In this paper, we show how one-class recognition of cognitive brain functions across multiple subjects can be performed at a good (above 85%) level of accuracy via appropriate choices of features. This betters the initial work of Hardoon and Manevitz (where such classification was first shown to be possible in principle, with 60% range on the same data) and work of various groups around the wor...

In statistical pattern recognition, the decision of which features to use is usually left to human judgment. If possible, automatic methods are desirable. Like multilayer perceptrons, learning subspace methods (LSMs) have the potential to integrate feature extraction and classification. In this paper, we propose two new algorithms, along with their neural-network implementations, to overcome ce...

Traditionally, human activity recognition has been achieved mainly by the statistical pattern recognition techniques such as the Nearest Neighbor Rule (NNR), and the state-space methods, e.g. the Hidden Markov Model (HMM). This paper proposes three novel approaches – the use of the Elman Network (EN) and two hybrids of Neural Network (NN) and HMM, i.e. HMM-NN and NN-HMM, to recognize ten simple...

Feature extraction methods and subsequent neural network performances are explored in this paper. Object recognition method ‘regionprops’ and moment invariants are used to extract basic characteristics from acquired bloodstain images. The extracted features are in return fed into a neural network for the purpose of pattern recognition. The blood drop in the image is first detected using sobel e...

This paper presents a feed forward back-propagation neural network model to predict the retained tensile strength and design chart in order to estimation of the strength reduction factors of nonwoven geotextiles due to installation process. A database of 34 full-scale field tests were utilized to train, validate and test the developed neural network and regression model. The results show that t...

This paper describes a novel design environment for cognitive systems tailored to the needs of flexibility, speed and transparence demanded in numerous application domains as e.g., automated visual inspection for quality control, mechatronics, medical applications, and other cognitive tasks. Our QuickCog design environment combines methods from statistical pattern recognition, neural networks, ...

This paper presents the development of Gurumukhi character recognition system of isolated handwritten characters by using Neocognitron at the first time. Wellknown neocognitron artificial neural network is chosen for its fast processing time and its good performance for pattern recognition problems. Here we have found the recognition accuracy of both learned and unlearned images of characters. ...

Charts are frequently embedded objects in digital documents and are used to convey a clear analysis of research results or commercial data trends. These charts are created through different means and may be represented by a variety of patterns such as column charts, line charts and pie charts. Chart recognition is as important as text recognition to automatically comprehend the knowledge within...