Statistics & Computer Science

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A methodology for identifying ?Web metrics? for ?Web analytics?
(2006) Wickramarachchi A P R; Malavisooriya L M
The need for perusing sound business practices when adopting an organizational web strategy is highlighted by many authors. Measuring effectiveness of business activities is a standard practice of any organization. However, few organizations actively take steps to assess successfulness of their web strategy. The main reason for this is due to the lack of a standard approach to measure the effectiveness of web sites. Many authors have come up with different types of metrics (measurements) for measuring the effectiveness of web sites. Two common approaches identified are; user centric measurements which depend on consumer surveys and site centric measurements which depend on server logs. The latter approach is preferred by many organizations Software tools have also been developed for collecting and analyzing results from web metrics. But still Organizations are faced with problems of identifying appropriate metrics for their web site. Different sections of a web site have different functions and objectives. Therefore different metrics are needed to measure the effectiveness of different sections of a web site. Thus a formal methodology for identifying suitable web metrics for a particular web site is invaluable for measuring the effectiveness of web sites. The research proposes a methodology for identifying appropriate web metrics for measuring the effectiveness of a web site; based on the objectives and functionalities of the web site. A set of metrics have been identified by combining the findings of previous researches which are evaluated through expert opinions. These metrics are the input for methodology development where a step wise approach is proposed to identify appropriate web metrics based on the functions of a particular web site.
An Enhanced Multi-Channel MAC for the IEEE 1609.4 Based Vehicular Ad Hoc Networks
(INFOCOM IEEE Conference on Computer Communications Workshops, 2010) Wang, Q.; Leng, S.; Fu, H.; Zang, Y.; Weerasinghe, K.G.H.D.
This paper proposes a multi-channel MAC scheme for Vehicular Ad Hoc Networks (VANETs), which dynamically adjusts the intervals of Control Channel (CCH) and Service Channels (SCHs). Markov modeling is conducted to optimize the intervals based on the traffic condition. The scheme also introduces a multi-channel coordination mechanism to provide the contention-free access in SCHs. Theoretical analysis and simulation results show that the proposed scheme is able to help IEEE 1690.4 MAC improve the saturation throughput of SCHs significantly, while maintaining the prioritized transmission of critical safety information on the CCH.
Analysis of key management in wireless sensor networks
(IEEE EIT?07, 2007) Dustin, M.; Shankarappa, J.; Petrowski, M.; Weerasinghe, K.G.H.D.; Fu, H.
A multitude of wireless sensor networks exist today in various fields, each having a specific objective in mind. Based on the objectives for each network, the security concerns can be different, dependent on such factors as the level of secrecy of the data being captured, the amount of computation done to the captured data, and the criticality of the data being available when needed. This paper aims to identify the various types of WSNs in existence today, review some of the key management schemes proposed by the community, and map each type of WSN to a set of these key management schemes that would be ideal to handle the security requirements for that network. Through our research, we aide in solving the question as to whether or not there exists any specific security concerns which are prevalent in a majority of WSNs in use today.
An android based live tour guide for Sri Lanka
(Sri Lanka Association for the Advancement of Science, 2016) de Silva, A.D.; Liyanage, S.R.
Anonymous service access for Vehicular Ad hoc Networks
(Information Assurance and Security (IAS), 2010 Sixth International Conference, 2011) Weerasinghe, K.G.H.D.; Fu, H.; Leng, S.
Communications through road side units in Vehicular Ad hoc Networks (VANETs) can be used to track the location of vehicles, which makes serious threat on users' privacy. In this paper, we propose and evaluate a novel location privacy enhancement protocol for VANETs. Firstly, we propose an Anonymous Online Service Access (AOSA) protocol. Secondly, we analytically evaluate the anonymity and the unlinkability of the proposed protocol. Finally, a series of simulation studies are conducted to evaluate the performance of our protocol in the real VANET environments such as Manhattan and Urban scenarios. According to analytical evaluation and simulations, our protocol provides higher level of anonymity and location privacy for on-line service access applications. Simulation results further show that our protocol is feasible and produces better performance in real VANET environments by producing higher success ratio and smaller delay.
An Application of 5-fold Cross Validation on a Binary Logistic Regression Model
(2016) Attanayake, A.M.C.H; Jayasundara, D.D.M.; Peiris, T.S.G.
Abstract Internal validation techniques can be used to check the predictive ability of the developed models. The most common internal validation techniques are split sample methods, cross validation methods and bootstrapping methods. The split sample methods are inefficient with the small size of data sets. The bootstrapping methods are efficient with the knowledge of computer programming languages. The cross validation methods are not very popular in practice. Therefore, in this study 5-fold cross validation method of cross validation techniques is applied to validate the predictive ability of a binary logistic regression model. The binary logistic regression model was fitted on a data set of UCI machine learning repository. Results of the cross validation reveal that low value of optimism and high value of c-statistic in the fitted regression model indicate an acceptable discrimination power of the developed model.
Application of fuzzy goal programming model to assess optimal multi crop cultivation planning
(Agriculture and Natural Resources, 2022) Hakmanage, N.M.; Chandrasekara, N.V.; Jayasundara, M.
Importance of the work: Planning for the optimal use of resources in agricultural systems considering uncertainty, with the objective of maximizing profit and production, will improve the social and economic conditions of farmers. Objectives: A rural farming area in Sri Lanka was used as a study site to apply the fuzzy goal programming (FGP) approach to identify the optimal cultivation plan and land resource allocation under uncertainty to optimize profit, production, labor, water use, fertilizer costs and land allocation. Materials & Methods: A tolerance-based FGP technique was used to quantify the fuzziness of different goals for the model. This study was carried out using 24 crops on a total land area under cultivation of 47.4 ha. These crops were categorized into three varieties: vegetable, fruit and other. Furthermore, the crops were classified into seven groups according to the required period of cultivation. Results: The proposed model suggested statistically significant increments of 11% and 10.6% for the net return and harvest amount, respectively, for the 24 crops compared to existing cultivation techniques.Main finding: The FGP multi-crop cultivation planning approach is a new application for the Sri Lankan rural farming community and it should be useful for agricultural planners, by allowing them to make more informed recommendations to the farming community. Crops that provide higher levels of production and profit than those currently being cultivated should be developed to extend cultivation under the supervision of agricultural experts or officers to obtain sustainable development of cultivation.
An Approach for Prediction of Weekly Prices of Green Chili in Sri Lanka: Application of Artificial Neural Network Techniques
(Journal of Agricultural Sciences – Sri Lanka, 2022) Basnayake, B.R.P.M.; Kaushalya, K.D.; Wickaramarathne, R.H.M.; Kushan, M.A.K.; Chandrasekara, N.V.
Purpose: Predicting the prices of crops is a principal task for producers, suppliers, governments and international businesses. The purpose of the study is to forecast the prices of green chili, which is a cash crop in Sri Lanka. Artificial neural networks were applied as they help to extract important insights from the bulk of data with a scientific approach. Research Method: The Time Delay Neural Network (TDNN), Feedforward Neural Network (FFNN) with Levenberg-Marquardt (LM) algorithm and FFNN with Scaled Conjugate Gradient (SCG) algorithm were employed on weekly average retail prices of green chili in Sri Lanka from the 1st week of January 2011 to the 4th week of December 2018. The performance of models was evaluated through the Mean Squared Error (MSE), Mean Absolute Error (MAE) and Normalized Mean Squared Error (NMSE). Findings: Among the three methods implemented, the FFNN model using the LM algorithm exhibited the highest accuracy with a minimum MSE of 0.0033, MAE of 0.0437 and NMSE of 0.2542. The model built using the SCG algorithm fitted data with a minimum MSE of 0.0033, MAE of 0.0458 and NMSE of 0.2549. Among the fitted TDNN models, the model with 8 input delays were a better model with an MSE of 0.0036, MAE of 0.0470 and NMSE of 0.3221. FFNNs outperformed TDNN in forecasting green chili prices of Sri Lanka. Originality/ Value: The neural network approach in forecasting the prices of green chili provides more accurate results to make decisions based on the trends and to identify future opportunities.
An Approach for Prediction of Weekly Prices of Green Chili in Sri Lanka: Application of Artificial Neural Network Techniques
(The Journal of Agricultural Sciences - Sri Lanka, 2022) Basnayake, B. R. P. M.; Kaushalya, K. D.; Wickaramarathne, R. H. M.; Kushan, M. A. K.; Chandrasekara, N. V. C.
Purpose: Predicting the prices of crops is a principal task for producers, suppliers, governments and international businesses. The purpose of the study is to forecast the prices of green chili, which is a cash crop in Sri Lanka. Artificial neural networks were applied as they help to extract important insights from the bulk of data with a scientific approach. Research Method: The Time Delay Neural Network (TDNN), Feedforward Neural Network (FFNN) with Levenberg-Marquardt (LM) algorithm and FFNN with Scaled Conjugate Gradient (SCG) algorithm were employed on weekly average retail prices of green chili in Sri Lanka from the 1st week of January 2011 to the 4th week of December 2018. The performance of models was evaluated through the Mean Squared Error (MSE), Mean Absolute Error (MAE) and Normalized Mean Squared Error (NMSE). Findings: Among the three methods implemented, the FFNN model using the LM algorithm exhibited the highest accuracy with a minimum MSE of 0.0033, MAE of 0.0437 and NMSE of 0.2542. The model built using the SCG algorithm fitted data with a minimum MSE of 0.0033, MAE of 0.0458 and NMSE of 0.2549. Among the fitted TDNN models, the model with 8 input delays were a better model with an MSE of 0.0036, MAE of 0.0470 and NMSE of 0.3221. FFNNs outperformed TDNN in forecasting green chili prices of Sri Lanka. Originality/ Value: The neural network approach in forecasting the prices of green chili provides more accurate results to make decisions based on the trends and to identify future opportunities.
Automated response recognition system for questionnaires
(Sri Lanka Association for the Advancement of Science, 2012) Fernando, M.A.I.D.; Chandrasekara, N.V.
COMPARISON OF PERFORMANCES OFSELECTED FORECASTING MODELS:AN APPLICATION TO DENGUE DATA IN COLOMBO, SRI LANKA
(Department of Statistics & Computer Science, Faculty of Science,& Research & Development Centre for Mathematical Modelling, Faculty of Science, University of Colombo, Sri Lanka., 2021) Attanayake, A.M.C.H.; Perera, S.S.N.; Liyanage, U.P.
Dengue is a one of the diseases in the world which has no exact treatment to recover from the disease. It is rapidly spreading throughout the world by causing large number of deaths [1]. In Sri Lanka, there is an increase of reported dengue cases over recent years. The majority of dengue cases reported in the Colombo district within the Sri Lanka. Effective dengue management and controlling strategies should be implemented to reduce the deaths from the disease. Modelling and predicting the distribution of the dengue will be useful in detecting outbreaks of the dengue and to execute controlling actions beforehand. The objective of this study is to develop an appropriate modelling technique to predict dengue cases. To accomplish this objective, we have chosen our study area as Colombo, Sri Lanka. Seven modelling techniques, namely, Na¨ıve, Seasonal Na¨ıve, Random Walk with Drift, Mean Forecasting, Autoregressive Integrated Moving Average, Exponential Smoothing and TBATS (Trigonometric, Box-Cox Transformation, ARMA errors, Trend and Seasonal components) [2] were chosen in this study to model dengue data. For model development process, monthly reported dengue cases in Colombo from January 2010 to December 2018 were used and validated using the data from January to December in 2019. Mean error, root mean squared error and mean absolute percentage error measurements were used to select the most parsimonious model to predict dengue cases in Colombo, Sri Lanka. Both Exponential and TBATS models were competed in predicting dengue cases by reporting minimum error measures. Therefore, results disclosed that among the selected methods either Exponential Smoothing model or TBATS model can be used to predict dengue cases in Colombo, Sri Lanka.
Comparison of support vector regression and artificial neural network models to forecast daily Colombo Stock Exchange
(Proceedings of the International Statistics Conference, Institute of Applied Statistics, Sri Lanka, 2011) Rangana, D.L.M.; Chandrasekara, N.V.; Tilakaratne, C.D.
Data centric adaptive in-network aggregation for wireless sensor networks
(IEEE/ASME International Conference, 2007) Weerasinghe, K.G.H.D.; Elhajj, I.; Krsteva, A.; Najm, M.A.
This paper presents and evaluates a data centric adaptive in-network aggregation algorithm for wireless sensor networks. In-Network data aggregation is used in wireless sensor networks to reduce the power consumption of sensor nodes. The accuracy of the aggregated results is highly sensitive to delays in the measurements. All existing methods use fixed time limit to accept delayed information for aggregation. The proposed method dynamically calculates the delay limit by using the historical behavior of each sensor. The presented simulation results illustrate the advantage of the developed algorithm.
Debris Run-Out Modeling Without Site-Specific Data
(International Journal of Advanced Computer Science and Applications, 2020) De Silva, N.M.T.; Wimalaratne, P.
Recent population growth and actions near hilly areas increase the vulnerability of occurring landslides. The effects of climate change further increase the likelihood of landslide danger. Therefore, accurate analysis of unstable slope behavior is crucial to prevent loss of life and destruction to property. Predicting landslide flow path is essential in identifying the route of debris, and it is essential necessary component in hazard mapping. Horvever, current methodologies of determining the flow direction of landslides require costly sitespecific data such as surface soil type, categories of underground soil layers, and other related field characteristics. This paper demonstrates an approach to predict the flow direction without site-specific data, taking a large landslide incident in Sri Lanka at Araranyaka region in the district of Kegalle as a case study. Spreading area assessment was based on deterministic eight-node (D8) and Multiple Direction Flow (MDF) flow directional .algorithms. Results acquired by the model were compared with the real Aranayaka landslide data set and the landslide hazard map of the area. Debris paths generated from the proof of concept software tool using the D8 algorithm showed greater than 760/o agreement, and MDF showed greater than 87oh agreement with the actual flow paths and other related statistics such as maximum width of the slide, run-out distance, and slip surface area.
Determining and Comparing Multivariate Distributions: An Application to AORD and GSPC with their related financial markets
(2016) Chandrasekara, N.V.; Mammadov, M.; Tilakaratne, C.D.
Many real world applications are associated with more than one variable and hence, identifying multivariate distributions associated with real world problems portrays great importance today. Many studies can be found in the literature in this aspect and most of them are associated with two variables/dimensions and the maximum dimension of multivariate distribution found in the literature is four. Different optimization techniques have been used by researchers to find multivariate distributions in their studies. Numerical methods can be identified as more preferable than analytical methods when the dimension of the problem is high. The main objective of this study is to identify the multivariate distribution associated with the return series of Australian all ordinary index (AORD) and those of the related financial markets and compare it with the multivariate distribution of return series of the US GSPC index and its related financial markets. No research were found in the literature which were aimed at finding aforesaid multivariate distribution and comparisons. Moreover no evidence found for identifying a multivariate distribution with six dimensions. Five financial markets: Amex oil index, Amex gold index, world cocoa index, exchange rate of Australian dollar to United States dollar and US GSPC index were found to be associated with AORD. Hence the attempt was to derive the multivariate distribution of return series of AORD and these five return series and therefore the optimization problem of the study is a six dimension problem which associated with forty three parameters need to be estimated. A local optimization technique and a global optimization technique were used to estimate the parameters of the multivariate distribution. Results exhibit that the parameter estimates obtained from the global optimization technique are better than the parameter estimates obtained from the local optimization technique. The multivariate distribution of return series of AORD and related financial markets is central, less peaked and have fat tails. A comparison was done with another multivariate distribution of a return series of a leading stock market index: GSPC and return series of its associated financial markets and found that both distributions are alike in shape. Two periods were identified in the AORD series and found that the shape of the multivariate distribution of one period is similar to the shape of the multivariate distribution of full data set while the shape of the multivariate distribution of the other period is dissimilar to that of full data set.
Enhancement of IEEE 802.11 modules in ns-2 and performance evaluation with error rate
(Proceedings of 43rd Annual Simulation Symposium (ANSS), 2010) Jin, K.; Weerasinghe, K.G.H.D.; Fu, H.
Ns-2 is being widely utilized to evaluate the wired and wireless networks on many research activities. The ns-2.33 distribution version includes an extension of IEEE 802.11 module which improves the core functions of 802.11 MAC and PHY protocols. Although this extension version provides well-designed MAC and PHY functions, it has some significant shortcomings in handling packet errors. When a packet error rate, one of the most important performance parameters in wireless network simulations, applies to the extension, a simulation program is interrupted with some fatal errors. Besides, as the packet errors are handled on the PHY layer in this version, MAC layer loses its own right of treating the packet errors. In this paper, we modify the extension version to correct the mentioned problems and verify the behavior of our modified version by simulation work. And also, we perform the ns-2 simulation to investigate the impact of error rate on IEEE 802.11p-based vehicular ad-hoc networks.
Enhancing unlinkability in Vehicular Ad Hoc Networks
(Intelligence and Security Informatics (ISI), 2011 IEEE International Conference, 2011) Weerasinghe, K.G.H.D.; Huirong, F.
Communication messages in Vehicular Ad-hoc Networks (VANETs) can be used to track movement of vehicles. In this paper, we address the problem of movement tracking and enhance location privacy without affecting security and safety of vehicles. By considering unique characteristics of VANETs, we firstly propose a synchronized pseudonym changing protocol based on the concept of forming groups among neighboring vehicles. Secondly, we analytically evaluate the anonymity and unlinkability of the proposed protocol. Finally, we do a series of simulations to evaluate the performance of our protocol in real VANET environments such as Manhattan and Urban. Simulation results show that our protocol is feasible and produces excellent performances. The main advantages of our protocol compared with the existing approaches include: 1) it makes larger anonymity set and higher entropy; 2) it reduces the tracking probability; 3) it can be used in both safety and non-safety communications; and 4) Vehicles need not suspend regular communication for changing pseudonyms.
An Ensemble Technique For Multi Class Imbalanced Problem Using Probabilistic Neural Networks
(Advances and Applications in Statistics, 2018) Chandrasekara, N.V.; Tilakaratne, C.D.; Mammadov, M.A.
The class imbalanced problem is one of the major difficulties encountered by many researchers when using classification tools. Multi class problems are especially severe in this regard. The main objective of this study is to propose a suitable technique to handle multi class imbalanced problem. Probabilistic neural network (PNN) is used as the classification tool and the directional prediction of Australian, United States and Srilankan stock market indices is considered as the application. We propose an ensemble technique to handle multi class imbalanced problem that is called multi class undersampling based bagging (MCUB) technique. This is a new initiative that has not been considered in the literature to handle multi class imbalanced problem by employing PNN. The results obtained demonstrate that the proposed MCUB technique is capable of handling multi class imbalanced problem. Therefore, the PNN with the proposed ensemble technique can be used effectively in data classification. As a further study, other classification tools can be used to investigate the performance of the proposed MCUB technique in solving class imbalanced problems.
ESAP: Efficient and scalable authentication protocol with conditional privacy for secure vehicular communications
(GLOBECOM Workshops (GC Wkshps), 2010 IEEE, 2010) Weerasinghe, K.G.H.D.; Fu, H.
Security mechanisms such as authentication, message integrity, and non-repudiation are extremely important features for both vehicle-to-vehicle and vehicle-to-infrastructure communications in vehicular ad hoc networks. This paper proposes an Efficient and Scalable Authentication Protocol (ESAP) that provides anonymity and conditional privacy for vehicular ad hoc networks based on the self-generated certificates. The proposed ESAP provides all required security services such as authentication, message integrity, non-repudiation and revoking malicious vehicles in an efficient and scalable manner while supporting user anonymity, location privacy and conditional privacy. Moreover, accuracy of this protocol does not depend on the availability of Road Side Units.
Estimating parameters of multivariate scaled t distribution of GSPC and its associated financial indices
(2015) Chandrasekara, N.V.; Mammadov, M.A.; Thilakaratne, C.D.