Pub Date : 2020-11-04DOI: 10.1109/IEMCON51383.2020.9284816
Agrippina Mwangi, Emmanuel Ndashimye, Bonaventure Karikumutima, S. K. Ray
Asthma claims thousands of lives every year, especially during the cold season, mostly because of delayed access to first-aid and lack of immediate and adequate medical care. Asthma-related fatalities are preventable by home remedies, exercises, prescribed treatments, and providing rapid response and care to patients in case of asthma attacks. The latter is paramount to many asthmatics who are not able to predict an asthma attack well in advance. This work proposes and deploys an IoT-alert system that a chronic asthma patient (or any asthma patient) can use to trigger an alert to inform emergency contacts in case of a sudden asthma attack. This research, done in Rwanda, aims to alert emergency contacts early enough for a rapid response in order to reduce the number of asthma-related deaths in the country. The proposed system, in its basic form, uses a keypad unit as an interaction point with the users, which when pressed triggers the alert system to send SMS messages to registered emergency contacts and sounds a buzzer to alert few of the immediate neighbours of the asthma patients living close by. The intention is to provide support and help the chronic asthma patients, particularly those living alone, to receive appropriate medical aid within five minutes of alerting the neighbors and emergency contacts during a sudden asthma attack. The keypad interaction unit, which is the primary component of this IoT-alert system, can be wall-mounted in any room so that patients can easily access it. A small functioning prototype of the proposed system has been successfully tested at Kigali and testing has proven its effectiveness in helping the asthma patients.
{"title":"An IoT-alert System for Chronic Asthma Patients","authors":"Agrippina Mwangi, Emmanuel Ndashimye, Bonaventure Karikumutima, S. K. Ray","doi":"10.1109/IEMCON51383.2020.9284816","DOIUrl":"https://doi.org/10.1109/IEMCON51383.2020.9284816","url":null,"abstract":"Asthma claims thousands of lives every year, especially during the cold season, mostly because of delayed access to first-aid and lack of immediate and adequate medical care. Asthma-related fatalities are preventable by home remedies, exercises, prescribed treatments, and providing rapid response and care to patients in case of asthma attacks. The latter is paramount to many asthmatics who are not able to predict an asthma attack well in advance. This work proposes and deploys an IoT-alert system that a chronic asthma patient (or any asthma patient) can use to trigger an alert to inform emergency contacts in case of a sudden asthma attack. This research, done in Rwanda, aims to alert emergency contacts early enough for a rapid response in order to reduce the number of asthma-related deaths in the country. The proposed system, in its basic form, uses a keypad unit as an interaction point with the users, which when pressed triggers the alert system to send SMS messages to registered emergency contacts and sounds a buzzer to alert few of the immediate neighbours of the asthma patients living close by. The intention is to provide support and help the chronic asthma patients, particularly those living alone, to receive appropriate medical aid within five minutes of alerting the neighbors and emergency contacts during a sudden asthma attack. The keypad interaction unit, which is the primary component of this IoT-alert system, can be wall-mounted in any room so that patients can easily access it. A small functioning prototype of the proposed system has been successfully tested at Kigali and testing has proven its effectiveness in helping the asthma patients.","PeriodicalId":6871,"journal":{"name":"2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"31 1","pages":"0012-0019"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77054754","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 : 2020-11-04DOI: 10.1109/IEMCON51383.2020.9284900
L. Nahar, S. Zafar, Faria Binta Rafiq
Internet of Things (IoT) empowers people to get more significant level of mechanization by creating framework, utilizing sensors and interconnecting gadgets and Internet. In ICU, silent checking is basic and the most significant action, as little delay in choice identified with patient's treatment may cause perpetual inability or even passing. Arranged sensors (either worn on the body or introduced in our living environmental factors), allow to acquire rich information collectively for our physical and mental prosperity. Currently to start with, the chances and difficulties of IoT can be featured in understanding this vision of things in terms of medicinal services. It can be given an opportunity to develop a new patient checking canny framework in order to improve clinical administration. Through this work, a crossover engineering have been offered over a solitary stage for a visual patient checking framework for Automatic Detection of hazard Situations and Alerts (ADSA) by utilizing a multi-camera framework and communitarian clinical sensors. The majority of ICU gadgets are furnished with different sensors to quantify wellbeing parameters, yet to screen it all the time. Here an IOT based framework have been proposed in the health care system, which can assist with fast correspondence, recognize crisis and start to communicate with human services staff. Furthermore, it can assist with starting proactive and speedy treatment. This medicinal services framework will lessen the plausibility of human mistakes, delays in correspondence and encourage specialist to save additional time in choice with precise perceptions.
{"title":"IOT Based ICU Patient Health Monitoring System","authors":"L. Nahar, S. Zafar, Faria Binta Rafiq","doi":"10.1109/IEMCON51383.2020.9284900","DOIUrl":"https://doi.org/10.1109/IEMCON51383.2020.9284900","url":null,"abstract":"Internet of Things (IoT) empowers people to get more significant level of mechanization by creating framework, utilizing sensors and interconnecting gadgets and Internet. In ICU, silent checking is basic and the most significant action, as little delay in choice identified with patient's treatment may cause perpetual inability or even passing. Arranged sensors (either worn on the body or introduced in our living environmental factors), allow to acquire rich information collectively for our physical and mental prosperity. Currently to start with, the chances and difficulties of IoT can be featured in understanding this vision of things in terms of medicinal services. It can be given an opportunity to develop a new patient checking canny framework in order to improve clinical administration. Through this work, a crossover engineering have been offered over a solitary stage for a visual patient checking framework for Automatic Detection of hazard Situations and Alerts (ADSA) by utilizing a multi-camera framework and communitarian clinical sensors. The majority of ICU gadgets are furnished with different sensors to quantify wellbeing parameters, yet to screen it all the time. Here an IOT based framework have been proposed in the health care system, which can assist with fast correspondence, recognize crisis and start to communicate with human services staff. Furthermore, it can assist with starting proactive and speedy treatment. This medicinal services framework will lessen the plausibility of human mistakes, delays in correspondence and encourage specialist to save additional time in choice with precise perceptions.","PeriodicalId":6871,"journal":{"name":"2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"33 1","pages":"0407-0413"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87155230","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 : 2020-11-04DOI: 10.1109/IEMCON51383.2020.9284933
Cherechi Ndukwe, M. Iqbal, J. Khan
This paper presents the design and development of a low-cost, open-source LoRa based Supervisory Control and Data Acquisition (SCADA) system for monitoring and control of small-sized microgrids located in remote locations. The proposed system is based on the 4th SCADA generation (IoT based), which features the combination of connectivity of components for data sharing. The proposed system employs LoRa physical level communication as the communication channel, and comprises of voltage and current sensors that act as the field instrumentation devices to acquire electrical parameters from the system. It also features LEDs that represent actuators for control. An Arduino Uno microcontroller is employed as the Remote Terminal Unit (RTU) to receive the parameters and process them for ttransmission. The Chirpstack IOT server, Node-Red, Influxdb, and Grafana are all installed locally on the Raspberry Pi (Master Terminal Unit, MTU) for data processing, storage, monitoring, and remote control of the actuators. The proposed system was set up in the Electrical Engineering Laboratory of the Memorial University to monitor a 130 W, 12 V solar PV panel with batteries Connected for energy storage. The acquired data is published on dashboards created on Grafana for monitoring and control.
本文介绍了一种低成本、开源的基于LoRa的监控和数据采集(SCADA)系统的设计和开发，用于监测和控制位于偏远地区的小型微电网。该系统基于第四代SCADA(基于物联网)，其特点是将组件的连接组合在一起，以实现数据共享。该系统采用LoRa物理级通信作为通信通道，并由电压和电流传感器作为现场仪表设备，从系统中获取电气参数。它还具有代表控制执行器的led。采用Arduino Uno微控制器作为远程终端单元(RTU)接收参数并对其进行处理以进行传输。Chirpstack物联网服务器、Node-Red、Influxdb和Grafana都安装在树莓派(Master Terminal Unit, MTU)上，用于数据处理、存储、监控和远程控制执行器。该系统安装在纪念大学的电气工程实验室，用于监测一个130w, 12v的太阳能光伏板，并连接电池进行储能。采集到的数据发布在Grafana上创建的仪表板上，用于监测和控制。
{"title":"Development of a Low-cost LoRa based SCADA system for Monitoring and Supervisory Control of Small Renewable Energy Generation Systems","authors":"Cherechi Ndukwe, M. Iqbal, J. Khan","doi":"10.1109/IEMCON51383.2020.9284933","DOIUrl":"https://doi.org/10.1109/IEMCON51383.2020.9284933","url":null,"abstract":"This paper presents the design and development of a low-cost, open-source LoRa based Supervisory Control and Data Acquisition (SCADA) system for monitoring and control of small-sized microgrids located in remote locations. The proposed system is based on the 4th SCADA generation (IoT based), which features the combination of connectivity of components for data sharing. The proposed system employs LoRa physical level communication as the communication channel, and comprises of voltage and current sensors that act as the field instrumentation devices to acquire electrical parameters from the system. It also features LEDs that represent actuators for control. An Arduino Uno microcontroller is employed as the Remote Terminal Unit (RTU) to receive the parameters and process them for ttransmission. The Chirpstack IOT server, Node-Red, Influxdb, and Grafana are all installed locally on the Raspberry Pi (Master Terminal Unit, MTU) for data processing, storage, monitoring, and remote control of the actuators. The proposed system was set up in the Electrical Engineering Laboratory of the Memorial University to monitor a 130 W, 12 V solar PV panel with batteries Connected for energy storage. The acquired data is published on dashboards created on Grafana for monitoring and control.","PeriodicalId":6871,"journal":{"name":"2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"59 1","pages":"0479-0484"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72722173","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 : 2020-11-04DOI: 10.1109/IEMCON51383.2020.9284830
Changshi Yang, Xianping Wang, Ying Luo, G. Stefanek
In the transition of courses from face-to-face to distance learning during the COVID-19 pandemic, students doing labs at home becomes a major challenge. In this paper, we proposed a feasible solution of building a low-cost portable IT laboratory with Windows-to-Go installed on a USB 3.1 flash drive. To verify the feasibility and usability of this portable IT laboratory, we setup two systems on a single test computer for performance comparison — Windows-to-Go installed on a USB 3.1 flash drive and Windows 10 installed on an internal SSD. Focusing on the needs of IT discipline, we manually compared the performance of installing, executing normal IT software and other common IT tasks on both systems. We also compared their performance benchmarks by PassMark. The experiment results show that this low-cost portable IT laboratory can be used as a candidate solution for students doing labs at home.
在2019冠状病毒病大流行期间，在课程从面对面学习向远程学习过渡的过程中，学生在家做实验成为一项重大挑战。在本文中，我们提出了一个可行的解决方案，即在USB 3.1闪存驱动器上安装Windows-to-Go，建立一个低成本的便携式IT实验室。为了验证这个便携式IT实验室的可行性和可用性，我们在一台测试计算机上设置了两个系统进行性能比较-安装在USB 3.1闪存驱动器上的Windows- To - go和安装在内部SSD上的Windows 10。专注于IT规程的需求，我们手动比较了在两个系统上安装、执行正常IT软件和其他常见IT任务的性能。我们还通过PassMark比较了它们的性能基准。实验结果表明，这种低成本的便携式IT实验室可以作为学生在家做实验的备选解决方案。
{"title":"Overcome IT Lab Challenge in COVID-19 with Windows-to-Go","authors":"Changshi Yang, Xianping Wang, Ying Luo, G. Stefanek","doi":"10.1109/IEMCON51383.2020.9284830","DOIUrl":"https://doi.org/10.1109/IEMCON51383.2020.9284830","url":null,"abstract":"In the transition of courses from face-to-face to distance learning during the COVID-19 pandemic, students doing labs at home becomes a major challenge. In this paper, we proposed a feasible solution of building a low-cost portable IT laboratory with Windows-to-Go installed on a USB 3.1 flash drive. To verify the feasibility and usability of this portable IT laboratory, we setup two systems on a single test computer for performance comparison — Windows-to-Go installed on a USB 3.1 flash drive and Windows 10 installed on an internal SSD. Focusing on the needs of IT discipline, we manually compared the performance of installing, executing normal IT software and other common IT tasks on both systems. We also compared their performance benchmarks by PassMark. The experiment results show that this low-cost portable IT laboratory can be used as a candidate solution for students doing labs at home.","PeriodicalId":6871,"journal":{"name":"2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"29 1","pages":"0791-0796"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74284494","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 : 2020-11-04DOI: 10.1109/IEMCON51383.2020.9284879
M. Goyal
Several research studies have been devoted for the last two decades to make estimates on or to forecast stock prices. Accurate stock prediction movement is still an open question for many companies and financial organizations. This article analyses the stock market prediction using deep learning model. The empirical results reveal the superiority of the LSTM recurrent deep learning model over Feed forward neural network and time series ARIMA model in terms four prediction metrics i.e. mean square error, root mean square error, mean average error and mean average percent error.
{"title":"Financial Time Series Stock Price Prediction using Deep Learning","authors":"M. Goyal","doi":"10.1109/IEMCON51383.2020.9284879","DOIUrl":"https://doi.org/10.1109/IEMCON51383.2020.9284879","url":null,"abstract":"Several research studies have been devoted for the last two decades to make estimates on or to forecast stock prices. Accurate stock prediction movement is still an open question for many companies and financial organizations. This article analyses the stock market prediction using deep learning model. The empirical results reveal the superiority of the LSTM recurrent deep learning model over Feed forward neural network and time series ARIMA model in terms four prediction metrics i.e. mean square error, root mean square error, mean average error and mean average percent error.","PeriodicalId":6871,"journal":{"name":"2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"82 9","pages":"0378-0383"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72619549","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 : 2020-11-04DOI: 10.1109/IEMCON51383.2020.9284945
Manasa Mainampati, B. Chandrasekaran
This paper describes how different algorithms are implemented on autonomous robots like GoPiGo robot and Turtlebot. A* and behavioral-based algorithms are programmed and used in this work. The GoPiGo robot is a vehicle control device; it can work in either manual or autonomous mode. Based on human instruction, the robot can switch back and forth between manual and autonomous control. A switching-based module is used to select the signals in manual mode and automated control signals in the automated mode. This research is a work in progress, and the next stage is using the Turtlebot to achieve the implementation of this work.
{"title":"Manual and Autonomous Mode based Navigation using Human Robot Interaction on a Robotic Vehicle","authors":"Manasa Mainampati, B. Chandrasekaran","doi":"10.1109/IEMCON51383.2020.9284945","DOIUrl":"https://doi.org/10.1109/IEMCON51383.2020.9284945","url":null,"abstract":"This paper describes how different algorithms are implemented on autonomous robots like GoPiGo robot and Turtlebot. A* and behavioral-based algorithms are programmed and used in this work. The GoPiGo robot is a vehicle control device; it can work in either manual or autonomous mode. Based on human instruction, the robot can switch back and forth between manual and autonomous control. A switching-based module is used to select the signals in manual mode and automated control signals in the automated mode. This research is a work in progress, and the next stage is using the Turtlebot to achieve the implementation of this work.","PeriodicalId":6871,"journal":{"name":"2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"9 1","pages":"0559-0564"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75941558","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 : 2020-11-04DOI: 10.1109/IEMCON51383.2020.9284914
David Airehrour, M. Cherrington, K. Rajagopal
According to the World Health Organization (WHO), about 91% of the global populace live in locations with air quality levels below WHO guidelines. Although ambient air pollution impacts both developed and developing nations, low- and middle-income nations suffer the highest consequences with the highest toll experienced in the Western Pacific and South East Asia regions. Poor air quality results in odour types ranging from chemical combustion smells to fungal smells. Fungal odour is formed due to damp indoor conditions like high humidity and temperature. This damp condition produces microscopic fungi which are allergens (substances that cause allergic reactions), irritants and toxic substances referred to as mould. Inhaling or touching mould spores may cause allergic reactions like sneezing, runny nose, redness of the eyes and skin rash and even respiratory complications. In this era of a global pandemic, having a general-purpose odour detection system becomes imperative. This research, therefore, aims to design and implement an odour detection system that can alert individuals or allergic sufferers to high content of toxic gases in their surroundings or homes. The study is a valuable resource for users to measure their air quality and to support those with respiratory vulnerabilities, especially in the Covid-19 environment of today.
{"title":"Odour Detection System for Allergy Sufferers","authors":"David Airehrour, M. Cherrington, K. Rajagopal","doi":"10.1109/IEMCON51383.2020.9284914","DOIUrl":"https://doi.org/10.1109/IEMCON51383.2020.9284914","url":null,"abstract":"According to the World Health Organization (WHO), about 91% of the global populace live in locations with air quality levels below WHO guidelines. Although ambient air pollution impacts both developed and developing nations, low- and middle-income nations suffer the highest consequences with the highest toll experienced in the Western Pacific and South East Asia regions. Poor air quality results in odour types ranging from chemical combustion smells to fungal smells. Fungal odour is formed due to damp indoor conditions like high humidity and temperature. This damp condition produces microscopic fungi which are allergens (substances that cause allergic reactions), irritants and toxic substances referred to as mould. Inhaling or touching mould spores may cause allergic reactions like sneezing, runny nose, redness of the eyes and skin rash and even respiratory complications. In this era of a global pandemic, having a general-purpose odour detection system becomes imperative. This research, therefore, aims to design and implement an odour detection system that can alert individuals or allergic sufferers to high content of toxic gases in their surroundings or homes. The study is a valuable resource for users to measure their air quality and to support those with respiratory vulnerabilities, especially in the Covid-19 environment of today.","PeriodicalId":6871,"journal":{"name":"2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"62 1","pages":"0193-0200"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80574295","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 : 2020-11-04DOI: 10.1109/IEMCON51383.2020.9284876
K. Shaukat, Talha Mahboob Alam, M. Ahmed, S. Luo, I. Hameed, Muhammad Shahid Iqbal, Jiaming Li, Muhammad Atif Iqbal
We live in a world where data is expanding exponentially. Most of the data is unstructured when obtained through the web. Many organizations, institutes, and governments worldwide gather public views regarding their products, services, or policies. With thousands of reviews about some product, service, or policy, it is impossible to conclude some kind of final thought from it. To handle this, there is a desperate need for a model that can extract meaningful information from data to make correct and timely decisions for the efficient growth of business and smooth running of an organization or government. Otherwise, the practice of collecting and storing data will be ineffective. In this study, we focused on conducting an extensive public survey on issues of Southern Punjab, carry out appropriate processing on collected data and predict trends in public opinion for decision-making. Natural Language Processing (NLP) and Machine Learning (ML) have dealt with this problem. Different data preprocessing techniques have been utilized to remove the noise from data. Our experiments stated that unemployment, poverty, education, and corruption are the major issues of the targeted region. This study will help government officials and non-governmental organizations to be focused on the extracted issues in the specific region.
{"title":"A Model to Enhance Governance Issues through Opinion Extraction","authors":"K. Shaukat, Talha Mahboob Alam, M. Ahmed, S. Luo, I. Hameed, Muhammad Shahid Iqbal, Jiaming Li, Muhammad Atif Iqbal","doi":"10.1109/IEMCON51383.2020.9284876","DOIUrl":"https://doi.org/10.1109/IEMCON51383.2020.9284876","url":null,"abstract":"We live in a world where data is expanding exponentially. Most of the data is unstructured when obtained through the web. Many organizations, institutes, and governments worldwide gather public views regarding their products, services, or policies. With thousands of reviews about some product, service, or policy, it is impossible to conclude some kind of final thought from it. To handle this, there is a desperate need for a model that can extract meaningful information from data to make correct and timely decisions for the efficient growth of business and smooth running of an organization or government. Otherwise, the practice of collecting and storing data will be ineffective. In this study, we focused on conducting an extensive public survey on issues of Southern Punjab, carry out appropriate processing on collected data and predict trends in public opinion for decision-making. Natural Language Processing (NLP) and Machine Learning (ML) have dealt with this problem. Different data preprocessing techniques have been utilized to remove the noise from data. Our experiments stated that unemployment, poverty, education, and corruption are the major issues of the targeted region. This study will help government officials and non-governmental organizations to be focused on the extracted issues in the specific region.","PeriodicalId":6871,"journal":{"name":"2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"6 1","pages":"0511-0516"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75762712","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 : 2020-11-04DOI: 10.1109/IEMCON51383.2020.9284919
S. Manoharan, Bilal Sariffodeen, K.T Ramasinghe, L.H Rajaratne, D. Kasthurirathna, J. Wijekoon
The soil composition around the world is depleting at a rapid rate due to overexploitation by the unsustainable use of fertilizers. Streamlining the availability of nutrient deficiency and fertilizer related knowledge among impoverished farming communities would promoter environmentally and scientifically sustainable farming practices. Thus, contributing to several Sustainable Development Goals set out by the United Nations. The most direct solution to the inappropriate fertilizer usage is to add only the necessary amounts of fertilizer required by plants to produce a significant yield without nutrition deficiencies. To this end this paper proposes a Smart Nutrient Disorder Identification system employing computer vision and machine learning techniques for identification purposes and a decentralized blockchain platform to streamline a bias-less procurement system. The proposed system yielded 88% accuracy in disorder identification, while also enabling secure, transparent flow of verified information.
{"title":"Smart Plant Disorder Identification using Computer Vision Technology","authors":"S. Manoharan, Bilal Sariffodeen, K.T Ramasinghe, L.H Rajaratne, D. Kasthurirathna, J. Wijekoon","doi":"10.1109/IEMCON51383.2020.9284919","DOIUrl":"https://doi.org/10.1109/IEMCON51383.2020.9284919","url":null,"abstract":"The soil composition around the world is depleting at a rapid rate due to overexploitation by the unsustainable use of fertilizers. Streamlining the availability of nutrient deficiency and fertilizer related knowledge among impoverished farming communities would promoter environmentally and scientifically sustainable farming practices. Thus, contributing to several Sustainable Development Goals set out by the United Nations. The most direct solution to the inappropriate fertilizer usage is to add only the necessary amounts of fertilizer required by plants to produce a significant yield without nutrition deficiencies. To this end this paper proposes a Smart Nutrient Disorder Identification system employing computer vision and machine learning techniques for identification purposes and a decentralized blockchain platform to streamline a bias-less procurement system. The proposed system yielded 88% accuracy in disorder identification, while also enabling secure, transparent flow of verified information.","PeriodicalId":6871,"journal":{"name":"2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON)","volume":"1 1","pages":"0445-0451"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82893724","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 : 2020-11-04DOI: 10.1109/IEMCON51383.2020.9284938
Gustavo Salazar-Chacón, L. Marrone
This Proof-of-Concept (PoC) research presents a conceptualization of main OpenFlow Southbound messages found in Software-Defined Networks. The use of an open-source network emulator has allowed to conduct traffic captures in order to contrast the theory with a high reliability PoC scenario. This paper begins by defining SDN according to Open Networking Foundation (ONF), as well as identifying characteristics, elements, and fundamental protocols of this technology that is changing the traditional paradigm of sending data. In order to test theory in practice, an OpenFlow network built with an Open-vSwitch (OVS), two hosts and external SDN Docker Controller were emulated using Mininet, a very efficient controller-based emulator. Later, SDN-packet captures were done for analysis. Connectivity tests are proposed to capture OpenFlow messages exchanged between different devices of the network, and with that, verifying its encapsulation, structure, and vulnerabilities. The ultimate aim of the present paper is to provide a clearer and more understandable vision of SDN and its operation in the real world.
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