Hybrid Neural Network Classifier Research Articles

Research on Feature Fusion Speech Emotion Recognition Technology for Smart Teaching

At present, there are numerous intelligent teaching applications based on various advanced pattern recognition technologies, such as face sign-in, classroom action recognition, student facial expression recognition, and other systems which have been gradually applied to major schools. Speech emotion recognition can analyze the characteristics of current teaching emotions, so as to discover the rules and details of teachers’ grasp of emotions. In order to realize the task of emotion classification in the process of intelligent teaching through machine learning technology, a teaching speech emotion recognition method with multifeature fusion and deep learning is proposed. The proposed method front-end processes the speech spectrum features in a multifeature fusion manner and combines them with an artificial neural network classifier to form the final speech emotion recognition model. First, after speech preprocessing, three features are selected and fused features using a network structure trained by parallel subnetworks. Then, the classifier used is a hybrid neural network classifier combining a convolutional neural network and a recurrent neural network. Finally, 100 open web courses were used to train and test the model. The test results show that the hybrid neural network sound model using multifeature fusion has good teaching speech emotion recognition capability.

A new hybrid neural network classifier based on adaptive neuron and multiplicative neuron

A new hybrid neural network classifier based on adaptive neuron and multiplicative neuron

Brain tumour detection and classification using hybrid neural network classifier

Brain tumour is one of the most harmful diseases, and has affected majority of people in the world including children. The probability of survival can be enhanced if the tumour is detected at its premature stage. Moreover, the process of manually generating precise segmentations of brain tumours from magnetic resonance images (MRI) is time-consuming and error-prone. Hence, in this paper, an effective technique is employed to segment and classify the tumour affected MRI images. Here, the segmentation is made with adaptive watershed segmentation algorithm. After segmentation, the tumour images were classified by means of hybrid ANN classifier. The hybrid ANN classifier employs cuckoo search optimisation technique to update the interconnection weights. The proposed methodology will be implemented in the working platform of MATLAB and the results were analysed with the existing techniques.

Brain tumour detection and classification using hybrid neural network classifier

Brain tumour is one of the most harmful diseases, and has affected majority of people in the world including children. The probability of survival can be enhanced if the tumour is detected at its premature stage. Moreover, the process of manually generating precise segmentations of brain tumours from magnetic resonance images (MRI) is time-consuming and error-prone. Hence, in this paper, an effective technique is employed to segment and classify the tumour affected MRI images. Here, the segmentation is made with adaptive watershed segmentation algorithm. After segmentation, the tumour images were classified by means of hybrid ANN classifier. The hybrid ANN classifier employs cuckoo search optimisation technique to update the interconnection weights. The proposed methodology will be implemented in the working platform of MATLAB and the results were analysed with the existing techniques.

Fault Diagnosis in Multi Phase Induction Machine using Mind Evolution Computation Algorithm Optimized Neural Network

This article proposes a new solution method for diagnosing faults in a multi phase induction motor using least mean square filter (LMS) and a new hybrid neural network with mind evolution computation algorithm. The entire procedure for teaching an artificial neural network (ANN) is popularly thought of among the toughest activities in system learning and also it has lately attracted lots of research workers. The proposed hybrid fault diagnosing method includes an efficient feature extractor based on LMS and a fault classifier based on a hybrid neural network. First, the LMS method is used to extract the effective features. The mind evolution computation algorithm is employed to train the neural network. The performance and efficiency of the presented hybrid neural network classifier is estimated by testing a total of 600 samples, which are modeled on the basis of the failure model. The average correct classification with and without mind evolution computation algorithm is about 98% and 96.17% for various fault signals respectively. The outcome got from the simulation analysis shows the potency of the proposed hybrid neural network for fault diagnosis in multi phase induction motor.

GOFAM: a hybrid neural network classifier combining fuzzy ARTMAP and genetic algorithm

Fuzzy ARTMAP (FAM), which is a supervised model from the adaptive resonance theory (ART) neural network family, is one of the conspicuous neural network classifier. The generalization/performance of FAM is affected by two important factors which are network parameters and presentation order of training data. In this paper we introduce a genetic algorithm to find a better presentation order of training data for FAM. The proposed method which is the combination of genetic algorithm with Fuzzy ARTMAP is called Genetic Ordered Fuzzy ARTMAP (GOFAM). To illustrate the effectiveness of GOFAM, several standard datasets from UCI repository of machine learning databases are experimented. The results are analyzed and compared with those from FAM and Ordered FAM which is used to determine a fixed order of training pattern presentation to FAM. Experimental results demonstrate the performance of GOFAM is much better than performance of Fuzzy ARTMAP and Ordered Fuzzy ARTMAP. In term of network size, GOFAM performs significantly better than FAM and Ordered FAM.

RBF와 LVQ 인공신경망을 이용한 요(尿) 딥스틱 선별검사에서의 요로감염 분류

Dipstick urinalysis is used as a routine test for a screening test of UTI (urinary tract infection) in primary practice because urine dipstick test is simple. The result of dipstick urinalysis brings medical professionals to make a microscopic examination and urine culture for exact UTI diagnosis, therefore it is emphasized on a role of screening test. The objective of this study was to the classification between UTI patients and normal subjects using hybrid neural network classifier with enhanced clustering performance in urine dipstick screening test. In order to propose a classifier, we made a hybrid neural network which combines with RBF layer, summation & normalization layer and L VQ artificial neural network layer. For the demonstration of proposed hybrid neural network, we compared proposed classifier with various artificial neural networks such as back-propagation, RBFNN and PNN method. As a result, classification performance of proposed classifier was able to classify 95.81% of the normal subjects and 83.87% of the UTI patients, total average 90.72% according to validation dataset. The proposed classifier confirms better performance than other classifiers. Therefore the application of such a proposed classifier expect to utilize telemedicine to classify between UTI patients and normal subjects in the future.

A hybrid neural network classifier combining ordered fuzzy ARTMAP and the dynamic decay adjustment algorithm

This paper presents a novel conflict-resolving neural network classifier that combines the ordering algorithm, fuzzy ARTMAP (FAM), and the dynamic decay adjustment (DDA) algorithm, into a unified framework. The hybrid classifier, known as Ordered FAMDDA, applies the DDA algorithm to overcome the limitations of FAM and ordered FAM in achieving a good generalization/performance. Prior to network learning, the ordering algorithm is first used to identify a fixed order of training patterns. The main aim is to reduce and/or avoid the formation of overlapping prototypes of different classes in FAM during learning. However, the effectiveness of the ordering algorithm in resolving overlapping prototypes of different classes is compromised when dealing with complex datasets. Ordered FAMDDA not only is able to determine a fixed order of training patterns for yielding good generalization, but also is able to reduce/resolve overlapping regions of different classes in the feature space for minimizing misclassification during the network learning phase. To illustrate the effectiveness of Ordered FAMDDA, a total of ten benchmark datasets are experimented. The results are analyzed and compared with those from FAM and Ordered FAM. The outcomes demonstrate that Ordered FAMDDA, in general, outperforms FAM and Ordered FAM in tackling pattern classification problems.

Fuzzy ARTMAP dynamic decay adjustment: An improved fuzzy ARTMAP model with a conflict resolving facility

This paper presents a hybrid neural network classifier of fuzzy ARTMAP (FAM) and the dynamic decay adjustment (DDA) algorithm. The proposed FAMDDA model is a conflict-resolving classifier that can perform stable and incremental learning while settling overlapping of hyper-rectangular prototypes of different classes in minimizing misclassification rates. The performance of FAMDDA is evaluated using a number of benchmark data sets. The results are analyzed and compared with those from FAM and a number of machine learning classifiers. The outcomes show that FAMDDA has a better generalization capability than FAM, and its performance is comparable with those from other classifiers. The effectiveness of FAMDDA is also demonstrated in an application pertaining to condition monitoring of a circulating water system in a power generation station. Implications on the effectiveness of FAMDDA from the application point of view are discussed.

A FULLY AUTOMATED OFFLINE HANDWRITING RECOGNITION SYSTEM INCORPORATING RULE BASED NEURAL NETWORK VALIDATED SEGMENTATION AND HYBRID NEURAL NETWORK CLASSIFIER

In this paper we propose a fully automated offline handwriting recognition system that incorporates rule based segmentation, contour based feature extraction, neural network validation, a hybrid neural network classifier and a hamming neural network lexicon. The work is based on our earlier promising results in this area using heuristic segmentation and contour based feature extraction. The segmentation is done using many heuristic based set of rules in an iterative manner and finally followed by a neural network validation system. The extraction of feature is performed using both contour and structure based feature extraction algorithm. The classification is performed by a hybrid neural network that incorporates a hybrid combination of evolutionary algorithm and matrix based solution method. Finally a hamming neural network is used as a lexicon. A benchmark dataset from CEDAR has been used for training and testing.

An automatic microcalcification detection system based on a hybrid neural network classifier

A hybrid intelligent system is presented for the identification of microcalcification clusters in digital mammograms. The proposed method is based on a three-step procedure: (a) preprocessing and segmentation, (b) regions of interest (ROI) specification, and (c) feature extraction and classification. The reduction of false positive cases is performed using an intelligent system containing two sub-systems: a rule-based and a neural network sub-system. In the first step of the classification schema 22 features are automatically computed which refer either to individual microcalcifications or to groups of them. Further reduction in the number of features is achieved through principal component analysis (PCA). The proposed methodology is tested using the Nijmegen and the Mammographic Image Analysis Society (MIAS) mammographic databases. Results are presented as the receiver operating characteristic (ROC) performance and are quantified by the area under the ROC curve ( A z ). In particular, the A z value for the Nijmegen dataset is 0.91 and for the MIAS is 0.92. The detection specificity of the two sets is 1.80 and 1.15 false positive clusters per image, at the sensitivity level higher than 0.90, respectively.