Soft computing

About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.

A satellite-based model for estimating PM2.5 concentration in a sparsely populated environment using soft computing techniques

Abstract: We applied three soft computing methods including adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and back-propagation artificial neural network (BPANN) algorithms for estimating the ground-level PM2.5 concentration. These models were trained by comprehensive satellite-based, meteorological, and geographical data. A 10-fold cross-validation (CV) technique was used to identify the optimal predictive model. Results showed that ANFIS was the best-performing model for predicting the variations in PM2.5 concentration. Our findings demonstrated that the CV-R2 of the ANFIS (0.81) is greater than that of the SVM (0.67) and BPANN (0.54) model. The results suggested that soft computing methods like ANFIS, in combination with spatiotemporal data from satellites, meteorological data and geographical information improve the estimate of PM2.5 concentration in sparsely populated areas. We used comprehensive dataset to develop a satellite-based model for estimating the PM2.5 concentration.Representative animations are created to visualize the spatiotemporal variation of the predictors.We applied ANFIS for the first time as a core model to estimate the spatiotemporal variation of PM2.5 concentration.We compared ANFIS with support vector machine and back-propagation artificial neural network.Adaptive model identification technique has been used to identify the optimal predictive model.

Fuzzy Logic in Management

Abstract: List of Figures. List of Tables. Introduction. 1: Management and intelligent support technologies. 1. Management. 2. Decision support systems. 3. Hyper knowledge - a brief summary of experiences. 4. New information technology, intelligent systems and soft computing. 5. Some lessons for future DSS. 2: Fuzzy sets and fuzzy logic. 1. Fuzzy sets. 2. Averaging operators. 3: Group decision support systems. 1. The arrow impossibility theorem: from classical to fuzzy approaches. 2. Consensus modeling in GDM. 3. A soft degree of consensus based on fuzzy logic with linguistic quantifiers. 4. An interactive system for consensus reaching. 5. The OCA approach to multicriteria multiperson consensus modeling. 6. Quality evaluation of elderly persons' homes using fuzzy quantifiers. 7. A decision support system for strategic planning through scenarios. 8. An artificial neural network evaluator for mortgage loan applications. 9. A linguistic approach to personnel evaluation. 10. An application to environmental policies. 4: Fuzzy real options for strategic planning. 1. A fuzzy approach to real option valuation. 2. Nordic Telekom Inc. 3. Summary. 5: A fuzzy approach to reducing the bullwhip effect. 1. The bullwhip effect, some additional details. 2. Fuzzy approaches to demand signal processing. 3. A hybrid soft computing platform for taming the bullwhip effect. 4. Summary. 6: Knowledge management. 1. Introduction. 2. The current state of knowledge management research. 3. Knowledge management: a conceptual framework. 4. Knowledge management strategies. 5. Knowledge management projects. 6. Research in knowledge management - some methodology issues. 7. IT solutions to support knowledge management. 7: Mobile technology applications. 1. Introduction. 2. Consumer survey in Finland. 3. An expert survey in Finland. 4. Acceptance of mobile commerce in Finland, Hong Kong and Singapore. 5. Mobile commerce products and services.

A Combination of PSO-Based Feature Selection and Tree-Based Classifiers Ensemble for Intrusion Detection Systems

Abstract: Due to the numerous attacks over the Internet, several early detection systems have been developed to prevent the network from huge losses. Data mining, soft computing, and machine learning are employed to classify historical network traffic whether anomaly or normal. This paper presents the experimental result of network anomaly detection using particle swarm optimization (PSO) for attribute selection and the ensemble of tree-based classifiers (C4.5, Random Forest, and CART) for classification task. Proposed detection model shows the promising result with detection accuracy and lower positive rate compared to existing ensemble techniques.

Applications of Data-Driven Modelling and Machine Learning in Control of Water Resources

Abstract: Traditionally, management and control of water resources is based on behavior-driven or physically based models based on equations describing the behavior of water bodies. Since recently models built on the basis of large amounts of collected data are gaining popularity. This modeling approach we will call data-driven modeling; it borrows methods from various areas related to computational intelligence—machine learning, data mining, soft computing, etc. The chapter gives an overview of successful applications of several datadriven techniques in the problems of water resources management and control. The list of such applications includes: using decision trees in classifying flood conditions and water levels in the coastal zone depending on the hydrometeorological data, using artificial neural networks (ANN) and fuzzy rule-based systems for building controllers for real-time control of water resources, using ANNs and M5 model trees in flood control, using chaos theory in predicting water levels for ship guidance, etc. Conclusions are drawn on the applicability of the mentioned methods and the future role of computational intelligence in modeling and control of water resources. 701 E. Chocolate Avenue, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com ITB8650 IDEA GROUP PUBLISHING This chapter appears in the book, Computational Intelligence in Control, edited by Masoud Mohammadian. Copyright © 2003, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.