A web based belief rule based expert system to predict flood

Islam, Raihan Ul; Andersson, Karl; Hossain, Mohammad Shahadat

doi:10.1145/2837185.2837212

Cited by 36 publications

(24 citation statements)

References 13 publications

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“…B ELIEF Rule Based Expert Systems (BRBES) are widely used to assess or evaluate the performance of a realworld system or to predict the occurrence of an event under uncertainty [63] [64]. The reason for this is that most of the real world systems are complex in nature due to the presence of infinite number of factors which are difficult to measure with 100% accuracy.…”

Section: Outline Of Belief Rule Based Expert Systemsmentioning

confidence: 99%

A Belief Rule Based Expert System for Datacenter PUE Prediction under Uncertainty

Hossain

Rahaman

Kor

et al. 2017

IEEE Trans. Sustain. Comput.

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Abstract-A rapidly emerging trend in the IT landscape is the uptake of large-scale datacenters moving storage and data processing to providers located far away from the end-users or locally deployed servers. For these large-scale datacenters power efficiency is a key metric, with the PUE (Power Usage Effectiveness) and DCiE (Data Centre infrastructure Efficiency) being important examples. This paper proposes a belief rule based expert system to predict datacenter PUE under uncertainty. The system has been evaluated using real-world data from a data center in the UK. The results would help planning construction of new datacenters and the redesign of existing datacenters making them more power efficient leading to a more sustainable computing environment. In addition, an optimal learning model for the BRBES demonstrated which has been compared with ANN and Genetic Algorithm; and the results are promising.

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Section: Outline Of Belief Rule Based Expert Systemsmentioning

confidence: 99%

A Belief Rule Based Expert System for Datacenter PUE Prediction under Uncertainty

Hossain

Rahaman

Kor

et al. 2017

IEEE Trans. Sustain. Comput.

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“…The program produced good results for various problem sizes. However, in future the algorithm will be further enhanced to address the problem of getting stuck in the local minima as well as to address different types of uncertainty found in various application domains, where belief rule based are widely used [27][28] [29][30] [31]. The GA with newly developed crossover operator will be used to support the learning procedures of belief rule base to obtain better accuracy in prediction.…”

Section: Discussionmentioning

confidence: 99%

A New Crossover Technique to Improve Genetic Algorithm and Its Application to TSP

Akter

Nahar

ShahadatHossain

et al. 2019

2019 International Conference on Electrical, Computer and Communication Engineering (ECCE)

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Optimization problem like TravellingSalesman Problem (TSP) can be solved by applying Genetic Algorithm (GA) to obtain perfect approximation in time. In addition, TSP is considered as a NP-hard problem as well as an optimal minimization problem. Selection, crossover and mutation are the three main operators of GA. The algorithm is usually employed to find the optimal minimum total distance to visit all the nodes in a TSP. Therefore, the research presents a new crossover operator for TSP, allowing the further minimization of the total distance. The proposed crossover operator consists of two crossover point selection and new offspring creation by performing cost comparison. The computational results as well as the comparison with available well-developed crossover operators are also presented. It has been found that the new crossover operator produces better results than that of other cross-over operators.

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“…The system contained a knowledge base, a BRBES main module, a configuration module, a BRB UI model, a training module, and an input module, as shown in Figure 7. The proposed system is an extension of our earlier proposed Web-BRBES [41]. A brief description of the components is given below.…”

Section: Brbes For Puementioning

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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A web based belief rule based expert system to predict flood

Cited by 36 publications

References 13 publications

A Belief Rule Based Expert System for Datacenter PUE Prediction under Uncertainty

A Belief Rule Based Expert System for Datacenter PUE Prediction under Uncertainty

A New Crossover Technique to Improve Genetic Algorithm and Its Application to TSP

Capacity Management of Hyperscale Data Centers Using Predictive Modelling

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