2020 2nd International Conference on Advancements in Computing (ICAC)最新文献_第4页

Evaluating Optimal Lockdown and Testing Strategies for COVID-19 using Multi-Agent Social Simulation 利用多智能体社会模拟评估COVID-19的最佳锁定和测试策略

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/ICAC51239.2020.9357132

P.M. Dunuwila, R. Rajapakse

{"title":"Evaluating Optimal Lockdown and Testing Strategies for COVID-19 using Multi-Agent Social Simulation","authors":"P.M. Dunuwila, R. Rajapakse","doi":"10.1109/ICAC51239.2020.9357132","DOIUrl":"https://doi.org/10.1109/ICAC51239.2020.9357132","url":null,"abstract":"COVID-19 pandemic has become a major concern due to its rapid spread throughout the world. We can observe some countries are successful in formulating effective strategies for managing the pandemic, while some are struggling. The research is based on the question of formulating effective policies for COVID-19 to reduce community transmission. While many countries are suffering from the pandemic, it is a critical issue that the policymakers should be concerned with formulating effective policies to address the problem. We use computational methods to foresee the future by creating a simulation model based on multi-agent and simulation methodology because it is not always possible to predict the future state of a complex adaptive system. The data are collected through a survey and the literature to calibrate the model parameters to build a constructive and realistic model. Once the model is constructed, the simulation results are compared with the real-world observations to validate the model. The implementation of the model follows an iterative process for improving the validity of the model. This paper presents the conceptual model of the system being investigated and its initial implementation, which needs to be calibrated further with empirical data before using it as a decision support tool.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130968590","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}

引用次数: 2

AI Approach In Monitoring The Physical And Psychological State Of Car Drivers And Remedial Action For Safe Driving 人工智能在汽车驾驶员身心状态监测中的应用及安全驾驶的补救措施

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/icac51239.2020.9357240

Sujeevan Shanmugarajah, Janani Tharmaseelan, Luckman Sivagnanam

{"title":"AI Approach In Monitoring The Physical And Psychological State Of Car Drivers And Remedial Action For Safe Driving","authors":"Sujeevan Shanmugarajah, Janani Tharmaseelan, Luckman Sivagnanam","doi":"10.1109/icac51239.2020.9357240","DOIUrl":"https://doi.org/10.1109/icac51239.2020.9357240","url":null,"abstract":"Road Accidents and casualties incited by drowsiness are an overall important social and monetary issue. The connection between drowsiness and accidents is bolstered by logical confirmations that relate to small-scale sleep. This project has focused on Driver drowsiness detection by using ECG signal extraction. This work expects to extract and arrange the basic four types of sleep through Wavelet Transform and machine learning calculations. The report covers a short theoretical introduction about the medicinal topic, features the extraction, filtering techniques, and afterward trains the extracted information through machine learning software. After that is covered, it demonstrates the results with two types of machine learning algorithms (active or drowsiness status) with WEKA software. The main benefit of this system is it will send a notification to the driver's mobile every second when he goes to sleeping status. Nowadays artificial intelligence cars are available with sleep assistance, however, the devices used on these cars are very expensive. So, our approach is to develop a system to predict the driver's drowsiness to reduce accidents caused by sleepiness at a low cost. The sleep / awake status is determined by both the factors RR peak's distance and R's amplitude","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132533067","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}

引用次数: 0

SURAKSHA E-Caretaker: Elders Falling Detection and Alerting System using Machine Learning SURAKSHA E-Caretaker:使用机器学习的老年人跌倒检测和警报系统

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/ICAC51239.2020.9357305

Lahiru Mendis, Sasini Hathurusinghe, H. Epa, Thisara Edirisinghe, J. Wickramarathne, Shalini Rupasinghe

{"title":"SURAKSHA E-Caretaker: Elders Falling Detection and Alerting System using Machine Learning","authors":"Lahiru Mendis, Sasini Hathurusinghe, H. Epa, Thisara Edirisinghe, J. Wickramarathne, Shalini Rupasinghe","doi":"10.1109/ICAC51239.2020.9357305","DOIUrl":"https://doi.org/10.1109/ICAC51239.2020.9357305","url":null,"abstract":"People become unable to perform tasks that were done at the younger ages as they were when the ages pass with time. Falls play a major issue in the lives of elderly people as the physical and mental quality of life is dependable on the effects of falls. This research presents an e-Caretaker SURAKSHA which is an elder falling detection and alerting system based on Machine Learning concepts. Researchers that have been done in this area have produced different solutions to detect only the falls but not to automatically detect and notify them to the caretakers. This solution serves as a smart wearable device that is capable of automatically monitoring real-time postures, detecting sudden falls, possible arrhythmia conditions of the heart of the fallen person, and daily route deviations along with the fallen location which is finally notified to the caretakers through a mobile application. According to the performed studies, python model development was used to implement the system through Machine Learning concepts by referring to the Markov model, Prophet model, and Naïve Bayes algorithms. This solution provides the results of this research with an accuracy of around 89.9% leading to a successful product in the domain.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131707899","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}

引用次数: 0

A Story of Two Surveys: for the Advancement of Sinhalese Mobile Text Entry Research 两个调查的故事:推进僧伽罗语移动文本输入研究

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/ICAC51239.2020.9357307

Shyam Reyal, Vijani S. Piyawardana, D. Kaveendri

引用次数: 0

Stock Market Price Forecasting using ARIMA vs ANN; A Case study from CSE 基于ARIMA和ANN的股票市场价格预测CSE的一个案例研究

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/ICAC51239.2020.9357288

G.W.R.I. Wijesinghe, R. Rathnayaka

{"title":"Stock Market Price Forecasting using ARIMA vs ANN; A Case study from CSE","authors":"G.W.R.I. Wijesinghe, R. Rathnayaka","doi":"10.1109/ICAC51239.2020.9357288","DOIUrl":"https://doi.org/10.1109/ICAC51239.2020.9357288","url":null,"abstract":"Stock market prediction or forecasting is a challenging task to predict the upcoming stock values. Stock prices are nonstationary and highly noisy because stock markets are affected by a variety of factors. Traditionally, the next lag of time series is effectively forecast by a variety of techniques like Simple Exponential Smoothing, ARIMA. In particular, ARIMA has shown its success in accuracy and precision in predicting the next time-series lags. As part of the literature, very few studies have focused on Colombo Stock Exchange (CSE) to find new predictive approaches for the forecasting of high volatility stock price indexes. Different statistical approaches and economic data strategies have been widely applied to define market price movements and trends and the trade volume levels in CSE over the last ten years. This article explores whether and how the newly developed deep learning algorithms for the projection of time series data, such as the Back Propagation Neural Network, are greater than traditional algorithms. The results show that Deep learning algorithms like BPNN outperform traditionally based algorithms like the model ARIMA. The MAE and MSE values relative to ARIMA and BPNN, which suggests BPNN 's superiority to ARIMA.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114845361","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}

引用次数: 6

Proximity based Intelligent Air Pollution Alerts for Garbage Disposal Sites 基于近距离的垃圾处理场空气污染智能警报

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/icac51239.2020.9357286

W.G.D.U. Wijerathne, M.L.M.P. Perera, R.H.C Nuwandika, R. Ranasinghe, K. Kahandawaarachchi, N. Gamage

{"title":"Proximity based Intelligent Air Pollution Alerts for Garbage Disposal Sites","authors":"W.G.D.U. Wijerathne, M.L.M.P. Perera, R.H.C Nuwandika, R. Ranasinghe, K. Kahandawaarachchi, N. Gamage","doi":"10.1109/icac51239.2020.9357286","DOIUrl":"https://doi.org/10.1109/icac51239.2020.9357286","url":null,"abstract":"Air pollution is one of the key trending challenges faced by the public at present. The garbage disposal sites are the major contributors which emit harmful gases (CO, CO2, CH4) where toxicity is at a higher level. This research attempts to fill the lacuna by providing an intelligent proximity-based air pollution detection system that alerts and makes the public aware of the danger and risk of the garbage dumps that are located near them via a mobile application. The device is developed to detect harmful gas with MG811, MQ7, MQ4 sensors with 0.80 accuracy. The device also contains a DHT22 temperature humidity sensor with 0.80 accuracy and SD011 particulate matter sensor which has a 0.95 accuracy. The application can notify the responsible authorities regarding the possible risks of the garbage dump and the health effects that can cause. The air toxicity is calculated with an accuracy of 0.75 and visualized, using the landfill classification and pollution level prediction algorithms with an accuracy of 0.96 in a geo proximity map with 0.92 percent accuracy.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126093628","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}

引用次数: 0

Learning Assistant to Acquire the Fundamental Language Skills for Non-Native Learners using AI 学习助手，以获得基本的语言技能，为非母语学习者使用人工智能

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/ICAC51239.2020.9357297

Praveenkumar Srikanthan, Roshan Nizar, Abishaan Ravikumar, Keerthigan Lalitharan, S. Harshanath, Jesuthasan Alosius

{"title":"Learning Assistant to Acquire the Fundamental Language Skills for Non-Native Learners using AI","authors":"Praveenkumar Srikanthan, Roshan Nizar, Abishaan Ravikumar, Keerthigan Lalitharan, S. Harshanath, Jesuthasan Alosius","doi":"10.1109/ICAC51239.2020.9357297","DOIUrl":"https://doi.org/10.1109/ICAC51239.2020.9357297","url":null,"abstract":"The ability to speak and learn a language properly requires good practice, experience and good learning strategies but the existing solutions do not provide proper guidance to learn a language with instant feedback. This research is an approach to devise an improved language learning assistant with practices that will help to improve the fundamental language skills for non-native learners and children who are in the early stage of their education. The four main skills focused on this application will be conversation, pronunciation, listening and grammatical skills. The implementation of this research is done by using technologies like natural language processing, machine learning, and deep learning approaches to come up with components to train the learner. The solution of this research is delivered by using a cross-platform application called GLIB which facilities to improve all the English language skills mentioned above along with guides, tips, practices, and feedback based on an evaluation to improve the English language.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122768383","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}

引用次数: 0

Evaluating Teaching Content and Assessments based on Learning Outcomes 评估教学内容和基于学习成果的评估

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/ICAC51239.2020.9357319

P. M. O. N. Pallegama, K. Kumari, D. Dissanayaka, A. V. Y. Ravihansi, Anuradha Karunasenna, Uthpala Samarakoon

{"title":"Evaluating Teaching Content and Assessments based on Learning Outcomes","authors":"P. M. O. N. Pallegama, K. Kumari, D. Dissanayaka, A. V. Y. Ravihansi, Anuradha Karunasenna, Uthpala Samarakoon","doi":"10.1109/ICAC51239.2020.9357319","DOIUrl":"https://doi.org/10.1109/ICAC51239.2020.9357319","url":null,"abstract":"A modularized syllabus content assigned to different units of a subject proves very useful to both teachings as well as the student community. In each module, learning outcomes are defined. In each learning outcome, lesson learning outcomes are defined. When the Teaching Content (Lecture content), Learning activities (Labs sheets and Tutorials), Final Question Papers are being made the subject learning outcome should be considered and it should be made within the subject learning outcomes. Then the teaching and learning process will be done properly. Nowadays Revised Bloom's Taxonomy standard is used to structure the Teaching Content, Learning Activities, and Final Question paper of a course in the best way. Currently, there is no proper solution to corporate above areas according to the Revised Bloom's Taxonomy. This paper discusses an automated system that provides the features to verify the module and lesson learning outcomes and their levels according to Revised Bloom's taxonomy and to verify that the teaching content and learning activities are within the learning outcomes. Beyond that, this system uses various technologies and algorithms to improve the accuracy and efficiency of this research. This automated system is able to achieve to the final outcome with the best accuracy and efficiency than the manual process.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121849203","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}

引用次数: 0

Intelligent Disease Detection System for Greenhouse with a Robotic Monitoring System 基于机器人监控系统的温室病害智能检测系统

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/ICAC51239.2020.9357143

S. Fernando, Ranusha Nethmi, Ashen Silva, Ayesh Perera, R. de Silva, P. Abeygunawardhana

{"title":"Intelligent Disease Detection System for Greenhouse with a Robotic Monitoring System","authors":"S. Fernando, Ranusha Nethmi, Ashen Silva, Ayesh Perera, R. de Silva, P. Abeygunawardhana","doi":"10.1109/ICAC51239.2020.9357143","DOIUrl":"https://doi.org/10.1109/ICAC51239.2020.9357143","url":null,"abstract":"Greenhouse farming plays a significant role in the agricultural industry because of its controlled climatic features. Recent examinations have stated that the mean creation of the yields under greenhouses is lessening due to disease events in the plants. These foods have become an imposing undertaking because these plants are being assaulted by different bacterial diseases, micro-organisms, and pests. The chemicals are applied to the plants intermittently without thinking about the necessity of each plant. Several problems have occurred in the greenhouse environment due to these causes. Therefore, there is a huge necessity for a system to detect diseases at an early stage. This research focused on designing a system to detect disease, which causes yellowish in greenhouse plants. Plant yellowing can be considered a significant problem of plants that grow under greenhouse-controlled environments. Through this research is focused on the most important and one of the most attention-grabbing crop tomato. There are specific diseases that cause yellowish the tomato plant, and they have been identified. The techniques utilized for early recognition of infection are image processing, machine learning, and deep learning. Index Terms-Greenhouses, Disease diagnosis, Image processing, Machine Learning, Deep Learning, Tomato Farming","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"898 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132273379","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}

引用次数: 8

Computational Model for Rating Mobile Applications based on Feature Extraction 基于特征提取的移动应用评定计算模型

2020 2nd International Conference on Advancements in Computing (ICAC) Pub Date : 2020-12-10 DOI: 10.1109/icac51239.2020.9357270

Inthuja Gunaratnam, D. Wickramarachchi

{"title":"Computational Model for Rating Mobile Applications based on Feature Extraction","authors":"Inthuja Gunaratnam, D. Wickramarachchi","doi":"10.1109/icac51239.2020.9357270","DOIUrl":"https://doi.org/10.1109/icac51239.2020.9357270","url":null,"abstract":"Google Play Store and App Store allow users to share their opinions and helps to measure users satisfaction level about the app through user comments. However, it's highly time-consuming to process all reviews manually. The usefulness of star ratings is limited for development teams since a rating represents an average of both positive and negative evaluations. Therefore, an automated solution is needed to systematically analyze reviews and other textual forms of data. The main objective of this research is to build a platform that rate apps by feature extraction and sentiment analysis to calculate the functionality index of apps based on metrics obtained by surveying 204 mobile phone users. The 5 topmost metrics obtained from them among the 16 metrics obtained from the literature review are usability, price, and frequency of updates, ad-freeness and battery consuming level. This research focuses on selected apps in music and audio category. To perform app rating indexes calculation of the overall app's reviews; data extraction, data cleaning, POS tagging, feature extraction, feature/feature values pairing, weighted feature rating, overall apps' rating and feature-wise app rating is done on textual data. The accuracy of the created model is measured by the level of satisfaction from users.","PeriodicalId":253040,"journal":{"name":"2020 2nd International Conference on Advancements in Computing (ICAC)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130036948","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}

引用次数: 3