THE IMPACT OF VITAMIN B12 DEFICIENCY ON NEUROLOGICAL HEALTH INSIGHTS FROM CLINICAL NEUROPHYSIOLOGY

Abstract

ABSTRACT Sun spot is a natural phenomenon which impact on various changes in the earth especially in weather, climatic conditions, disasters and diseases etc. The existing methods on prediction of sun spots are majorly focused on mathematical and statistical implications. In order to make more productivity, in this research, we applied Artificial Neural network and Machine Learning Methods on sun spots data set to prediction accuracy of various sunspot data. The results and accuracy are based in the training of dataset with SVM classifiers and Machine Learning Methods. The merits of this research prove the prediction accuracy of sun spot numbers and LSTM models are to be applied for the sunspot predictions which performs better in accuracy. SVM classifiers hold various advanced algorithms to make training on data to achieve better accuracy and performance. Among the various kinds of SVM classifiers in the area of prediction Linear SVM, Quadratic SVM and Cubic SVM are been considered for this performance evaluation especially in Prediction accuracy. The main parameter applied for prediction is Rooted Mean Square Error (RMSE). The experimentation part take place using the MATAB tool. An improved Vanilla LSTM model is proposed to overcome the various challenges in the existing models. The main objective of this model is to make predictions using sun spots. This Vanilla Long-Short Term Memory (LSTM) model is applied with optimized hyper parameters with fine tuning i.e. batch size, epoch, and optimizer etc., Adam optimizer is applied for the optimization during the process. Single layer is used and the optimized hyper parameters provide better results.  The prediction process is accomplished by data set and is pre processed with normalization then the sequence is created and the LSTM architecture is established. Based the training and testing data the prediction process is done. The model evaluates the similarity measures such as Absolute Error, Relative Error and Related Mean Square Error (RMSE). The performance of the model is estimated by comparing with the existing Stacked LSTM and Vanilla LSTM model.