2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET)最新文献_第3页

IoT based Health Monitoring & Automated Predictive System to Confront COVID-19 基于物联网的健康监测和自动预测系统，以应对COVID-19

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322811

Md Mashrur Sakib Choyon, Maksudur Rahman, Md. Mohsin Kabir, M. Mridha

{"title":"IoT based Health Monitoring & Automated Predictive System to Confront COVID-19","authors":"Md Mashrur Sakib Choyon, Maksudur Rahman, Md. Mohsin Kabir, M. Mridha","doi":"10.1109/HONET50430.2020.9322811","DOIUrl":"https://doi.org/10.1109/HONET50430.2020.9322811","url":null,"abstract":"As the whole world is striving to combat the Coronavirus disease (COVID-19), healthcare and health monitoring systems are struggling to confront the virus. Many cases have been observed where the COVID-19 could not be identified at a specific time. Furthermore, any effective strategy that can monitor the coronavirus state in the human body has not been established yet. As a result, patients of the coronavirus could not receive proper treatment when necessary. Therefore, the death toll due to COVID-19 is rising. This paper proposes a systematic approach to combat the COVID-19 pandemic more efficiently by combining the concept of ‘Internet of Things’ (IoT) and machine learning (ML). The paper also gives a brief idea about how IoT can be used to monitor the health status and also to detect the severity of coronavirus in a human body by using some of the biological data such as body temperature, heart pulse, etc. from the patient's body. The developed system can provide healthcare, maintain distant communication, and emergency medical support to the patients. This paper proposes a practical solution with the help of the developed health monitoring system that can mitigate the loss done by the COVID-19.","PeriodicalId":245321,"journal":{"name":"2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET)","volume":"377 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122658061","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: 16

The Burden of Artificial Intelligence on Internal Security Detection 人工智能对内部安全检测的负担

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322823

Tsung-Yu Ho, Wei-An Chen, Chiung-Ying Huang

引用次数: 0

High Speed Tracking with Machine Intelligence 高速跟踪与机器智能

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322830

Mehran Chowdhury, Md. Hasibur Rahman Khan, Md. Badiuzzaman Pranto, R. H. Ashique, Mohammad Monirujjaman Khan

引用次数: 2

Design and Analysis of Solar Water Pumping with Storage for Irrigation in Iran 伊朗太阳能抽水蓄能灌溉系统设计与分析

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322660

M. Zamanlou, M. Iqbal

引用次数: 4

Development of An e-commerce Sales Chatbot 电子商务销售聊天机器人的开发

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322667

Mohammad Monirujjaman Khan

引用次数: 12

Human Abnormality Classification Using Combined CNN-RNN Approach 结合CNN-RNN方法的人体异常分类

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322814

Md. Mohsin Kabir, Farisa Benta Safir, Saifullah Shahen, Jannatul Maua, Iffat Ara Binte Awlad, M. Mridha

{"title":"Human Abnormality Classification Using Combined CNN-RNN Approach","authors":"Md. Mohsin Kabir, Farisa Benta Safir, Saifullah Shahen, Jannatul Maua, Iffat Ara Binte Awlad, M. Mridha","doi":"10.1109/HONET50430.2020.9322814","DOIUrl":"https://doi.org/10.1109/HONET50430.2020.9322814","url":null,"abstract":"With the advent of big data, Facial Expression Recognition (FER) has become a promising area in the Deep Learning domain. The facial expression reflects our mental activities and provides useful information on human behaviors. With the increasing improvement of the deep learning-based classification method, special demands for human stability measurement using facial expression have emerged. Recognizing human abnormalities such as drug addiction, autism, criminal mentality, etc., are quite challenging due to the limitation of existing FER systems. Besides, there are no existing datasets that consist of helpful images that describe the true expressions of the human face that can detect human abnormality. To achieve the best performance on human abnormality recognition we have created a Normal and Abnormal Humans Facial Expression (NAHFE) dataset. In this paper, we propose a new model by stacking the Convolutional Neural Network and Recurrent Neural Network (RNN) together. The proposed combined method consists of convolution layers followed by the recurrent network. The associated model extracts the features within facial portions of the images and the recurrent network considers the temporal dependencies which exist in the images. The proposed combined architecture has been evaluated based on the mentioned NAHFE dataset and it has achieved state-of-the-art performance to detect human abnormalities.","PeriodicalId":245321,"journal":{"name":"2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122675600","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

Rapid Thermal Annealing of Screen-printable Atmospheric Cu Pastes for PERC Solar Cell PERC太阳能电池网印常压铜浆料的快速热退火研究

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322819

Sarah Grempels, S. Huneycutt, A. Ebong, R. Dharmadasa, K. Ankireddy, T. Druffel

{"title":"Rapid Thermal Annealing of Screen-printable Atmospheric Cu Pastes for PERC Solar Cell","authors":"Sarah Grempels, S. Huneycutt, A. Ebong, R. Dharmadasa, K. Ankireddy, T. Druffel","doi":"10.1109/HONET50430.2020.9322819","DOIUrl":"https://doi.org/10.1109/HONET50430.2020.9322819","url":null,"abstract":"This paper reports on the investigation of the rapid thermal annealing of screen-printable Cu paste for cost-effective metallization of PERC cells. The preliminary results of screen-printable contacted solar cells show that the challenges of Cu including rapid diffusion into Si at high temperature and oxidation in the atmospheric ambient are overcome. The open circuit voltage (VOC) of 662 mV, shunt resistance of 25000 Ω-cm2, the reverse saturation current density (JO2) of 4.4E-9 A/m2 attest to this fact. The VOC was found to increase as the belt speed was varied from 300–350 inches per minute (IPM). The peak temperature varied somewhat for the belt speed with highest peak temperature of 590°C at 300 IPM and 584°C for the 350 IPM. The efficiency peaked at the 300IPM belt speed, which showed the lowest series resistance and highest fill factor (FF). However, the electroluminescence micrographs show similar brightness between the cells sintered with belt speeds of 300 IPM and 350 IPM. The result supports the observation that Cu pastes favors fast belt speeds, which enables uniform formation of local back surface field (BSF). Finally, this result shows a viable future for atmospheric Cu paste to replace the expensive Ag metal.","PeriodicalId":245321,"journal":{"name":"2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127713289","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

Communication Security in VANETs VANETs中的通信安全

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322839

Abeer Gauher, Alia Umrani, Yousra Javed

引用次数: 0

Impact of Analogue Switch-Off on Availability of TVWS: A Quantitative Assessment 模拟信号关闭对TVWS可用性的影响:定量评估

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322824

Steven Wandale, Gracian Taulo, D. Kondwani, Patrick Musivapo, C. Mikeka

引用次数: 0

Forecasting of Electricity Generation for Hydro Power Plants 水电厂发电量预测

2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET) Pub Date : 2020-12-14 DOI: 10.1109/HONET50430.2020.9322841

U. Javed, M. Fraz, Imran Mahmood, M. Shahzad, Omar Arif

{"title":"Forecasting of Electricity Generation for Hydro Power Plants","authors":"U. Javed, M. Fraz, Imran Mahmood, M. Shahzad, Omar Arif","doi":"10.1109/HONET50430.2020.9322841","DOIUrl":"https://doi.org/10.1109/HONET50430.2020.9322841","url":null,"abstract":"In today's world of modern technology and rapid increasing demand of electronics, electricity has become an essential and a vital part of our daily life. The under-developed or developing countries face several different challenges in order to manage demand and supply of electricity. The gap between demand and supply of electricity has a very strong effect on the economic growth. The forecasting of energy will play an important role for the policy makers to timely identify the sudden change in demand of electricity under given conditions. To this end, we developed a model for forecasting of electricity generation from hydro-power plants. Besides the parameters from hydropower plant, the forecasting model incorporates the temperature and rainfall in the catchment area of the dam. In this research work we analyzed the data for the energy generation trends on live data of Tarbela Dam, which consist of daily electricity generation for the last five years augmented with the temperature and rainfall data of the dam catchment area. Moreover, we have applied different supervised machine learning and time-series based models to forecast the energy production. The proposed solution is based on future forecasting of energy generation for hydro power plant, which can assist the policy makers in better decision making. The proposed research work will help in minimizing the increasing gap between energy demand and production considering weather conditions of the area. It can also help the power plant management to detect any anomaly or a failure in the electricity production of electricity by studying the deviation from the predicted trend. Our proposed method can forecast the production of electricity generation with Mean Absolute Error of 2.47 only.","PeriodicalId":245321,"journal":{"name":"2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134443052","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: 1