Design and implementation of an academic expert system through big data analysis

Choi, Do Young; Lee, Hyeonbyeong; Bok, Kyoungsoo; Yoo, Jaesoo

doi:10.1007/s11227-020-03446-0

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…An expert assessment was carried out on the basis of a competence approach, taking into consideration the ranks from [25,26]. Determining the number of experts in the group was calculated on the basis of selective observation [27]. As experts, 10 specialists in the chosen area were selected.…”

Section: Selection Of Parameters For Evaluating the Systems For Remov...mentioning

confidence: 99%

Determining the performance indicators of employing combined methods for removing space objects from near-earth orbits

Dron’

Hilorme

Golubek

et al. 2022

EEJET

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A methodology for assessing the relative effectiveness of alternative options for building space object diversion systems has been improved. An algorithm for assessing the effectiveness of the system of removal of space objects from near-Earth orbit based on the method of integral assessment is given. It makes it possible to simplify the process of optimal choice of the method to divert space objects and determine efficiency in the early phases of the life cycle of rocket and space technology objects. The use of the appropriate toolset makes it possible to build a system for assessing the effectiveness of projects for the removal of space objects from low Earth orbits using various diversion methods (active, passive, combined). The analysis of defining world indicators of evaluation of objects of rocket and space technology based on regulations by international space agencies has been carried out. An indicator of the total integrated relative efficiency of projects of space object diversion systems from low Earth orbits has been proposed, which makes it possible to build the removal of passive, active, and combined methods for leveling the risks of space activities. It is argued that the selected combined system using an autophagic launch vehicle could reduce environmental losses and, as a result, reduce compensation payments to owners of space objects. The possibilities of building combined systems with reusable engines have been considered in order to reduce such indicators as the period of diversion and reduction of operating costs due to fuel economy.

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Section: Selection Of Parameters For Evaluating the Systems For Remov...mentioning

confidence: 99%

Determining the performance indicators of employing combined methods for removing space objects from near-earth orbits

Dron’

Hilorme

Golubek

et al. 2022

EEJET

View full text Add to dashboard Cite

show abstract

“…To sum up, by determining the set function of active intrusion intention and attack behavior of complex networks, the metabolic diffusion process of active intrusion of complex networks is estimated [35,36]. Based on the evaluation results of metabolic diffusion process of active intrusion of complex networks, a grey prediction model of active intrusion metabolism of complex networks is constructed, and the construction of metabolic grey prediction model based on big data and Internet of things technology is realized.…”

Section: Construction Of the Metabolic Grey Prediction Modelmentioning

confidence: 99%

The Metabolism Grey Prediction Model Based on Big Data and Internet of Things Technology

Ren

Sun

Shan

et al. 2022

Wireless Communications and Mobile Computing

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In view of the uncertainty and diversity of the metabolic grey prediction model in the prediction process, resulting in poor prediction effect, a metabolic grey prediction model based on big data and Internet of things technology is constructed. Establish the aerobic metabolic process of human telecontrol and put forward the detection index of aerobic metabolic cycle function in human telecontrol; on this basis, use the metabolic grey prediction model analysis algorithm to determine the active intrusion intention of complex network, establish the intrusion intention attack behavior set function, establish the internal operation architecture under the technology of big data and Internet of things, and realize the construction of metabolic grey prediction model. The experimental results show that the constructed model can realize data prediction, with high confidence level and good effect.

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“…Advancements in this area of research have contributed to the growth of expert systems. With the development of data mining, many big data-driven expert systems have been proposed [ 25 , 26 , 27 ]. This suggests that big data can widen the search scope of expert systems and also improve their training performances.…”

Section: Introductionmentioning

confidence: 99%

“…The growth of feature selection (including feature extraction) is manifested in the fruitful results of recent research. The outcomes derived from a large amount of data, in particular, have drawn the attention of experts across all fields [ 27 , 32 ]. However, the goal of feature selection for expert systems is to learn autonomously from a large amount of data to create a better model with better training results.…”

Section: Introductionmentioning

confidence: 99%

A Robust Design-Based Expert System for Feature Selection and COVID-19 Pandemic Prediction in Japan

Chen

2022

Healthcare

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Expert systems are frequently used to make predictions in various areas. However, the practical robustness of expert systems is not as good as expected, mainly due to the fact that finding an ideal system configuration from a specific dataset is a challenging task. Therefore, how to optimize an expert system has become an important issue of research. In this paper, a new method called the robust design-based expert system is proposed to bridge this gap. The technical process of this system consists of data initialization, configuration generation, a genetic algorithm (GA) framework for feature selection, and a robust mechanism that helps the system find a configuration with the highest robustness. The system will finally obtain a set of features, which can be used to predict a pandemic based on given data. The robust mechanism can increase the efficiency of the system. The configuration for training is optimized by means of a genetic algorithm (GA) and the Taguchi method. The effectiveness of the proposed system in predicting epidemic trends is examined using a real COVID-19 dataset from Japan. For this dataset, the average prediction accuracy was 60%. Additionally, 10 representative features were also selected, resulting in a selection rate of 67% with a reduction rate of 33%. The critical features for predicting the epidemic trend of COVID-19 were also obtained, including new confirmed cases, ICU patients, people vaccinated, population, population density, hospital beds per thousand, middle age, aged 70 or older, and GDP per capital. The main contribution of this paper is two-fold: Firstly, this paper has bridged the gap between the pandemic research and expert systems with robust predictive performance. Secondly, this paper proposes a feature selection method for extracting representative variables and predicting the epidemic trend of a pandemic disease. The prediction results indicate that the system is valuable to healthcare authorities and can help governments get hold of the epidemic trend and strategize their use of healthcare resources.

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Design and implementation of an academic expert system through big data analysis

Cited by 7 publications

References 24 publications

Determining the performance indicators of employing combined methods for removing space objects from near-earth orbits

Determining the performance indicators of employing combined methods for removing space objects from near-earth orbits

The Metabolism Grey Prediction Model Based on Big Data and Internet of Things Technology

A Robust Design-Based Expert System for Feature Selection and COVID-19 Pandemic Prediction in Japan

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