2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC)最新文献_第3页

R10-HTC 2018 Keynotes R10-HTC 2018主题演讲

2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2018-12-01 DOI: 10.1109/r10-htc.2018.8629799

引用次数: 0

Eduscope.Mobile - Mobile Application for Teaching and Learning Eduscope。移动-用于教学和学习的移动应用程序

2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2018-12-01 DOI: 10.1109/R10-HTC.2018.8629820

K.H. M Fernando, I. Gunasekara, J.V.G. A Krishani, A.G. C Dilshani, Dr. Malitha Wijesundara

引用次数: 0

R10-HTC 2018 Tutorial Sessions R10-HTC 2018教程

2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2018-12-01 DOI: 10.1109/r10-htc.2018.8629804

引用次数: 0

Sign Language Recognition for Speech and Hearing Impaired by Image Processing in MATLAB 基于图像处理的语音和听力障碍手语识别

2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2018-12-01 DOI: 10.1109/R10-HTC.2018.8629823

Parama Sridevi, Tahmida Islam, Urmi Debnath, Noor A Nazia, Rajat Chakraborty, C. Shahnaz

引用次数: 7

Detecting Adverse Drug Reaction with Data Mining And Predicting its Severity With Machine Learning 用数据挖掘检测药物不良反应并用机器学习预测其严重程度

2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2018-12-01 DOI: 10.1109/R10-HTC.2018.8629806

Tanvir Islam, Nadib Hussain, Samiul Islam, Amitabha Chakrabarty

{"title":"Detecting Adverse Drug Reaction with Data Mining And Predicting its Severity With Machine Learning","authors":"Tanvir Islam, Nadib Hussain, Samiul Islam, Amitabha Chakrabarty","doi":"10.1109/R10-HTC.2018.8629806","DOIUrl":"https://doi.org/10.1109/R10-HTC.2018.8629806","url":null,"abstract":"Adverse Drug Reaction (ADR) is one of the many uncertainties that are considered a fatal threat to the pharmacy industry and the field of medical diagnosis. Utmost care is taken to test a new drug thoroughly before it is introduced and made available to the public. However, these pre-clinical trials are not enough on their own to ensure safety. The increasing concern to the ADRs has motivated the development of statistical, data mining and machine learning methods to detect the Adverse Drug Reactions. With the availability of Electronic Health Records (EHRs), it has become possible to detect ADRs with the mentioned technologies. In this work, we have proposed a hybrid model of data mining and machine learning to identify different Adverse Reactions and predict the intensity of the outcome. We have used the Proportionality Reporting Ratio (PRR) along with the precision point estimator test called the Chi-Square test to find out the different relationships between drug and symptoms called the drug-ADR association. This output from the data mining technique is used as an input to the machine learning algorithms such as Random Forest and Support Vector Machine (SVM) to predict the intensity of the outcome of ADR, depending on a patient’s demographic data such as gender, weight, age, etc. In this work, we have achieved an accuracy of 91% to predict 'death' as the outcome from an ADR.","PeriodicalId":404432,"journal":{"name":"2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133737115","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

Implementation of an Oscilloscope Vector Network Analyzer for Teaching S-Parameter Measurements 用于教学s参数测量的示波器矢量网络分析仪的实现

2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2018-12-01 DOI: 10.1109/R10-HTC.2018.8629807

C. Ambatali

引用次数: 3

Towards Smart Farming: Accurate Prediction of Paddy Harvest and Rice Demand 走向智慧农业:水稻收成和需求的准确预测

2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2018-12-01 DOI: 10.1109/R10-HTC.2018.8629843

M. R. S. Muthusinghe, Palliyaguru S. T., W. Weerakkody, A. M. H. Saranga, W. Rankothge

{"title":"Towards Smart Farming: Accurate Prediction of Paddy Harvest and Rice Demand","authors":"M. R. S. Muthusinghe, Palliyaguru S. T., W. Weerakkody, A. M. H. Saranga, W. Rankothge","doi":"10.1109/R10-HTC.2018.8629843","DOIUrl":"https://doi.org/10.1109/R10-HTC.2018.8629843","url":null,"abstract":"Rice is the predominant staple food in Asian countries. It has a major impact on the social and economic development of these countries. Therefore, it is very important to keep the sustainability between paddy cultivation and consumer demand. Paddy crop yield and demand for rice of a country depend on numerous factors such as rainfall, humidity, citizen's life styles etc. Hence, the prediction of future harvest and demand is a complex process. There is a requirement for a platform that predicts on future harvest and demands based on all affecting factors. We have proposed a platform that targets the smart farming concepts for paddy, with following modules: (1) a prediction module to predict paddy harvest and (2) a prediction module to predict rice demand. We have developed the prediction modules using two machine learning algorithms: (1) Recurrent Neural Network (RNN) and (2) Long Short-Term Memory (LSTM). The performances of algorithms were evaluated using real data sets for the Sri Lankan context. Our results show that the prediction modules are giving accurate results in a short time.","PeriodicalId":404432,"journal":{"name":"2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132469131","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: 19

R10-HTC 2018 Technical Program Committee R10-HTC 2018技术计划委员会

2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2018-12-01 DOI: 10.1109/r10-htc.2018.8629815

引用次数: 0

Heat Stroke Alert System 中暑警报系统

2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2018-12-01 DOI: 10.1109/R10-HTC.2018.8629819

Y. Marambe, D. Niroshani, P. Rathnayake, S. Dayananda, Dhammika H. De Silva

引用次数: 2

Detecting Cervix Type Using Deep learning and GPU 基于深度学习和GPU的子宫颈类型检测

2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) Pub Date : 2018-12-01 DOI: 10.1109/R10-HTC.2018.8629824

Bijoy M B, V. Shilimkar, J. B

{"title":"Detecting Cervix Type Using Deep learning and GPU","authors":"Bijoy M B, V. Shilimkar, J. B","doi":"10.1109/R10-HTC.2018.8629824","DOIUrl":"https://doi.org/10.1109/R10-HTC.2018.8629824","url":null,"abstract":"Cervical cancer is the second most occurring cancer in women of all age groups. It causes cells on the cervix to grow out of control. Cervical cancer is caused by a virus called human papillomavirus aka HPV. In the early stages of cancer, there will be very little symptoms which make it difficult to detect. If cancer is detected at an early stage, then proper and effective medication can be started at the right time. Usual methods available for detection of cervical cancer largely depend on human expertise. With the advancements in medical imaging technology, computerized methods were also developed to detect the cancerous cells at an early stage. The type of treatment for cervical cancer is primarily determined by the cervix type of the patient and hence its type detection is very important. Thus, we have proposed a method to classify the cervix type using deep learning technology. A CNN model is created and trained from the scratch, along with two other models which are trained using transfer learning technology. From the experimental results, a validation accuracy of 0.6523 is achieved. We also trained the parallel models using GPU and speed of about six fold (x6) is achieved","PeriodicalId":404432,"journal":{"name":"2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"357 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122718036","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