2021 5th International Conference on Computing Methodologies and Communication (ICCMC)最新文献

Aspect-Oriented Sentiment Classification using BiGRU-CNN model 基于BiGRU-CNN模型的面向方面情感分类

2021 5th International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2021-04-08 DOI: 10.1109/ICCMC51019.2021.9418242

Dr. Sindhu C, Bihanga Som, S. Singh

{"title":"Aspect-Oriented Sentiment Classification using BiGRU-CNN model","authors":"Dr. Sindhu C, Bihanga Som, S. Singh","doi":"10.1109/ICCMC51019.2021.9418242","DOIUrl":"https://doi.org/10.1109/ICCMC51019.2021.9418242","url":null,"abstract":"People on the Internet have generated a large amount of commentary data to share their opinions about products and services in their daily lives which include large commercial value. For these comment sentences, they often include several comment aspects, and the sentiment varies on these aspects, making the overall meaning of the sentence meaningless for polarization. The purpose of the aspect-level sentiment classification is to recognize target’s sense extremity in context. Deep Learning is evolving in an increasingly mature direction, and the utilization of deep learning methods to detect emotion has become increasingly popular. A sentiment classification model is propsoed by combining a convolutional neural network and a bidirectional gated recurrent unit.Bidirectional gated recurrent unit is similar to Long short-term memory, a time cyclic neural network with a lesser processing complexity. The model first extracts sequence features of the text through the bidirectional gated recurrent unit and then extracts the local static features of the text through the convolutional neural network. Finally, the Sigmoid classifier is used for the final sentiment classification.","PeriodicalId":131747,"journal":{"name":"2021 5th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"324 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115011781","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

Modern Stage of Health Services in IoT Era 物联网时代健康服务的现代阶段

2021 5th International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2021-04-08 DOI: 10.1109/ICCMC51019.2021.9418259

Megha Gupta, Nida Khan, Divya Choudhary

引用次数: 0

Analysis of Effectiveness of Augmentation in Plant Disease Prediction using Deep Learning 基于深度学习的植物病害预测增强效果分析

2021 5th International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2021-04-08 DOI: 10.1109/ICCMC51019.2021.9418266

Jithy Lijo

引用次数: 4

Advanced IOT based System for Cricketers Health Supervision 基于物联网的先进板球运动员健康监督系统

2021 5th International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2021-04-08 DOI: 10.1109/ICCMC51019.2021.9418314

Syed. Karimunnisa, Ashok Bekkanti, U. Haritha, Gayatri Parasa, C. Z. Basha

引用次数: 4

Mining Tourists’ Opinions on Popular Indian Tourism Hotspots using Sentiment Analysis and Topic Modeling 利用情感分析和话题建模挖掘游客对印度热门旅游热点的看法

2021 5th International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2021-04-08 DOI: 10.1109/ICCMC51019.2021.9418341

Shefali Singh, Tureen Chauhan, Vibhas Wahi, P. Meel

{"title":"Mining Tourists’ Opinions on Popular Indian Tourism Hotspots using Sentiment Analysis and Topic Modeling","authors":"Shefali Singh, Tureen Chauhan, Vibhas Wahi, P. Meel","doi":"10.1109/ICCMC51019.2021.9418341","DOIUrl":"https://doi.org/10.1109/ICCMC51019.2021.9418341","url":null,"abstract":"User-generated content is an exploration area of interest with regards to web 2.0. The development of social networks and community-based websites have changed the manner in which individuals utilize the Internet. It makes individuals no longer restricted to pursuing the data given by professional channels, but to making individual profiles, producing personalized content, or sharing photographs, recordings, blogs, and so forth. This sort of data comprises the current online user-generated content. With the continuous development of the travel industry, the quantity of online travel review websites has also increased. Indian Tourism is popular for its rich culture and diversity and hence Government of India has increased the number of new tourist destinations to expand their popularity and presence. Researchers have proposed various studies to increase tourism network using Big Data. Techniques of Sentiment Analysis along with Topic Modelling have been used to unearth patterns and observations from online reviews. This paper aims to mine reviews of 10 popular travel destinations in India. Using sentiment analysis technique, the proposed research work has explored the polarity of various reviews extracted from TripAdvisor. Data collection was done by using the web framework Scrapy to acquire more than 10,000 reviews for these destinations. This paper also analyzes the result of doing Topic Modeling on reviews for individual destinations. Results conclude that Joy is the most common emotion in all the visitor’s experiences. Indian tourism decision quality can be improved by the help of the results from this study.","PeriodicalId":131747,"journal":{"name":"2021 5th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127514544","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

A Blockchain based Framework for IoT Security 基于区块链的物联网安全框架

2021 5th International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2021-04-08 DOI: 10.1109/ICCMC51019.2021.9418485

R. Premkumar, P. Sathya

{"title":"A Blockchain based Framework for IoT Security","authors":"R. Premkumar, P. Sathya","doi":"10.1109/ICCMC51019.2021.9418485","DOIUrl":"https://doi.org/10.1109/ICCMC51019.2021.9418485","url":null,"abstract":"Millions of Internet of Things (IoT) devices are associated with ordinarily to trade information or data over the web. Notwithstanding, IoT protection and security are significant difficulties because of expanding the quantity of IoT gadgets that the data are gathered from IoT gadgets. Blockchain has stood out to address protection and security worry in IoT gadgets because of its trademark highlight which incorporates permanence, review capacity, and decentralization. Regular blockchain is costly in computational and has restricted versatility and devours more memory and transfer speed which causes the postponement for IoT biological systems. This paper shows two novel commitment, Lightweight adaptable blockchain and disseminated time-based agreement. The Lightweight adaptable accomplishes decentralization by shaping an overlie network where high asset gadgets together deal with the blockchain. The time-based consensus that preparing overhead and deferral. A confident approach is used by the group heads to endlessly reduce the handling overhead for confirm new squares. Blockchain is progressively being utilized to give a circulated, secure, trusted, and private structure for the energy exchange. Be that as it may, existing arrangements experience the ill effects of the absence of protection, handling and parcel overheads, and dependence on Trusted Third Parties to make sure about the exchange. To address these difficulties, this paper proposes a Secure Private Blockchain-based system as a piece of this commitment.","PeriodicalId":131747,"journal":{"name":"2021 5th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124906405","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

Image feature extraction and analysis algorithm based on multi-level neural network 基于多层次神经网络的图像特征提取与分析算法

2021 5th International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2021-04-08 DOI: 10.1109/ICCMC51019.2021.9418309

Erhui Xi

引用次数: 2

Multi Domain Fake News Analysis using Transfer Learning 基于迁移学习的多领域假新闻分析

2021 5th International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2021-04-08 DOI: 10.1109/ICCMC51019.2021.9418411

Pratyush Goel, Samarth Singhal, Snehil Aggarwal, Minni Jain

{"title":"Multi Domain Fake News Analysis using Transfer Learning","authors":"Pratyush Goel, Samarth Singhal, Snehil Aggarwal, Minni Jain","doi":"10.1109/ICCMC51019.2021.9418411","DOIUrl":"https://doi.org/10.1109/ICCMC51019.2021.9418411","url":null,"abstract":"Fake news detection is a significant problem where information is available from multiple sources across the internet. Most of the research on fake news has only targeted politics-related articles, but such models would not be robust enough to tackle fake news in the real world. To solve this problem, this research work incorporated transfer learning using attention-based transformers (BERT, RoBERTa, XLNet, DeBERTa, GPT2) and trained them on multi-domain datasets FakeNews AMT and Celebrity across different domains i.e. Politics, Entertainment, Sports, Business, Education and Technology. The proposed model has obtained state-of-the-art results while doing multi-domain and cross-domain testing, having beaten previous papers conformably. Also, the model has achieved a 99.3% accuracy on FakeNewsAMT and 84% accuracy on celebrity dataset. We believe the synergy of transfer learning in a multi-domain setting will make a robust model, which would be relevant in the real world. This idea originated from the fact that multi-domain research’s critical challenge is that data distribution is varying, and the key benefit of transfer learning is that it can perform well even when it is trained and tested on different data distributions.","PeriodicalId":131747,"journal":{"name":"2021 5th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124953744","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

Data mining in Cloud Computing 云计算中的数据挖掘

2021 5th International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2021-04-08 DOI: 10.1109/ICCMC51019.2021.9418489

D. Pranav, Devansh Punj, Tushar Dubey, Pronika Chawla

引用次数: 0

Ancient Horoscopic Palm Leaf Binarization Using A Deep Binarization Model - RESNET 基于深度二值化模型RESNET的古代占星棕榈叶二值化

2021 5th International Conference on Computing Methodologies and Communication (ICCMC) Pub Date : 2021-04-08 DOI: 10.1109/ICCMC51019.2021.9418461

B. Nair B J, Ashwin Nair

{"title":"Ancient Horoscopic Palm Leaf Binarization Using A Deep Binarization Model - RESNET","authors":"B. Nair B J, Ashwin Nair","doi":"10.1109/ICCMC51019.2021.9418461","DOIUrl":"https://doi.org/10.1109/ICCMC51019.2021.9418461","url":null,"abstract":"Binarization of ancient documents is a challenging task. Nowadays lot of traditional binarization algorithms exist with good accuracy but those algorithms cannot remove all kind of noises which are present in the same ancient documents. In traditional RESNET batch normalization is not using because of that it takes too much time for training. But proposed RESNET uses batch normalization which will increase the speed of the model training. Also, it is true huge data set can’t be used at same time for enhancement. So, the deep learning models like RESNET will remove noise from ancient documents with good accuracy. The modified RESNET model will give good accuracy in ancient degraded image enhancement. Residual network will remove the noises like ink bleed and uneven illumination. In modified RESNET model with batch normalization which will increase the speed of the training phase. Proposed work is mainly based on modified RESNET with Convolution and Batch normalization along with Relu as one block like which five blocks are used for image binarization. It is working based on two phase method like down-sampling and up-sampling which is used to efficiently binarize the degraded ancient palm leaf manuscript with an accuracy of 95.38%.","PeriodicalId":131747,"journal":{"name":"2021 5th International Conference on Computing Methodologies and Communication (ICCMC)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125188522","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: 13