https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/3102 2026-04-29T13:05:00Z 2026-04-29T13:05:00Z Jayawardhana, S.C.G. https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/3142 2016-04-26T09:09:01Z 2015-05-26T00:00:00Z

Title: Level Estimation Model for Cultivation Suitability based on Meteorological Parameters Authors: Jayawardhana, S.C.G. Abstract: From the early ages, rice is the staple food of Sri Lankans. Paddy is usually cultivated as a wetland crop, whichever rain fed or irrigated. There are number of factors that affect the productivity of paddy cultivation. These factors can be categorized in to methodology, climatic, economic, physical location. Despite technological advancements imposed to the paddy cultivation related to any of the above factors, climate still remains as the major cause of agricultural productivity. Hence this research focuses on determining the effect of climatic factors to the paddy yield through computer science based approach. In order to estimate unknown paddy yield levels, the relationship between meteorological factors (e.g. temperature (max), temperature (min), rainfall and evaporation) and paddy yield has been modeled using machine learning techniques. As the initial step, a parameter value forecasting model was developed. Forecasted outputs used as input to cultivation level estimation module and based on cultivation levels, seasonal shifts identification process was carried out. RapidMiner data mining tool has been used for model generation and artificial neural networks for learning purpose. Both Forecasting module and cultivation level estimation module showed satisfactory level of accuracy. As a tropical country, Sri Lanka may not have expected highest level of accuracy for parameter value forecasting. However 2 this forecasting model was able to achieve more than 50% of average prediction trend accuracy for all the considered meteorological parameter fore castings. The accuracy can be boosted with the availability of precise and significant data and increasing the number of considering factors. Through this research, the research objectives have been achieved up to satisfactory level by applying computer science concepts and evaluation by the domain experts endorsed the applicability of the research concept.

2015-05-26T00:00:00Z Jayasiri, M.T.C. https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/3141 2016-04-26T09:09:01Z 2015-05-26T00:00:00Z

Title: Human Stress Inference Framework in a Non-Invasive Manner Authors: Jayasiri, M.T.C. Abstract: In our daily life, work related stress is recognized as one of the most serious health problem. It can a ect to the emotional and cognitive well-being of individuals as well as the quality of human life. Detection of stress at its early stage, may help to prevent most physiological as well as behavioral disorders. Since stress is a subjective measurement, detection is very challengeable. Use of standard psychological questionnaire is one of commonly used stress detection approach based on analysis of answers given by individuals. But it is not suitable for real time stress detection. Some of studies in this area are focused on physiological signals based stress detection such as analysis of signals like GSR and heart rate variability. But most of them are invasive and expensive techniques and also lead to disturbance of daily activities and changes of the normal behavior of users. Behavioral characteristics based stress detection is another possible way that researches are focused on. It has been demonstrated as a non-invasive stress detection approach. Signs of stress can be seen in the changes of the human behavior. It is visible to the outside environment if there are deviation from the normal behavior patterns when a person becomes stressed. Most of studies were used single models of behavioral characteristics to capture the behavior pattern changes of individuals in stress detection. This study is focused on detecting human stress while working with a computer by considering multiple behavioral characteristics, namely dynamic keystroke patterns, mouse movements and dynamic facial changes. As we all experienced there can be many reasons to become stress when working with a computer such as work overload, strict deadlines and etc. In generally, combination of several classi ers in order to produce an accurate output is most preferred rather than choosing a best individual classi er. This may help to produce an improved performance in more accurate manner. To address the drawbacks of stress detection using single model this study proposed a framework which consists of multiple feature based classi ers. Research study is demonstrated multiple behavioral feature extraction in a personalized manner from individuals to identify patterns and train those, under three di erent neural network classi er systems. It shows that, it can be obtained higher accuracy in stress detection by combining these three classi er systems. It come up with a solution, based on fuzzy integral which is one of most preferred fusion techniques and has high accuracy than measuring stress using single classi er. Finally research study provides cost e ective stress detection approach with increased accuracy.

2015-05-26T00:00:00Z Horawalavithana, Y.S. https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/3140 2016-04-26T09:09:01Z 2015-05-26T00:00:00Z

Title: Cloud based publish/subscribe model for Top-k matching over continuous data streams Authors: Horawalavithana, Y.S. Abstract: Publish/subscribe systems are widely recognized in processing continuous queries over data streams and are augmented by algorithms coming from the eld of data stream processing. Existing functions which are capable of matching publications & subscriptions in state-ofthe- art publish/subscribe systems are depended on a stateless function which provides only a Boolean decision on whether a given publication is to be noti ed to relevant subscriber or not. But in such systems, the large quantity of received publications may be considered as a sort of spam, while a system that delivers too few publications might be recognized as non-working. In our study, we propose an advanced publish/subscribe matching model to control the unpredictable number of delivered publications over a continuous data-stream, where at a given time t our model limits the number of delivered publications by parameter k, while ranks them within a size w of sliding window. A general scoring mechanism is exploited where publications get scored against personalized user subscription spaces based on the relevancy. We adopt an inverted-list data structure to index the subscription space to enhance the e ciency of matching process. Also we focus on the problem of selecting the k-most diverse items from a relevant result set, in a dynamic setting where Top-k results change over time. We formalize the above problem of continuous k-diversity as MAXDIVREL which maps to the independent dominating set problem in graph theory, which is NP-hard. An incremental indexing mechanism is proposed for handling streaming publications that is based on Locality Sensitive Hashing (LSH) to diversify Top-k results continuously. Our prototype model is implemented in a cloud based message broker system and we have designed it to scale on top of Amazon Web Services (AWS): a scalable cloud-service provider. We explore the natural behavior of ranked publications mathematically modeled by zipf property. Based on the experiments across many diversity methods, MAXDIVREL exhibits the strongest natural behavior. Also the proposed LSH indexing mechanism produces MAXDIVREL diverse set of results at 70% accuracy by comparing with naive optimal method. Finally, we report the experimental results concerning the performance & e ciency of the proposed indexing mechanisms on a variety of synthetic datasets.

2015-05-26T00:00:00Z Harischandra, N.S. https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/3139 2016-04-26T09:09:01Z 2015-05-26T00:00:00Z

Title: Real-time Simulation of Aero-optical Distortions Due to Air Density Fluctuations at Supersonic Speed Authors: Harischandra, N.S. Abstract: Aero-optical distortions produced by supersonic and hypersonic boundary layers were extensively experimentally studied using wind tunnels and CFD techniques in the last decades. But on the other hand, implementations of visual simulations of shock phenomenon have been given signi cantly less-attention. Moreover there is not an openly available simulation model of aero-optical distortions due to shock waves that can be used for ight simulators and games. We present a novel approach to simulate aero-optical distortions due to shock waves generated by a supersonic jet by considering the physics background of the shock phenomenon. While exploring the prevalent physics theories on shock waves, three main types of shock waves have been identi ed: normal shock, bow shock and oblique shock. From those, the shock waves generated by a supersonic jet are called oblique shock waves. The optical distortion is simulated by calculating the index of refraction for oblique shock waves. The refractive index for the shock wave was calculated, by considering the mean characteristics of supersonic ows. Ray tracing is a good technique to simulate refraction in higher quality. But the computer cost is considerably high. Thus this simulation is implemented using environment mapping techniques in OpenGL. Even though the ow characteristics are not uniform across the shock wave the results shows that this approach is a better way to simulate aero-optical distortions in real time.

2015-05-26T00:00:00Z