We investigate sequence machine learning techniques on raw radio signal time-series data. By applying deep recurrent neural networks we learn to discriminate between several application layer traffic types on top of a constant envelope modulation without using an expert demodulation algorithm. We show that complex protocol sequences can be learned and used for both classification and generation...

Geological facies interpretation is essential for reservoir studying. The method of classification and identification seismic traces is a powerful approach for geological facies classification and distinction. Use of neural networks as classifiers is increasing in different sciences like seismic. They are computer efficient and ideal for patterns identification. They can simply learn new algori...

In the field of artificial intelligence, adaptive learning technique refers to combinations of artificial neural networks. In this thesis the adaptive learning technique is been implemented to carry out the Detection and Localization of heart sound. It can be implemented in the NS2 and MATLAB In ns2 part logic will identify the sound of heart and in matlab part heart sound is detected. The issu...

This paper proposes a short-term load forecasting method for natural gas using deep learning. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields like image recognition and speech processing. This paper explores many aspects of using deep neural networks for time series forecasting. It is determined that the proposed ...

This study uses a revolutionary image recognition method, deep learning, for the classification of potentially malignant pulmonary nodules. Deep learning is based on deep neural networks. We report results of our initial findings and compare performance of deep neural nets using a combination of different network topologies and optimization parameters. Classification accuracy, sensitivity and s...

The timely diagnosis of brain tumors is currently a complicated task. objective was to build an image classification model detect the existence or not by adding header ResNet-50 architecture. CRISP-DM methodology used for data mining. A dataset 3847 MRI images used, 2770 training, 500 validation, and 577 testing. were resized 256 × scale then generator created that responsible dividing pixels 2...