An improved fuzzy rule-based system using evidential reasoning and subtractive clustering for environmental investment prediction

Yang, Lijiao; Ye, Feifei; Li, Jun; Wang, Ying Ming; Hu, Haibo

doi:10.1016/j.fss.2021.02.018

Cited by 18 publications

(6 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposal to use the apparatus of expert systems based on fuzzy logic is based on the statement about a high level of economic uncertainty under conditions of martial law. It is worth noting that most of the available research, for example [12][13][14], focuses on solving individual problems in accordance with the considered sector of the economy. At the same time, the use of a typical fuzzy FuzzyKIDE ES is aimed at forming a holistic view of the possible directions of development of the sector under consideration.…”

Section: Discussion Of Results Of the Study Of Forecasts Of Trends In...mentioning

confidence: 99%

“…One example of the use of an improved fuzzy system based on rules is the original development used to predict investments in the field of ecology [14]. The cited paper proposes a new model for forecasting investments using a fuzzy rules-based system (FRBS), an "evidence-based reasoning" (ER) approach, and a subtractive clustering algorithm (SC), called FRBS-ERSC.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting development trends in the information technology industry using fuzzy logic

Dudnyk

Sokolovska

2023

EEJET

View full text Add to dashboard Cite

The IT industry occupies a significant place in the economy of the world. Trends in its development directly affect changes in various sectors of the economy. This paper considers the dynamics of IT industry under conditions of increased entropy of the functioning environment caused by crises, military conflicts, non-standard events. The mathematical basis of the study is an apparatus of fuzzy logic. The proposed technique for analyzing and forecasting the state and dynamics of the IT sector makes it possible to take into account the influences of a set of factors of different nature and direction of action, which contributes to obtaining a reasonable assessment under conditions of inaccurate information. The software platform for implementing the methodology is the authentically designed fuzzy expert system (ES) FuzzyKIDE. The conceptual foundations of the platform structure have been defined. A modification of the fuzzy inference mechanism has been proposed, which, unlike existing samples, makes it possible to eliminate saving intermediate results and reduces the load on the database. The composition and structure of the knowledge base (KB) for ES have been proposed. Fuzzy rules are based on predetermined relationships between key factors of influence. The technology of ES operation is represented using simulated scenarios with obtaining predictive results. The analysis of the final data of expert consultations proves the ES operability, providing for the possibility of KB significant expansion in the process of industrial operation. The use of ES is aimed at forming a holistic view of possible directions of IT sector development. Maintaining the actual state of ES KB is a condition for early warning of the emergent negative/crisis phenomena. The FuzzyKIDE expert consulting system is proposed as a tool to support management decision-making based on the analysis and forecasting of the state and dynamics of IT sector under conditions of high uncertainty

show abstract

Section: Discussion Of Results Of the Study Of Forecasts Of Trends In...mentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Forecasting development trends in the information technology industry using fuzzy logic

Dudnyk

Sokolovska

2023

EEJET

View full text Add to dashboard Cite

show abstract

“…Although the type-1 fuzzy logic system has been widely used in many analytical and forecasting problems [ 9 , 10 ]. However, because the type-1 fuzzy membership functions have a crisp value, they have difficulty dealing with data with high uncertainty [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…Research to predict investment environment based on an improved fuzzy rule system using evidence reasoning and subtractive clustering [ 10 ]. A whale optimization algorithm and a least-squares estimator develop multi-objective prediction using an aim-object-based asymmetric neuro-fuzzy system.…”

Section: Introductionmentioning

confidence: 99%

Application of Interval Type-2 Fuzzy Logic System and Ant Colony Optimization for Hydropower Dams Displacement Forecasting

Mai

Bui

Doan

2023

Int. J. Fuzzy Syst.

View full text Add to dashboard Cite

Forecasting the dam displacement is an important problem in dam monitoring. By using multi-temporal monitoring data to make predictions and forecast dam structure health, it is possible to detect abnormalities over time of the dam so that remedial measures can be taken promptly to minimize risks. The paper proposes a method of applying the interval type-2 fuzzy logic system (IT2FLS) and ant colony optimization (ACO) technique to forecast hydroelectric dams’ displacement. The method consists of two stages: The first is to design an IT2FLS according to the input and output data with base rules and newly constructed membership functions. The second is to find the optimal parameters for an interval type-2 fuzzy logic system based on the ACO technique and give the final fuzzy forecasting system. Experimental data are measured from hydroelectricity monitoring data of Ialy, Gia Lai Province, Vietnam, in the period from 2004 to 2016. The proposed method is compared with some other methods to compare prediction efficiency and accuracy. The experimental results show that the proposed model has the best performance compared to other standard methods in both accuracy and stability of prediction. This shows that a predictive model based on the IT2FLS is a promising method and can be used for forecasting time-series data. Supplementary Information The online version contains supplementary material available at 10.1007/s40815-022-01452-3.

show abstract

“…Studies were applied to this act or process of incorporating or combining various elements into a whole. This designed fuzzy rule-based system 5 increases the value or price of topics like hybrid intelligent systems. Related studies have been developed for fuzzy and to integrate fuzzy into the theory of the universe reasonably.…”

Section: Introductionmentioning

confidence: 99%

An intelligent fuzzy inference rule‐based expert recommendation system for predictive diabetes diagnosis

Nagaraj

Deepalakshmi

2022

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Diabetes is one of the most common and hazardous diseases, which can affect almost every organ in the body. Diagnosis of diabetes requires determining all vital parameters related to the disease. However, the nature of the data from those parameters is very uncertain, affecting the process of disease diagnosis. This article proposes an intelligent fuzzy inference rule-based predictive diabetes diagnosis model (IFIR_PDDM), providing content recommendations to patients with diabetes. The suggested model employs an inference technique that medical specialists have validated for recommendations. IFIR_PDDM comprises three elements used to forecast the risk of diabetes disease. Initially, a fuzzy membership function utilizes medical recommendations and statistical methodologies. Medical specialists then validate the mining-based rules using a decision tree rule induction technique. The proposed model predicts the risk of diabetes disease using fuzzy inference based on Mamdani's technique. Based on this information, the recommendations for a normal life, nutrition, exercise, and medications are given to patients. We used an electronic health record (EHR) medical and clinical dataset from the PIMA Indian Diabetes dataset to develop our proposed model and assess its performance. The proposed model takes less time for diabetes diagnosis, and the expert recommendation system uses the fuzzy inference method.

show abstract

An improved fuzzy rule-based system using evidential reasoning and subtractive clustering for environmental investment prediction

Cited by 18 publications

References 46 publications

Forecasting development trends in the information technology industry using fuzzy logic

Forecasting development trends in the information technology industry using fuzzy logic

Application of Interval Type-2 Fuzzy Logic System and Ant Colony Optimization for Hydropower Dams Displacement Forecasting

An intelligent fuzzy inference rule‐based expert recommendation system for predictive diabetes diagnosis

Contact Info

Product

Resources

About