A belief rule based expert system to assess Lung Cancer under uncertainty

Hossain, Mohammad Shahadat; Hasan, Muhammad Asif; Uddin, Mejbah; Islam, Md. Monirul; Mustafa, Rashed

doi:10.1109/iccitechn.2015.7488106

Cited by 7 publications

(5 citation statements)

References 14 publications

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“…Furthermore, the receiver operating characteristic (ROC) curve provides detailed visualization and comparative assessment of the different methods [45]. Therefore, it is used in different domains, such as clinical applications [46], atmospheric science, and many other fields [47]. Additionally, the ROC curve is used to assess the accuracy of trained disjunctive and conjunctive BRBs, ANN, and ANFIS for the prediction of PUE.…”

Section: Resultsmentioning

confidence: 99%

Capacity Management of Hyperscale Data Centers Using Predictive Modelling

et al. 2019

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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.

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Section: Resultsmentioning

confidence: 99%

Capacity Management of Hyperscale Data Centers Using Predictive Modelling

et al. 2019

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“…2 illustrates the workflow of a Deep Learning integrated BRBES method. The initial BRB can be created using experts' opinion or based on the method described in [36].…”

Section: Deep Learning Inspired Belief Rule-based Expert System (Bmentioning

confidence: 99%

“…First, the input data is mapped to matching degrees of the referential values of the antecedent attributes during input transformation as described in [36]. Input transformation is marked as 'a' in Fig.…”

Section: A Input Transformationmentioning

confidence: 99%

A Deep Learning Inspired Belief Rule-Based Expert System

2020

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Recent technological advancements in the area of the Internet of Things (IoT) and cloud services, enable the generation of large amounts of raw data. However, the accurate prediction by using this data is considered as challenging for machine learning methods. Deep Learning (DL) methods are widely used to process large amounts of data because they need less preprocessing than traditional machine learning methods. Various types of uncertainty associated with large amounts of raw data hinder the prediction accuracy. Belief Rule-Based Expert Systems (BRBES) are widely used to handle uncertain data. However, due to their incapability of integrating associative memory within the inference procedures, they demonstrate poor accuracy of prediction when large amounts of data is considered. Therefore, we propose the integration of an associative memory based DL method within the BRBES inference procedures, allowing to discover accurate data patterns and hence, the improvement of prediction under uncertainty. To demonstrate the applicability of the proposed method, which is named BRB-DL, it has been fine tuned against two datasets, one in the area of air pollution and the other in the area of power generation. The reliability of the proposed BRB-DL method, has also been compared with other DL methods such as Long-Short Term Memory and Deep Neural Network, and BRBES by taking into account of the air quality dataset from Beijing city and the power generation dataset of a combined cycle power plant. BRB-DL outperforms the above-mentioned methods in terms of prediction accuracy. For example, the Mean Square Error value of BRB-DL is 4.12 whereas for Long

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“…Predicting the stage of NSCLC is a complex problem and so an expert system is essential for handling all sorts of uncertainty. A belief rule base expert system which is designed and developed from Belief Rule-Based Inference Methodology using the Evidential Reasoning (RIMER) approach [13][14] [25][26][27][28][29][30][31][32][33][34][35][36][37] is used in this paper. All sorts of uncertainty are able to address by this approach.…”

Section: IImentioning

confidence: 99%

A Belief Rule Base Expert System for staging Non-Small Cell Lung Cancer under Uncertainty

Biswas

Chowdhury

Nahar

et al. 2019

2019 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON)

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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).

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A belief rule based expert system to assess Lung Cancer under uncertainty

Cited by 7 publications

References 14 publications

Capacity Management of Hyperscale Data Centers Using Predictive Modelling

Capacity Management of Hyperscale Data Centers Using Predictive Modelling

A Deep Learning Inspired Belief Rule-Based Expert System

A Belief Rule Base Expert System for staging Non-Small Cell Lung Cancer under Uncertainty

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