2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4)最新文献_第6页

Road Traffic Density Surveillance System using IR Sensor, Image Processing and Raspberry Pi: A Comparative Analysis 基于红外传感器、图像处理和树莓派的道路交通密度监测系统的比较分析

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689444

Priyansh Maheshwari, N. V. Shrivas, Ashok Kumawat

{"title":"Road Traffic Density Surveillance System using IR Sensor, Image Processing and Raspberry Pi: A Comparative Analysis","authors":"Priyansh Maheshwari, N. V. Shrivas, Ashok Kumawat","doi":"10.1109/C2I454156.2021.9689444","DOIUrl":"https://doi.org/10.1109/C2I454156.2021.9689444","url":null,"abstract":"In our present time, the use of vehicles has become widespread which has given rise to the problem of traffic congestion a common sight in day-to-day life. And with the increased sales of vehicles in recent years the problem is almost considered as a known factor that gives rise to noise and air pollution in coming years. This imminent problem gives validation to the use of de-congestion methods, some of which are more efficient traffic signs and constant traffic monitoring to prevent a standstill event that is known to devolve into traffic jams. Traffic monitoring can be done with the help of Frame comparison as well as Edge Detection in tandem with IR sensors that give a rough estimate of how far the traffic congestion goes. In this paper we have focused on the merging of three different approaches viz. the IR Sensor in passive mode, Real-time Background Subtraction in a semi-active mode, and Real-time Canny Image Processing in an active mode in an efficient manner in a prototype and analyzed their performance using Raspberry pi for selection of a preferred system. The results obtained reduce traffic congestion using the dynamic times allocated to the higher traffic lanes until an average time, which is assumed with lower traffic lanes. This method is inexpensive and easily implemented with Raspberry Pi's help to run it arguably efficiently on medium-traffic areas.","PeriodicalId":190895,"journal":{"name":"2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4)","volume":"259 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116219007","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

Student Productivity Surveillance Algorithm 学生生产力监控算法

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689278

Vishal V Angadi, Anjali N Dixit, Veena S

引用次数: 0

A 5/6 GHz MMR Structure Based UWB Bandpass Notch Filter Embedded with a Spur line for Wireless Applications 一种基于5/6 GHz MMR结构的嵌入式直线UWB带通陷波滤波器

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689370

J. Panda, S. Dash, S. Mishra, A. Bakshi

引用次数: 0

Automatic Detection of Bacilli Bacteria from Ziehl-Neelsen Sputum Smear Images Ziehl-Neelsen痰涂片图像中杆菌的自动检测

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689283

V. Shwetha, K. Prasad, C. Mukhopadhyay, Barnini Banerjee, Abir Chakrabarti

引用次数: 4

Skin Disease Classification using Machine Learning Algorithms 使用机器学习算法进行皮肤病分类

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689338

K. Swamy, B. Divya

{"title":"Skin Disease Classification using Machine Learning Algorithms","authors":"K. Swamy, B. Divya","doi":"10.1109/C2I454156.2021.9689338","DOIUrl":"https://doi.org/10.1109/C2I454156.2021.9689338","url":null,"abstract":"Skin diseases are the large number of spread diseases in the world. Their diagnoses are very difficult because of its difficulties in skin texture, presence of hair on skin and color. It is required to develop methods like machine learning in order to increase the accuracy of diagnosis for various types of skin diseases. Machine learning techniques are widely used in medical fields for diagnosis. These algorithms use feature values from images as input to make a decision. The process consists of three stages-The feature extraction stage, the training stage and the testing stage. The process makes use of machine learning technology to train itself with the various skin images. The objective of this process is to increase accuracy of skin disease detection. Three important features in image classification are texture, color, shape, and combination of these. In this work, color and texture features are used to classify the skin disease. Normal skin color is different from the skin with disease. Smoothness, coarseness, and regularity is effectively identified using texture features in the images. Hence, these two features are explored to identify skin disease effectively. In this work, entropy, variance and maximum histogram value of Hue-Saturation-Value(HSV) features are used. These features are used to build machine learning algorithm by using Decision Tree(DT) and Support Vector Machine(SVM). At first level, entropy measure is used to split the tree. At second level, variance is used to get leafs for textures. In color features, maximum histogram value of HSV measure is used to split the tree. Accuracy is used to test the performance of the proposed algorithm.","PeriodicalId":190895,"journal":{"name":"2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123907031","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: 6

Enhanced Security using Advanced Encryption Standards in Face Recognition 在人脸识别中使用高级加密标准增强安全性

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689403

C. Vanitha, S. Malathy, K. Anitha, S. Suwathika

引用次数: 2

An Intelligent Driver Monitoring System 智能驾驶员监控系统

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689295

P. Supraja, P. Revati, K.Sushmitha Ram, J. C.

引用次数: 2

Conference Details 会议的细节

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/c2i454156.2021.9689407

引用次数: 0

The Impact of YouTube Personalization Algorithm on User-Generated Content Creation in India YouTube个性化算法对印度用户生成内容创作的影响

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689393

Akshatha Ilangovan, Prof. Richard Beswick, B. Sarojini

引用次数: 0

Power Management System for PV Fed DC Drive with Discrete Controller 基于离散控制器的光伏直流电源管理系统

2021 2nd International Conference on Communication, Computing and Industry 4.0 (C2I4) Pub Date : 2021-12-16 DOI: 10.1109/C2I454156.2021.9689423

V. K, A. T., K. Chitra, M. Lakshmanan, B. D

引用次数: 1