Pothole detection








Abstract

As a new form of malicious software phishing websites appear frequently in recent years which cause great harm to online financial services and data security. In this paper we design and implement an intelligent model for detecting phishing websites. In this model we extract 10 different types of features such as title keyword and link text information to represent the website. Heterogeneous classifiers are then built based on these different features. We propose a principled ensemble classification algorithm to combine the predicted results from different phishing detection classifiers. Hierarchical clustering technique has been employed for automatic phishing categorization. Case studies on large and real daily phishing websites collected from Kingsoft Internet Security Lab demonstrate that our proposed model outperforms other commonly used anti-phishing methods and tools in phishing website detection. With widespread adoption of smartphones some of humans tough problems are being tackled with mobile applications. One of many such problems is road traffic accidents and congested traffic in metropolitan cities. According to the World Health Organization (WHO) road accidents accounted for 53 339 fatalities in Nigeria in the year 2010 and with growing number of vehicle users traf?c is growing day by day. Technology more specifically mobile phone technology has evolved to enable miniature devices the capability of containing powerful sensors. The functionalities of these sensors such as accelerometers present in smartphones is what this study exploits to develop a system capable of automatically detecting potholes in real-time and monitoring road traffic conditions. Machine learning techniques; Support vector machines based on K-means clustering are applied to the data obtained from such sensors to estimate road/traffic conditions. Previous work in this area puts the onus on the user and pays little attention to giving incentives for the tedious and mundane task of cataloging potholes or any other road anomalies. As such this study goes beyond simply detecting or estimating road/traffic conditions and derives utility for the user by making use of the data collected to enable prevention of potholes while driving and visualizing roads traffic which would inform decisions on alternate routes. The developed system is evaluated using data obtained from the crawdad.org database and a test drive on Ilorin roads shows promising results.


Modules


Algorithms

CNN


Modification

application




Price

₹12000 (INR)


Year