News. Phi Delta Epsilon最新文献_第5页

Active Data Collection of Health Data in Mobile Devices 移动设备中健康数据的主动数据收集

News. Phi Delta Epsilon Pub Date : 2022-01-01 DOI: 10.5220/0011300700003277

A. Machado, Heitor Cardoso, Plinio Moreno, Alexandre Bernardino

引用次数: 0

Bridging the Gap between Real and Synthetic Traffic Sign Repositories 弥合真实和合成交通标志存储库之间的差距

News. Phi Delta Epsilon Pub Date : 2022-01-01 DOI: 10.5220/0011301100003277

D. Silva, António Fernandes

引用次数: 0

Traffic Sign Repositories: Bridging the Gap Between Real and Synthetic Data 交通标志库:弥合真实数据和合成数据之间的差距

News. Phi Delta Epsilon Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-37317-6_4

D. Silva, A. Fernandes

引用次数: 0

An Ensemble-based Dimensionality Reduction for Service Monitoring Time-series 基于集成的服务监控时间序列降维方法

News. Phi Delta Epsilon Pub Date : 2022-01-01 DOI: 10.5220/0011273700003277

Farzana Anowar, S. Sadaoui, Hardi Dalal

引用次数: 1

Analysis of Ensemble of Neural Networks and Fuzzy Logic Classification in Process of Semantic Segmentation of Martian Geomorphological Settings 火星地貌设置语义分割过程中神经网络集成与模糊逻辑分类分析

News. Phi Delta Epsilon Pub Date : 2022-01-01 DOI: 10.5220/0011315200003277

Kamil Choromański, J. Kozakiewicz, M. Sobucki, M. Pilarska-Mazurek, R. Olszewski

引用次数: 0

Convolutional Neural Networks for Structural Damage Localization on Digital Twins 基于卷积神经网络的数字孪生结构损伤定位

News. Phi Delta Epsilon Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-37317-6_5

Marco Parola, Federico A. Galatolo, Matteo Torzoni, M. G. Cimino

引用次数: 0

Linguistic Feature-based Classification for Anger and Anticipation using Machine Learning 基于语言特征的愤怒和预期分类使用机器学习

News. Phi Delta Epsilon Pub Date : 2022-01-01 DOI: 10.5220/0011289300003277

K. Ramakrishnan, Vimala Balakrishnan, Kumanan Govaichelvan

{"title":"Linguistic Feature-based Classification for Anger and Anticipation using Machine Learning","authors":"K. Ramakrishnan, Vimala Balakrishnan, Kumanan Govaichelvan","doi":"10.5220/0011289300003277","DOIUrl":"https://doi.org/10.5220/0011289300003277","url":null,"abstract":"Growing number of online discourses enables the development of emotion mining models using natural language processing techniques. However, language diversity and cultural disparity alters the sentiment orientation of words depending on the community and context. Therefore, this study investigates the impacts of linguistic features, namely lexical and syntactic, in predicting the presence two emotions among Malaysian YouTube users, anger and anticipation. Term Frequency-Inverse Document Frequency (TF-IDF), Unigrams, Bigrams and Parts-of-Speech Tags were used as features to observe the classification performance. The dataset used in this study contains 2500 YouTube comments by Malaysian users on 46 Covid-19 related videos. Comments were extracted from three prominent Malaysian-centric English news channels: Channel News Asia (CNA), The Star News, and New Strait Times, ranging from 16 March 2020 - 30 April 2020 (i.e., first lockdown phase). Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbour and Multinomial Naive Bayes were the six classification algorithms tested, with results indicating Support Vector Machine with TF-IDF provided the best performance, achieving accuracy of 76% and 73% for anger and anticipation, respectively.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"46 1","pages":"140-147"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90790926","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

RRConvNet: Recursive-residual Network for Real-life Character Image Recognition RRConvNet:用于现实人物图像识别的递归残差网络

News. Phi Delta Epsilon Pub Date : 2022-01-01 DOI: 10.5220/0011270400003277

Tadele Mengiste, B. Belay, Bezawork Tilahun, Tsiyon Worku, Tesfa Tegegne

{"title":"RRConvNet: Recursive-residual Network for Real-life Character Image Recognition","authors":"Tadele Mengiste, B. Belay, Bezawork Tilahun, Tsiyon Worku, Tesfa Tegegne","doi":"10.5220/0011270400003277","DOIUrl":"https://doi.org/10.5220/0011270400003277","url":null,"abstract":": Variations in fonts, styles, and ways to write a character have been the major bottlenecks in OCR research. Such problems are swiftly tackled through advancements in deep neural networks (DNNs). However, the number of network parameters and feature reusability are still the issues when applying Deep Convolutional Neural networks(DCNNs) for character image recognition. To address these challenges, in this paper, we propose an extensible and recursive-residual ConvNet architecture (RRConvNet) for real-life character image recognition. Unlike the standard DCCNs, RRConvNet incorporates two extensions: recursive-supervision and skip-connection. To enhance the recognition performance and reduce the number of parameters for extra convolutions, layers of up to three recursions are proposed. Feature maps are used after each recursion for reconstructing the target character. For all recursions of the reconstruction method, the reconstruction layers are the same. The second enhancement is to use a short skip-connection from the input to the reconstruction output layer to reuse the character features maps that are already learned from the prior layer. This skip-connection could be also used as an alternative path for gradients where the gradient is too small. With an overall character recognition accuracy of 98.2 percent, the proposed method achieves a state-of-the-art result on both publicly available and private test datasets.","PeriodicalId":88612,"journal":{"name":"News. Phi Delta Epsilon","volume":"57 1","pages":"110-116"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89143033","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

Identifying Users' Emotional States through Keystroke Dynamics 通过击键动力学识别用户的情绪状态

News. Phi Delta Epsilon Pub Date : 2022-01-01 DOI: 10.5220/0011367300003277

S. Marrone, Carlo Sansone

引用次数: 1

Calculating the Credibility of Test Samples at Inference by a Layer-wise Activation Cluster Analysis of Convolutional Neural Networks 用卷积神经网络分层激活聚类分析计算推理时测试样本的可信度

News. Phi Delta Epsilon Pub Date : 2022-01-01 DOI: 10.5220/0011274000003277

Daniel Lehmann, M. Ebner

引用次数: 0