The ability to determine infarction thickness using magnetic resonance perfusion modulated imaging (PWI) should assist physicians to decide how vigorously to treat severe stroke victims. Algorithms for predicting tissue fate have indeed been created, although they are largely based on hand-crafted characteristics extracted from perfusion pictures, which seem to be susceptible to background subtraction approaches. Researchers show how deep convolution neural networks (CNNs) can be used to predict final stroke infarction thickness only using primary perfusion data throughout this paper. The number of recoverable tissues determines the alternative treatments for patients with acute ischemic stroke. The accuracy of this measurement technique is currently restricted by a set threshold and limited imagining paradigms. The values collection from real-time sensors was used to create and evaluate this suggested deep learning-based stroke illness statistical method. Several deep-learning systems (CNN-LSTM, LSTM, and CNN-Bidirectional LSTM) that specialize in time series analysis prediction and classification were analyzed and compared. These findings show that noninvasive technologies that can simply measure brainwave activity by itself can forecast and track stroke illnesses in real-time throughout ordinary life are feasible. When compared with the previous measuring approaches, these findings are predicted to lead to considerable improvements in early stroke diagnosis at a lower cost and with less inconvenience.
Today’s markets are rather matured and arbitrage opportunities remain for a very short time. The main objective of the paper is to devise a stock market ontology-based novel trading strategy employing machine learning to obtain maximum stock return with the highest stock ratio. The paper aims to create a dynamic portfolio to obtain high returns. In this work, the impact of the applied machine learning techniques on the Chinese market was studied. The problem of investing a particular total amount in a large universe of stocks is considered. The Chinese stocks traded on Shanghai Stock Exchange and Shenzhen Stock Exchange are chosen to be the entire universe. The inputs that are considered are fundamental data and company-specific technical indicators unlike the macroscopic factors considered in the existing systems. In the stock market document repository, ontological constructs with Word Sense Disambiguation (WSD) algorithm improve the conceptual relationships and reduce the ambiguities in Ontological construction. The machine learning techniques Kernel Regression and Recurrent Neural Networks are used to start the analysis. The predicted values of stock prices from the Artificial Neural Network provided quite accurate results with an accuracy level of 97.55%. In this study, the number of nodes will be selected based on Variance-Bias plots by tracking the error on the in-sample data set and the validation data set.
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