Research Article a May 2018 © www.ijarcsse.com , All Rights Reserved Page | 21 International Journals of Advanced Research in Computer Science and Software Engineering ISSN: 2277-128X (Volume-8, Issue-5) An Improvement on Deep Time Growing Neural Network on Biological Signals: Review Manpreet Kaur Research Scholar, Guru Kashi University, Talwandi Sabo, Punjab, India Er. Jashanpreet Kaur Assistant Professor, Guru Kashi University, Talwandi Sabo, Punjab, India Abstract: A novel method for learning the cyclic contents of stochastic time series: the deep time-growing neural network (DTGNN). The DTGNN combines supervised and unsupervised methods in different levels of learning for an enhanced performance. It was employed by a multiscale learning structure to classify cyclic time series (CTS), in which the dynamic contents of the time series are preserved in an efficient manner. This paper suggests a systematic procedure for finding the design parameter of the classification method for a one versus-multiple class application. In this paper different authors research papers are reviewed and different problems are stored and now these problems are resolved. Keywords: DNN, HMM, CTS, MLP, TDNN etc. I. INTRODUCTION Time series classification has been a topic of study over decades. Several supervised and unsupervised methods have been suggested with which to learn the dynamic contents of time series. Dynamic time warping, hidden Markov model (HMM), and artificial neural network are three well known methods extensively employed in many contexts, e.g., automatic speech recognition [1]–[3]. Nevertheless, the development of a classification method sophisticated for the cyclic time series (CTS) had been overlooked in the model level, even though a number of methods were subjectively applied to the CTS [4]–[6]. A CTS is described as a no stationary time series exhibiting repetitive characteristics. Unlike periodic time series, the cyclic duration of a CTS can be inconsistent, but repetitive patterns are observed. This cyclic behavior attributes special features to the time series that can be exploited by a classifier to enhance its classification performance. The importance of developing a sophisticated method for the CTS classification is realized when considering that a recording of many natural phenomena and biological activities resembles a CTS. As an example, phonocardiogram (PCG) is a recording of the sounds emanating from the mechanical activity of a heart. This is considered as a typical CTS where the cycle duration is affected by a number of physiological activities, e.g., respiration. Several studies reported the importance of having a reliable decision support system for screening pediatric cardiac disease in primary healthcare centers, as the screening accuracy is still considerably low [7], [8]. The main challenge for developing a decision support system for screening cardiac disease is a reliable method for processing and classifying the PCG signal. Such a need is seen in different medical applications, in which the biological signal is cyclic, and the classification of the signal can be important, and sometimes critical to patient monitoring, as is the case, for instance, with the classification of the patterns associated with electroencephalograms (EEGs). Biological signals with cyclic characteristics often show no stationary behavior not only within the cycles, but over them in the cycle-to-cycle variation. This associates a high level of complexity with the signal that makes the development of the classifier, a big challenge. Unlike many industrial applications, the origin of the complexities in the majority of the biological signals has yet to be fully understood. As a result, the stochastic models can provide a better learning than deterministic ones, especially when it comes with a general model for diverse medical applications. Such a model needs to be capable of coping with the complexities of the signals. A multiscale structure for classifying CTS based on a novel deep machine learning method. This paper introduces the idea of exploiting the cyclic contents of the time series in a certain fashion named the time-growing sectors. This idea serves as the means for building a novel architecture of the time-growing neural network (TGNN), which we call it the deep TGNN (DTGNN), in which an efficient classification is performed through multiple mapping. The proposed structure preserves the dynamic contents of CTS by the concise feature vector that provides optimal discrimination power. It is theoretically and experimentally proved that the DTGNN offers superior learning compared with two other alternatives, the time delayed neural network (TDNN) and the multilayer perceptron (MLP). In this paper, the support vector machine (SVM) performs the ultimate binary classification using the outcomes of the DTGNN as the