Global Transitions Proceedings最新文献_第9页

Comparative performance analysis for speech digit recognition based on MFCC and vector quantization 基于MFCC和矢量量化的语音数字识别性能比较分析

Global Transitions Proceedings Pub Date : 2021-11-01 Epub Date: 2021-08-12 DOI: 10.1016/j.gltp.2021.08.013

Datta Rakshith KS , Rudresh MD , Shashibhushsan G

{"title":"Comparative performance analysis for speech digit recognition based on MFCC and vector quantization","authors":"Datta Rakshith KS , Rudresh MD , Shashibhushsan G","doi":"10.1016/j.gltp.2021.08.013","DOIUrl":"10.1016/j.gltp.2021.08.013","url":null,"abstract":"<div><p>The main goal of this research work is to experimentally verify the importance of spoken Speech digit signal in person authentication in controlling applications. The motivation is based on the earlier work of demonstrating the feasibility of using spoken speech digit utterance signal for person security and controlling applications. This paper work also discusses the. Comparative analysis of the cepstral analysis with the mel frequency cepstral coefficient (MFCC) by using vector quantization feature matching technique. All digits speech digit from zero utterance to nine digit utterance data has been collected for 15 subjects in three different sessions. For the thus collected spoken speech digit data, the feature extraction techniques such as cepstral and MFCC were applied to extract the Cepstral and MFCC features. In the next stage of work vector quantization was used for feature matching for both Cepstral and MFCC features and performance were recorded for two different session data. By comparing the performance of Cepstral plus VQ with the MFCC plus VQ, we can conclude that feature extraction technique MFCC gives the better performance than cepstral feature for spoken digit utterance data.</p></div>","PeriodicalId":100588,"journal":{"name":"Global Transitions Proceedings","volume":"2 2","pages":"Pages 513-519"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.gltp.2021.08.013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85639383","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: 11

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 Epub Date: 2021-08-12 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

Sentimental analysis of Indian regional languages on social media 印度地区语言在社交媒体上的情感分析

Global Transitions Proceedings Pub Date : 2021-11-01 Epub Date: 2021-08-13 DOI: 10.1016/j.gltp.2021.08.039

Kakuthota Rakshitha, Ramalingam H M, M Pavithra, Advi H D, Maithri Hegde

{"title":"Sentimental analysis of Indian regional languages on social media","authors":"Kakuthota Rakshitha, Ramalingam H M, M Pavithra, Advi H D, Maithri Hegde","doi":"10.1016/j.gltp.2021.08.039","DOIUrl":"10.1016/j.gltp.2021.08.039","url":null,"abstract":"<div><p>The idea of sentimental analysis is getting attention for the last few years. The key challenges in a sentimental analysis are the collection of huge data from the sources, applying appropriate algorithms or techniques, and classifying them into different sentiments. In this fast-spreading internet world, social media provides a platform for individuals to express their sentiments. With the changing ways of things in different areas in our day-to-day life, the way of expressing one's view or opinion has also changed. People tend to express themselves in their regional language or in a way convenient to them. These individual reviews play an important role in decision-making. With the huge amount of data that is obtained on social media, it is of no use if the opinions are not classified based on their sentiments. This paper provides information about the tweets posted by the customer are positive, negative, or neutral. For this the proposed model first scrape the tweets from Twitter by using Twitter APIs, then later by using text blob, the customer reviews are given different sentiment scores and classify them as positive, negative, or neutral by using text classification model.</p><p>This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/)</p><p>Peer-review under responsibility of the scientific committee of the 8th International Conference on Through-Life Engineering Service – TESConf 2019.</p></div>","PeriodicalId":100588,"journal":{"name":"Global Transitions Proceedings","volume":"2 2","pages":"Pages 414-420"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.gltp.2021.08.039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90688845","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: 18

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 Epub Date: 2021-08-13 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

Agroxpert - Farmer assistant agroexpert—农民助理

Global Transitions Proceedings Pub Date : 2021-11-01 Epub Date: 2021-08-12 DOI: 10.1016/j.gltp.2021.08.016

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

引用次数: 6

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 Epub Date: 2021-08-13 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

Deep Learning Precision Farming: Grapes and Mango Leaf Disease Detection by Transfer Learning 国际计算系统及其应用会议(ICCSA - 2021)

Global Transitions Proceedings Pub Date : 2021-11-01 Epub Date: 2021-08-12 DOI: 10.1016/j.gltp.2021.08.002

U Sanath Rao , R Swathi , V Sanjana , L Arpitha , K Chandrasekhar , Chinmayi , Pramod Kumar Naik

引用次数: 22

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 Epub Date: 2021-08-18 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 Epub Date: 2021-08-12 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 Epub Date: 2021-08-22 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