development of an autonomous weeding machine requires a vision system capable of detecting and locating the position of the crop. it is important for the vision system to be able to recognize the accurate position of the crop stem to be protected during weeding. several shape features of corn plants and common weed species in the location were extracted by means of morphological operations. eff...

A classification technique using Support Vector Machine (SVM) classifier for detection of rolling element bearing fault is presented here.  The SVM was fed from features that were extracted from of vibration signals obtained from experimental setup consisting of rotating driveline that was mounted on rolling element bearings which were run in normal and with artificially faults induced conditio...

objective(s): this study addresses feature selection for breast cancer diagnosis. the present process uses a wrapper approach using ga-based on feature selection and ps-classifier. the results of experiment show that the proposed model is comparable to the other models on wisconsin breast cancer datasets. materials and methods: to evaluate effectiveness of proposed feature selection method, we ...

Objective(s): This study addresses feature selection for breast cancer diagnosis. The present process uses a wrapper approach using GA-based on feature selection and PS-classifier. The results of experiment show that the proposed model is comparable to the other models on Wisconsin breast cancer datasets. Materials and Methods: To evaluate effectiveness of proposed feature selection method, we ...

In recent years, deep learning based on artificial neural network (ANN) has achieved great success in pattern recognition. However, there is no clear understanding of such neural computational models [1], for instance, for a trained ANN-classifier, we have no idea of why some classes are easy to be predicted correctly, but some are difficult to be predicted correctly. In this paper, we try to u...

This paper presents a new approach based on HMM/ANN hybrid for online signature verification. The hybrid HMM/ANN model is constructed by using a type of time delay Neural Networks as local probability estimators for an HMM, where a posterior probability of the model is worked out by the Viterbi algorithm, given an observation sequence. The proposed HMM/ANN hybrid has a strong discriminant abili...

Classifier performance is often enhanced through combining multiple streams of information. In the context of multistream HMM/ANN systems in ASR, a confidence measure widely used in classifier combination is the entropy of the posteriors distribution output from each ANN, which generally increases as classification becomes less reliable. The rule most commonly used is to select the ANN with the...

Area of classifying satellite imagery has become a challenging task in current era where there is tremendous growth in settlement i.e. construction of buildings, roads, bridges, dam etc. This paper suggests an improvised k-means and Artificial Neural Network (ANN) classifier for land-cover mapping of Eastern Himalayan state Sikkim. The improvised k-means algorithm shows satisfactory results com...

This paper investigates a new approach based on Artificial Neural Networks (ANNs) for real-time fault classification in power transmission lines which can be used in digital power system protection. The technique uses sampled current and voltage data of each phase at one terminal as inputs to the corresponding ANN. The ANN outputs indicate the type of the fault within a time less than 5 ms. The...