Classification of stress is imperative especially with regard to automobile drivers since stress level of the driver forms a major factor for accidents. This paper deciphered the classification of stress of automobile drivers using Radial Basis Function Kernel Support Vector Machine (SVM) classifier. The nonlinear separation of features in feature space was deciphered by this kernel trick. Pertinent feature extraction was done from ECG and EMG signals of the driver. Features extracted intuitively showed correlation with stress. This was made solid after getting a high classification accuracy of 100% using SVM using 10 fold cross validation. SVM performance was compared with that of kNN classifier and cross validation showed that kNN had only 81.26, 62.13 and 88.93% of classification rate, sensitivity and specificity where for SVM these parameters were 100%.

No abstract

Plant pathogens prediction is the prerequisite for timely and productive control of plant pathogens within complicated environments. However, the white mold is a complicated disease in a brinjal plant. Hence, to vanquish these difficulties a novel Deep Learning Integration (DLI) Techniques has been proposed. In Proposed system, classification is carried out by Support Vector Machine (SVM) and prediction is carried out by Convolutional Neural Network (CNN) Algorithm to predict the plant illness in Brinjal with high accuracy of 99.4%.

No abstract

The early diagnosis of Parkinson’s Disease (PD) is a challenging practice for doctors. Currently, there are no separate diagnostics and tests to be done to predict onset PD. However, the PD can be predicted through repeated clinical trials and tests. Sometimes, early prediction of PD can become tedious based on trials and tests. The computer-aided prediction will help medical professionals predict PD accurately during one’s onset stages to improve the PD patients’ quality of life. Hence, early prediction of PD is essential. In this article, Convolution Neural Networks (CNN) is proposed to classify PD patients and healthy individuals. The brain MRI images are given as input for the proposed methodology. The CNN deep neural network will first extract the features from the images. Then, it will classify the PD patients and healthy individuals from the extracted features. The automatic feature extraction will improve the accuracy of the classifier and reduce human error. The brain MRI images are taken from the PPMI dataset for experimentation. The sensitivity, specificity, and accuracy are calculated to assess the performance of the proposed methodology. The loss is also calculated to verify the performance of the classifier. It is observed that the CNN classifier has produced a higher accuracy of more than 98% in classifying PD patients and healthy individuals when compared to multi-layer perceptron deep learning.