2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML)最新文献

Building Domain-Specific Lexicons: An Application to Financial News 构建特定领域词汇:在金融新闻中的应用

2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML) Pub Date : 2019-08-01 DOI: 10.1109/Deep-ML.2019.00013

Savaş Yıldırım, Dhanya Jothimani, Can Kavaklioglu, A. Bener

{"title":"Building Domain-Specific Lexicons: An Application to Financial News","authors":"Savaş Yıldırım, Dhanya Jothimani, Can Kavaklioglu, A. Bener","doi":"10.1109/Deep-ML.2019.00013","DOIUrl":"https://doi.org/10.1109/Deep-ML.2019.00013","url":null,"abstract":"Natural Language Processing (NLP) has gained attention in the recent years. Previous research (such as WordNet and Cyc) has focused on developing an all purpose (generalised) polarised lexicons. However, these lexicons do not provide much information in different domains such as Finance and Medical Sciences. Using these lexicons for text classification could affect the prediction accuracy. Therefore, there is a need for building domain-and context-specific lexicons. To achieve this, in this work, a label based propagation based word embedding algorithm has been proposed to obtain positive and negative lexicons. The proposed algorithm works on the principle of graph theory and word embedding. The proposed algorithm is tested on Dow Jones news wires text feed to classify the Financial news as hot and non-hot. Three classifiers, namely, Logistic Regression, Random Forest and XGBoost, employing polarised lexicons, seed words and random words were used. The performance of classifiers in all cases was evaluated using accuracy. Lexicons generated using the proposed approach were effective in classifying the Financial news articles as hot and non-hot compared to classifiers using seed words and random words. Proposed label propagation with word embedding algorithm generates context-specific lexicons, which aids in helps in better representation of text in natural processing tasks and avoids the problem of dimensionality.","PeriodicalId":228378,"journal":{"name":"2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127282795","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

Cursive Handwritten Text Recognition using Bi-Directional LSTMs: A Case Study on Urdu Handwriting 基于双向lstm的草书手写文本识别:以乌尔都语手写为例

2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML) Pub Date : 2019-08-01 DOI: 10.1109/Deep-ML.2019.00021

Shahbaz Hassan, Ayesha Irfan, Ali Mirza, I. Siddiqi

引用次数: 20

2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML) Pub Date : 2019-08-01 DOI: 10.1109/Deep-ML.2019.00017

Betul Ay, G. Aydin, Zeynep Koyun, Mehmet Demir

引用次数: 16

DEEP-ML 2019 Organizing Committee DEEP-ML 2019组委会

2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML) Pub Date : 2019-08-01 DOI: 10.1109/deep-ml.2019.00006

引用次数: 0

A Comparison of Clustering Measures on Raw Signals of Welding Production Data 焊接生产数据原始信号聚类方法的比较

2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML) Pub Date : 2019-08-01 DOI: 10.1109/Deep-ML.2019.00019

Selvine G. Mathias, Daniel Grossmann, G. Sequeira

{"title":"A Comparison of Clustering Measures on Raw Signals of Welding Production Data","authors":"Selvine G. Mathias, Daniel Grossmann, G. Sequeira","doi":"10.1109/Deep-ML.2019.00019","DOIUrl":"https://doi.org/10.1109/Deep-ML.2019.00019","url":null,"abstract":"Production data from industries today have a heterogeneous structure, which makes it difficult to analyze and derive some viable inferences. Because of the varying pattern of data, whether labeled or unlabeled, numerical or categorical, any strict standard or optimization procedure using production data is a difficult task. Applying machine learning (ML) algorithms to analyze production data has therefore become an essential requirement for industries. In this study, production data obtained from welding seams is used. We analyze raw signals of electrical current and voltage in the form of arrays obtained from welding processes by applying clustering algorithms. Each process is represented by a group number in the procured data and the corresponding welds of a group are divided into optimal number of clusters on the basis of results given by metrics such as Silhouette Scores and Adjusted Rand's Index. As a validation of the metrics, we use Davies-Bouldin Index to compare and optimize our results. We conclude that a multi-clustering technique can be devised to profile clusters of welding data using only current and voltage signals.","PeriodicalId":228378,"journal":{"name":"2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126325863","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

Interpretable Machine Learning in Healthcare through Generalized Additive Model with Pairwise Interactions (GA2M): Predicting Severe Retinopathy of Prematurity 通过具有两两相互作用的广义加性模型(GA2M)在医疗保健中的可解释机器学习:预测早产儿严重视网膜病变

2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML) Pub Date : 2019-08-01 DOI: 10.1109/Deep-ML.2019.00020

Tamer Karatekin, S. Sancak, G. Celik, S. Topçuoğlu, G. Karatekin, Pınar Kırcı, A. Okatan

引用次数: 17

A Deep Learning Based Distributed Smart Surveillance Architecture using Edge and Cloud Computing 基于边缘和云计算的深度学习分布式智能监控架构

2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML) Pub Date : 2019-08-01 DOI: 10.1109/Deep-ML.2019.00009

Halil Can Kaskavalci, Sezer Gören

引用次数: 20

Comparative Study of Neural Networks and Decision Trees for Application in Trading Financial Futures 神经网络与决策树在金融期货交易中的比较研究

2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML) Pub Date : 2019-08-01 DOI: 10.1109/Deep-ML.2019.00015

Saulius Blaziunas, A. Raudys

引用次数: 2

DEEP-ML 2019 Program Committee DEEP-ML 2019项目委员会

2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML) Pub Date : 2019-08-01 DOI: 10.1109/deep-ml.2019.00007

Ahmad Javaid

{"title":"DEEP-ML 2019 Program Committee","authors":"Ahmad Javaid","doi":"10.1109/deep-ml.2019.00007","DOIUrl":"https://doi.org/10.1109/deep-ml.2019.00007","url":null,"abstract":"Ahmad Javaid, The University of Toledo, Spain Angelo Genovese, Università Degli Studi Di Milano, Italy Atul Negi, University of Hyderabad, India Bahadorreza Ofoghi, The University of Melbourne, Australia Bruno Veloso, INESC Technology and Science, Porto, Portugal Changwei Hu, Yahoo Research, USA Cihan Varol Sam, Houston State University, USA Daniele Apiletti, Polytechnic University of Turin, Italy Dhiya Al-Jumeily, Liverpool John Moores University, UK Fahimeh Farahnakian, University of Turku, Finland George Vouros, UNIPI, Greece Gulustan DOGAN, Yildiz Technical University, Turkey Huiru (Jane) Zheng, Ulster University, UK Huseyin Seker, The University of Northumbria at Newcastle, UK Jims Marchang, Sheffield Hallam University, UK Jose de Jesus Rubio Avila, SEPI-ESIME UA-IPN, Mexico Lei Zhang, East China Normal University, China Mahardhika Pratama, Nanyang Technology University, Singapore Nizar Bouguila, Concordia University, Canada Noor Akhmad Setiawan, Universitas Gadjah Mada, Indonesia Rabiah Ahmad, Universiti Teknikal Malaysia, Malaysia Santanu Pal, Universität des Saarlandes, Germany Serap SAHIN, Izmir Institute of Technology, Turkey Sevil SEN, Hacettepe University, Turkey Sotiris Kotsiantis, University of Patras, Greece Sung-Bae Cho, Yonsei University, Korea Tomoyuki Uchida, Hiroshima City University, Japan Valentina Emilia Balas, Aurel Vlaicu University of Arad, Romania Rosangela Ballini, University of Campinas, Brazil Yevgeni Bodyanskiy, Kharkiv National University of Radio Electronics, Ukraine Giovanna Castellano, University of Bari, Italy Antonio Dourado, University of Coimbra, Portugal Christian Eitzinger, Profactor GmbH, Steyr-Gleink, Austria Jus Kocijan, University of Nova Gorica, Slovenia Daniel Leite, Federal University of Lavras, Brazil Chin Teng Lin, University of Technology Sydney, Australia Seiichi Ozawa, Kobe University, Japan Daniel Sanchez, University of Granada, Spain Daniela Zaharie, West University of Timisoara, Romania Dhanya Jothimani, Ryerson University, Canada","PeriodicalId":228378,"journal":{"name":"2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124374711","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

Risk Parity Models for Portfolio Optimization: A Study of the Toronto Stock Exchange 投资组合优化的风险平价模型:以多伦多证券交易所为例

2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML) Pub Date : 2019-08-01 DOI: 10.1109/Deep-ML.2019.00014

Dhanya Jothimani, A. Bener

引用次数: 2