• Manpreet Kaur
• Er. Jashanpreet Kaur

a novel strategy for taking in the cyclic substance of stochastic time arrangement: the profound time-developing neural system. The DTGNN consolidates administered and unsupervised strategies in various levels of learning for an upgraded execution. It was utilized by a multiscale learning structure to group cyclic time arrangement (CTS), in which the dynamic substance of the time arrangement are protected in a proficient way.This paper recommends a precise methodology for finding the outline parameter of the characterization strategy for a one versus-different class application. A novel approval technique is additionally proposed for assessing the auxiliary hazard, both in a quantitative and a subjective way. The impact of the DTGNN on the execution of the classifier is measurably approved through the rehashed irregular sub inspecting utilizing distinctive arrangements of CTS, from various therapeutic applications. In this paper Respiration dataset and ECG signals are tested and out of these signals average respiration rate 18.25 is achieved.

"A modified deep machine learning method for classifying cyclic time series of biological signals using time growing neural network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 7, page no.40-44, July-2018, Available :http://www.jetir.org/papers/JETIR1807005.pdf

"A modified deep machine learning method for classifying cyclic time series of biological signals using time growing neural network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 7, page no. pp40-44, July-2018, Available at : http://www.jetir.org/papers/JETIR1807005.pdf