Browsing by Author "Ramashini, M."

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    Identification of Anomalous Clients’ Request by Analyzing Server Log File using Apache Hadoop Framework and Tableau
    (Uva Wellassa University of Sri Lanka, 2019-02) Bavathuja, V.; Raahini, S.; Ramashini, M.; Wimaladharma, S.T.C.I.
    Information systems provide information about its state and operation in the form of log records. These records are composed of log entries containing information related to a specific event, which can be related to security. Potential security breaches can be revealed by analyzing log files and looking for anomalies that occurred at a certain time during the device operation. Log files from proxy server of Uva Wellassa University of Sri Lanka will be analyzed using Hadoop Framework and Apache Pig in order to identify anomalous clients’ Request. Anomalous clients’ request identification refers to the problem of finding pattern in data that do not conform to expected behavior. These nonconforming patterns are often referred to as anomalies, outliers or exceptions in different application domains. Log files of a proxy server are created and maintained by the server itself and analyzing theses files will offer a valuable insight into server usage while they can be used in various applications, such as detecting intrusions on the web. The log files will be stored in Hadoop Distributed File System. Data preprocessing and analyzation will be done using Apache Pig: a platform for analyzing large data sets. The analyzed data will be reported through Tableau dashboard. According to the research study, the total number of records after cleaning is 817,426 and 856 unique IP addresses have accessed the proxy server from the period of Thursday, 26 April 2018 01:14:48.138 to the period of Friday, 27 April 2018 10:31:23.834. Several findings including the total visits and bandwidth were found and displayed using graph and charts. This information along with other findings can be applied to find solutions for many legitimate problems such as, user/customer behavior analysis, etc.
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    Nitrogen Level Measuring System for Rice Cultivation
    (Uva Wellassa University of Sri Lanka, 2016) Liyanage, K.L.K.R.; Ramashini, M.
    A major reason of the high production cost in rice cultivation is due to the fertilizer cost. Farmers apply fertilizer not based on the plant condition but on an exact date starting from seeding. This may lead to either over an application or under an application of fertilizer since the rice growth is not uniform throughout the crop area. Both of these cases would lead to nitrogen deficiency that gives a lower yield. Modern agricultural researchers discourage the use of traditional farming and encourage precision farming. Precision farming is described as the production inputs like seed, fertilizer etc. should be applied only when needed for the most economic production in order to obtain the highest output. One of the most effective tools to determine when to apply fertilizer in what amount is the Leaf Colour Chart (LCC). The colour matching is relative to the colour perception of a person. Therefore, the recommended way is to do the matching process by the same person. The usage of LCC is also limited to a certain period of a day due to the effect of sunlight. This research aims to develop a mobile application that automates the usage of LCC to overcome its limitations. The mobile application is capable of identifying a leaf sample to the LCC window it matches the most. The standard values are set according to the standards imposed by Rice Research and Development Institute. After a series of testing, it was found out that the results of LCC and the mobile application's readings show a minimum difference based on Z-test one proportion test. Keywords: Leaf Colour Chart, RRDI, LCC, Nitrogen deficiency, Z-test one proportion