Stock Price Prediction using ML and LSTM based Deep Learning models

Stock Price Prediction has become an important area of research for such a very long time. A lot of research has already been made to predict the stock in a best possible manner and to gain more profit from that , Now adays some market hypothesis believe that it is nearly very difficult to predict the stock price accurately but at the same time some machine learning techniques proved that choosing of right model and appropriate variables may lead to scenario where stock prices and their movement can be easily pre-dicted with great accuracy. Prediction of stock price becomes easy due to the introduc-tion of data mining techniques which helps the researchers to identify meaningful pat-terns and find the best possible results by working on the technical analysis of stock. In this research we have implemented some of the machine learning and deep learning techniques to gain more insights of varying stock prices with respect to time the purpose of introducing the Deep Learning model is that they can predict more accurate results as they are the advanced version of Machine Learning models. We have also compared these Machine Learning and Deep Learning models so that we can get the best possible model for our project

Machine Learning Methods for Rapid Inspection of Automated Fiber Placement Manufactured Composite Structures

The advanced manufacturing capabilities provided through the automated fiber placement (AFP) system has allowed for faster layup time and more consistent production across a number of different geometries. This contributes to the modern production of large composite structures and the widespread adaptation of composites in industry in general and aerospace in particular. However, the automation introduced in this process increases the difficulty of quality assurance efforts and inspection. The AFP process can induce a number of manufacturing defects including wrinkles, twists, gaps, and overlaps. The manual identification of these defects is often laborious and requires a measure of expert knowledge. A software package for the assistance of the inspection process has been used in conjunction with automated inspection hardware for the automated inspection, identification, and characterization of AFP manufacturing defects. Image analysis algorithms were developed and demonstrated on a number of defect types. Defects are identified in scan images and exact size and shape characteristics are extracted for export

Hybrid Theory-Machine Learning Methods for the Prediction of AFP Layup Quality

The advanced manufacturing capabilities provided through the automated fiber placement (AFP) system has allowed for faster layup time and more consistent production across a number of different geometries. This contributes to the modern production of large composite structures and the widespread adaptation of composites in industry in general and aerospace in particular. However, the automation introduced in this process increases the difficulty of quality assurance efforts. Industry available tools for predicting layup quality are either limited in scope, or have extremely high computational overhead. With the advent of automated inspection systems, direct capture of semantic inspection data, and therefore complete quality data, becomes available. It is therefore the aim of this document to explore and develop a technique to combine semantic inspection data and incomplete but fast physical modeling tool into a comprehensive hybridized model for predicting and optimizing AFP layup quality. To accomplish this, a novel parameterization of Gaussian Process Regression is developed such that nominal behavior is dictated through theory and analytic models, with latent variables being accounted for in the stochastic aspect of the model. Coupled with a unique clustering approach for data representation, it is the aim of this model to improve on the current state of the art in quality prediction as well as provide a direct path to process parameter optimization