In india, road transportation dominates all other means of transportation mechanisms. Well maintained roadways make the travel smooth and comfortable for the passengers. Most of the time road irregularities such as potholes and humps create disturbances to cozy travel and cause major damages to vehicles. Detecting road potholes and road roughness levels is a key to road condition monitoring, which impacts on transport safety and driving comfort.This paper proposes a method that aims to monitor road surfaces, detect road potholes and humps and predict their severity by analyzing the vertical vibration signals produced by the vehicle while it moves. The proposed sytem uses smartphone accelerometer to capture the vehicle vibrations in which Z-axis reading corresponds to the vehicle vertical vibrations. Gaussian model based mining algorithm is proposed for the abnormal event detection, X-Z ratio filtering is applied for event classification as pothole or hump. Severity estimation algorithm is proposed, which makes use of the relation between vertical acceleration and relative vertical displacement of the vehicle.

Diabetic Retinopathy (DR) is considered as the complication of Diabetes Mellitus that damages the blood vessels in the retina. This is characterized as a serious vision-threatening problem in most of the diabetic subjects. Effective automatic classification of diabetic retinopathy is a challenging task in the medical field. The feature extraction plays an eminent role in the effective classification of disease. The proposed work focuses on the extraction of Haralick and Anisotropic Dual-Tree Complex Wavelet Transform (ADTCWT) features that can perform reliable DR classification from retinal fundus images. The Haralick features are based on second-order statistics and ADTCWT reliably extracts the directional features in images. The proposed work concentrates on both binary classification as well as multiclass classification of DR. The system is evaluated across various classifiers such as Support Vector Machine (SVM), Random Forest, Random Tree, J48 classifiers by giving input image features extracted from the MESSIDOR, KAGGLE and DIARETDB0 databases. The performances of the classifiers are analyzed by comparing specificity, precision, recall, False Positive Rate (FPR) and accuracy values for each classifier. The evaluation results show that by applying the proposed feature extraction method, Random Forest outperforms all the other classifiers with an average accuracy of 99.7% and 99.82% for binary and multiclass classification respectively.