Stock market price prediction is difficult and complex task. Prediction in stock very unstable Process. Price are most of the time tend to follow patterns those more or less regular curve. Machine Learning techniques use different predictive models algorithms predict automate things reduce human effort. This research paper focuses on Long Short Term Memory (LSTM) future company using each day c...

Short-term load forecasting (STLF) plays an important role in facilitating efficient and reliable operations of power systems optimizing energy planning the electricity market. To improve accuracy prediction, adaptive clustering long short-term memory network is proposed to effectively combine process prediction process. More specifically, adopts maximum deviation similarity criterion algorithm...

For a power supplier, meeting demand-supply equilibrium is of utmost importance. Electrical energy must be generated according to demand, as large amount electrical cannot stored. the proper functioning supply system, an adequate model for predicting load necessity. In present world, in almost every industry, whether it healthcare, agriculture, and consulting, growing digitization automation pr...

Deep learning has brought about a phenomenal paradigm shift in digital steganography. However, there is as yet no consensus on the use of deep neural networks reversible steganography, class steganographic methods that permits distortion caused by message embedding to be removed. The underdevelopment field steganography with can attributed perception perfect reversal seems scarcely achievable, ...

Abstract Sequential recommendation is a stream of studies on recommender systems, which focuses predicting the next item user interacts with by modeling dynamic sequence user-item interactions. Since being born to explore tendency variable-length temporal sequence, Recurrent Neural Networks (RNNs) have been paid much attention in this area. However, inherent defects caused network structure RNN...