2021 6th International Conference on Information Technology Research (ICITR)最新文献

{"title":"Mellow: Stress Management System For University Students In Sri Lanka","authors":"K.G.P.R Chandrasiri, A.A. Chandrasena, L.H.C.R De Silva, H.W.V.O. Jayasinghe, G. T. Dassanayake, Oshadha Seneweera","doi":"10.1109/ICITR54349.2021.9657419","DOIUrl":"https://doi.org/10.1109/ICITR54349.2021.9657419","url":null,"abstract":"Stress, if defines naively, is the body's response to pressure. Sri Lanka is a South Asian country where mental health is not given a much of a concern comparing with the Western and European countries. The Mental Health Foundation stated that some stress responses can be managed, and some can be useful but too much stress can cause negative effects and long-term stress may affect human's physical and mental health in advance [1]. According to the statistics of the World Health Organization (WHO) 5%-10% of the Sri Lanka population suffer from mental health problems which needs the attention of the professionals [2]. The present case study aims at the efficient and effective management of stress of University Undergraduates of Sri Lanka who can be a victim of stress due to several reasons. The proposed system aims to help the university undergraduates by providing them with a mobile application which collects and calculates the current stress levels and stress scores of undergraduates using different techniques and suggesting the stress relieving activities for that specific stress level via a recommendation system which contains multiple stress relieving activities which can be used to cater with different stress levels.","PeriodicalId":188174,"journal":{"name":"2021 6th International Conference on Information Technology Research (ICITR)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125424868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Image Breaking Method For Lung Isolation from Chest X-rays","authors":"Chanduka A Samarasinghe, G. U. Ganegoda","doi":"10.1109/ICITR54349.2021.9657412","DOIUrl":"https://doi.org/10.1109/ICITR54349.2021.9657412","url":null,"abstract":"Most of the techniques that are available for chest x ray image segmentation are not purely based on image processing. Majority of the existing work involves a limited preprocessing part, and the rest is conducted with the leverage of Machine learning and Convolutional neural networks. This study strives to fill this void by introducing a fully image processing-based lung segmentation method. This paper discusses a novel method introduced to segment lungs from chest x ray images purely based on image processing. The method introduced here is named as “Image Breaking method” due to its unique wary of breaking the CXR in to segments to maximize the data extraction. Reason for selecting Xray images is X Ray images are common and reachable in almost all the general hospitals and for most of the lung related diseases CXRs are the most common primary medical imaging technique that is used. Because of its complexity and lesser quality of the image, chest x ray images are hard to segmentate only based on image processing. Here we have taken an attempt to provide guidance for consumers the pathways that can be taken to achieve this task only using image processing. It needs lesser resources; lesser line of code and all the steps are based on experience we gained through experiments. Final evaluation showed that this method provides a fairly good output when it compared with structural similarity to images that are segmented by specialists.","PeriodicalId":188174,"journal":{"name":"2021 6th International Conference on Information Technology Research (ICITR)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125067292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vision Based Intelligent Shelf-Management System","authors":"H.A.M Priyanwada, K.A.D Dilanka Madhushan, C. Liyanapathirana, L. Rupasinghe","doi":"10.1109/ICITR54349.2021.9657405","DOIUrl":"https://doi.org/10.1109/ICITR54349.2021.9657405","url":null,"abstract":"Currently supermarkets are more popular, and the local stores are leaving the competition. when people go to supermarkets, they find various items stocked on seemingly unlimited shelves. Supermarket shelves needed to be filled with the items accordingly. The most common problems in the supermarkets are identifying the empty shelves, on-shelf availability, and future sales. The labors cannot always track the empty shelves and on shelf availability levels due to their workloads. Moreover, it is a time-consuming method for the labors which can affect the customer satisfaction and business profit. Every month, supermarkets buy the required number of products from related manufacturing companies by analyzing the previously purchased products and their sales. This is usually done manually by managing excel sheets which is also time consuming and not reliable. Especially during the seasonal times or pandemic situations they cannot use the manual method which must also be done as fast as possible. Therefore, this system can be used to assist in empty shelf detection, percentage of on-shelf availability and in the prediction of future sales. The implementation of on-shelves percentage detection service is done using machine learning. Machine learning processes are carried out for implementing the necessary functionalities and algorithms. Initially, the camera captures clear and real time images regularly. Then the system processes and detects the image similar to the threshold percentage or detect the empty shelves. When the system detects the threshold percentage or empty shelves, the system will provide an alert to the labors. The Implementation of the predicting the future supply and demands is done using time series analysis using several existing machine learning algorithms by utilizing historical data. In this research the prediction of future sales and demand in the supermarkets is done by considering the customers' behavior, the variety of product groups they buy and seasonal changes. These predictions are made on the assumption of a constant per capital supply of products and demand in our system.","PeriodicalId":188174,"journal":{"name":"2021 6th International Conference on Information Technology Research (ICITR)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123700427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heuristics-Based SQL Query Generation Engine","authors":"Chinthani Sugandhika, S. Ahangama","doi":"10.1109/ICITR54349.2021.9657317","DOIUrl":"https://doi.org/10.1109/ICITR54349.2021.9657317","url":null,"abstract":"A database is one of the prime media to store data. Most of the time, relational databases are preferred over other databases due to their ability to represent complex relationships between data. Languages like Structured Query Language (SQL) are used to retrieve data stored in relational databases. Information stored in these databases is often accessed by naïve users who do not possess high competencies in technical database querying. Therefore, Natural Language Interfaces to Databases (NLIDB) are being developed to translate natural language into SQL queries and retrieve the corresponding database results. This paper proposes a novel NLIDB called SQL Query Generation Engine which has been developed using a heuristics-based approach. The system was tested with more than 200 natural language queries and has shown an overall accuracy of 93%.","PeriodicalId":188174,"journal":{"name":"2021 6th International Conference on Information Technology Research (ICITR)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132566224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Rule based Approach for Hemorrhage Detection in Digital Fundus Photographs","authors":"S. C. Munasingha, Primesh Pathirana, Kodithuwakkuge Keerthi Priyankara, Ravindu Gimantha Upasena, Akira Ikeda","doi":"10.1109/ICITR54349.2021.9657220","DOIUrl":"https://doi.org/10.1109/ICITR54349.2021.9657220","url":null,"abstract":"Hemorrhages are one of the earliest signs of Diabetic Retinopathy, hence accurate detection of hemorrhages is crucial in an automated DR detection system. In this paper, a novel and robust rule based methodology for automated detection of hemorrhages is proposed. We present an ensemble technique for hemorrhage classification by incorporating size-based classification, color-statistic-based classification, and shape-based classification along with a novel dual step filtering approach for candidate detection. Finally, we present an experimental study carried out on DIARETDB database using the proposed method to detect and segment hemorrhages in retinal images.","PeriodicalId":188174,"journal":{"name":"2021 6th International Conference on Information Technology Research (ICITR)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132271551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning-Based Automated Tool to Detect Sinhala Hate Speech in Images","authors":"Evangeli Silva, Maheshi Nandathilaka, Sandupa Dalugoda, Thanu Amarasinghe, S. Ahangama, G. T. Weerasuriya","doi":"10.1109/ICITR54349.2021.9657453","DOIUrl":"https://doi.org/10.1109/ICITR54349.2021.9657453","url":null,"abstract":"Social media platforms have emerged rapidly with technological advancements. Facebook, the most widely used social media platform has been the primary reason for the spread of hatred in Sri Lanka in the recent past. When a post with Sinhala hate content is reported on Facebook, it is translated to the English language before the review of the moderators. In most instances, the translated content has a different context compared to the original post. This results in concluding that the reported post does not violate the established policies and guidelines concerning hate content. Hence, an effective approach needs to be in place to address the aforementioned problem. This research project proposes a solution through an automated tool that is capable of detecting hate content presented in Sinhala phrases extracted from Facebook posts/memes. The tool accepts an image that contains Sinhala texts, extracts the text using a Convolutional Neural Network (CNN) model, preprocesses the text using Natural Language Processing (NLP) techniques, analyzes the preprocessed text to identify hate intensity level and finally classifies the text into four main domains named Political, Race, Religion and Gender using a text classification model.","PeriodicalId":188174,"journal":{"name":"2021 6th International Conference on Information Technology Research (ICITR)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132629441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Kidland: An Augmented Reality-based approach for Smart Ordering for Toy Store","authors":"W.M.C.D Wijayalath, R.M.T.T Ranasinghe, Suriyaa Kumari, M.S.B.W.T.M.P.S.B. Thennakoon, H.D Vithanage, S. Chandrasiri","doi":"10.1109/ICITR54349.2021.9657326","DOIUrl":"https://doi.org/10.1109/ICITR54349.2021.9657326","url":null,"abstract":"Augmented reality (AR) is an iconic topic that can be applied in different domains in modern world technology. With the rapid development of technologies, eCommerce (Online Shopping) has become closer to human life. As a result, AR was started implemented with eCommerce platforms by the developers. With the busy lives and the pandemic situation, people are limited to visiting toy stores while providing a solution. An AR-based virtual toy store is proposed with 3D Toy generation for visualizing selected toys, a Virtual tour for enhancing the remote virtual shopping experience, and an Indoor navigation system visualizing the path within large scale shopping malls are new features of the proposed system. The majority of the existing eCommerce platforms are missing image search features. As a solution, “KidLand” has implemented an image search engine, suggesting add-on-related items and nearest branches using machine learning algorithms. An intelligent chatbot uses a reinforcement learning algorithm and Natural Language Understanding (NLU) to give possible solutions regarding the toy store. As a solution to the language literacy problem, developed a chatbot that can chat both English and Sinhala languages. “Kidland” was developed to provide the users the next level of shopping experience with attractive features of AR technology with marketing and use advanced technologies overcoming the issues of ordinary eCommerce platforms. In Sri Lanka, this system has been identified as a solution for the issues with ordinary shopping platforms.","PeriodicalId":188174,"journal":{"name":"2021 6th International Conference on Information Technology Research (ICITR)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114536407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fleet management with real-time data analytics","authors":"R. Rathnayaka, K.V.J.P. Ekanayake, Hhmp Rathnayake, H. R. Jayetileke","doi":"10.1109/ICITR54349.2021.9657406","DOIUrl":"https://doi.org/10.1109/ICITR54349.2021.9657406","url":null,"abstract":"Significant effort has to be devoted to surviving the businesses relying on fleet vehicles in the year 2020 and ahead as the novel coronavirus (COVID-19) epidemic became pandemic. Executing profitable business while keeping the staff safe and productive is a critical challenge to deal with. To find a solution, we focus on driver management out of major functions in fleet management such as vehicle, driver, and operation management. We were unable to identify a study conducted to capture real-time data on a ride in a fleet. Therefore, to fill that gap we implemented a cost-effective real-time Fleet Management System (FMS) using data analytics with the use of ESP32 SIM800L with reprogrammable capabilities. Fleet can use this system to monitor real-time data on vehicle location, remaining time to the destination, vehicle speed, and distance traveled. Moreover, the system can be personalized as it has reprogrammable features to be enabled or disabled based on the customer's preference. Once the data is captured through the Global Positioning System (GPS) receiver, data will be transmitted via General Packet Radio Service (GPRS) to two remote servers. One server is hosted locally with SQL and where the other is hosted in a cloud environment with a Firebase realtime database. The vehicle location is tracked using GPS. For fast data transfer, 3G Global System for Mobile communications (GSM) with ESP32 800L microprocessor was used. A web-based graphical user interface is developed to analyse and present the transmitted data. Vehicle information can be viewed and located on the web application in form of google maps. Real-time data analytics is used with Firebase's real-time database. Furthermore, Short Message Service (SMS) facility is made available for the driver to communicate with configured mobile numbers","PeriodicalId":188174,"journal":{"name":"2021 6th International Conference on Information Technology Research (ICITR)","volume":"161 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120990284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Last Page","authors":"S. Haven","doi":"10.1093/ejil/chy007","DOIUrl":"https://doi.org/10.1093/ejil/chy007","url":null,"abstract":"","PeriodicalId":188174,"journal":{"name":"2021 6th International Conference on Information Technology Research (ICITR)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117334810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}