Smart Computing and Systems Engineering (SCSE)
Permanent URI for this communityhttp://repository.kln.ac.lk/handle/123456789/18936
Browse
6 results
Search Results
Item Language identification at word level in Sinhala-English code-mixed social media text(IEEE International Research Conference on Smart computing & Systems Engineering (SCSE) 2019, Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Shanmugalingam, K.; Sumathipala, S.Automatic analyzing and extracting useful information from the noisy social media content are currently getting attention from the research community. It is common to find people easily mixing their native language along with the English language to express their thoughts in social media, using Unicode characters or the Unicode characters written in Roman Scripts. Thus these types of noisy code-mixed text are characterized by a high percentage of spelling mistakes with phonetic typing, wordplay, creative spelling, abbreviations, Meta tags, and so on. Identification of languages at word level become a necessary part for analyzing the noisy content in social media. It would be used as an intimidate language identifier for chatbot application by using the native languages. For this study we used Sinhala-English codemixed text from social media. Natural Language Processing (NLP) and Machine Learning (ML) technologies are used to identify the language tags at the word level. A novel approach proposed for this system implemented is machine learning classifier based on features such as Sinhala Unicode characters written in Roman scripts, dictionaries, and term frequency. Different machine learning classifiers such as Support Vector Machines (SVM), Naive Bayes, Logistic Regression, Random Forest and Decision Trees were used in the evaluation process. Among them, the highest accuracy of 90.5% was obtained when using Random Forest classifierItem Use of LIME for Human interpretability in Sinhala document classification(IEEE International Research Conference on Smart computing & Systems Engineering (SCSE) 2019, Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Kumari, P. K. S.; Haddela, P.S.With advancement of technology in Sri Lanka, use of Sinhala text usage has grown rapidly over the time where automatic categorization is helpful for efficient content management. As a result, experts tend to use machine learning application to categorize this large volume of data in an efficient and accurate manner. Most of these learning models are operating in a black-box where there is no way to understand how the model has decided which category an instance is assigned. Understanding the reason behind why learning model makes these predictions is very important to trust such models and to provide reasonable justifications in real world application. Intention of this research is to present the work carried on related to document classification model prediction interpretation where a set of text classifiers has been studied with use of SinNG5, freely available Sinhala Document corpusItem Distribution cost optimization using Big Data Analytics, Machine Learning and Computer Simulation for FMCG Sector(IEEE International Research Conference on Smart computing & Systems Engineering (SCSE) 2019, Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Adikari, A.M. C.; Amalan, T. P.Developments in information and communication technology has made significant impact on every sector. Unfortunately, limited research exists regarding information systems for the distribution networks in Supply Chain. This study made an effort to investigate the linkage between information systems and transportation cost optimization in FMCG (Fast Moving Consumer Goods) sector. Information systems should support the management at operational and strategic level. The study focused on the operational level implementation of information system with machine learning and big data analytics. Factors, variables and constraints affecting the cost of transportation were identified from industry experts and literature. Then a case study approach applied by analyzing the distribution network data of a Sri Lankan FMCG company. A quantitative model was developed to reflect the transport cost structure and a software model was developed considering the constraints and the cost structure, to reduce the cost of transportation by big data analytics, machine learning and computer simulation. Developed model has been compared with the existing model of transportation in the FMCG manufacturer to benchmark the optimization. In proposed model, the usage of vehicles are reduced, thereby minimizing the transportation cost by increasing the consolidation possibilities, route planning and stacking models.Item A study on classifying the store positioning from the transactional data(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Takahashi, M.; Tanaka, Y.This paper describes a customer analysis for store positioning, using data gathered from supermarkets in Japan. Among the retail industry in Japan, there are many types of reward cards used for customer retention purposes. The rewards cards or “Point Card”, is originally aimed for customer analysis purposes, but at present the full benefits have not been extracted due to issues in data analytics. This reward card has only become a method of simply distributing “virtual money” to the customer. For the efficient use of gathering data, we propose a classification method of the customer based on the objectives of visiting stores. In this study, the customers were classified into their objectives.Item 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.Item 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.