IMPROVED PSO BASED DRIVER’S DROWSINESS DETECTION USING FUZZY CLASSIFIER

In this drowsiness detection framework two actions including brain and visual features are utilised to distinguish the various levels of drowsiness. These actions are provided by the EEG and EOG signal brain actions. From the EEG and EOG signals the peculiarities like mean, peak, pitch, maximum, minimum, standard deviation are assessed . In these peculiarities we decide on some best attributes - peak and pitch employing an IPSO strategy that picks up the best threshold esteem. These signals are then offered into the STFT which is employed to discover the signal length, producing a STFT network from the intermittent hamming window,the output of which are energy signals alpha and beta. These energy signals are offered into the MCT to get an alpha mean and a beta mean -the most chosen and outstanding attributes. These are then subjected to fuzzy based classification to give a precise result checking over the maximum values in the alpha and the beta series . &nbsp

Detection of Diseases in Flora Through Leaf Image Classification by Convolution Neural Network

The quality of human existence and economic standing are significantly impacted by agriculture. It is the foundation of a nation's economic structure. Therefore, early diagnosis of plant diseases is crucial in both the agricultural sector and in people's daily life. Hunger and starvation are caused by agricultural losses due to plant diseases, especially in less developed nations where access to disease-controlling measures is limited and yearly losses of 30 to 50 percent for main crops are not unusual. Due to inadequate diagnosis of plant diseases, many plants die. Initially, diagnosis of plant disease was performed using MATLAB and machine learning algorithms including SVM. But these diagnoses did not provide accurate results. Also, in previous works website has not been created. To overcome this problem, a CNN model has been proposed that detects plant diseases. This CNN model has been deployed to the website. On this website, the image can be uploaded, and the disease gets predicted according to the image. The detected disease gets displayed on the website. To the CNN model, 15 cases have been fed, including both healthy and unhealthy leaves. The proposed model achieves a greater accuracy of more than 95%. This work offers a major benefit to the farmers by helping them in detecting plant diseases without requiring any special hardware or software