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Design of an Intelligent Bracelet Based on IOT and LORA Protocol to Identify New COVID-19 Contact Cases 2

Design of an Intelligent Bracelet Based on IOT and LORA Prot...

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2nd international conference on Digital Age and Technological advances for Sustainable Development, ICdata 2021

作者： Zouari, Karima Houmanat, Omar Cherti, Ilias Bahaj, Mohamed Lab L.M.I.E.T Hassan Ist University Faculty of Sciences and Technologies Mathematics and Computer Science Department Settat Morocco Mohammed v University Ecole Nationale Supérieure d'Informatique and Systems Analysis Internet of Things and Mobile Services Master Rabat Morocco

ISBN: (纸本)9781665429016

COVID-19 is one of the most dangerous pandemics in human history. First emerged in Wuhan, China, in December 2019, it has become a concern to all countries of the world. This virus only needs a short distance to spread rapidly between humans, and even if we could locate all the infected individuals, this would not solve the problem. The real solution is to identify all the people who have come in contact with the virus carrier. This paper is a study on online monitoring, intelligent assisted diagnosis, and treatment assistance for covid-19 in terms of early identification, social distancing, and quarantine. While focusing on wireless healthcare networks, the internet of things, and cloud computing. A federated or hybrid approach leverages the strengths and mitigates the weaknesses of both data Medical Technology and IOT to detect and alert people who have been exposed to covid-19. © 2021 IEEE.

关键词： COVID-19

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Proposing XLSR Multilingual Model for Vietnamese Language Recognition

Proposing XLSR Multilingual Model for Vietnamese Language Re...

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IEEE international conference on Research, Innovation and Vision for the Future

作者： Phat Nguyen Huu Thach Ho Sy Chau Nguyen Le Bao Minh Nguyen Anh Luong Nguyen Thien Hieu Nguyen Duc Minh-Trien Pham Dat Vu Tien Quang Tran Minh Hanoi University of Science and Technology Hanoi Vietnam Hanoi-Amsterdam Highschool for the Gifted Hanoi Vietnam Tran Phu High School Hanoi Vietnam VNU University of Engineering and Technology Hanoi Vietnam Haiphong University Haiphong Vietnam Ho Chi Minh City University of Technology Hochiminh Vietnam Vietnam National University HoChiMinh City Hochiminh Vietnam

ISBN: (纸本)9781665461672

The article presents issues related to the cross-lingual speech representations (XLS-R) model that can apply to training Vietnamese data. The goal of the article is to create an application for voice assistant software or virtual assistant in the home with Vietnamese data. The results show that the proposed model can improve the accuracy up to 61 % which has practical applicability.

关键词： Training Computational modeling Virtual assistants Refining Training data Speech recognition data models

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Ultra-Low Power Wireless Sensor Network for Air Quality Monitoring System

Ultra-Low Power Wireless Sensor Network for Air Quality Moni...

引用

Green Energy, computing and Sustainable Technology (GECOST), international conference on

作者： Sahiru Randula Sachintha Balasooriya Lasith Yasakethu School of Engineering Sri Lanka Technological Campus Sri Lanka Graduate School of Engineering Kyoto University of Advance Science Japan

ISBN: (纸本)9781665486644

data Gathering and Monitoring is one of the major use cases in the area of wireless sensor network scenarios. Common technologies have capabilities for basic data gathering and monitoring, but the key challenges to data gathering of the surrounding environment are energy consumption is difficult in those technologies and the lifetime of such systems is low. Also, the energy cost of data gathering and monitoring systems is *** research work highlights an Ultra-Low Power data Gathering and Monitoring System to gather and monitor data to fulfill the conditions of regular data gathering with ultralow power utilization for the battery-powered as well as rechargeable systems and sensor nodes. Also, in this work, we show a comparison between several current using data gathering and monitoring systems for low power design with their technologies as well as low power data gathering using the microcontrollers-based autonomous system. The proposed system provides instantaneous air quality readings on several gasses alone side with temperature and humidity. Also, this system can gather data from the air more efficiently and monitor gathered data with low power consumption with the use of microcontrollers for better energy efficacious technique with marginal information inception and allows for minor power expendable and more economical processing of data. The use of a wireless system in the proposed system can provide capabilities for operating in remote environments with reliable air-quality tracking.

关键词： Temperature measurement Temperature sensors Wireless sensor networks Power demand Microcontrollers Humidity Air pollution

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Automatic Tuberculosis and COVID-19 cough classification using deep learning

Automatic Tuberculosis and COVID-19 cough classification usi...

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2022 IEEE international conference on Electrical, Computer, and Energy technologies, ICECET 2022

作者： Pahar, Madhurananda Klopper, Marisa Reeve, Byron Warren, Rob Theron, Grant Diacon, Andreas Niesler, Thomas Stellenbosch University Department of Electrical and Electronic Engineering Stellenbosch South Africa Stellenbosch University Division of Molecular Biology and Human Genetics Stellenbosch South Africa TASK Applied Science Cape Town South Africa

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

We present a deep learning based automatic cough classifier which can discriminate tuberculosis (TB) coughs from COVID-19 coughs and healthy coughs. Both TB and COVID-19 are respiratory diseases, contagious, have cough as a predominant symptom and claim thousands of lives each year. The cough audio recordings were collected at both indoor and outdoor settings and also uploaded using smartphones from subjects around the globe, thus containing various levels of noise. This cough data include 1.68 hours of TB coughs, 18.54 minutes of COVID-19 coughs and 1.69 hours of healthy coughs from 47 TB patients, 229 COVID-19 patients and 1498 healthy patients and were used to train and evaluate a CNN, LSTM and Resnet50. These three deep architectures were also pre-trained on 2.14 hours of sneeze, 2.91 hours of speech and 2.79 hours of noise for improved performance. The class-imbalance in our dataset was addressed by using SMOTE data balancing technique and using performance metrics such as F1-score and AUC. Our study shows that the highest F1-scores of 0.9259 and 0.8631 have been achieved from a pre-trained Resnet50 for two-class (TB vs COVID-19) and three-class (TB vs COVID-19 vs healthy) cough classification tasks, respectively. The application of deep transfer learning has improved the classifiers' performance and makes them more robust as they generalise better over the cross-validation folds. Their performances exceed the TB triage test requirements set by the world health organisation (WHO). The features producing the best performance contain higher order of MFCCs suggesting that the differences between TB and COVID-19 coughs are not perceivable by the human ear. This type of cough audio classification is non-contact, cost-effective and can easily be deployed on a smartphone, thus it can be an excellent tool for both TB and COVID-19 screening. © 2022 IEEE.

关键词： COVID-19

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Ability to Apply Mathematical Modeling on Account of DBDT Algorithm

Ability to Apply Mathematical Modeling on Account of DBDT Al...

引用

Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： Yongming Lu Department of Mathematics Ganzhou Teachers College Ganzhou Jiangxi Province Jiangxi China

ISBN: (纸本)9781665493970

In the era of big data, more and more information is shared, which provides sufficient resources for the development of social science and technology. All these dynamic developments are inseparable from mathematical models. In the information environment, students' modeling ability and consciousness are at the lower middle level. Improving this ability is a huge task to be solved. It is a good breakthrough to cultivate students' mathematical modeling application ability from the perspective of algorithm. Try to use algorithm theory to solve the suspicious and difficult points of mathematical modeling application ability. The research on improving the application ability of mathematical modeling on account of DBDT algorithm is a subject of contemporary significance, which must be strengthened. This paper studies the definition, concept and related knowledge on account of DBDT algorithm, and points out a series of knowledge and theories to improve the application ability of mathematical modeling on account of DBDT algorithm. In the text, the data is tested, and the results show that the improvement of mathematical modeling application ability on account of DBDT algorithm achieves 82.20%, 90.54%, 93.05% and 98.12% high efficiency in terms of system feasibility, innovation, fault tolerance and self-optimization performance.

关键词： Technological innovation Heuristic algorithms Social sciences Fault tolerant systems Transforms Mathematical models data models

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Centralization or Decentralization? How Can Digital Transformation Empowers Firm Performance?

Centralization or Decentralization? How Can Digital Transfor...

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IEEE international conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Cao Yifei Hao Lingfeng Hua Zhang Kang Xingyao College of Economics and Management Shenyang Aerospace University Shenyang China Sun Wah International Business School Liaoning University Shenyang China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

Digitalization is a systematic shift driven by information technology. It is a long-term strategy aimed at improving firms' economic performance and fostering sustainable development. In this study, we utilize big data from China's A-share listed enterprises spanning from 2007 to 2022 to empirically examine the mechanisms through which digitalization affects firm performance. Research indicates that firm digitalization has a positive impact on firm performance. Specifically, for every 1 unit increase in the degree of firm digitalization, its performance increases by 0.36% in the current year and 0.26% in the following year. The result remains robust after undergoing a series of robustness tests. Furthermore, ownership concentration exerts positive moderating effects. When the ownership concentration increases by 1 unit, the positive impact of digitalization on enterprise performance will increase by 0.0001 unit. This study provides practical recommendations for businesses to effectively utilize digital technologies and achieve high-quality development.

关键词： Technological innovation Cloud computing Costs Systematics Digital transformation Decision making Companies Big data Real-time systems Business

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Social Media Event Summarization using Neural Networks

Social Media Event Summarization using Neural Networks

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Smart technologies in computing, Electrical and Electronics (ICSTCEE), international conference on

作者： Sucharitha Yadala Anitha Gurram Yamunadevi S Veeresh U S Bhargavi Latha TN Chitti Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering & Technology Hyderabad India Computer Science and Engineering CMR Institute of Technology Hyderabad India Computer Science and Engineering Panimalar Engineering College Tamilnadu India School of Computer Science and Engineering REVA University Bengaluru Karnataka India Kalasalingam Academy of Research and Education Krishnankoil India

Micro blogging sites have become extremely popular, with a huge number of tweets being posted daily on a broad assortment of subjects. Recent studies have exposed that a significant part of these tweets are regarding “events”, and the discovery of new events in the tweet-stream has pulled in a lot of attention. In the Natural Language Processing (NLP) field, automatic text summarization is viewed as an extremely tricky problem. The content substance on the internet, specifically, is rising at an outstanding level. The capacity to decode through such immense volumes of data, to facilitate mine the require knowledge, is a foremost undertaking and needs an automatic system to serve with the present warehouse of data. A decent text summarization framework should comprehend the entire content, reorganize information, and create coherent, useful and amazingly tiny summaries to pass on the significant information of the innovative content. In this article, an inventive content summarization framework has been built, which combines RNN with LSTM and encoder-decoder structure. Our framework is assessed on the LCSTS dataset, which is an excellent corpus of Chinese short content summarization dataset created from “SinaWeibo”. Empirical outcomes exhibit the adequacy of the proposed framework, which outperforms all the existing approaches. Particularly, the proposed framework enhances the semantic consistency by 5% in terms of human assessment.

关键词： Social networking (online) Semantics Neural networks Blogs Natural language processing data mining

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A Survey on Machine Learning Techniques Using Quantum computing

A Survey on Machine Learning Techniques Using Quantum Comput...

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international conference on Emerging Research in Electronics, Computer science and Technology (ICERECT)

作者： Manjunath T N Adarsh Hiremath Niranjanamurthy M Sheng-Lung Peng Shrihari M R Pushpa S K Department of ISE BMS Institute of Technology and Management Bengaluru India Department of AI and ML BMS Institute of Technology and Management Bengaluru India Department of Creative Technologies and Product Design National Taipei University of Business Taipei Taiwan Department of CSE SJC Institute of Technology Chickballapur India

Over the course of the last seven decades the world of computing has taken revolutionary reformations in the scope of classical computing. Classical computers manipulate ones and zeroes to crunch through operations given to them by users. An innovative strategy known as quantum computing leverages the concepts of quantum physics to solve issues that are too complex for conventional computers to handle. Two of the domains of science that are now progressing the fastest are theoretical machine learning and quantum computing theory. In recent years, researchers have begun examining how classic machine learning techniques may be improved by quantum computing. Hybrid techniques that integrate conventional and quantum algorithms are part of quantum machine learning. Instead of using standard data, quantum techniques may be utilized to examine quantum states. However, quantum algorithms have the potential to greatly expand current data science techniques. In this paper we review the contribution carried out by various researchers in the field of Quantum Machine Learning and later we look at certain techniques associated with it for its use.

关键词： Computers Computer science Quantum algorithm Machine learning algorithms Machine learning Quantum state data science

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An Empirical Study of Genomic Sequencing Workflow in the Cloud

An Empirical Study of Genomic Sequencing Workflow in the Clo...

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international conference on Futuristic technologies (INCOFT)

作者： Mandeep Kumar Gagandeep Kaur University Institute of Computing Chandigarh University Mohali India Locuz Enterprise Solutions Ltd Delhi India

Cloud computing is a well-known and long lasting paradigm that has transformed how large and small businesses manage their computational resources. The cloud and network’s global footprint match the global nature of science, with security and access controls that allow genomics researchers to manage data sharing. It’s also changing the way genomicists and other researchers collaborate and work with massive archive datasets. Researchers must understand the cloud and the new modes of analysis and collaboration it enables as it makes inroads into genomics. This work discusses genomic sequencing workflow procedures and software, as well as discuss how to setup a genomic sequencing workflow in the cloud with appropriate services, using the AWS Cloud as an example. We performed three genomic sequencing workflow execution Case Studies that use appropriate software with different hardware in the cloud. We ran a small-scale genomic sequencing workload with 290 MB of input data (FAST5) and a large-scale genomic sequencing workload with 232 GB of input data (FAST5). We provide a comprehensive performance evaluation of the genomic sequencing workflow with our execution Case Studies and comparisons to bare metal servers. The final output of the workflow will be used for filtration, validation, and clinical report generation as per clinical guidelines.

关键词： Performance evaluation Sequential analysis Cloud computing Genomics Random access memory Metals Software

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Disaster Management Using Artificial Intelligence 26th

Disaster Management Using Artificial Intelligence

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26th annual international conference on Advanced computing and Communications, ADCOM 2020

作者： Savio Rajan, K. Rajan, Amith Abraham Waltin, Steve Maria Joseph, Tom Anjali, C. Department of Computer Science and Engineering Mar Baselios College of Engineering and Technology Kerala Trivandrum India

ISBN: (纸本)9789811900181

Natural calamities are known to inject a feeling of helplessness into the minds of the people involved with it, and that is exactly the situation for the personnel who are in charge of helping with the situation. Primarily focusing on the issue of rescue personnel not being able to locate the affected casualties, a solution to the same is proposed via two distinct technologies, social media analytics and aerial person detection. When any kind of disaster occurs, social media gets flooded with different kinds of data such as images, videos, texts, etc. These could be posted by people who are directly or indirectly affected by the occurrence of the disaster. In this paper, a social media analytics method is proposed to monitor social media for disaster-based posts and give detailed insights to take necessary action. Another major challenge experienced by search and rescue teams during a disaster situation is the search for survivors and victims and also to reach out to distant areas. Drones can be used for person detection in case of emergency situations. It can fly through a particular area and help in the detection of people stranded in emergency situations such as floods, isolated buildings, etc. Here, another method is also proposed for performing person detection from an aerial viewpoint, and all of these are integrated into a web app from which the user can select the features according to the need. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Antennas

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