple stakeholders, large user bases and complex goals. Consequently, even experts have difficulties in evaluating these systems, especially in an integrated and comprehensive way, as well as on an aggregate level, and thus, there is currently little knowledge about the actual impact and results of e-government. Expert systems are a candidate solution to evaluate such complex e-government systems. However, it is difficult for expert systems to cope with uncertain evaluation data that are vague, inconsistent, highly subjective or in other ways challenging to formalize. This paper presents an approach that can handle uncertainty in e-government evaluation: The combination of Belief Rule Base (BRB) knowledge representation and Evidential Reasoning (ER). This approach is illustrated with a concrete prototype, known as the Belief Rule Based Expert System (BRBES) and implemented in the local e-government of Bangladesh. The results have been compared with a recently developed method of evaluating e-government, showing that the BRBES approach is more accurate and reliable. The BRBES can be used to identify the factors that need to be improved in e-government projects and can juxtapose different scenarios. Thus, the system can be used to facilitate decision making processes under uncertainty.

Asthma is a common chronic disease that affects millions of people around the world. The most common signs and symp-toms of asthma are cough, breathlessness, wheeze, chest tightness and respiratory rate. They cannot be measured ac-curately since they consist of various types of uncertainty such as vagueness, imprecision, randomness, ignorance and in-completeness. Consequently, traditional disease diagnosis, which is performed by a physician, cannot deliver accurate results. Therefore, this paper presents the design, develop-ment and application of a decision support system for as-sessing asthma under conditions of uncertainty. The Belief Rule-Based Inference Methodology Using the Evidential Rea-soning (RIMER) approach was adopted to develop this expert system, which is named the Belief Rule-Based Expert System (BRBES). The system can handle various types of uncertainty in knowledge representation and inference procedures. The knowledge base of this system was constructed by using re...

This paper describes a prototype clinical decision support system (CDSS) for risk stratification of patients with cardiac chest pain. A newly developed belief rule-based inference methodology-RIMER was employed for developing the prototype. Based on the belief rule-based inference methodology, the prototype CDSS can deal with uncertainties in both clinical domain knowledge and clinical data. Moreover, the prototype can automatically update its knowledge base via a belief rule base (BRB) learning module which can adjust BRB through accumulated historical clinical cases. The domain specific knowledge used to construct the knowledge base of the prototype was learned from real patient data. We simulated a set of 1000 patients in cardiac chest pain to validate the prototype. The belief rule-based prototype CDSS has been found to perform extremely well. Firstly, the system can provide more reliable and informative diagnosis recommendations than manual diagnosis using traditional rules when there are clinical uncertainties. Secondly, the diagnostic performance of the system can be significantly improved after training the BRB through accumulated clinical cases.

An intelligent belief rule base (BRB) based system with internet of things (IoT) integration can evaluate earthquake prediction (EP). This ingenious and rational system can predict earthquake by aggregating changed animal behavior combined with environmental and chemical changes which are taken as real time inputs from sensors. The BRB expert system blends knowledge demonstration criterion like attribute weight, rule weight, belief degree. The intelligent BRB system with IoT predicts the probable occurrence of the earthquake in a region based on the sign and symptoms culled by the persistent sensors. The final result taken from Intelligent BRB system with IoT integration is compared with expert and fuzzy-based system. The projected method gives a better prediction than the up-to-date expert system and fuzzy system

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, Communicated by V. Loia.

An intelligent belief rule base (BRB) based system with internet of things (IoT) integration can evaluate earthquake prediction (EP). This ingenious and rational system can predict earthquake by aggregating changed animal behavior combined with environmental and chemical changes which are taken as real time inputs from sensors. The BRB expert system blends knowledge demonstration criterion like attribute weight, rule weight, belief degree. The intelligent BRB system with IoT predicts the probable occurrence of the earthquake in a region based on the sign and symptoms culled by the persistent sensors. The final result taken from Intelligent BRB system with IoT integration is compared with expert and fuzzy-based system. The projected method gives a better prediction than the up-to-date expert system and fuzzy system

Among the various natural calamities, flood is considered one of the most catastrophic natural hazards, which has a significant impact on the socioeconomic lifeline of a country. The Assessment of flood risks facilitates taking appropriate measures to reduce the consequences of flooding. The flood risk assessment requires Big data which are coming from different sources, such as sensors, social media, and organizations. However, these data sources contain various types of uncertainties because of the presence of incomplete and inaccurate information. This paper presents a Belief rule-based expert system (BRBES) which is developed in Big data platform to assess flood risk in real time. The system processes extremely large dataset by integrating BRBES with Apache Spark while a web-based interface has developed allowing the visualization of flood risk in real time. Since the integrated BRBES employs knowledge driven learning mechanism, it has been compared with other data-driven learning mechanisms to determine the reliability in assessing flood risk. Integrated BRBES produces reliable results comparing from the other datadriven approaches. Data for the expert system has been collected targeting different case study

Acute coronary syndrome (ACS) is responsible for the obstruction of coronary arteries, resulting in the loss of lives. The onset of ACS can be determined by looking at the various signs and symptoms of a patient. However, the accuracy of ACS determination is often put into question since there exist different types of uncertainties with the signs and symptoms. Belief rule-based expert systems (BRBESs) are widely used to capture uncertain knowledge and to accomplish the task of reasoning under uncertainty by employing belief rule base and evidential reasoning. This article presents the process of developing a BRBES to determine ACS predictability. The BRBES has been validated against the data of 250 patients suffering from chest pain. It is noticed that the outputs created from the BRBES are more dependable than that of the opinion of cardiologists as well as other two expert system tools, namely artificial neural networks and support Communicated by V. Loia.

Bronchopneumonia is an acute or chronic inflammation of the lungs, in which the alveoli and/or interstitial are affected. Usually the diagnosis of Bronchopneumonia is carried out using signs and symptoms of this disease, which cannot be measured since they consist of various types of uncertainty. Consequently, traditional disease diagnosis, which is performed by a physician, cannot deliver accurate results. Therefore, this paper presents the design, development and application of an expert system for assessing the suspicion of Bronchopneumonia under uncertainty. The Belief Rule-Based Inference Methodology using the Evidential Reasoning (RIMER) approach was adopted to develop this expert system, which is named the Belief Rule-Based Expert System (BRBES). The system can handle various types of uncertainty in knowledge representation and inference procedures. The knowledge base of this system was constructed by using real patient data and expert opinion. Practical case studies were used to validate the system. The system-generated results are more effective and reliable in terms of accuracy than from the results generated by a manual system.

E-government development is often complex, with multiple stakeholders, large user bases and complex goals. Consequently, even experts have difficulties in evaluating these systems, especially in an integrated and comprehensive way, as well as on an aggregate level, and thus, there is currently little knowledge about the actual impact and results of e-government. Expert systems are a candidate solution to evaluate such complex e-government systems. However, it is difficult for expert systems to cope with uncertain evaluation data that are vague, inconsistent, highly subjective or in other ways challenging to formalize. This paper presents an approach that can handle uncertainty in e-government evaluation: The combination of Belief Rule Base (BRB) knowledge representation and Evidential Reasoning (ER). This approach is illustrated with a concrete prototype, known as the Belief Rule Based Expert System (BRBES) and implemented in the local e-government of Bangladesh. The results have been compared with a recently developed method of evaluating e-government, showing that the BRBES approach is more accurate and reliable. The BRBES can be used to identify the factors that need to be improved in e-government projects and can juxtapose different scenarios. Thus, the system can be used to facilitate decision making processes under uncertainty.

Asthma is a common chronic disease that affects millions of people around the world. The most common signs and symp-toms of asthma are cough, breathlessness, wheeze, chest tightness and respiratory rate. They cannot be measured ac-curately since they consist of various types of uncertainty such as vagueness, imprecision, randomness, ignorance and in-completeness. Consequently, traditional disease diagnosis, which is performed by a physician, cannot deliver accurate results. Therefore, this paper presents the design, develop-ment and application of a decision support system for as-sessing asthma under conditions of uncertainty. The Belief Rule-Based Inference Methodology Using the Evidential Rea-soning (RIMER) approach was adopted to develop this expert system, which is named the Belief Rule-Based Expert System (BRBES). The system can handle various types of uncertainty in knowledge representation and inference procedures. The knowledge base of this system was constructed by using re...

Nowadays, the recognition of facial expression draws significant attention in various domains. In view of this, a realtime facial expression recognition system has been developed using a Deep Learning approach, which can classify ten emotions, including angry, disgust, fear, happy, mockery, neutral, sad, surprise, think, and wink. In addition, an integrated expert system has also been developed by integrating Deep Learning with a Belief Rule Base to support the assessment of the overall mental state of a person over a period of time from video streaming data under uncertainty. In this research, data-driven and knowledge-driven approaches are integrated together to assess the mental state of an individual. Such a system could enable the identification of a suspect before committing any crime beforehand by the law enforcement agency. The performance of this integrated system is found reliable than existing methods of facial expression assessment. Contribution-The paper presents a noble method of computing the overall mental condition of a person by integrating CNN and BRBES under uncertainty.

Motivations: Ischemic Heart Disease (IHD) is one of the most common causes of the death in many countries [1]. The disease is unpredictable in onset and hence, requires rigorous diagnosis in regards to multiple signs and symptoms such as cardiac pain, breathlessness, sweating, palpitation, nausea vomiting and hypertension [2]. However, these signs and

Bronchopneumonia is an acute or chronic inflammation of the lungs, in which the alveoli and/or interstitial are affected. Usually the diagnosis of Bronchopneumonia is carried out using signs and symptoms of this disease, which cannot be measured since they consist of various types of uncertainty. Consequently, traditional disease diagnosis, which is performed by a physician, cannot deliver accurate results. Therefore, this paper presents the design, development and application of an expert system for assessing the suspicion of Bronchopneumonia under uncertainty. The Belief Rule-Based Inference Methodology using the Evidential Reasoning (RIMER) approach was adopted to develop this expert system, which is named the Belief Rule-Based Expert System (BRBES). The system can handle various types of uncertainty in knowledge representation and inference procedures. The knowledge base of this system was constructed by using real patient data and expert opinion. Practical case studies were used to validate the system. The system-generated results are more effective and reliable in terms of accuracy than from the results generated by a manual system.

Non small cell Lung cancer (NSCLC) is one of the most well-known types of Lung cancer which is reason for cancer related demise in Bangladesh. The early detection stage of NSCLC is required for improving the survival rate by taking proper decision for surgery and radiotherapy. The most common factors for staging NSCLC are age, tumor size, lymph node distance, Metastasis and Co morbidity. Moreover, physicians' diagnosis is unable to give more reliable outcome due to some uncertainty such as ignorance, incompleteness, vagueness, randomness, imprecision. Belief Rule Base Expert System (BRBES) is fit to deal with above mentioned uncertainty by applying both Belief Rule base and Evidential Reasoning approach .Therefore, this paper represents the architecture, development and interface for staging NSCLC by incorporating belief rule base as well as evidential reasoning with the capability of handling uncertainty. At last, a comparative analysis is added which indicate that the outcomes of proposed expert system is more reliable and efficient than the outcomes generated from traditional human expert as well as Support Vector Machine (SVM) or Fuzzy Rule Base Expert System (FRBES).

Safety assessment is one of important aspects in health management. In safety assessment for practical systems, three problems exist: lack of observation information, high system complexity and environment interference. Belief rule base with attribute reliability (BRB-r) is an expert system that provides a useful way for dealing with these three problems. In BRB-r, once the input information is unreliable, the reliability of belief rule is influenced, which further influences the accuracy of its output belief degree. On the other hand, when many system characteristics exist, the belief rule combination will explode in BRB-r, and the BRB-r based safety assessment model becomes too complicated to be applied. Thus, in this paper, to balance the complexity and accuracy of the safety assessment model, a new safety assessment model based on BRB-r with considering belief rule reliability is developed for the first time. In the developed model, a new calculation method of the belief rule reliability is proposed with considering both attribute reliability and global ignorance. Moreover, to reduce the influence of uncertainty of expert knowledge, an optimization model for the developed safety assessment model is constructed. A case study of safety assessment of liquefied natural gas (LNG) storage tank is conducted to illustrate the effectiveness of the new developed model.

Uterine cancer is one of the conspicuous cancers for women in both developed and developing countries including Bangladesh. Now, in the world it is the sixth most common cancer among women and fourteenth most common cancer overall. The high occurrence of uterine cancer in women has increased significantly in the last years. This involves many factors to be measured and evaluated, which are related to the signs and symptoms of this disease. These factors usually expressed in quantitative and qualitative ways. In addition, a hierarchical relationship exists among these factors. Since qualitative factors cannot be measured in a quantitative way, resulting various types of uncertainties such as incompleteness, vagueness, imprecision.

Belief  rule-based  inference  system  introduces  a  belief  distribution  structure  into the conventional  rule-based  system,  which  can  effectively  synthesize  incomplete  and  fuzzy information.  In  order  to  optimize  reasoning  efficiency  and  reduce  redundant  rules,  this  paper proposes a rule reduction method based on regularization. This method controls the distribution of  rules  by  setting  corresponding  regularization  penalties  in  different  learning  steps  and reduces redundant rules. This paper first proposes the use of the Gaussian membership function to  optimize  the  structure  and  activation  process  of the  belief  rule  base,  and  the  corresponding regularization penalty construction method. Then, a step-by-step training method is used to set a  different  objective  function  for  each  step  to  control  the  distribution  of  belief  rules,  and  a reduction  threshold  is  set  according  to  the  distribution  information  of  the  belief  rule  base  to perform rule reduction. Two experiments will be conducted based on the synthetic classification data  set  and  the  benchmark  classification  data  set  to  verify  the  performance  of  the  reduced belief rule base.

Smart risk assessment systems are becoming more and more important in the society. If the chances of reducing and managing certain risks are increased, the impacts can be controlled and reduced significantly. This article surveys different belief-rule-based decision support systems and various wireless sensor network technologies that can be used in collaboration to build interesting risk assessment applications. We propose a model for building such an environment and describe a potential application of our proposed model for assessing flood risks in a case study.

Meditation is a practice, by regularly applying one can develop calm and quite mind. It is essential not only for developing pure mind but also for sound health. Therefore, the assessment of meditation is very important in every day of our life. It can be measured by using various human sensing factors such as sleep pattern, Galvanic Skin Response (GSR), Heart rate Variability (HRV), pulse difference etc. However, assessing meditation level is complex due to the presence of various types of uncertainties such as vagueness, ignorance, imprecision, incompleteness associated with these human sensing factors. This paper presents the design, development and application of an expert system to assess meditation under uncertainty. The belief rule-based inference methodology using evidential reasoning approach (RIMER), which can handle various types of uncertainties, employed to develop this expert system. The knowledge-base has been constructed by taking account of meditation practitioner’s real data and expert’s opinion. The practitioner’s case studies have been used to validate the expert system. It has been observed that the system generated results are more reliable than from the expert assessment.

Smart risk assessment systems are becoming more and more important in the society. If the chances of reducing and managing certain risks are increased, the impacts can be controlled and reduced significantly. This article surveys different belief-rule-based expert systems and various wireless sensor network technologies that can be used in collaboration to build interesting risk assessment applications. We propose a model for building such an environment and describe a potential application of our proposed model for assessing flood risks in a case study.

This paper presents a comprehensive study of the flood analysis and prediction using Geographical Information system (GIS). Different scientists and researchers from all over the world had performed detailed analysis of flood risk assessment specifically for human population and to take precautionary measurements before or after the critical condition of disaster occurs using Remote sensing and satellite images. In this research study, we had performed detailed analysis of Flood Prediction techniques based on GIS using Ad hoc wireless Sensor Network Architecture. We had also proposed a Model for Flood Risk Analysis and prediction, which would be very helpful for us in calculating the impact of Flood damage in disaster hit regions. The GIS domain proves to be very helpful for us in geographical survey and to identify the tsunamis causing vast potential and economical damage. In this research study, we had also used Arc GIS simulation tool to identify pre and post disaster flood risk analysis. Our Research study focuses on various geographical information Systems specifically designed for Flood Disaster management and to analyze necessary input parameters including soil moisture, air pressure, direction of wind, humidity and rain fall. These parameters would be very helpful for us in modelling real life scenarios specifically in case of flood disasters. Our proposed model is also very helpful for us in predicting the upcoming disasters and to take necessary actions by emergency and rescue authorities to save the life of thousands of people before this critical condition occurs.