Project duration-cost-quality prediction model based on Monte Carlo simulation

Uncertainty in water quality model predictions is inevitably high due to natural stochasticity, model uncertainty, and parameter uncertainty. An integrated modelling system (modified-BASINS) under uncertainty is described and demonstrated for use in receiving-water quality prediction and watershed management. A Monte Carlo simulation was used to investigate the effect of various uncertainty types on output prediction. Without pollution control measures in the watershed, the concentrations of total nitrogen (T-N) and total phosphorus (T-P) in the Hwaong Reservoir, considering three uncertainty types, would be less than about 4.4 and 0.23 mg L−1, respectively, in 2012, with 90% confidence. The effects of two watershed management practices, wastewater treatment plants (WWTP) and constructed wetlands (WETLAND), were evaluated. The combined scenario (WWTP + WETLAND) was the most effective at improving reservoir water quality, bringing concentrations of T-N and T-P in the Hwaong Reservoir to less than 3.4 and 0.14 mg L−1, 24 and 41% improvements, respectively, with 90% confidence. Overall, the Monte Carlo simulation in the integrated modelling system was practical for estimating uncertainty and reliable in water quality prediction. The approach described here may allow decisions to be made based on the probability and level of risk, and its application is recommended.

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Research On Neural Network Quality Prediction Model Based On Genetic Algorithm

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/267/4/042026 ◽

2019 ◽

Vol 267 ◽

pp. 042026

Author(s):

Xia Li ◽

Yiru Dai ◽

Jin Cheng

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Prediction Model ◽

Quality Prediction ◽

Model Based ◽

Network Quality

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A Novel Monte-Carlo Simulation-Based Model for Malware Detection (eRBCM)

Electronics ◽

10.3390/electronics10222881 ◽

2021 ◽

Vol 10 (22) ◽

pp. 2881

Author(s):

Muath Alrammal ◽

Munir Naveed ◽

Georgios Tsaramirsis

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Information Systems ◽

Malware Detection ◽

New Model ◽

Malicious Activity ◽

Model Based ◽

Simulation Based ◽

Computer Based ◽

Reinforcement Model

The use of innovative and sophisticated malware definitions poses a serious threat to computer-based information systems. Such malware is adaptive to the existing security solutions and often works without detection. Once malware completes its malicious activity, it self-destructs and leaves no obvious signature for detection and forensic purposes. The detection of such sophisticated malware is very challenging and a non-trivial task because of the malware’s new patterns of exploiting vulnerabilities. Any security solutions require an equal level of sophistication to counter such attacks. In this paper, a novel reinforcement model based on Monte-Carlo simulation called eRBCM is explored to develop a security solution that can detect new and sophisticated network malware definitions. The new model is trained on several kinds of malware and can generalize the malware detection functionality. The model is evaluated using a benchmark set of malware. The results prove that eRBCM can identify a variety of malware with immense accuracy.

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