Automated Expert System Knowledge Base Development Method for Information Security Risk Analysis

Vitkus, Donatas; Steckevičius, Žilvinas; Goranin, Nikolaj; Kalibatiene, Diana; Čenys, Antanas

doi:10.15837/ijccc.2019.6.3668

Cited by 13 publications

(8 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Knowledge base is a type of database in an expert system where it stores all the information from various facts, rules and relationships [23]. The more information stored in the knowledge base, the more accurate the expert system [24].…”

Section: Methods 21 Expert Systemmentioning

confidence: 99%

expert system on diagnosis of mental diseases

Jain

Aggarwal

Singhal

et al. 2023

J. Soft Comput. Explor.

View full text Add to dashboard Cite

Mental disorder is one of the most serious problems in today's time. Mental disorders can be classified into different sub-disorders according to changes in human behavior and mental condition. According to reports one out of seven people suffered from mental disorders. In this research paper, our main emphasis is to build an expert system that diagnoses people based on their symptoms, so people can diagnose themselves early before going to the doctor. Expert Systems are one of the most important applications in artificial intelligence that solves complex problems without human help. We provide different rules, facts, and relationships among different symptoms in our knowledge base, from which users can query their problems and get their results. We used SWI-prolog to build an expert system. There are a few types of disorders, such as mental disorders, neurodevelopmental disorders, eating disorders, etc.

show abstract

Section: Methods 21 Expert Systemmentioning

confidence: 99%

expert system on diagnosis of mental diseases

Jain

Aggarwal

Singhal

et al. 2023

J. Soft Comput. Explor.

View full text Add to dashboard Cite

show abstract

“…Symbolic AI methods based on knowledge-driven bulid a rule system according to the working principle and historical fault data of chemical plants. By hitting the rules with the values of process parameters and change rules, it can predict whether abnormal conditions will occur and infer the causes of abnormal conditions [121] .…”

Section: Knowledge-driven Methodsmentioning

confidence: 99%

Artificial Intelligence Methods Applied to Catalytic Cracking Processes

Yang

Lei

et al. 2023

Big Data Min. Anal.

View full text Add to dashboard Cite

Fluidic Catalytic Cracking (FCC) is a complex petrochemical process affected by many highly non-linear and interrelated factors. Product yield analysis, flue gas desulfurization prediction, and abnormal condition warning are several key research directions in FCC. This paper will sort out the relevant research results of the existing Artificial Intelligence (AI) algorithms applied to the analysis and optimization of catalytic cracking processes, with a view to providing help for the follow-up research. Compared with the traditional mathematical mechanism method, the AI method can effectively solve the difficulties in FCC process modeling, such as high-dimensional, nonlinear, strong correlation, and large delay. AI methods applied in product yield analysis build models based on massive data. By fitting the functional relationship between operating variables and products, the excessive simplification of mechanism model can be avoided, resulting in high model accuracy. AI methods applied in flue gas desulfurization can be usually divided into two stages: modeling and optimization. In the modeling stage, data-driven methods are often used to build the system model or rule base; In the optimization stage, heuristic search or reinforcement learning methods can be applied to find the optimal operating parameters based on the constructed model or rule base. AI methods, including data-driven and knowledge-driven algorithms, are widely used in the abnormal condition warning. Knowledge-driven methods have advantages in interpretability and generalization, but disadvantages in construction difficulty and prediction recall. While the data-driven methods are just the opposite. Thus, some studies combine these two methods to obtain better results.

show abstract

“…The three main types of abnormal working condition warning techniques currently in use are analytical model-based techniques, knowledge-based techniques, and data-driven techniques [4]. Knowledge-based methods analyze and reason using knowledge and experience, but obtaining knowledge and experience is more challenging [5]. Data-driven methods predict changes in the data via mining.…”

Section: Introductionmentioning

confidence: 99%

Fused Data-Driven Approach for Early Warning Method of Abnormal Conditions in Chemical Process

2023

View full text Add to dashboard Cite

The utilization of data-driven methods in chemical process modeling has been extensively acknowledged due to their effectiveness. However, with the increasing complexity and variability of chemical processes, predicting and warning of anomalous conditions have become challenging. Extracting valuable features and constructing relevant warning models are critical problems that require resolution. This research proposed a novel fused method that integrates K-means density-based spatial clustering of applications with noise (DBSCAN) clustering and bi-directional long short-term memory multilayer perceptron (Bi-LSTM-MLP) to enable early warning of abnormal conditions in chemical processes. The paper applied the proposed method to analyze the early warning using actual process data from Eastman Tennessee and the atmospheric pressure reduction unit as an example. In the TE model and example, the root mean square error (RMSE) of this method is 0.006855 and 0.052546, respectively, which is quite low when compared to other methods. The experimental results confirmed the effectiveness of our approach.

show abstract

Automated Expert System Knowledge Base Development Method for Information Security Risk Analysis

Cited by 13 publications

References 27 publications

expert system on diagnosis of mental diseases

expert system on diagnosis of mental diseases

Artificial Intelligence Methods Applied to Catalytic Cracking Processes

Fused Data-Driven Approach for Early Warning Method of Abnormal Conditions in Chemical Process

Contact Info

Product

Resources

About