Browsing by Author "Ariyadasa, H.M.S.N."
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Item Data Milling Approach to Predict Climate Changes in Sri Lanka(Uva Wellassa University of Sri Lanka, 2016) Kumarasinghe, K.H.S.; Ariyadasa, H.M.S.N.Knowledge of climate data or weather data in a country is essential for business, society, agriculture and energy applications. Therefore, extracting some hidden knowledge relevant to business, society, agriculture and energy by using different techniques are essential to enrich the knowledge base. The technique, data mining can answer questions that have not traditionally been solved or very time consumed to resolve. Therefore, this technique can be used to predict climate changes by using available data. Climate change prediction is a vital application in meteorology. It is one of the most scientifically, technically challenging problem across the world in the last century. Many climate predictions like rainfall prediction, thunderstorm prediction, predicting cloud conditions are major challenges for atmospheric research. Predicting the climate changes is essential to help to precautious for the climate. This paper presents the data mining technique for predict precipitation changing patterns and temperature changing patterns using classification technique. The classification is done using decision tree algorithm with 50 years average data. The data were collected from WorldClim website, which includes climate data of many countries in the world. Data was collected from 10,000 random places in Sri Lanka. The data were in a satellite image format. Around 10,000 of random data samples were extracted using ArcGIS application including attributes Temperature, Precipitation, Altitude, Bioclim, etc. The Standard Knowledge Discovery in Databases (KDD) process was applied to the dataset to discover the hidden pattern in climate. After removing data inconsistencies in the pre-processing stage, smoothing, generalization and aggregation were applied in the transformation stage. A data model for the climate data was developed and trained by using j48 decision tree classifier algorithm. WEKA was selected as the data mining tool and it was produced a decision tree relevant to the data set. The finding of this research deep-rooted again, the different altitude levels and precipitation levels affect the temperature norm. Keywords: Climate change, Data mining, J48, Classification, Weka, WordclimItem DIGITAL FORENSIC STEGANALYSIS OF ENCRYPTED INFORMATION WITH SPECIAL REFERENCE TO THE MP4 VIDEOS(Uva Wellassa University of Sri Lanka, 2018) Palliyage, S.L.; Kankanamge, N.D.; Ariyadasa, H.M.S.N.Computerized information correspondence has turned into an integral piece of foundation these days and the security and the privacy of them assumes an essential part. Cryptography and Steganography are being two major methods having a wide usage, which secure information. Steganography conceal secret information and does not leave evident proof of information modification. Out of numerous techniques of application of Steganography, current study focused on End of File Injection technique. The study investigated the Steganalysis of Mp4 type videos, which were encrypted utilizing the above technique. A pool of Mp4 videos consisted of several qualities and capacities were tested and evaluated. A detector system was developed which was capable of identifying the presence of Steganography within the Mp4 videos, thus the system can be further used to scan the suspected Mp4 videos and give the results whether it has embedded data or not. The system reported a higher accuracy level of detecting Mp4 Stego-videos, which used the said techniques for data embedding. Further studies are needed to cover other video formats and other techniques of Steganography. The development of the field would reveal new paths in digital forensic investigations.Item Smart Tour Planner for Sri Lanka(Uva Wellassa University of Sri Lanka, 2019-02) Kumar, P.; Deemantha, S.P.S.; Lakmal, E.K.H.; Ariyadasa, H.M.S.N.; Wimaladharma, S.T.C.I.Tourism industry is an asset to Sri Lankan economy. It contributes around 5% in national revenue. The development of tourism industry is significantly slow as compared to the other tourist countries because we are not using new technologies in this field. In recent years, several applications are developed for the tourism industry. However, those applications are only used to provide the information about the places. The most important thing when it comes to going for a tour is to manage the time. Moreover, managing time at an unknown place is very difficult. Climatic changes also affect the tourists to visit some places. To overcome these issues, we have proposed an Android based mobile application that can help tourists to plan their tour to Sri Lanka before arriving here. The application covers 5 major areas with the methodology. 1) Finding the shortest route using “Nearest Neighbor algorithm” that covers the as many as possible places to visit with respect to the tourist’s budget, time and tour type. 2) An intelligent system that records the time spend at a place, then by using machine learning algorithms using Google’s “TensorFlow” that can predict the time needed to visit that particular place for new tourists. 3) A schedule for tourists that they have to follow during their tour that helps to manage time. 4) Alerting the tourists for any emergency situations (flood, tsunami, Land sliding) using crowdsourcing. 5) Comment summarization and sentiment analysis that can give a brief idea about the places those tourists are planning to visit. At last, user gets a scheduled tour that he can follow up during visit to Sri Lanka. The evaluation of this application depends upon the time and money saved due to scheduling of the tour, and that saved time is use to visit some more places. This application helps the tourists to plan their tour easily that significantly increase the tourism in the country and will positively affects the country’s economy.