IRSPAS 2016
Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/15651
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Item Real-time big data video analytics for unorganized traffic environments(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Ranaweera Arachchi, I.; Jayalal, S.; Rajapakse, C.Traffic on local roads has reached such a level that it is necessary to address the issue of traffic congestion and seek complex transport solutions for the city. Increase of the number of vehicles on the road becomes one of the key reasons for increasing traffic congestion. Traffic congestion is associated with massive financial and manhour loss and therefore attempts to alleviate this has been of keen interest. The basis of almost all those approaches is traffic monitoring and analysis, leading to having an effective traffic management system. Most traffic management systems are applied in well-organized traffic environments such as highways, where driver discipline is high. But in unorganized urban environments as seen in Sri Lanka, road traffic behavior vary from the accepted standards. Driver and pedestrian indiscipline cause huge traffic congestions in urban areas. Hence in such a scenario, a system that monitors road traffic on different traffic environments is very useful. There are several existing techniques such as Magnetic Loops, Microwave RADAR, Infrared Detectors, Ultrasonic Detectors and Camera Based Systems. Traffic monitoring systems require short processing time, low processing cost and high reliability. Therefore, according to the literature, camera-based monitoring is the best-suited technique for traffic monitoring. Real-time video analytics are part of a centralized approach to modern traffic management which is defined as computer vision-based surveillance that provides algorithms for object detection, tracking, classification and trajectory analysis using real-time traffic surveillance video. It usually uses roadside cameras (CCTV) to obtain traffic information and transmit it to central servers, exhibiting real-time operability of big data. In this study, several approaches and algorithms for moving object detection, based on temporal differencing method, optical flow method, background filtering are compared and a novel real-time vehicle detection and classification algorithm based on background filtering will be proposed and re-engineered in order to be applicable to challenging unorganized traffic environments. The solution will classify vehicles individually and their trajectories in real time in unorganized traffic environments in order to analyze the behaviors of the drivers as well as pedestrians on the road. We use OpenCV which is a library of programming functions mainly aimed at real-time computer vision, for implementing and testing algorithms. Data will be collected via pre-recorded video clips from Kiribathgoda junction in the western province, for the testing purpose and real- time CCTV surveillance video is going to be used as the input for implementation. A comprehensive data analysis is required to be conducted to address the higher processing requirement of such videos. The solution will be validated for performance subsequently. The final objective of this research is to come up with an optimum algorithm for vehicle detection and classification in unorganized traffic environments which would help to analyze the behavior of road users. The solution will lead to reduced traffic congestion in the country by enhancing the efficiency and effectiveness of traffic monitoring and analyzing systems.Item Detection of cyber bullying on social media networks(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Priyangika. S.; Jayalal, S.Social Media is becoming an integral part of people’s daily lives today. It is an effective way of sharing one’s life experiences, special occasions, achievements and other events with their friends and family. Although it is a fruitful way to communicate with groups, some people find themselves being insulted or offended by others who are involved in certain post or conversations. These insulations can be based on racism, using profanity or any other vulgar or lewd language. This cyber bullying needs to be monitored and controlled by the social media site owners since it will highly effect on the number and safety of the active site membership. Currently, there is no automated process of identifying offensive comments by the social network site itself. It can be only diagnosed by humans after reading the comments, flagging or reporting them to the owner of the site or blocking the offender. Considering the massive big data set generated in social media daily, automatically detection of offensive statements is required to reduce insulation effectively. For this purpose, text classification approach can be applied where a given text will be categorized as insulting or not, through learning from a pre-learned model. In order to develop the model, data was collected from the popular data repository site named www.kaggle.com. The dataset consists of comments posted on Facebook and Twitter. Firstly the dataset was divided into training data set and test data set. Then the collected data was preprocessed by removing the unwanted strings, correcting words and eliminating duplicate data fields. In the next step, features or keywords were extracted which are qualified to distinguish a statement as ‘insulting’ using N-grams model and counting methods. Feature selection is done using Chi- Squared test and finally apply classification algorithms for separating insulting comments and non-insulting comments from a dataset given. Machine learning algorithms such as Support Vector Machines (SVM), Naïve Bayes, Logistic Regression and Random Forest are used for this. Out of the classification algorithms, SVM is to be performed better than other algorithms since this is a two-class classification problem and a comment is to be classified only into two separate classes which are ‘insulting’ and ‘neutral’. With an exact separation of a given comment into ‘insulting’ and ‘neutral’ category, cyberbullying happening through offensive comments posted on social media sites can be detected.Item Predicting box office success of movies using sentiment analysis and opinion mining(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Basnayake, H.; Jayalal, S.Movies and social media come together as a result of people sharing their opinions on social media and movie makers using the same platforms for movie promotions. From movie makers to movie goers, many parties are interested in the success or failure of a movie. Forecasting the success of a movie before its release has been a difficult task for many industry analysts. Since film industry’s unpredictable nature, many analysts have come up with different algorithms and mechanisms to predict the success of a movie. One of the mechanisms to predict the box office success is hype analysis. Hype is one of the factors that drive people to the theatres to watch a new movie. Box office opening of a new movie depends on this hype and it will boost up the total box office collection. Hype can be estimated through social media platforms like Twitter. Twitter can be used as a corpus for sentiment analysis and opinion mining. A movie’s success cannot be predicted in a high accurate level solely based on social factors. Classical factors like movie’s brand name, cast, director, etc. are also important aspects in movie’s performance at box office and should be considered as well. However, a highly accurate method for movie box office prediction integrating both social and classical factors is yet to be introduced for this research area. In this study, tweets related to the particular movie before releasing are collected using an archiver tool and are used as input data. Then the collected data is preprocessed in order to get a clean dataset. As a part of sentiment analysis and opinion mining, feature selection is performed using N-gram method in order to filter out irrelevant data records and unlike Bag of words method, this does not require an extensive dictionary of words since it uses combinations of words and letters. Afterwards the data related to classical factors are integrated with the proposed formula in order to predict the opening box office collection of the movie. The proposed formula is an extension of a formula used in a previous research and the new extension represent the inclusion of classical factors. Finally, the results are compared with actual box office data and the previous formula results in order to compare and determine the level of accuracy. Based on initial results, the proposed formula showed of an accuracy level more than 85 percent when the results were compared with actual box office data. Even though it produced a higher accuracy level, the results produced were less than the actual box office values. Thus further testing is needed to determine the actual accuracy level.