2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第2页

Modifying LSTM Posteriors with Manner of Articulation Knowledge to Improve Speech Recognition Performance 利用发音知识方式修改LSTM后验以提高语音识别性能

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00122

Pradeep Rengaswamy, K. S. Rao

引用次数: 2

Predicting Secondary Equity Offerings (SEOs) Using Machine Learning 利用机器学习预测二级股权发行(seo)

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00198

Linlin Cui, Jianhua Chen, Wentao Wu

{"title":"Predicting Secondary Equity Offerings (SEOs) Using Machine Learning","authors":"Linlin Cui, Jianhua Chen, Wentao Wu","doi":"10.1109/ICMLA.2018.00198","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00198","url":null,"abstract":"This paper explores the application of machine learning techniques in finance to predict if a publicly-traded firm will issue a Seasoned Equity Offering (SEO) by analyzing the firm's 10-Q filing documents with Security and Exchange Commissions (SEC). Specifically, using the information content in the Management Discussion and Analysis section (MD&A) of 10-Q filings, we train five different algorithms, including Logistic Regression (LR), Support Vector Classification (SVC), Multinomial Naïve Bayes (NB), Artificial Neural Network (ANN) and Random Forest (RF). Two types of features, unigrams and phrases are considered. Term frequency-inverse document (TF-IDF) scores are used as independent variables in these models. Experimental results show that the accuracy of phrases-only models has a range of 0-2% improvement for LR, NB, and RF compared with unigrams-only models. The accuracy of phrase-only model for SVC is close to that of unigrams-only model. The 74.53% accuracy of unigrams-only model for SVC classifier performs the best among all tested classifiers. The precision of all models varies between 60% and 75%, while the recall varies between 55% and 85%. Further, we tune model parameters of one linear model (LR) and one non-linear model (RF) to see how these parameters will impact the models' performance. Finally, we apply RF to find the most important features on prediction and find that \"merger\" is the most important feature in both unigrams-only model and phrases-only model. We conclude that text mining with SEC financial document filings could be an effective tool to predict important corporate events such as SEO.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"25 3 1","pages":"1219-1224"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79714174","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

Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data 基于不平衡临床数据的叠置去噪自编码器死亡率风险预测

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00087

Zakhriya Alhassan, D. Budgen, Riyad Alshammari, Tahani Daghstani, A. McGough, N. A. Moubayed

{"title":"Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data","authors":"Zakhriya Alhassan, D. Budgen, Riyad Alshammari, Tahani Daghstani, A. McGough, N. A. Moubayed","doi":"10.1109/ICMLA.2018.00087","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00087","url":null,"abstract":"Clinical data, such as evaluations, treatments, vital sign and lab test results, are usually observed and recorded in hospital systems. Making use of such data to help physicians to evaluate the mortality risk of in-hospital patients provides an invaluable source of information that can ultimately help with improving healthcare services. In particular, quick and accurate predictions of mortality can be valuable for physicians who are making decisions about interventions. In this work we introduce the use of a predictive Deep Learning model to help evaluate the mortality risk for in-hospital patients. Stacked Denoising Autoencoder (SDA) has been trained using a unique time-stamped dataset (King Abdullah International Research Center – KAIMRC) which is naturally imbalanced. The results are compared to those from common deep learning approaches, using different methods for data balancing. The proposed model demonstrated here aims to overcome the problem of imbalanced data, and outperforms common deep learning approaches with an accuracy of 77.13% for the Recall macro","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"148 1","pages":"541-546"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88651825","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: 14

Semi-Supervised Deep Learning System for Epileptic Seizures Onset Prediction 半监督深度学习系统癫痫发作预测

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00191

Ahmed M. Abdelhameed, M. Bayoumi

{"title":"Semi-Supervised Deep Learning System for Epileptic Seizures Onset Prediction","authors":"Ahmed M. Abdelhameed, M. Bayoumi","doi":"10.1109/ICMLA.2018.00191","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00191","url":null,"abstract":"The advance prediction of seizures before its onset has been a challenging task for scientists for a long time. It is still the epileptic patients' hope to find an effective way of preventing seizures to improve the quality of their lives. In this paper, using an innovative mixing of unsupervised and supervised deep learning techniques, we propose a novel epileptic seizure prediction system using electroencephalogram (EEG) recordings from the human brains. The proposed system is built upon classifying between the interictal and the preictal brain states. The proposed system uses two-dimensional deep convolutional autoencoder for learning the best discriminative spatial features from the multichannel unlabeled raw EEG recordings. A Bidirectional Long Short-Term Memory recurrent neural network is used for classification based on the temporal information. To help achieve faster learning and reliable convergence for our system, the transfer learning technique is used for initializing the weights for the patient-specific networks. Within, up to one hour of prediction window, our system achieved an average sensitivity of 94.6% and average low false prediction alarm rate of 0.04FP/h which makes it one of the most efficient among state-of-the-art methods.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"17 1","pages":"1186-1191"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87288418","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: 21

Development of a Deep-Learning-Based Method for Breast Ultrasound Image Segmentation 基于深度学习的乳腺超声图像分割方法研究

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00179

Rania Almajalid, J. Shan, Yaodong Du, Ming Zhang

{"title":"Development of a Deep-Learning-Based Method for Breast Ultrasound Image Segmentation","authors":"Rania Almajalid, J. Shan, Yaodong Du, Ming Zhang","doi":"10.1109/ICMLA.2018.00179","DOIUrl":"https://doi.org/10.1109/ICMLA.2018.00179","url":null,"abstract":"Breast cancer is one of the deadliest cancers that cause women death globally. Ultrasound imaging is one of the commonly used diagnostic tools for detection and classification of breast abnormalities. In the past decades, computer-aided diagnosis (CAD) systems have been developed to improve the accuracy of diagnosis made by radiologists. In particular, automatic breast ultrasound (BUS) image segmentation is a critical step for cancer diagnosis using CAD. However, accurate tumor segmentation is still a challenge as a result of various ultrasound artifacts. This paper developed a novel segmentation framework based on deep learning architecture u-net, for breast ultrasound imaging. U-net is a convolutional neural network architecture designed for biology image segmentation with limited training data. It was originally proposed for neuronal structure segmentation in microscopy images. In our work, we modified and improved the method for BUS image segmentation. On a database of 221 BUS images, we first applied pre-processing techniques including contrast enhancement and speckle reduction to improve the image quality. Then the u-net model was trained and tested through two-fold cross-validation. In order to increase the size of training set, data augmentation strategies including rotation and elastic deformation were applied. Finally, a post-processing step that removed noisy region(s) from the segmentation result finalized the whole method. The area error metrics, dice coefficient, and similarity rate were calculated to evaluate the performance on the testing sets. We compared our method with another two fully automatic segmentation methods on the same dataset. Our method outperformed the other two significantly with the dice coefficient = 0.825 and similarity rate = 0.698. Experiment results showed that the modified u-net method is more robust and accurate in breast tumor segmentation for ultrasound images.","PeriodicalId":6533,"journal":{"name":"2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"11 1","pages":"1103-1108"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85251957","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: 59

Ensemble of Learning Project Productivity in Software Effort Based on Use Case Points 基于用例点的软件工作中学习项目生产力的集成

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00232

Mohammad Azzeh, A. B. Nassif, Shadi Banitaan, Cuauhtémoc López Martín

引用次数: 5

Classifying Eligibility Criteria in Clinical Trials Using Active Deep Learning 使用主动深度学习对临床试验中的资格标准进行分类

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00052

C. Chuan

引用次数: 5

Q-Learning Acceleration via State-Space Partitioning

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00050

Haoran Wei, Kevin Corder, Keith S. Decker

引用次数: 2

Time Series Neural Networks for Real Time Sign Language Translation 实时手语翻译的时间序列神经网络

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00043

Sujay S. Kumar, T. Wangyal, Varun Saboo, R. Srinath

引用次数: 23

Improving Web Application Firewalls through Anomaly Detection 通过异常检测改进Web应用防火墙

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI: 10.1109/ICMLA.2018.00124

Gustavo Betarte, Eduardo Giménez, R. Martínez, Álvaro Pardo

引用次数: 15