Smart Computing and Systems Engineering - 2018 (SCSE 2018)

Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/18937

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Agent based modeling for unordered traffic in Sri Lanka – An investigation into pedestrian behavior
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Rathnayaka, K.R.K.S.
Rising traffic congestion is an inescapable condition in large and growing metropolitan areas across the world. Main entities of a traffic scenario are pedestrians and vehicles. Police make different rules to control the traffic congestions and from an infrastructure development perspective, authorities take actions to construct underground and overhead pedestrian bridges, fences along pavements, islands, etc. However, most of these initiatives end up with unexpected results, mostly since traffic congestion is an emerging macro-level pattern of complex micro-level behaviors of pedestrians and drivers. The study proposes Agent-Based Modeling and Simulation (ABMS) approach, which applies computational methods to study the issues in complex systems. When considering a simulation environment, software agents interact with each other similar to the way real world vehicles and pedestrians behave. This lets us study traffic congestion emerging as a macro-level pattern. Identifying the overall impact of behaviors of drivers and pedestrians to the congestion by extending the previous work, is the aim of this research. The research uses ABMS environment called NetLogo to develop the simulator and Kiribathgoda junction in Western Province, Sri Lanka as the testbed. Coming up with an effective traffic simulator for the unordered traffic conditions in Sri Lanka, which could be used by policy makers to analyze different traffic congestion scenarios and test different solutions to reduce traffic, is the main objective of this research.
AHP integrated MILP approach to minimize transportation cost to prioritize distribution requirements
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Madushika, I.K.; Wijayanayake, A.
Customer satisfaction can be considered as the most important factor for any business as it is tightly linked to revenue and determines the company’s growth and the sustainability. Further it is the leading indicator of customer repurchases and loyalty. Final outcome of the effective supply chain (SC) management is to make the customer loyal and if failed it would result to transfer the customer towards the competitor. Understanding this importance, research in supply chain management (SCM) has grown significantly in recent years. Many organizations have identified that customer satisfaction (CS) and the SC cost are linked and it is impossible to optimize both at the same time. Many studies have been done under different situations to minimize transportation cost (TC) as it ultimately reduces a tremendous amount of SC cost. The need for a reliable approach to optimize customer satisfaction while minimizing the transportation cost has been raised in many occasions as improving customer satisfaction is a goal sought by many businesses in the logistic industry. This requirement becomes critical when the distributor has to select a set of customer orders to be delivered when the supply is less than the demand. Therefore, the objective of this study was to develop a model to find a way to optimally satisfy the customer orders, while minimizing the transportation cost. As a result, a customer focused approach is presented by incorporating Analytic Hierarchy Process (AHP) and then employing a mixed integer linear programming (MILP) model to find the optimal solution. The proposed model addresses customer satisfaction while minimizing the transportation costs
Analysis and detection of potentially harmful Android applications using machine learning
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Kavneth, G.A.S.; Jayalal, S.
With the rapid advancement of technology today, smartphones have become more and more powerful and attract a huge number of users with new features provided by mobile device operating systems such as Android and iOS. Android extended its lead by capturing 86% of the total market in 2017 (Gartner, 2017) and became the most popular mobile operating system. However, this huge demand and freedom has made the hackers and cybercriminals more curious to generate malicious apps towards the Android operating system. Thus, research on effective and efficient mobile threat analysis becomes an emerging and important topic in cybersecurity research area. This paper proposes a static-dynamic hybrid malware detecting scheme for Android applications. While the static analysis could be fast, and less resource consuming technique and dynamic analysis can be used for high complexity and deep analysis. The suggested methods can automatically deliver an unknown application for both static and dynamic analysis and determine whether Android application is a malware or not. The experimental results show that the suggested scheme is effective as its detection accuracy can achieve to 93% ∼ 100%. The findings have been more accurate in identifying Android malwares rather than separating those two static and dynamic behaviors. Furthermore, this research compares the machine learning algorithms for static and dynamic analysis of the Android malwares and compare the accuracy by the data used to train the machine learning models. It reveals Deep Neural Networks and SVM can be used for and higher accuracy. In addition, era of the training and testing dataset highly effect the accuracy of the results regarding Android applications.
Analysis of historical accident data to determine accident prone locations and cause of accidents
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Ifthikar, A.; Hettiarachchi, S.
Road traffic accidents causes great distress and destroy the lives of many individuals. Inspite of different attempts to solve this problem, it still resides as a major cause of death. This paper proposes a system to analyse historical accident data and subsequently identify accident-prone areas and their relevant causes via clustering accident location coordinates. This system, once developed, can be used to warn drivers and also to aid fully autonomous automobiles to take precautions at accident-prone areas.
Applicability of crowdsourcing for traffic-less travelling in Sri Lankan context
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Senanayake, J.M.D.; Wijayanayake, J.
Traffic is one of the most significant problem in Sri Lanka. Valuable time can be saved if there is a proper way to predict the traffic and recommend the best route considering the time factor and the people’s satisfaction on various transportation methods. Therefore, in this research using crowdsourcing together with data mining techniques, data related to user mobility were collected and studied and based on the observations, an algorithm has been developed to overcome the problem. By using developed techniques, the best transportation method can be predicted. Therefore, people can choose what will be the best time slots & transportation methods when planning journeys. The algorithm correctly predict the best traffic-less traveling method for the studied area of each given day & the given time. Throughout this research it has been proven that to determine the best transportation method in Sri Lankan context, data mining concepts together with crowdsourcing can be applied. Based on a thorough analysis by extending the data set of the collection stage, it was shown that this research can be extended to predict the best transportation method with consideration of existing traffic in all the areas.
An approach to coexistence analysis between agility and ERP implementation
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Rajakaruna, R.J.P.K.; Wijayanayake, J.
Business organizations tend to re-engineer their business processes by adopting Enterprise Resource Planning (ERP) systems in order to gain a competitive advantage. ERPs offer countless benefits by enabling an enterprise to operate as an integrated, process oriented and real time enterprise. But the issue is re-engineering with ERP ranks among slow-moving, costly and challenging processes of an organization. Many ERP specialists regard agile approaches positively, to mitigate the common ERP implementation challenges. Agile implementation of ERPs is still under research area. This research discusses on the need of agile approaches in ERP implementations and how agility and ERP implementations can coexist. In this case our research question is “Can the common ERP implementation challenges be solved by using agile approaches?” and if so, “How these challenges can be solved?” This study also seeking for uplift the level of awareness on the applicability of agility for ERP implementation projects and these findings can be effectively used by ERP Implementers, Vendors, Consultants, Project Managers and Researchers in their respective projects.
An assessment of machine learning-based training tools to assist Dyslexic patients
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Sathsara, G.W.C.; Rupasinghe, T.D.; Sumanasena, S.P.
Dyslexia is a language based disability, where the patients often have difficulties with reading, spelling, writing and pronouncing words. The reading speed of Dyslexics tend to be lower than their equivalents, because of slow letter and word processing. Inspite of this disorder, a dyslexic person can be trained to read in normal speed. There are manual methods and some technical improvements can be reported such as the live-scribe smart pen, Dragon Naturally Speaking, Word processors, and Video Games. This study provides an assessment about the Machine Learning (ML) based techniques used for Dyslexic patients via a systematic review of literature, and a proposed ML based algorithm that will lay foundation for future research in the areas of machine learning, augmented and healthcare training devices.
Automatic smart parking system using Internet of Things (IoT)
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Rishan, U.M.
Internet of Things (IoT) plays a vital role in connecting the surrounding environmental things to the network. The IoT is a system of interrelated computing devices that are provided with identifier and the ability to transfer data over a network without requiring human and computer interaction. These type of technologies are used to connect un-internet devices to the network from any remote location. With the number of vehicles on the roads climbing steeply over the last few years, motorists face problems in parking vehicles in designated slots in the city. In this paper a Smart Parking System is designed which enables the user to find the nearest parking area and provide the information about the availability of parking slot to the motorist. The system mainly focuses on reducing the time of finding the parking area and avoids unnecessary travelling through filled parking lots in a parking area. Thus it reduces fuel consumption and minimizes carbon emissions as well.
Classification of vehicles by video analytics for unorganized traffic environments
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Arachchi, I.M.R.; Jayalal, S.; Rajapakse, C.
Traffic monitoring is essential for infrastructure planning and transportation. The objective of traffic monitoring is to have an effective traffic management system. Traffic management systems would be effective in well-organized traffic environments, where it has very disciplinary behaviors and less in inefficiencies. But in unorganized urban environments like Sri Lanka, road traffic behaviours are varying from standard structured ways which lead to discompose the traffic management. An effective monitoring system requires short processing time, low processing cost and high reliability. The paper proposes a novel vehicle detection and classification algorithm based on background filtering and re-engineered with suitable changes in order to be applicable to challenging unorganized traffic environments. The solution is successfully classifying vehicles individually and their trajectories in unorganized traffic environments in order to monitor the behaviors of the drivers. The system gives 74.4% average accuracy in vehicle detection and 55% accuracy in vehicle classification while counting each vehicle passed by. We used OpenCV functions for implementing and testing algorithms. Data was collected through pre-recorded video clips from footbridge crossing at Colombo Fort in western province Sri Lanka, for the testing. The ultimate objective of this research was to come up with a best-suited algorithm for vehicle detection and classification (hybrid solution) in unorganized traffic environments which would help to analyze the behaviors of road users. The solution will lead to help reduce unorganized traffic congestions by enhancing the efficiency and effectiveness of traffic monitoring and analyzing systems those are used for intelligent traffic management systems and traffic simulation models.
Computer aided segmentation approach for Melanoma skin cancer detection
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Sivathmeega, S.; Kariapper, R.K.A.R.; Rathnayaka, R.M.K.T.
Skin cancer is the most common type of cancer in the world and nowadays, this incidence is increasing rapidly. In recent years, there has been a fairly rapid increment in melanoma skin cancer patients. Melanoma, this the deadliest form of skin cancer, must be diagnosed earlier as soon as possible for effective treatment. To diagnose melanoma earlier, skin lesion should be segmented accurately. However, the segmentation of the melanoma skin cancer lesion in traditional approach is a challenging task due to the number of false positives is large and time consuming in prediction. Hence, the development of automated computer vision system becoming as an essential tool to segment the skin lesion from given photograph of patient’s cancer affected area and to overcome those difficulties, which were found in the earlier methods. This work was done through image processing techniques. Some of these techniques are widely used in similar applications, as is the case of the canny edge detection for finding the lesion boundary. Other techniques are watershed segmentation for segmenting the lesion from skin, multilevel thresholding for merging the lesion, and active contour for increasing the accuracy. Though the personnel in the medical field had introduced new methodologies to improve the accuracy by addressing the challenges and mainly focusing on the accuracy, the approach in this study achieved 97.54% sensitivity, 97.69% specificity, and 97.56% accuracy.
Coordination and control in virtual teams in software industry
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Koggalahewa, L.; Wijayanayake, J.
The last couple of decades has witnessed a steady, irreversible trend towards globalization. Economic forces have relentlessly turned national markets into global markets while emerging competition and corporations reach across national boundaries. More than a decade ago, seeking lower costs and access to skilled resources, many organizations began to experiment with remotely located manufacturing and service facilities. The ready availability of skilled IT personnel at very competitive prices in developing nations like India, and the rapid infrastructure development in these countries made it a ready industry to make this transition. With this trend, emerged the concept of virtual teams. Virtual teams are work arrangements where team members are geographically dispersed and work interdependently through the use of electronic communication media to achieve common goals. There is a difficulty in developing strategies for various team processes in virtual teams. The objective of this research was to identify the factors influencing working of virtual teams and strategies to facilitate better coordination and control among them in the context of software development. Literature revealed the factors that affect the coordination process of virtual teams in software development. The factors identified include level of authority of team members, leadership style, media synchronicity of the team, distribution of information within the team and experience in working together. Then a conceptual model was developed to analyze the impact of each factor in virtual team coordination. A detailed questionnaire was used to obtain views of industrial experts. The results concluded that the developed model is significant and it explains sixty-six percent of the working of virtual teams. It was shown that, level of authority and media synchronicity are the most significant of the factors. The applicability of the model was verified by conducting interviews with software industry personnel. The study also focused on finding reasons for using virtual teams, pros, cons and problems faced by virtual teams in Sri Lankan context. The findings can be used to better coordinate software projects with the use of virtual teams.
A cross-functional collaborative model for supplier evaluation for the sustainability of a firm
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Jayarathne, K.; Wijayanayake, A.; Weerabahu, S.
Business organizations have emphasized the importance of sustainability in their business processes. Sustainability of a firm can be measured on social, environmental and economic benefit indicators known as “triple bottom line”. Supplier selection process is one of the critical issues of sustainability activities faced by supply chain managers to maintain a strategically competitive position in the industry and supplier selection can significantly affect in achieving the triple bottom line. Given the current context, technological factors immensely affect the sustainability of a firm. Communication and web based systems related technology is a vital factor to build sustainable supply chain relationships. Thus, technological aspects can be taken into consideration under different sustainable criteria for supplier selection, though it has not been considered yet as a major factor. Analytical Network Process (ANP) has been incorporated to compute the supplier evaluated score that was computed by each department against each supplier. Then an Integer Linear Programming (ILP) model has been used to integrate the judgments of the multiple decision-makers. This research addresses the supplier selection decisions by groups of experts, which improves the quality and accuracy of the decisions made. In this model, both subjective and objective factors related to supplier selection are incorporated in order to optimize the procurement process aligning to the sustainability of the firm.
A data mining approach for the analysis of undergraduate examination question papers
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Brahmana, A.; Kumara, B.T.G.S.; Liyanage, A.L.C.J.
Examinations play a major role in the teaching, learning and assessment process. Questions are used to obtain information and assess knowledge and competence of students. Academics who are involved in teaching process in higher education mostly use final examination papers to assess the retention capability and application skills of students. Questions that used to evaluate different cognitive levels of students may be categorized as higher order questions, intermediate order questions and lower order questions. This research work tries to derive a suitable methodology to categorize final examination question papers based on Bloom’s Taxonomy. The analysis was performed on computer science related end semester examination papers in the Department of computing and information systems of Sabaragamuwa University of Sri Lanka. Bloom’s Taxonomy identifies six levels in the cognitive domain. The study was conducted to check whether examination questions comply with the requirements of Bloom’s Taxonomy at various cognitive levels. According to the study the appropriate category of the questions in each examination, the paper was determined. Over 900 questions which obtained from 30 question papers are allocated for the analysis. Natural language processing techniques were used to identify the significant keywords and verbs which are useful in the determination of the suitable cognitive level. A rule based approach was used to determine the level of the question paper in the light of Bloom’s Taxonomy. An effective model which enables to determine the level of examination paper can derive as the final outcome.
Data mining model for identifying high-quality journals
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Jayaneththi, J.K.D.B.G.; Kumara, B.T.G.S.
The focus in local universities over the last decade, have shifted from teaching at undergraduate and postgraduate levels to conducting research and publishing in reputed local and international journals. Such publications will enhance the reputation on the individual and the university. The last two decades has seen a rapid rise in open access journals. This has led to quality issues and hence chossing journals for publication has become an issue. Most of these journals focus on the monetary aspect and will publish articles that previously may not have been accepted. Some of the issues include design of the study, methodology and the rigor of the analysis. This has great consequences as some of these papers are cited and used as a basis for further studies. Another cause for concern is that, the honest researchers are sometimes duped, into believing that journals are legitimate and may end up by publishing good material in them. In addition, at present, it is very difficult to identify the fake journals from the legitimate ones. Therefore, the objective of the research was to introduce a data mining model which helps the publishers to identify the highest quality and most suitable journals to publish their research findings. The study focused on the journals in the field of Computer Science. Journal Impact Factor, H-index, Scientific Journal Rankings, Eigen factor Score, Article Influence Score and Source Normalized Impact per Paper journal metrics were used for building this data mining model. Journals were clustered into five clusters using K-Means clustering algorithm and the clusters were interpreted as excellent, good, fair, poor and very poor based on the results.
Detecting plagiarism in multiple Sinhala documents
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Ganepola, G.A.U.E.; Wijayasiriwardhane, T.K.
Availability of unlimited information resources over the Internet and the advancement of the Internet search engines such as Google to locate those resources much easily have contributed to an increase of plagiarism. Though there are a number of software tools available for detecting plagiarism in multiple English documents, no such a tool is yet available for the Sinhala language. This paper presents a novel language dependent approach to detect plagiarism in multiple Sinhala documents. It uses stemming, stop word removal and synonym replacement for text preprocessing and term frequency-inverse document frequency (tf-idf) and cosine similarity for similarity comparison. A prototype software tool was developed and interlinked with an operational Sinhala WordNet to demonstrate the viability of the proposed approach. The prototype tool was validated against a sample of Sinhala assignments from secondary school students. The assignments were also examined by an expert to determine whether they had actually been plagiarized. When compared the results of the prototype tool against those of the expert judgment, we found that our proposed approach for plagiarism detection in multiple Sinhala documents performs with an accuracy of over 80%.
Determinants of successful implementation of Green Supply Chain Management: From literature review perspective
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Thushanthani, T.; Rupasinghe, T.D.
The purpose of this study is to identify the Green Supply Chain Management (GSCM) best practices and explore the factors influencing the succesful adoption of green supply chain management practices. The authors have used a systematic review of literature approach to collate 27 articles ranging from automobile, beverages, construction, electrical, hospitality, power generating and, general industries. The findings are revealed under five categories namely; green procurement, green design, green packaging, green operations, green manufacturing and reverse logistics incorporating 48 critical success factors under five themes, namely; Organizational Commitment (OC), Knowledge Base (K), Operational Dynamics (OD) , Market Pressure (MP) and Exogenous (E).
Developing a concept to convert LD/STL to VHDL
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Dharmarathna, G.H.R.O.
A Programmable Logic Controller (PLC) is a microprocessor based solid state device which is a very significant control component unit in industrial automation systems. Ladder diagram (LD) is a form of graphical language type PLC programming. LDs and Statement Lists (STL) are used to program PLCs. Both of these programming methods represent the schematics of electrical relay circuit diagram. Since LD programs are executed in a sequential and cyclic way, the operational efficiency and performance of PLC will be limited by the length of the ladder diagram and the operational speed of the microprocessor. Field Programmable Gate Array (FPGA) is a new technology used in industrial process control systems. VHDL (VHSIC-HDL- Very High Speed Integrated Circuit - Hardware Description Language) programming is used to program FPGA devices. Because of its parallel execution system and reconfigurable hardware structure, FPGA has excellent performance. Therefore, flexible and high speed systems can be implemented using FPGA. The main aspect of this research was to develop a concept to convert LD/STL to VHDL. By using Siemens - STEP 7 Micro/WIN - version 4.0.81 and Xilinx® – ISE Design Suite version 14.6 software, this concept was developed to convert Bit Logic LDs into VHDL. After identifying the Boolean logic of the STL code, inputs and outputs are declared in the entity part and PLC to FPGA conversion logic is defined in the architecture part of the VHDL code. To overcome the performance limitations of microprocessor based PLCs, FPGA based PLC implementation is suggested as a better approach.
An e-pest surveillance and advisory system to empower farmers in managing rice pests and diseases in Sri Lanka
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Ponnamperuma Arachchi, J.; Bandara, D.M.B.N.; Perera, S.P.M.G.N.H.; Nilakshi, S.V.; Nugaliyadde, L.; Sisira Kumara, W.A.G.
An e-pest surveillance and advisory system called “Govi Vedaduru” was developed with two broad objectives in mind; to enable rice farmers in Sri Lanka to manage pest and disease problems efficiently and in a cost-effective manner and to facilitate relevant authorities with better monitoring and control of pest and disease incidents. Many farmers are not competent to identify pests and diseases related issues and decide the correct management practices themselves. They expect the assistance of the field officers of government agriculture extension services for this purpose. However, lack of officers and the knowledge gaps that exist among them hinder the achieving these expectations. Hence, farmers do not receive the correct advice in time and crop get damaged leading to higher production cost. The Govi Vedaduru mobile application was designed to provide an advisory service through smart phones enabling the farmers to obtain expert guidance from the Rice Research Stations of Department of Agriculture (DOA), in identifying their field problems and remedial measures to follow. A user friendly mobile interface was developed in local languages (initially Sinhala) to upload data about the pest and disease problems. The system was initially piloted in five areas (yaya) of rice cultivation representing two agro-ecological zones in Galle district in Sri Lanka. A follow up survey of the participant farmers indicated that they received timely, useful and trustworthy advice that helped them with correct remedial measures. The reports generated via web application with limited incident data showed the system’s capability of providing valuable information to relevant authorities for monitoring and planning purposes.
An efficient data perturbation scheme for preserving privacy on a numerical database
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Iuon-Chang Lin; Cheng-Yi Tsai; Li-Cheng Yang
The data retention within an organization may increase rapidly with time. In order to reduce cost of organization, they may choose a third-party storage provider. There is a leakage crisis when provider cannot be trusted. Another scenario is a dealer collects all transaction data and provides it to a data analysis company for marketing purpose. For these reasons and beyons, preserving privacy in database becomes an important issue. This paper concerns the prediction of disclosure risk in numerical database. It presents an efficient noise generation that relies on Huffman coding algorithm and builds a noise matrix that can add noise intuitively to original value. Moreover, we adopt clustering technique before generating noise. The result shows that the running time of noise generation of clustering scheme is faster than non-clustering scheme.
Evaluation of higher education institutions using aspect based sentiment analysis
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Balachandran, L.; Kirupananda, A.
Demand for formal higher education programs among the younger generation in Sri Lanka, has grown over the past decade. The demand growth has fueled the opening up of many local and internationally affiliated institutes offering a diverse range of degree programs. The selection of the appropriate course from these institutes is challenging given the wide choice. In order to select the appropriate institute, students use the Internet for reviews and user comments, especially from social network sites like Facebook, Twitter and Google plus. This search, involves a cost in terms of time spent for reading the comments and processing whether the standing of the ratings for the program and the institution are appropriate. This task is challenging because of the difficulty to extract sentiment information from a massive set of online reviews. A solution is proposed, using an aspect based sentiment evaluation system that assesses institutions by considering the reviews provided, to overcome this problem. This concept is based on Natural Language Processing (NLP). A web based, automated application tool that retrieves review data from social media networks on the institution and the features of the program, analyzes the sentiment value and provides a rating has been developed.