International Journal of Computational Intelligence and Applications

A support vector machine (SVM) optimized by genetic algorithm (GA)-based damage identification method is proposed in this paper. The best kernel parameters are obtained by GA from selection, crossover and mutation, and utilized as the model parameters of SVM. The combined vector of mode shape ratio and frequency rate is used as the input variable. A numerical example for a simply supported bridge with five girders is provided to verify the feasibility of the method. Numerical simulation shows that the maximal relative errors of GA-SVM for the damage identification of single, two and three suspicious damaged elements is 1.84%. Meanwhile, comparative analyzes between GA-SVM and radical basis function (RBF), back propagation networks optimized by GA (GA-BP) were conducted, the maximal relative errors of RBF and GA-BP are 6.91% and 5.52%, respectively. It indicates that GA-SVM can assess the damage conditions with better accuracy.

The detection and diagnosis of faults in technical systems are of great practical significance and paramount importance for the safe operation of the plant. The early detection of fault can help avoid system shutdown, breakdown and even catastrophe involving human fatalities and material damage. Since the operator cannot monitor all the variables simultaneously, an automated approach is needed for the real time monitoring and diagnosis of the system. This paper presents the design and development of adaptive neuro-fuzzy inference system (ANFIS) model based for the fault detection of pneumatic valve in cooler water spray system in cement industry. The fault detection model is developed by using two different approaches, namely, ANFIS and feed forward network with back propagation algorithm (BPN). The training and testing data required are developed for the ANFIS model and BPN model that were generated at different operating conditions by operating the pneumatic valve and by creating various faults in real time in a laboratory experimental model. The performance of the developed ANFIS model and back propagation were tested and also compared for a total of 19 faults in pneumatic valve used in cooler water spray system. Obtained results of the ANFIS performed better than BPN.

H5N1 avian influenza outbreak detection is a significant issue for early warning of epidemics. This paper proposes domain knowledge-based joint one class classification model for avian influenza outbreak. Instead of focusing on manipulations of the one class classification model, we delve into the one class avian influenza dataset, divide it into sub-classes by domain knowledge, train the sub-class classifiers and unify the result of each classifier. The proposed joint method solves the one class classification and features selection problems together. The experiment results demonstrate that the proposed joint model definitely outperforms the normal one class classification model on the animal avian influenza dataset.

The best option to deal with a complex system that is too cumbersome to be treated in a centralized way is to decompose it into a number of sub-systems and optimize them in a distributed and decentralized way to reach the desired system objective. These sub-systems can be viewed as a multi-agent system (MAS) with self-learning agents. Furthermore, another challenge is to handle the constraints involved in real world optimization problems. This paper demonstrates the theory of probability collectives (PC) in the collective intelligence (COIN) framework, supplemented with a penalty function approach for constraint handling. The method of deterministic annealing in statistical physics, game theory and Nash equilibrium are at the core of the PC optimization methodology. Three benchmark problems have been solved with the optimum results obtained at reasonable computational cost. The evident strengths and weaknesses are also discussed to determine the future direction of research.

Nucleus segmentation is one of important steps in the automatic white blood cell differential counting. In this paper, we proposed a technique to segment images of the nucleus. We analyze a set of white-blood-cell-nucleus-based features using color fuzzy texture spectrum (Base 5). We applied artificial neural network for classification. We compared the results with moment based features. The classification performances are evaluated by class wise classification rates. The results show that the features using nucleus alone could be utilized to achieve a classification rate of 99.05% on the test sets.

This paper presents a novel technique for the classification of suspicious areas in digital mammograms. The proposed technique is based on clustering of input data into numerous clusters and amalgamating them with a Support Vector Machine (SVM) classifier. The technique is called multi-cluster support vector machine (MCSVM) and is designed to provide a fast converging technique with good generalization abilities leading to an improved classification as a benign or malignant class. The proposed MCSVM technique has been evaluated on data from the Digital Database of Screening Mammography (DDSM) benchmark database. The experimental results showed that the proposed MCSVM classifier achieves better results than standard SVM. A paired t-test and Anova analysis showed that the results are statistically significant.