From the past decade there has been drastic development and deployment of digital data stored in electronic health record (EHR). Initially, it is designed for getting patient general information and performing health care tasks like billing, but researchers focused on secondary and most important use of these data for various clinical applications. In this paper we used deep learning based clinical note multi-label multi class approach using GloVe model for feature extraction from text notes, Auto-Encoder for training based on model and Navie basian classification and we map those classes for multi- classes. And we perform experiments with python and we used libraries of keras, tensor flow, numpy, matplotlib and we use MIMIC-III data set. And we made comparison with existing works CNN, skip-gram, n-gram and bag-of words. The performance results shows that proposed frame work performed good while classifying the text notes.

The research activity considered in this paper concerns about efficient approach for modeling and prediction of air quality. Poor air quality is an environmental hazard that has become a great challenge across the globe. Therefore, ambient air quality assessment and prediction has become a significant area of study. In general, air quality refers to quantification of pollution free air in a particular location. It is determined by measuring different types of pollution indicators in the atmosphere. Traditional approaches depend on numerical methods to estimate the air pollutant concentration and require lots of computing power. Moreover, these methods cannot draw insights from the abundant data available. To address this issue, the proposed study puts forward a deep learning approach for quantification and prediction of ambient air quality. Recurrent neural networks (RNN) based framework with special structured memory cells known as Long Short Term Memory (LSTM) is proposed to capture the dependencies in various pollutants and to perform air quality prediction. Real time dataset of the city Visakhapatnam having a record of 12 pollutants was considered for the study. Modeling of temporal sequence data of each pollutant was performed for forecasting hourly based concentrations. Experimental results show that proposed RNN-LSTM frame work attained higher accuracy in estimating hourly based air ambience. Further, this model may be enhanced by adopting bidirectional mechanism in recurrent layer.

The present paper aims at performance evaluation of Doubly Truncated Generalized Laplace Mixture Model and Hierarchical clustering (DTGLMM-H) for image analysis concerned to various practical applications like security, surveillance, medical diagnostics and other areas. Among the many algorithms designed and developed for image segmentation the dominance of Gaussian Mixture Model (GMM) has been predominant which has the major drawback of suiting to a particular kind of data. Therefore the present work aims at development of DTGLMM-H algorithm which can be suitable for wide variety of applications and data. Performance evaluation of the developed algorithm has been done through various measures like Probabilistic Rand index (PRI), Global Consistency Error (GCE) and Variation of Information (VOI). During the current work case studies for various different images having pixel intensities has been carried out and the obtained results indicate the superiority of the developed algorithm for improved image segmentation.

Probability distributions formulate the basic framework for developing several segmentation algorithms. Among the various segmentation algorithms, skin colour segmentation is one of the most important algorithms for human computer interaction. Due to various random factors influencing the colour space, there does not exist a unique algorithm which serve the purpose of all images. In this paper a novel and new skin colour segmentation algorithms is proposed based on bivariate Pearson type II mixture model since the hue and saturation values always lies between 0 and 1. The bivariate feature vector of the human image is to be modeled with a Pearson type II mixture (bivariate Beta mixture) model. Using the EM Algorithm the model parameters are estimated. The segmentation algorithm is developed under Bayesian frame. Through experimentation the proposed skin colour segmentation algorithm performs better with respect to segmentation quality metrics such as PRI, VOI and GCE. The ROC curves plotted for the system also revealed that the proposed algorithm can segment the skin colour more effectively than the algorithm with Gaussian mixture model for some images.

In this paper, we introduce a face recognition algorithm based on doubly truncated multivariate Gaussian mixture model with DCT under logarithm domain. In face recognition, the face image is subject to the variation of illumination. The effect of illumination cannot be avoided by mere consideration of DCT coefficients as feature vector. The illumination effect can be minimized by utilizing DCT coefficients under logarithm domain and discarding sum of the DCT coefficients which represents the illumination in the face image. Here, it is assumed that the DCT coefficients under logarithm domain after adjusting the illumination follow a doubly truncated multivariate Gaussian mixture model. The truncation on the feature vector has a significant influence in improving the recognition rate of the system using EM algorithm with K-means or hierarchical clustering, the model parameters are estimated. A face recognition system is developed under Bayesian frame using maximum likelihood. The performance of the system is demonstrated by using the databases namely, JNTUK and Yale and comparing it’s performance with the face recognition system based on GMM. It is observed that the proposed face recognition system outperforms the existing systems.