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The selection of a particular neural network model belonging to the Pareto front is a problem that exists in all multi-objective algorithms. This paper proposes a novel solution to this problem based on a linear combination of the outputs of the two extremes in the Pareto front, which form an ensemble. The decision support TOPSIS method is used to determine which linear combination creates the best...
Image thresholding is a very important phase in the image analysis process. However, different images have different characteristics making the traditional process of thresholding by one algorithm a very challenging task. That is because any thresholding method may be perform well for some images but for sure it will not be suitable for all images. In this paper, intelligent thresholding by training...
This paper presents a novel image edge detection method based on a simplified pulse coupled neural network with anisotropic interconnections (PCNNAI) by applying an anisotropic linking mechanism. PCNNAI utilizes the anisotropic linking mechanism to create an adaptive synaptic weight matrix to achieve the anisotropic interconnection model among neurons. Therefore, the neurons corresponding to edge...
Combining pattern recognition is the promising direction in designing an effective classifier systems. There are several approaches of collective decision-making, among them voting methods, where the decision is a combination of individual classifiers' outputs are quite popular. This article focuses on the problem of fuser design which uses continuous outputs of individual classifiers to make a decision...
This article presents two classifiers based on machine learning methods, aiming to detect physiologic anomalies considering Poincaré plots of heart rate variability. It was developed a preprocessing procedure to encoding the plots, based on the Cellular Features Extraction Method. Simulation of different classifiers, artificial neural networks and support vector machine, has been performed and the...
Linear regression and classification techniques are very common in statistical data analysis but they are often able to extract from data only linear models, which can be a limitation in real data context. Aim of this study is to build an innovative procedure to overcome this defect. Initially, a multiple linear regression analysis using the best-subset algorithm was performed to determine the variables...
One of the major issues concerning the Artificial Neural Networks (ANNs) design is a proper adjustment of the weights of the network. There have been a number of studies comparing the performance of evolutionary and gradient based ANNs learning. But the results of the studies, sometime conflicting to each other although the same and standard dataset development had been used. Motivated by this finding,...
A novel soft computing system to optimize a dental milling process is proposed. The model is based on the initial application of several statistical and projection methods as Principal Component Analysis and Cooperative Maximum Likelihood Hebbian Learning to analyze the structure of the data set and to identify the most relevant variables. Finally, a supervised neural model and identification techniques...
According to some biological observations, generating output variability is one of the characteristics expected from a memory model. In this paper a BAM inspired chaotic model is used to mimic this functionality of the brain. Chaos gives the potential to create deterministic variability and control its degree of uncertainty. Using some time series generated by the trained network, largest lyapunov...
We propose a classification model for the cognitive level of question items in examinations based on Bloom's taxonomy. The model implements the artificial neural network approach, which is trained using the scaled conjugate gradient learning algorithm. Several data preprocessing techniques such as word extraction, stop word removal, stemming, and vector representation are applied to a feature set...
This paper presents the methodology how to utilize sensor networks in order to predict human's thermal comfort and sensation. The neural network was dynamically organized on the basis of correlations with the thermal sensation of the occupants and many other values in the sensor network, and the structure of the neural network was updated cyclically. In this paper, the air-conditioning system in an...
Multivariate quality control charts show some advantages to monitor several variables in comparison with the simultaneous use of univariate charts, nevertheless, there are some disadvantages. The main problem is how to interpret the out-of-control signal of a multivariate chart. The MEWMA quality control chart is a very powerful scheme to detect small shifts in the mean vector. There are no previous...
Pattern recognition is very challenging multidisciplinary research area attracting researchers and practitioners. Gesture recognition is a specialized pattern recognition task with the goal of interpreting human gestures via mathematical models. One of the usages of gesture recognition is the sign language recognition which is the basic communication method between deaf people. Since there is lack...
This paper describes a method using image processing and genetic algorithm-neural network (GA-NN) for automated Mycobacterium tuberculosis detection in tissues. The proposed method can be used to assist pathologists in tuberculosis (TB) diagnosis from tissue sections and replace the conventional manual screening process, which is time-consuming and labour-intensive. The approach consists of image...
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