Pub Date : 2018-12-01DOI: 10.1109/R10-HTC.2018.8629820
K.H. M Fernando, I. Gunasekara, J.V.G. A Krishani, A.G. C Dilshani, Dr. Malitha Wijesundara
Smart Phones and Tablets are very common devices among people at Present. They have many additional features i.e. camera, internet, GPS etc than the basic phone has. Smart devices make easier the work of social, business and academic life of people. M-Learning is used to make easier works of academic life. M-Learning describes the Teaching and Learning using mobile devices. Eduscope.Mobile represents the M-Learning by providing virtual classroom. It will cover the whole classroom scenario. The lecturer can do their lectures from anywhere as well as students can learn the lectures from anywhere. Eduscope.Mobile is a cross-platform mobile application for M-Learning. It provides Live session facility for both Lecturers and students to connect from anywhere to the lecture at the same time. It creates a virtual classroom for lecturers and students. Lecturers and Students perform any activity in a normal classroom by using this mobile application.
{"title":"Eduscope.Mobile - Mobile Application for Teaching and Learning","authors":"K.H. M Fernando, I. Gunasekara, J.V.G. A Krishani, A.G. C Dilshani, Dr. Malitha Wijesundara","doi":"10.1109/R10-HTC.2018.8629820","DOIUrl":"https://doi.org/10.1109/R10-HTC.2018.8629820","url":null,"abstract":"Smart Phones and Tablets are very common devices among people at Present. They have many additional features i.e. camera, internet, GPS etc than the basic phone has. Smart devices make easier the work of social, business and academic life of people. M-Learning is used to make easier works of academic life. M-Learning describes the Teaching and Learning using mobile devices. Eduscope.Mobile represents the M-Learning by providing virtual classroom. It will cover the whole classroom scenario. The lecturer can do their lectures from anywhere as well as students can learn the lectures from anywhere. Eduscope.Mobile is a cross-platform mobile application for M-Learning. It provides Live session facility for both Lecturers and students to connect from anywhere to the lecture at the same time. It creates a virtual classroom for lecturers and students. Lecturers and Students perform any activity in a normal classroom by using this mobile application.","PeriodicalId":404432,"journal":{"name":"2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"7 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":"121723998","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}
Pub Date : 2018-12-01DOI: 10.1109/r10-htc.2018.8629804
Provides an abstract for each of the tutorial presentations and may include a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings.
{"title":"R10-HTC 2018 Tutorial Sessions","authors":"","doi":"10.1109/r10-htc.2018.8629804","DOIUrl":"https://doi.org/10.1109/r10-htc.2018.8629804","url":null,"abstract":"Provides an abstract for each of the tutorial presentations and may include a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings.","PeriodicalId":404432,"journal":{"name":"2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"23 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":"129014050","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}
Pub Date : 2018-12-01DOI: 10.1109/R10-HTC.2018.8629823
Parama Sridevi, Tahmida Islam, Urmi Debnath, Noor A Nazia, Rajat Chakraborty, C. Shahnaz
The paper presents the model of a sign language interpreter that can verbalize American Sign Language (ASL). This robust model is based on creating a human-computer interface (HCI) using the user's hand gesture only. The combination of Hardware and software interfaces-webcam and MATLAB 2016a-performs the feature extraction process from the image captured from real-time video of hand signs. These features are compared with the features of the database images and after some image processing techniques in MATLAB, the system generates outputs depending on the prediction of highest resemblance. As the model is free from any other apparatus or accessories, it is solely practical and easy to use. This model provided satisfactory accuracy in our tests without any need of any constant or unicolor background. The proposed technique, together with a vast source database, will definitely be highly beneficial for mitigating the communication gap between the people with speaking and hearing abilities and those without them.
{"title":"Sign Language Recognition for Speech and Hearing Impaired by Image Processing in MATLAB","authors":"Parama Sridevi, Tahmida Islam, Urmi Debnath, Noor A Nazia, Rajat Chakraborty, C. Shahnaz","doi":"10.1109/R10-HTC.2018.8629823","DOIUrl":"https://doi.org/10.1109/R10-HTC.2018.8629823","url":null,"abstract":"The paper presents the model of a sign language interpreter that can verbalize American Sign Language (ASL). This robust model is based on creating a human-computer interface (HCI) using the user's hand gesture only. The combination of Hardware and software interfaces-webcam and MATLAB 2016a-performs the feature extraction process from the image captured from real-time video of hand signs. These features are compared with the features of the database images and after some image processing techniques in MATLAB, the system generates outputs depending on the prediction of highest resemblance. As the model is free from any other apparatus or accessories, it is solely practical and easy to use. This model provided satisfactory accuracy in our tests without any need of any constant or unicolor background. The proposed technique, together with a vast source database, will definitely be highly beneficial for mitigating the communication gap between the people with speaking and hearing abilities and those without them.","PeriodicalId":404432,"journal":{"name":"2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"60 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":"124128399","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}
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.
{"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}
Pub Date : 2018-12-01DOI: 10.1109/R10-HTC.2018.8629807
C. Ambatali
In this paper, the feasibility of the use of a two- channel digital oscilloscope synthesized with a signal generator to create a two-port vector network analyzer (VNA) is experimentally validated. The sinusoidal transmitted and reflected signals are digitized by the oscilloscope and the data displayed is used to calculate the S-parameters of a device under test (DUT). The hardware used in this system is a common low-spec oscilloscope, a signal generator, and a coupler. All of these can be found or built in a classroom setting and can be used to demonstrate network analysis for education of students instead of using commercial VNAs which are expensive. The measured S-parameters gathered in this setup is within 4 decibels in magnitude and 20 degrees in phase compared to measurements on a commercial VNA.
{"title":"Implementation of an Oscilloscope Vector Network Analyzer for Teaching S-Parameter Measurements","authors":"C. Ambatali","doi":"10.1109/R10-HTC.2018.8629807","DOIUrl":"https://doi.org/10.1109/R10-HTC.2018.8629807","url":null,"abstract":"In this paper, the feasibility of the use of a two- channel digital oscilloscope synthesized with a signal generator to create a two-port vector network analyzer (VNA) is experimentally validated. The sinusoidal transmitted and reflected signals are digitized by the oscilloscope and the data displayed is used to calculate the S-parameters of a device under test (DUT). The hardware used in this system is a common low-spec oscilloscope, a signal generator, and a coupler. All of these can be found or built in a classroom setting and can be used to demonstrate network analysis for education of students instead of using commercial VNAs which are expensive. The measured S-parameters gathered in this setup is within 4 decibels in magnitude and 20 degrees in phase compared to measurements on a commercial VNA.","PeriodicalId":404432,"journal":{"name":"2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"67 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":"131464454","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}
Pub Date : 2018-12-01DOI: 10.1109/R10-HTC.2018.8629843
M. R. S. Muthusinghe, Palliyaguru S. T., W. Weerakkody, A. M. H. Saranga, W. Rankothge
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.
{"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}
Pub Date : 2018-12-01DOI: 10.1109/R10-HTC.2018.8629819
Y. Marambe, D. Niroshani, P. Rathnayake, S. Dayananda, Dhammika H. De Silva
The Heat Stroke Alert System (HSAS) is a system that aims at providing alerts if an individual is at risk of experiencing a heat stroke. The undetectable nature of a heat stroke, has been a major issue with athletes and sportsmen, leading to the death of a few school-level athletes in the recent past. Heat exhaustion if unattended may lead to death due to damage of internal organs of the body like the brain and kidneys. The proposed system which is mainly intended in saving lives from untimely deaths due to heat stroke, consist of a wearable device with a mobile application. It is based on four factors which can be used to determine if an individual is experiencing a heat stroke, and accordingly generate pre-alerts and critical alerts taking these four factors into consideration. The device is composed of sensors to detect the four factors under consideration. The location of the athlete and medical services too will be tracked by the system. The proposed system will be the first of its kind in the market to detect heat strokes.
{"title":"Heat Stroke Alert System","authors":"Y. Marambe, D. Niroshani, P. Rathnayake, S. Dayananda, Dhammika H. De Silva","doi":"10.1109/R10-HTC.2018.8629819","DOIUrl":"https://doi.org/10.1109/R10-HTC.2018.8629819","url":null,"abstract":"The Heat Stroke Alert System (HSAS) is a system that aims at providing alerts if an individual is at risk of experiencing a heat stroke. The undetectable nature of a heat stroke, has been a major issue with athletes and sportsmen, leading to the death of a few school-level athletes in the recent past. Heat exhaustion if unattended may lead to death due to damage of internal organs of the body like the brain and kidneys. The proposed system which is mainly intended in saving lives from untimely deaths due to heat stroke, consist of a wearable device with a mobile application. It is based on four factors which can be used to determine if an individual is experiencing a heat stroke, and accordingly generate pre-alerts and critical alerts taking these four factors into consideration. The device is composed of sensors to detect the four factors under consideration. The location of the athlete and medical services too will be tracked by the system. The proposed system will be the first of its kind in the market to detect heat strokes.","PeriodicalId":404432,"journal":{"name":"2018 IEEE Region 10 Humanitarian Technology Conference (R10-HTC)","volume":"35 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":"123498052","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}
Pub Date : 2018-12-01DOI: 10.1109/R10-HTC.2018.8629824
Bijoy M B, V. Shilimkar, J. B
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
{"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}
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