International Conference on Advances in Computing and Technology (ICACT)
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Item Behavior & Biometrics Based Masquerade Detection Mobile Application(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Chandrasekara, P.; Rajapaksha, S.; Abeywardana, H.; Sanjeevan, P.; Abeywardena, K. Y.Mobile phone has become an important asset when it comes to personal security since one’s mobile is now a virtual safe for that person. This is due to the sensitivity of the details which are stored in these devices. To protect the information inside a mobile phone the manufacturers use conventional technologies such as password protection, face recognition or finger print protection. Nevertheless, it is clear that these security methods can be bypassed by several other techniques as shoulder surfing, finger print or face recognition by pass with 3D printing. Due to these concerns post authentication is an increasingly discussed topic in the security domain. However, there are very few applied researches done on the post authentication of mobile platforms. In order to protect the phone from an unauthorized user a novel method is proposed by the authors. The aim of the research is to detect the illegitimate user by monitoring the behavior of the user. In order to detect the behavior four main parameters are proposed. Namely, Key stroke dynamics using a customized keyboard, location detection, voice recognition and App usage. Initially machine learning is used to train this mobile application with the authentic user’s behavior and they are stored in a central database. After the initial training period the application is monitoring the usage comparing it with the existing data of the legitimate user. Another unique feature is the inbuilt prevention mechanism which is designed to be executed when an illegitimate user is detected. The entire storage content will be encrypted and a current location alert along with a captured photo of the intruder will be sent to a pre-defined account of the real user in a cloud platform. The real user can log into the account remotely and obtain the phone’s location and the photo of the intruder. Furthermore, this application is proposed as an inbuilt application in order to avoid the deletion of app or uninstallation of the app by the intruder. With this proposed post authentication application “AuthDNA”, a user is able to protect sensitive information of the mobile device in case of theft and bypassing of initial authentication.Item Identification of Papaya Fruit Diseases using Deep Learning Approach(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Munasingha, L.V.; Gunasinghe, H.N.; Dhanapala, W. W. G. D. S.The diseases are a major problem faced by all the farmers including fruit farmers. It is a threat for large farmlands because these diseases spread throughout the land and make the fruits inedible, which at the end impact badly on the farmer’s income. Hence early disease detection is very important for the farmers to prevent or to control the propagation of the diseases. The traditional method of fruit disease detection and identification is naked eye observation. Even if this method is sufficient for a home gardener, it is a very inefficient one that requires experience and expertise. As a solution for this problem several computerized approaches are being developed using Machine Learning and Image Processing techniques in the resent researches. In our proposed work, we considered Papaya fruit, as it is a very popular fruit cultivation in Sri Lanka. In this study we have implemented a computerized model for papaya disease identification using Convolutional Neural Network (CNN). Among various diseases of papaya fruit, anthracnose, black spot, powdery mildew, phytophthora and ringspot were chosen. These are commonly found in Sri Lankan papaya cultivation. We have collected diseased images using a digital camera in normal conditions from papaya farms. Some of the images were found from the publicly available images on the internet. Then we have trained a deep CNN for these images. The network is able to classify images into five major papaya diseases. The system can finally identify the disease once a new image fed to it. The model performed ~92% of classification accuracy for new images. With compared to previous research done using Support Vector Machine (SVM), there is an increase of ~2%. Furthermore, it could be seen that the Black Spot disease was identified very easily by the model. Powdery Mildew was the most difficult disease to recognize. The results of this study reveal that this method is an accurate, reliable and efficient where it could be useful as an aid for expertise.Item A Simple Machine Learning Approach for Identifying Promotional Short Message Service (SMS) Messages.(Faculty of Computing and Technology, University of Kelaniya, Sri Lanka., 2017) Dias, D.S.; Dias, N.G.J.Mobile phones play an integral part in the modern lives of humans. Short Message Services (SMS) Messages have become a popular mode for simple communication. Its’ simplicity, costeffectiveness and large audience has attracted the attention of advertising industry to send targeted promotional messages to mobile phones. In Sri Lanka, a survey conducted in Colombo, yielded that 3 out of 5 SMS messages received our promotional messages. Even though extensive research has been carried out in detecting junk SMS messages, the amount of research conducted on filtering promotional SMS messages is rare. The purpose of this research is to evaluate the success and accuracy of utilizing a simple machine learning algorithm to identify promotional SMS messages. Here, we have used a feed-forward neural network based on a statistical model, which was trained with a training data set consisting of promotional as well as non-promotional messages. Each test message was broken down in to individual words and filtered through by cleaning to form keywords which will have consist of a weight and probability value. With each message that is used to train, these values will be updated according to whether it is a promotional or a non-promotional message. When a message is tested through this neural network, the words of the message will be matched against the keyword’s weight and probability, which will then calculate a resultant probability. By setting a par-value, we can classify the test as a promotional or a non-promotional message. The proposed model yielded a 100% accuracy when tested within the given test data set. In order to get successful results for broader test data sets, the model has to be trained comprehensively with proper amount of promotional and non-promotional messages. Optionally, the results obtained from the feed forward neural network for incoming messages, can then be fed back in to the feed forward neural network for further training. As future work, we intend to take this solution to an android-based mobile application that extracts promotional messages from the incoming SMS messages as well as from a server, and display them to the user based on his preferences.Item Driver Assist Traffic Signs Detection and Recognition System(Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2016) Manisha, U.K.D.N.; Liyanage, S.R.Traffic signs or road signs are signs that are initiated at the roads to provide information of the overcoming behavior of the road to drivers and pedestrians. Since 1930s with the increment of the use of vehicles, road signs were introduced in Europe. Latterly many countries have adopted them to standardize their signs to enhance the safety of road users. Since the number of vehicles is an increasing factor in the world, road traffic became an increasing factor. Specially in urban areas the pedestrian activities at the road is generally high along with the road traffic. It is possible that drivers may lose their concentration to the traffic signs because of closing vehicles and pedestrian activities. There are many notification boards with various colors and textures at road sides. This also may cause the problem that hard to detect the traffic signs clearly to the eyes. Violating traffic signs may cause drivers to make accidents and also unnecessary problems like penalties from the law. To ensure more safety and convenient drive, automation of traffic signs recognition took apart. Computer Vision is a promising approach for addressing this problem which is an interdisciplinary field that emphasis, how the computers can be made to gain high level understanding from digital images. First automated traffic signs recognition was reported in Japan in 1984. Since then number of methods have been developed for traffic signs detection and recognition. This paper presents ‘Driver Assist Traffic Signs Detection and Recognition System’ which is capable of detecting, recognizing and indicating traffic signs at the road side to the driver to ensure a safety and convenient drive by acknowledging the behavior of the road. The proposed system mainly consists with two phases which are detection phase and recognition phase. In both phases I have used classifiers with different technologies which are computer vision image processing techniques and machine learning techniques respectively. In detection phase I have used a cascade classifier to analyze the each frame of the input to find traffic signs of it. For the purpose of training the classifier I have provided over 3000 positive samples of images with region of interests (ROIs) which includes traffic signs and provided over 15000 negative samples of images which does not include any traffic signs. Haar-like features of the images were used to train the classifier with a proper false alarm rate. Aspect ratio changes for most of 3D objects with the location of the camera. Since the classifier is very sensitive to the aspect ratio of the traffic sign I have to use many training images as possible to achieve almost all the orientations of traffic signs to the training set of images. The main objective of the detection phase is to classify the presence of traffic signs and return the coordinates of the sign for each frame. In recognition phase I have used machine learning techniques to train a category classifier support vector machine (SVM) to recognize and indicate the detected traffic signs by the detector. Histogram of Oriented Gradient (HOG) features were used to train the SVM by extracting the features from the training sets and stores them in separate classes as separate categories. For each coordinate that returned by the detector, used to crop the original frame and make an input image to the category classifier. For each input image the category classifier gives a separate score for each category by matching the HOG features of the image. The highest score gives the nearest category and I have obtained an optimal score value to ensure the accuracy of the recognition phase. The main objective of the recognition phase is to choose the correct category of the detected traffic sign by the detector and indicates the traffic sign category. In the detection phase I used LBP and HOG as the feature extraction methods along with the Haar like feature and obtained that the higher accurate technique is to use Haar like features. In recognition phase I chose 11 categories of traffic signs for the training process. I have obtained an optimal value of -0.04 as the score for the best accuracy of the recognition phase. The proposed system can detect, recognize and indicates traffic signs with great accuracy not only at the daylight but at night also and can be implemented to use in any vehicle. Detection process achieves over 88% accuracy and in recognition process accuracy of classify the category of a detected sign is over 98%. In real time testing overall system achieves over 88% of accuracy over 45-50 km/h speed.