2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)最新文献_第7页

Covid19 Infection Detection and Classification Using CNN On Chest X-ray Images 基于CNN的胸部x线图像covid - 19感染检测与分类

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663614

Ashwini Dasare, Harsha S

{"title":"Covid19 Infection Detection and Classification Using CNN On Chest X-ray Images","authors":"Ashwini Dasare, Harsha S","doi":"10.1109/DISCOVER52564.2021.9663614","DOIUrl":"https://doi.org/10.1109/DISCOVER52564.2021.9663614","url":null,"abstract":"Covid-19 has opened up a plethora of worries to the world since the past 2 years. The infection rate and death rate are increasing rapidly. It has worsened by the number of genetic mutations this virus has undergone. Timely detection of the disease is the only way out to handle this health emergency. Severity of this disease is when the virus attacks the major volume of the lung and results in pneumonia. To diagnose the pneumonia the first preferred modality is chest X-ray. There are two solid reasons why the Computer Aided Diagnosis (CAD) system is the need of the hour. First, the volume of X-rays generated for a huge number of infected patients to be assessed and second being the requirement of accuracy in diagnosis. Radiologists find it difficult to assess the severity through bare eyes and most of the time end up making a wrong conclusion which is chaotic decision. With the advent of technology, deep learning algorithms are proving to be most appropriate because of its ability to deliver expected accuracy and capacity to handle huge volume of data. This paper proposed a Deep Learning based Computer Aided Diagnosis System that accepts Chest X-ray image of a patient as input and classifies them as pneumonia or non-pneumonia. The Deep learning model is built and is trained with over 5000 chest X-ray images. Thus, trained model is then tested and validated and an accuracy of 96.66% is achieved. However, since the data is not real time, this work does not claim medical accuracy. The validation plots of the training loss and accuracy and validation loss and accuracy have been validated through regression.","PeriodicalId":413789,"journal":{"name":"2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123495280","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

Fuzzy Logic Based Stereo Matching Method for Images with Variation in Exposure Conditions 基于模糊逻辑的曝光条件变化图像立体匹配方法

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663728

A. Shetty, Navya Thirumaleshwar Hegde, A. Vaz

引用次数: 0

Role of Embedded Computing Systems in Biomedical Applications–Opportunities and Challenges 嵌入式计算系统在生物医学应用中的作用——机遇与挑战

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663646

K. Nandini, G. Seshikala

{"title":"Role of Embedded Computing Systems in Biomedical Applications–Opportunities and Challenges","authors":"K. Nandini, G. Seshikala","doi":"10.1109/DISCOVER52564.2021.9663646","DOIUrl":"https://doi.org/10.1109/DISCOVER52564.2021.9663646","url":null,"abstract":"Embedded computing systems are an amalgamation of various electronic devices, sensors, and processor that is designed to perform specific tasks. In the biomedical field, embedded computing systems are typically used to process data and storage purposes. In recent years, there has been a surge in interest in embedded computing systems for biomedical applications because of their high level of dependability and ability to provide effective healthcare solutions and services. These systems play an important in various applications such as electronic devices, specialized health care systems, reliable wearable electronic gadgets, electrocardiograms, and others. With tremendous technological innovations, such as the assimilation of computing systems with the IoT (Internet of Things) and AI (artificial intelligence) ML (Machine Learning) are the driving factors which has led to gain tremendous usage in the present scenario, and it will continue to grow exponentially in future with global advancements. This technological transformation could certainly create a revolution in embedded computing systems having the global market estimated to be USD 86.5 billion in 2020 and projected to reach USD 116.2 billion by 2025; at a CAGR of 6.1% from 2020 to 2025. This paper attempts to brief the role, opportunities, and challenges of embedded computing devices in the biomedical field particularly to health care applications.","PeriodicalId":413789,"journal":{"name":"2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"14 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132454570","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: 0

Room Light Intensity Control with Temperature Monitoring System Using Arduino 基于Arduino的室内光强控制与温度监测系统

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663580

Calvin Marian Netto, Cifha Crecil Saldanha, Davin Dsouza

引用次数: 1

Multitaper Spectrogram for Classification of Speech and Music With Pretrained Audio Neural Networks 基于预训练音频神经网络的多锥度谱图语音和音乐分类

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663695

G.B Rakshith, K. Narendra, Sanjeev Gurugopinath

引用次数: 0

Energy-Efficient VM Scheduling in the Cloud Environment using Reinforcement Learning 基于强化学习的云环境下节能虚拟机调度

2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) Pub Date : 2021-11-19 DOI: 10.1109/DISCOVER52564.2021.9663658

Isha Bhandary, K. Atul, A. Athani, Somashekar Patil, D. Narayan

{"title":"Energy-Efficient VM Scheduling in the Cloud Environment using Reinforcement Learning","authors":"Isha Bhandary, K. Atul, A. Athani, Somashekar Patil, D. Narayan","doi":"10.1109/DISCOVER52564.2021.9663658","DOIUrl":"https://doi.org/10.1109/DISCOVER52564.2021.9663658","url":null,"abstract":"Cloud data centers consume a huge amount of energy in the form of electrical energy for their operation. They also emit carbon dioxide and impact the balance of nature. This management of exponentially increasing load and the minimization of energy use along with the impact on the environment is the biggest challenge a cloud service provider (CSP) faces. CSPs establish and maintain data center farms, which enable the delivery of cloud services to millions of clients. The reduction in energy usage by data centers while also minimizing the number of service level agreement (SLA) violations is a major challenge. In this work, we have proposed a reinforcement learning (RL)-based dynamic virtual machine (VM) consolidation mechanism wherein the host load is predicted by considering previous and current host utilization. The learning agent chooses a suitable-power mode for the hosts. Load balancing is done for the over-utilized hosts and dynamic VM consolidation is performed for the under-utilized hosts. The VM scheduling is performed using an energy-aware best fit method. Ourproposed model shows a significant drop in the number of SLA violations and energy consumption when compared to the ARIMA model.","PeriodicalId":413789,"journal":{"name":"2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)","volume":"422 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126713811","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: 0