Global Transitions Proceedings最新文献_第9页

Debunking health fake news with domain specific pre-trained model 揭穿健康假新闻与领域特定的预训练模型

Global Transitions Proceedings Pub Date : 2021-11-01 DOI: 10.1016/j.gltp.2021.08.038

Santoshi Kumari, Harshitha K Reddy, Chandan S Kulkarni, Vanukuri Gowthami

{"title":"Debunking health fake news with domain specific pre-trained model","authors":"Santoshi Kumari, Harshitha K Reddy, Chandan S Kulkarni, Vanukuri Gowthami","doi":"10.1016/j.gltp.2021.08.038","DOIUrl":"10.1016/j.gltp.2021.08.038","url":null,"abstract":"<div><p>During this covid pandemic it is clearer than ever how much health misinformation effects. It is much easier now to publish health related articles online without validation, these articles are shared across social media contributing to the spread of health fake news. This Health fake news are spread with intent to damage image of person or product, to increase sells of a product or to promote a product. In recent research papers, many useful health misinformation detection models use BERT (Bidirectional Encoder Representations from Transformers) which is pretrained on unlabeled data extracted from English Wikipedia and book corpus and are mostly dealt with health misinformation on social media. Therefore, a self - ensemble SCIBERT (Scientific BERT) based model that makes use of domain specific word embeddings is proposed for detection of health misinformation specifically in news which is less explored and a dataset combining existing FakeHealth dataset and custom dataset that contains health articles scraped from news fact checking website Snopes.com. Classification results exhibits that the proposed model provides weighted F1 score of 0.715.</p></div>","PeriodicalId":100588,"journal":{"name":"Global Transitions Proceedings","volume":"2 2","pages":"Pages 267-272"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.gltp.2021.08.038","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79537801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Performance enhancement of dual axis solar tracker system for solar panels using proteus ISIS 7.6 software package 利用Proteus ISIS 7.6软件包增强太阳能帆板双轴跟踪系统性能

Global Transitions Proceedings Pub Date : 2021-11-01 DOI: 10.1016/j.gltp.2021.08.049

Prajakta Pawar, Priyanka Pawale, Tanuja Nagthane, Mohan Thakre, Nayana Jangale

{"title":"Performance enhancement of dual axis solar tracker system for solar panels using proteus ISIS 7.6 software package","authors":"Prajakta Pawar, Priyanka Pawale, Tanuja Nagthane, Mohan Thakre, Nayana Jangale","doi":"10.1016/j.gltp.2021.08.049","DOIUrl":"10.1016/j.gltp.2021.08.049","url":null,"abstract":"<div><p>In this article, a standalone solar tracking system is intended and successfully synchronized using proteus 7.6 ISIS. Best at instantly aligning a solar panel with the position of the sun, resulting in a 40% increase in efficiency. A low-cost solar tracking detector is being used to rotate the solar panel paired to the motor at such a specific angle to rotate 180°s in 8 steps per day with better accuracy. A microcontroller (Atmega3285P) is used as a control unit, and ADC ports are used to interconnect sensor units. To interconnect a solar array, the motor driver ULN 293D is also used to rotate the solar panel at the highest solar power angle. The modeling is performed in two ways, using four LDRs and eight LDRs, and the results of both are measured. The simulation and analysis results show that the maximum current drawn by the solar tracking sensor is less than 0.5 mA. “Proteus 7.6 ISIS” and “Arduino UNO” and “Code Vision AVR” are used to write the program code and to burn over Atmega3285P for parameters estimation.</p></div>","PeriodicalId":100588,"journal":{"name":"Global Transitions Proceedings","volume":"2 2","pages":"Pages 455-460"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.gltp.2021.08.049","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77876176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Unsupervised Aspect Extraction Algorithm for opinion mining using topic modeling 基于主题建模的意见挖掘无监督方面提取算法

Global Transitions Proceedings Pub Date : 2021-11-01 DOI: 10.1016/j.gltp.2021.08.005

Azizkhan F Pathan , Chetana Prakash

{"title":"Unsupervised Aspect Extraction Algorithm for opinion mining using topic modeling","authors":"Azizkhan F Pathan , Chetana Prakash","doi":"10.1016/j.gltp.2021.08.005","DOIUrl":"10.1016/j.gltp.2021.08.005","url":null,"abstract":"<div><p>With the massive use of electronic gadgets and the developing fame of web-based media, a great deal of text information is being produced at the rate never observed. It is not feasible for people to pursue all information produced and discover what is being investigated in their area of interest. To determine topics in large textual documents Topic modeling is used. Topic Modeling Algorithms are Unsupervised Machine Learning approaches which are widely used and have proven to be successful in the area of Aspect-based Opinion Mining to extract ‘latent’ topics, which are aspects of interest. In this paper, the approaches that are widely used for topic modeling are examined and compared to find their importance in detecting topics based on metrics such as Perplexity and Coherence. As a result, Latent Dirichlet Allocation is a good topic modeling algorithm compared to Latent Semantic Analysis and Hierarchical Dirichlet Process for aspect extraction process in aspect-based opinion mining. Also, we have proposed an unsupervised aspect extraction algorithm based on topic models for Aspect-based Opinion mining.</p></div>","PeriodicalId":100588,"journal":{"name":"Global Transitions Proceedings","volume":"2 2","pages":"Pages 492-499"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.gltp.2021.08.005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77974404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Application of IOT and machine learning in crop protection against animal intrusion 物联网和机器学习在作物保护中的应用

Global Transitions Proceedings Pub Date : 2021-11-01 DOI: 10.1016/j.gltp.2021.08.061

K Balakrishna, Fazil Mohammed, C.R. Ullas, C.M. Hema, S.K. Sonakshi

引用次数: 18

Agroxpert - Farmer assistant agroexpert—农民助理

Global Transitions Proceedings Pub Date : 2021-11-01 DOI: 10.1016/j.gltp.2021.08.016

Vandana Nayak , Pranav R Nayak N , Sampoorna , Aishwarya , N.H. Sowmya

引用次数: 6

A novel road side unit assisted hash chain based approach for authentication in vehicular Ad-hoc network 一种基于路旁单元辅助哈希链的车载自组织网络认证新方法

Global Transitions Proceedings Pub Date : 2021-11-01 DOI: 10.1016/j.gltp.2021.08.014

Farooque Azam , Sunil Kumar , Neeraj Priyadarshi

{"title":"A novel road side unit assisted hash chain based approach for authentication in vehicular Ad-hoc network","authors":"Farooque Azam , Sunil Kumar , Neeraj Priyadarshi","doi":"10.1016/j.gltp.2021.08.014","DOIUrl":"10.1016/j.gltp.2021.08.014","url":null,"abstract":"<div><p>Growth of connected vehicles in vehicular social network (VSN) for the ease of driving, route recommendation and infotainment services involve huge exchange of messages between the vehicles. This exchange of messages is prone to several security attacks. Also, the messages must be from authentic source for the success of VSN. Thus, VSN attracts industries, researchers and academia for the development and deployment of a network for its secured communication. Vehicular Ad-hoc Network (VANET) can provide a promising solution for connected vehicles. VANET is a framework in the intelligent transportation systems (ITS) which provide communication between vehicles also known as vehicle to vehicle (V2V) and to infrastructure also known as vehicle to infrastructure (V2I) communications using dedicated short range communication (DSRC) and wireless Access for Vehicular Environment (WAVE). Several security and privacy majors have been taken up by the researchers in the literature. Also most of the scheme completely relies on the trusted authority (TA) for all decisions. However, because of the strict time constraints, the delay must be minimum and incurs less computational and communication overhead. In this research work, RSU assisted hash chain based approach has been proposed to mitigate security and privacy attacks. RSU based scheme thus reduces the complete dependency on the TA. Simulation results show the effectiveness of proposed work as compared to existing work mentioned in this paper.</p></div>","PeriodicalId":100588,"journal":{"name":"Global Transitions Proceedings","volume":"2 2","pages":"Pages 163-168"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.gltp.2021.08.014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87451120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

AGRIC: A quality farming 农业:优质农业

Global Transitions Proceedings Pub Date : 2021-11-01 DOI: 10.1016/j.gltp.2021.08.020

U Prajna , B.S. Prajwal , N Shrinidhi , A Shree vidya Rao , Bhat Rukmini

引用次数: 0

ResNet-50 vs VGG-19 vs training from scratch: A comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images ResNet-50 vs VGG-19 vs Training from Scratch:胸部x线图像肺炎分割分类的比较分析

Global Transitions Proceedings Pub Date : 2021-11-01 DOI: 10.1016/j.gltp.2021.08.027

A. Victor Ikechukwu, S. Murali, R. Deepu, R.C. Shivamurthy

{"title":"ResNet-50 vs VGG-19 vs training from scratch: A comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images","authors":"A. Victor Ikechukwu, S. Murali, R. Deepu, R.C. Shivamurthy","doi":"10.1016/j.gltp.2021.08.027","DOIUrl":"10.1016/j.gltp.2021.08.027","url":null,"abstract":"<div><p>In medical imaging, segmentation plays a vital role towards the interpretation of X-ray images where salient features are extracted with the help of image segmentation. Without undergoing surgery, clinicians employ various modalities ranging from X-rays and CT-Scans to ultrasonography, and other imaging techniques to visualise and examine interior human body organ and structures. To ensure appropriate convergence, training a deep convolutional neural network (CNN) from scratch is tough since it requires more computational time, a big amount of labelled training data and a considerable degree of experience. Fine-tuning a CNN that has been pre-trained using, for instance, a huge set of labelled medical datasets, is a viable alternative. In this paper, a comparative study was done using pre-trained models such as VGG-19 and ResNet-50 as against training from scratch. To reduce overfitting, data augmentation and dropout regularization was used. With a recall of 92.03%, our analysis showed that the pre-trained models with proper finetuning was comparable with Iyke-Net, a CNN trained from scratch.</p></div>","PeriodicalId":100588,"journal":{"name":"Global Transitions Proceedings","volume":"2 2","pages":"Pages 375-381"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.gltp.2021.08.027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80056819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 64

Crop yield forecasting using data mining 利用数据挖掘进行作物产量预测

Global Transitions Proceedings Pub Date : 2021-11-01 DOI: 10.1016/j.gltp.2021.08.008

Pallavi Kamath, Pallavi Patil, Shrilatha S, Sushma, Sowmya S

引用次数: 28

Comparative analysis of deep learning models for COVID-19 detection 新型冠状病毒肺炎深度学习检测模型的对比分析

Global Transitions Proceedings Pub Date : 2021-11-01 DOI: 10.1016/j.gltp.2021.08.030

Santoshi Kumari, Ediga Ranjith, Abhishek Gujjar, Siranjeevi Narasimman, H S Aadil Sha Zeelani

{"title":"Comparative analysis of deep learning models for COVID-19 detection","authors":"Santoshi Kumari, Ediga Ranjith, Abhishek Gujjar, Siranjeevi Narasimman, H S Aadil Sha Zeelani","doi":"10.1016/j.gltp.2021.08.030","DOIUrl":"10.1016/j.gltp.2021.08.030","url":null,"abstract":"<div><p>Corona virus disease also acknowledged as COVID-19 outbreak, a worldwide pandemic is one of the most acute and severe viruses in recent time. The rate of COVID cases rise rapidly around the world. Although vaccines have been developed, deep learning (DL) techniques shown as a useful method for clinical diagnosis and other fields. Deep structured learning also known as Deep learning is method based on artificial neural network with interpretation learning. This paper aims to do a comparative analysis on medical images like computer tomography scans (CT scan) and X-ray by means of different deep learning systems. This analysis discusses about structures developed for COVID-19 analysis via deep learning performances on Inception, VGG, Xception, Resnet models and provide insights and on data sets to train these neural networks. A comparative analysis is done for considering the better deep learning model for detection. The main aim of this paper is to ease medical experts and help them to understand the ways of deep learning techniques and how they can be prospective used to combat COVID-19.</p></div>","PeriodicalId":100588,"journal":{"name":"Global Transitions Proceedings","volume":"2 2","pages":"Pages 559-565"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.gltp.2021.08.030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89737685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10