Browsing by Author "Charles, E.Y.A."

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Analysis of Road Traffic Accidents Using Data Mining
    (Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2017) Liyanaarachchi, K.L.P.P.; Charles, E.Y.A.
    Accident happens unexpectedly and unintentionally, typically resulting in damage or injury or in fatalities. Data mining is the extraction of implicit, previously unknown, and potentially useful information from data collected for various purposes. The main objective of this research is to identify more accurate and useful patterns that would exists in the road traffic accident data using data mining techniques. It is believed that these patterns can be utilized to take measures to reduce the number of accidents or the severity of the accidents. As part of this research work, details of accidents occurred in Colombo district in the year 2015 were collected from the Traffic Headquarters, Colombo, Sri Lanka. A data set with 9487 accident incidents each detailed with 55 features was created from the collected data. This data consists four types of accidents, namely, Fatal (154), Grievous (877), Non-Grievous (2028) and Vehicle damage only (6428). There are a quite a few published studies on traffic accident analysis using data mining methods. In most of these studies, J48 classifier has produced higher accuracy than other methods. So far no such study has been reported on accidents occurred in Sri Lankan roads. A correlation analysis was performed on the data set and as a result 10 attributes were removed. In this study, the J48 decision tree classifier was usedin two ways. In the first one all four type of accidents were considered. The decision tree built with 70% of the data was able to achieve an average accuracy of 71.4687%. In the second analysis, three types Fatal, Grievous and nongrievous types were combined into one class and named as Injured. This approach was taken to reduce the effect of the vehicle damage only class, which is around 68% of the total data. The decision tree built with this merged classes was able to achieve an accuracy of 78.7288 % using a tenfold cross validation. The decision tree was converted into 20 rules, which can predict the type of accident based on the identified attribute values. The results were found to be helpful to identify the factors influencing traffic accidents and can be further analyzed to find more subtle reasons or situations that are causing accidents.
  • Thumbnail Image
    Item
    E-Waste Generation and Management Practices in Jaffna and Nallur Divisional Secretariat Divisions, Sri Lanka – A Base Line Study
    (Department of Zoology, University of Kelaniya, Kelaniya, Sri Lanka, 2014-06) Kayathiri, K.; Charles, E.Y.A.; Surendran, S.N.
    The post-conflict development state of Jaffna District has resulted in increased e-waste generation and the problems associated with it. In this phase of development, Jaffna peninsula faces several environmental problems which need an urgent attention. This baseline study was conducted to estimate the magnitude and flow of e-waste generation and to understand the e-waste management practices in Jaffna district. It was conducted in the Jaffna and Nallur Divisional Secretariat Divisions in Jaffna District from February to August 2013 targeting households and corporate as key consumers. Using skip sampling method, random samples of two hundred households in each GN divisions were selected and interviewed. Six offices were selected to represent government, banks and telecom companies. Results of the household survey were compared with the usage of items reported in the 2004 – Consumer Finance and Socio-Economic Survey conducted by the Central Bank of Sri Lanka. The survey revealed that the usage of refrigerators has increased to 58% from 12.8% while the usage of washing machines has increased to 34% from 0.8%. The usage of televisions has increased to 98% from 43.9%. The survey at the corporate reveled that out of their total electrical and electronic equipments desktop computer shows high percentage (28%) of usage. Significantly greater percentage (22%) of LED/LCD screen monitors are used while the usage of CRT monitors is comparatively less (6%). A large number of (9%) air conditioners are in use. 80% of the households are keeping their e-waste at home and are reluctant to throw them as waste. Similarly all public offices keep e-waste until they are auctioned. Large enterprises send their e-waste to the head offices which are located in Colombo. There are no e-waste collection centers in Jaffna. Even though a large number of people can distinguish (70%) e-waste, 100% of households are not aware of what happens to the equipment they have discarded as general waste. Similarly the corporates are also unaware of it. The study also tried to estimate the increase of equipment usage through the electricity consumption. There is a high increase in electrical and electronic equipment usage in Jaffna district which will increase the e-waste dramatically. Also there is a severe lack of knowledge regarding e-waste. Establishing an e-waste collection centre in Jaffna district is an essential need. General public should be made aware of the negative impacts of uncontrolled disposal of e-waste.