2021 International Conference on Computational Performance Evaluation (ComPE)最新文献_第10页

Face Mask Detection for Preventing the Spread of Covid-19 using Knowledge Distillation 基于知识蒸馏的口罩检测预防新冠肺炎传播

2021 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2021-12-01 DOI: 10.1109/ComPE53109.2021.9752237

Ambika Lakhera, Priyansh Jain, Ruchi Gajjar, Manish I. Patel

{"title":"Face Mask Detection for Preventing the Spread of Covid-19 using Knowledge Distillation","authors":"Ambika Lakhera, Priyansh Jain, Ruchi Gajjar, Manish I. Patel","doi":"10.1109/ComPE53109.2021.9752237","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9752237","url":null,"abstract":"The coronavirus pandemic (COVID-19) has unfolded hastily throughout the entire world. This pandemic disease can spread through droplets and can be airborne. Hence, the use of face masks in public places is crucial to stop its spread. The present study aims to develop a system that can identify masked or non-masked faces; whether it is a normal mask, transparent mask, or a face alike mask. The face mask detection system is developed with the help of Convolutional Neural Networks (CNN). The model compression technique of Knowledge Distillation has been used to make the machine lesser computation and memory intensive so that it is simple to install the model on a few embedded gadgets and cell computing platforms. Using the model compression technique and GPU systems will help boom the calculation velocity of the model and drop the storage space required for calculations. The experimental outcomes show that the developed detector is capable to classify diverse types of masks. Also, it can classify video images in real-time. Using the Knowledge Distillation on the baseline model can improve the testing accuracy from 88.79% to 90.13%. The proposed unique system can be implemented to assist in the prevention of COVID-19 spread and detect various mask types.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114868604","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

Image Processing Methods for Face Recognition using Machine Learning Techniques 使用机器学习技术的人脸识别图像处理方法

2021 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2021-12-01 DOI: 10.1109/ComPE53109.2021.9752410

T. R. Ganesh Babu, K. Shenbagadevi, V. S. Shoba, S. Shrinidhi, J. Sabitha, U. Saravanakumar

引用次数: 0

Image Quality Enhancement : A Linear Programming Approach 图像质量增强:线性规划方法

2021 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2021-12-01 DOI: 10.1109/ComPE53109.2021.9752053

J. Patni, Saurabh Agarwal, M. Kumar, Priyal Agarwal

引用次数: 0

Performance Analysis of WSN by varying number of clusters 不同簇数的WSN性能分析

2021 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2021-12-01 DOI: 10.1109/ComPE53109.2021.9752419

Gaurav Bathla, Lokesh Pawar, Rohit Bajaj, Harjeet Kaur, Navjot Singh

{"title":"Performance Analysis of WSN by varying number of clusters","authors":"Gaurav Bathla, Lokesh Pawar, Rohit Bajaj, Harjeet Kaur, Navjot Singh","doi":"10.1109/ComPE53109.2021.9752419","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9752419","url":null,"abstract":"A Wireless Sensor Network (WSN) consist of huge number of Sensor Nodes (SN’s) which are powered by irreplaceable battery source. So as to get best outcome of WSN, better network lifetime, equal load balancing among all SN’s and scalability of the network among the network are to be taken care off. For getting optimal lifetime from sensor network, clustering is one of the best techniques. Basically it is a process of placing similar kind of nodes together which works parallel with each other. One elected node from a cluster is responsible for transmitting data cluster’s aggregated data to the resource opulence node known as sink or Base Station (BS). An efficient scheme is required for formation of clusters & electing CH for network optimization. This work focuses on analysing network performance by varying number of clusters during the network operation. In the end it checks the lifetime (stable & Overall) of the network by varying probability factor for number of CH’s (Cluster Head) in network. Result analysis shows that lifetime of the network decreases gradually while increasing the probability of CH (Cluster head) however there is not as much difference observed in Stable Lifetime.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134129034","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

A Smart System for Detection and Classification of Pests Using YOLO AND CNN Techniques 基于YOLO和CNN技术的害虫检测与分类智能系统

2021 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2021-12-01 DOI: 10.1109/ComPE53109.2021.9752185

Sarapu Likith, B. R. Reddy, K. Sripal Reddy

引用次数: 1

Study on Machine-Learning Algorithms in Crop Yield Predictions specific to Indian Agricultural Contexts 机器学习算法在印度农业作物产量预测中的应用研究

2021 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2021-12-01 DOI: 10.1109/ComPE53109.2021.9752260

S. Sharma, D. Sharma, J. Verma

{"title":"Study on Machine-Learning Algorithms in Crop Yield Predictions specific to Indian Agricultural Contexts","authors":"S. Sharma, D. Sharma, J. Verma","doi":"10.1109/ComPE53109.2021.9752260","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9752260","url":null,"abstract":"Prior and well-grounded produces evaluation is vital in quantifying a well and financial assessment at the field level for discovering agricultural commodity strategic action plans for import-export policies and increasing farmer incomes. Crop production projections are performed utilizing machine learning algorithms to estimate a higher crop yield, which is one of the most difficult challenges in the agriculture business. Because of the growing importance of agricultural yield prediction, this article takes an in-depth look at how Machine Learning (ML) approaches may be utilized to forecast crop production. The present state of agricultural yield worldwide is discussed first, followed by a brief introduction of extensively utilized features and forecasting procedures. Forecasting crop yields is a serious issue in agriculture, plus there is a large dataset that makes it arduous for farmers to select seeds and forecast yields. In today’s circumstances, since the extension in population, agricultural production must be raised simultaneously to fulfill people’s wants. This paper is a detailed study of various aspects of crop yielding in India using machine learning techniques and artificial intelligence.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130557425","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

Secure Cloud-based Remote Monitoring of Environmental Factors using Mobile and Web Apps for Industry Automation 使用移动和Web应用程序对工业自动化环境因素进行安全的基于云的远程监控

2021 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2021-12-01 DOI: 10.1109/ComPE53109.2021.9752238

S. R., Sharath Cherian Thomas, Achint Mathews, Nishtha, Swathi C. Prabhu

{"title":"Secure Cloud-based Remote Monitoring of Environmental Factors using Mobile and Web Apps for Industry Automation","authors":"S. R., Sharath Cherian Thomas, Achint Mathews, Nishtha, Swathi C. Prabhu","doi":"10.1109/ComPE53109.2021.9752238","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9752238","url":null,"abstract":"Climate factors like temperature and humidity has a major role to play in the productivity in an office workspace. Similarly, products like medicine and food have to be manufactured and stored in optimum environmental conditions. Thus, monitoring the factors like temperature and humidity of different part of an office space or manufacturing unit is very important. Studying these factors over time will help in finding the optimum temperature for different parts of the office. Periodic storage of the data on secure remote location such as a SQL database on the cloud opens possibilities for future analysis. Presence of an app-based and web-based portal for monitoring the temperature and humidity data makes remote monitoring easy. Such a system can ensure the conditions are monitored at all times and alerts can be given in case of a sudden peak or drop. Security and confidentiality of the data is very important and thus limiting the access to the database and even the web and app-based portals can ensure the data is visible only to authorized personal.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125849886","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

Deep Learning Techniques for Anomaly based Intrusion Detection System: A Survey 基于异常的入侵检测系统的深度学习技术综述

2021 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2021-12-01 DOI: 10.1109/ComPE53109.2021.9751909

Y. Kumar, Lokesh Chouhan, Basant Subba

引用次数: 0

High Accuracy Predictive Model on Breast Cancer Using Ensemble Approach of Supervised Machine Learning Algorithms 基于监督机器学习算法集成方法的乳腺癌高精度预测模型

2021 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2021-12-01 DOI: 10.1109/ComPE53109.2021.9752254

Chaitanya Kaul, Neeraj Sharma

{"title":"High Accuracy Predictive Model on Breast Cancer Using Ensemble Approach of Supervised Machine Learning Algorithms","authors":"Chaitanya Kaul, Neeraj Sharma","doi":"10.1109/ComPE53109.2021.9752254","DOIUrl":"https://doi.org/10.1109/ComPE53109.2021.9752254","url":null,"abstract":"This research article is based on the ensemble approach of different supervised machine learning algorithms to identify the early stages of breast cancer problems. The World Health Organization (WHO) approved that existence of the breast tumor is high for the women in developing countries and it is one of the significant research issues in current scenario in the real world. In this research article researcher used the 30 features to extract and predict accurate prediction on breast cancer using ensemble approach of supervised machine learning algorithms. It is a great challenge in designing a machine learning model to evaluate the performance of the classification of breast tumor. Implementing an efficient classification methodology will support in resolving the complications in analyzing breast cancer. This proposed model employs four machine learning (ML) algorithms Decision tree classifiers, Random Forest KNN, and support vector machine (SVM) and found support vector machine (SVM) which given the high accuracy of 0.976688 among them for the categorization of breast tumor in women. This classification includes the two levels of disease as benign or malignant. The researcher also used the other parameters and evaluated this predictive model using Precision, Recall and F1-Score. The data analysis report is proved that this predictive model is having 98% accuracy level to predict the cancer at early stages in women.","PeriodicalId":211704,"journal":{"name":"2021 International Conference on Computational Performance Evaluation (ComPE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122347182","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

SITF: an algorithm for secured image transmission using Fractals SITF:一种使用分形的安全图像传输算法

2021 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2021-12-01 DOI: 10.1109/ComPE53109.2021.9752105

Sneh Lata Aswal, A. Negi, A. Saxena

引用次数: 0