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The process of partitioning into different objects of an image is segmentation. In different major fields like face tracking, Satellite, Object Identification, Remote Sensing and majorly in medical field segmentation process is very important to find the different objects in the image. To investigate the functions and processes of human boy in radiology magnetic resonance imaging (MRI) will be used. MRI technique is using in many hospitals for the diagnosis purpose widely in finding the stage of a particular disease. In this paper, we proposed a new method for detecting the tumor with enhanced performance over traditional techniques such as K-Means Clustering, fuzzy c means (FCM). Different research methods have been proposed by researchers to detect the tumor in brain. To classify normal and abnormal form of brain, a system for screening is discussed in this paper which is developed with a framework of artificial intelligence with deep learning probabilistic neural networks by focusing on hybrid clustering for segmentation on brain image and crystal contrast enhancement. Feature's extraction and classification are included in the developing process. Performance in Simulation of proposed design has shown the superior results than the traditional methods. [ABSTRACT FROM AUTHOR]

Recognition of finger vein patterns is essential technique that analyses the finger vein patterns to enable accurate authentication of an individual. A proper, accurate and quick learning of patterns is essentially required for improving the classification pattern. It is essential in developing an intelligent algorithm to effectively study and classify the patterns. In this paper, we develop an improved deep learning hybrid model for feature extraction and classification. A dimensional reduction deep neural network (DR-DNN) model has included a dimensional reduction model for extracting the essential features by reducing the dimensionality of feature datasets. A convolutional neural network (CNN) helps in classifying the benign vein patterns from the malignant vein patterns. The effectiveness is compared against existing deep learning classifiers to measure how effective the deep learning model is used for classifying finger vein patterns for biometric authentication. The results shows that the proposed method achieves an accuracy rate of 97.16% for the proposed method, where the other existing methods including CNN, Recurrent Neural Network (RNN) and Deep Neural Nets (DNN) has an accuracy rate of 86%, 80.66% and 88.31%, respectively. [ABSTRACT FROM AUTHOR]

Aiming at the low effectiveness of short texts feature extraction, this paper proposes a short texts classification model based on the improved Wasserstein-Latent Dirichlet Allocation (W-LDA), which is a neural network topic model based on the Wasserstein Auto-Encoder (WAE) framework. The improvements of W-LDA are as follows: Firstly, the Bag of Words (BOW) input in the W-LDA is preprocessed by Term Frequency–Inverse Document Frequency (TF-IDF); Subsequently, the prior distribution of potential topics in W-LDA is replaced from the Dirichlet distribution to the Gaussian mixture distribution, which is based on the Variational Bayesian inference; And then the sparsemax function layer is introduced after the hidden layer inferred by the encoder network to generate a sparse document-topic distribution with better topic relevance, the improved W-LDA is named the Sparse Wasserstein-Variational Bayesian Gaussian mixture model (SW-VBGMM); Finally, the document-topic distribution generated by SW-VBGMM is input to BiGRU (Bidirectional Gating Recurrent Unit) for the deep feature extraction and the short texts classification. Experiments on three Chinese short texts datasets and one English dataset represent that our model is better than some common topic models and neural network models in the four evaluation indexes (accuracy, precision, recall, F1 value) of text classification. [ABSTRACT FROM AUTHOR]