2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER)最新文献_第2页

Human Action Recognition by Concatenation of Spatio-Temporal 3D SIFT and CoHOG Descriptors using Bag of Visual Words 基于视觉词袋的时空三维SIFT和CoHOG描述符串联的人体动作识别

2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER) Pub Date : 2022-10-14 DOI: 10.1109/DISCOVER55800.2022.9974645

R. Divya Rani, C. J. Prabhakar

{"title":"Human Action Recognition by Concatenation of Spatio-Temporal 3D SIFT and CoHOG Descriptors using Bag of Visual Words","authors":"R. Divya Rani, C. J. Prabhakar","doi":"10.1109/DISCOVER55800.2022.9974645","DOIUrl":"https://doi.org/10.1109/DISCOVER55800.2022.9974645","url":null,"abstract":"In this paper, Spatio-Temporal Interest Points (STIPs) based technique is presented to recognize human actions using Bag of Visual Words (BoVW) representation. First, we extract densely sampled local 3-Dimensional Scale Invariant Feature Transform (3D SIFT) and global Co-occurrence Histograms of Oriented Gradients (CoHOG) feature descriptors from input video sequences. The discriminative features are selected by applying the Linear Discriminant Analysis (LDA) dimensionality reduction technique. The optimal features selected from both 3D SIFT and CoHOG features are concatenated to produce a single feature vector. To generate visual vocabulary we used k-means++ clustering. The prominent visual words are considered by the Term-Frequency Inverse-Document Frequency (TF.IDF) weighing scheme and are used to generate histograms. The Support Vector Machine (SVM) classifier is used for action classification. The proposed method is evaluated using two popular human action recognition datasets, such as the KTH dataset, and the Weizmann dataset. The experimental results obtained for our proposed method are compared with the state-of-the-art human action recognition techniques which demonstrate that the proposed method achieves the highest recognition accuracy 98.00% for KTH dataset and 98.7% for Weizmann dataset.","PeriodicalId":264177,"journal":{"name":"2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121247199","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

Comparative Analysis of Machine Learning Algorithms for Disease Detection in Apple Leaves 苹果叶片病害检测的机器学习算法比较分析

2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER) Pub Date : 2022-10-14 DOI: 10.1109/DISCOVER55800.2022.9974840

Andhavaram Mohan Sai, Nagamma Patil

{"title":"Comparative Analysis of Machine Learning Algorithms for Disease Detection in Apple Leaves","authors":"Andhavaram Mohan Sai, Nagamma Patil","doi":"10.1109/DISCOVER55800.2022.9974840","DOIUrl":"https://doi.org/10.1109/DISCOVER55800.2022.9974840","url":null,"abstract":"Leaves serve as unique indicators to distinguish the diseased leaves because the image information of the leaf changes when it is suffering from some disease. To detect these diseases, we need to recognize the patterns formed by the diseases in the leaves. Generally, plants are observed with a naked eye by either experts or farmers to detect and identify the plants. But this method can be expensive and time processing; therefore, it is essential to automate crop disease diagnosis in regions with few experts. This work revolves around an approach to developing a plant disease detection model based on apple leaves. The proposed methodology uses the following three feature extraction techniques: Hu Moments, Haralick Texture, and Color Histogram. The research work provides a comparative analysis of machine learning models for detecting diseases in apple leaves, namely: Black Rot, Cedar Apple Rust, and Apple Scab. The model is evaluated on a subset of the “Plant Village Dataset” dealing with apple leaves. Out of all the machine learning models fitted, Random Forest has obtained the highest test accuracy of 98.125 percent.","PeriodicalId":264177,"journal":{"name":"2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121725750","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

Prediction of Heart Disease using Computational Algorithms 使用计算算法预测心脏病

2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER) Pub Date : 2022-10-14 DOI: 10.1109/DISCOVER55800.2022.9974702

V. Shobha, C. Smitha, Ashwini Kodipalli

{"title":"Prediction of Heart Disease using Computational Algorithms","authors":"V. Shobha, C. Smitha, Ashwini Kodipalli","doi":"10.1109/DISCOVER55800.2022.9974702","DOIUrl":"https://doi.org/10.1109/DISCOVER55800.2022.9974702","url":null,"abstract":"Nowadays, peoples are suffering from heart diseases steadily because of their ignorance towards their physical-fitness. Globally there are 32% population are suffering from heart disease by world health organization(WHO).The death rates are increasing because of heart attacks and even the peoples are suffering across the global for all kind of Genders because of heart problems. The cases of heart attack is increasing day-by-day. The prediction of heart diseases are very needful to the health sectors includes the hospitals, sanatoriums, nursing and medical because it is difficult to analyze the huge data. The better prediction of heart disease can prevent the life threats. There are many algorithms had used to predict the heart disease but In this paper many Machine Learning Classification algorithms are applied such as Logistic Regression, K-Nearest Neighbor(KNN), Decision tree-classifier, Random Forest, Naive Bayes and support vector machine. Here we have done the comparative study of all the algorithms mentioned in the above lines. This heart disease dataset is collected from kaggle.com. The objective of the paper is to find the better accuracy provided by the algorithm. Several outcomes has achieved and verified using accuracy and confusion matrix.","PeriodicalId":264177,"journal":{"name":"2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133049578","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

Detection and Mitigation of Low and Slow DDoS attack in an SDN environment SDN环境下低速和慢速DDoS攻击的检测与缓解

2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER) Pub Date : 2022-10-14 DOI: 10.1109/DISCOVER55800.2022.9974724

A. N. H. D. Sai, B. H. Tilak, N. S. Sanjith, Padi Suhas, R. Sanjeetha

引用次数: 2

Sarcasm Detection in English Text using Tweets and Headlines 基于推文和标题的英语文本讽刺检测

2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER) Pub Date : 2022-10-14 DOI: 10.1109/DISCOVER55800.2022.9974808

Prajwal K Naik, Snigdha S Chenjeri, S. Sruthy, H. Mamatha

引用次数: 1

Text Summarization using NLP Technique 使用NLP技术的文本摘要

2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER) Pub Date : 2022-10-14 DOI: 10.1109/DISCOVER55800.2022.9974823

Balaji N, M. N, D.Lalitha Kumari, Sunil Kumar P, Bhavatarini N, Shikah Rai A

{"title":"Text Summarization using NLP Technique","authors":"Balaji N, M. N, D.Lalitha Kumari, Sunil Kumar P, Bhavatarini N, Shikah Rai A","doi":"10.1109/DISCOVER55800.2022.9974823","DOIUrl":"https://doi.org/10.1109/DISCOVER55800.2022.9974823","url":null,"abstract":"The text data online is increasing massively; hence, producing a summarized text document is essential. We can create the summarization of multiple text documents either manually or automatically. A manual approach may be tedious and a time-consuming process. The resulting composition may not be accurate when processing lengthy articles; hence the second approach, i.e., the automated summary generation process, is essential. Training machine learning models using these processes makes space and time-efficient summary generation possible. There are two widely used methods to generate summaries, namely, Extractive summarization and abstractive summarization. The extractive technique scans the original document to find the relevant sentences and extracts only that information from it. The abstractive summarization technique interprets the original text before generating the summary. This process is more complicated, and transformer architecture-based pre-trained models are used for comparing the text & developing the outline. This research analysis uses the BBC news dataset to evaluate and compare the results obtained from the machine learning models.","PeriodicalId":264177,"journal":{"name":"2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER)","volume":"268 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121155404","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

Development of Electrical Parameters Measurement Device with IoT Functionality 具有物联网功能的电参数测量设备的开发

2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER) Pub Date : 2022-10-14 DOI: 10.1109/DISCOVER55800.2022.9974623

M. Ranjan, Anshuman Anand, A. Rao, A. Padmasali

引用次数: 0

Scalable, Cost Effective IoT Based Medical Oxygen Monitoring System for Resource Constrained Hospital Environment 可扩展、经济高效的基于物联网的医疗氧气监测系统，用于资源受限的医院环境

2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER) Pub Date : 2022-10-14 DOI: 10.1109/DISCOVER55800.2022.9974660

Joel J. P. C. Rodrigues, R. K. Chandra Shekar

{"title":"Scalable, Cost Effective IoT Based Medical Oxygen Monitoring System for Resource Constrained Hospital Environment","authors":"Joel J. P. C. Rodrigues, R. K. Chandra Shekar","doi":"10.1109/DISCOVER55800.2022.9974660","DOIUrl":"https://doi.org/10.1109/DISCOVER55800.2022.9974660","url":null,"abstract":"Oxygen therapy is one of the critical treatments employed in epidemics, pandemics, and natural calamities. Recent covid pandemic worldwide witnessed many deaths due to improper management, delayed delivery, and wastage of medical oxygen. Therefore, efficient utilization of available oxygen is very important. To monitor and manage oxygen, several hospitals employ IoT-based systems. Scalability is an essential feature in such monitoring systems in order to cater to the needs of a sudden surge in the number of patients requiring oxygen. The most commonly employed technique to monitor and manage an oxygen cylinder uses a pressure sensor where scaling up is an issue. Therefore, in this paper, a scalable solution that efficiently measures and monitors the available oxygen in the cylinder is proposed. The approach measures oxygen level using a weight sensor module and raises alerts during critical conditions such as low oxygen level and blockage or leakage of oxygen. The proposed system is a cost-effective, plug-and-play system that aids rapid deployment thereby providing timely care to the patients. Also, it does not require any change in the existing infrastructure making it suitable for a resource-constrained environment. The proposed system supports a web-based dashboard and mobile app that can be remotely accessed.","PeriodicalId":264177,"journal":{"name":"2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124114278","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

Route establishment in MANETs; Ameliorated with Tunicate Swarm Algorithm and Cross layer Interaction manet中路由的建立采用束状虫群算法和跨层交互进行改进

2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER) Pub Date : 2022-10-14 DOI: 10.1109/DISCOVER55800.2022.9974683

Sadanand Inamdar, Jayashree I. Kallibaddi

引用次数: 0

Characterization of horticulture and general lighting-LEDs and Luminaires 园艺和一般照明的特性。led和灯具

2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER) Pub Date : 2022-10-14 DOI: 10.1109/DISCOVER55800.2022.9974863

R. Sowmya, C. P. Kurian, S. Narasimhan

引用次数: 0