2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)最新文献_第6页

Feature-Based Understanding of Human Emotions 基于特征的人类情感理解

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/IRI.2019.00051

Jonathon Moody, D. Jeong, Soo-Yeon Ji

引用次数: 1

Renovating Database Queries with Query AutoAwesome 使用Query AutoAwesome更新数据库查询

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/IRI.2019.00048

Chetna Suryavanshi, C. Dyreson, Jonathan Adams

引用次数: 0

Deep Multi-Task Learning for Interpretable Glaucoma Detection 可解释青光眼检测的深度多任务学习

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/IRI.2019.00037

Nooshin Mojab, V. Noroozi, Philip S. Yu, J. Hallak

{"title":"Deep Multi-Task Learning for Interpretable Glaucoma Detection","authors":"Nooshin Mojab, V. Noroozi, Philip S. Yu, J. Hallak","doi":"10.1109/IRI.2019.00037","DOIUrl":"https://doi.org/10.1109/IRI.2019.00037","url":null,"abstract":"Glaucoma is one of the leading causes of blindness worldwide. The rising prevalence of glaucoma, with our aging population, increases the need to develop automated systems that can aid physicians in early detection, ultimately preventing vision loss. Clinical interpretability and adequately labeled data present two major challenges for existing deep learning algorithms for automated glaucoma screening. We propose an interpretable multi-task model for glaucoma detection, called Interpretable Glaucoma Detector (InterGD). InterGD is composed of two major complementary components, segmentation and prediction modules. The segmentation module addresses the lack of clinical interpretability by locating the optic disc and optic cup regions in a fundus image. The prediction module utilizes a larger dataset to improve the performance of the segmentation task and thus mitigate the problem of limited labeled data in a segmentation module. The two components are effectively integrated into a unified multi-task framework allowing end-to-end training. To the best of our knowledge, this work is the first to incorporate interpretability into glaucoma screening employing deep learning methods. The experiments on three datasets, two public and one private, demonstrate the effectiveness of InterGD in achieving interpretable results for glaucoma screening.","PeriodicalId":295028,"journal":{"name":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115930607","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: 16

IRI 2019 Foreword

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/iri.2019.00005

引用次数: 0

Analysis of Temporal Relationships between ASD and Brain Activity through EEG and Machine Learning 通过脑电图和机器学习分析ASD与脑活动的时间关系

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/IRI.2019.00035

Yasith Jayawardana, M. Jaime, S. Jayarathna

{"title":"Analysis of Temporal Relationships between ASD and Brain Activity through EEG and Machine Learning","authors":"Yasith Jayawardana, M. Jaime, S. Jayarathna","doi":"10.1109/IRI.2019.00035","DOIUrl":"https://doi.org/10.1109/IRI.2019.00035","url":null,"abstract":"Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that impairs normative social cognitive and communicative function. Early diagnosis is crucial for the timely and efficacious treatment of ASD. The Autism Diagnostic Observation Schedule Second Edition (ADOS-2) is the current gold standard for diagnosing ASD. In this paper, we analyse the short-term and long-term relationships between ASD and brain activity using Electroencephalography (EEG) readings taken during the administration of ADOS-2. These readings were collected from 8 children diagnosed with ASD, and 9 low risk controls. We derive power spectrums for each electrode through frequency band decomposition and through wavelet transforms relative to a baseline, and generate two sets of training data that captures long-term and short-term trends respectively. We utilize machine learning models to predict the ASD diagnosis and the ADOS-2 scores, which provide an estimate for the presence of such trends. When evaluating short-term dependencies, we obtain a maximum of 56% accuracy of classification through linear models. Non-linear models provide a classification above 92% accuracy, and predicted ADOS-2 scores within an RMSE of 4. We use a CNN model to evaluate the long-term trends, and obtain a classification accuracy above 90%. Our findings have implications for using EEG as a non-invasive bio-marker for ASD with minimal feature manipulation and computational overhead.","PeriodicalId":295028,"journal":{"name":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115531495","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: 17

Deep Learning and Data Sampling with Imbalanced Big Data 不平衡大数据下的深度学习与数据采样

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/IRI.2019.00038

Justin M. Johnson, T. Khoshgoftaar

{"title":"Deep Learning and Data Sampling with Imbalanced Big Data","authors":"Justin M. Johnson, T. Khoshgoftaar","doi":"10.1109/IRI.2019.00038","DOIUrl":"https://doi.org/10.1109/IRI.2019.00038","url":null,"abstract":"This study evaluates the use of deep learning and data sampling on a class-imbalanced Big Data problem, i.e. Medicare fraud detection. Medicare offers affordable health insurance to the elderly population and serves more than 15% of the United States population. To increase transparency and help reduce fraud, the Centers for Medicare and Medicaid Services (CMS) have made several data sets publicly available for analysis. Our research group has conducted several studies using CMS data and traditional machine learning algorithms (non-deep learning), but challenges associated with severe class imbalance leave room for improvement. These previous studies serve as baselines as we employ deep neural networks with various data-sampling techniques to determine the efficacy of deep learning in addressing class imbalance. Random over-sampling (ROS), random under-sampling (RUS), and combinations of the two (ROS-RUS) are applied to study how varying levels of class imbalance impact model training and performance. Classwise performance is maximized by identifying optimal decision thresholds, and a strong linear relationship between minority class size and optimal threshold is observed. Results show that ROS significantly outperforms RUS, combining RUS and ROS both maximizes performance and efficiency with a 4 x speedup in training time, and the default threshold of 0.5 is never optimal when training data is imbalanced. To the best of our knowledge, this is the first study to provide statistical results comparing ROS, RUS, and ROS-RUS deep learning methods across a range of class distributions. Additional contributions include a unique analysis of thresholding as it relates to the minority class size and state-of-the-art performance on the given fraud detection task.","PeriodicalId":295028,"journal":{"name":"2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122047907","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: 31

An Ensemble Machine Learning Method for Single and Clustered Cervical Cell Classification 基于集成机器学习的单个和聚类宫颈细胞分类方法

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/IRI.2019.00043

Mohammed Kuko, M. Pourhomayoun

引用次数: 14

IRI 2019 Keynotes

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/iri.2019.00012

引用次数: 0

Preliminary Study of Relationship between Health Behavior and Breast Cancer 健康行为与乳腺癌关系的初步研究

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/IRI.2019.00069

Kuo-Chung Chu, Min-Yang Xiao, C. Chang, Cheng-Hsiang Hsiao, Yan-Chen Jiang, Po-Yao Tsai

引用次数: 1

Automated Neural Network Construction with Similarity Sensitive Evolutionary Algorithms 基于相似性敏感进化算法的自动神经网络构建

2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2019-07-01 DOI: 10.1109/IRI.2019.00052

Haiman Tian, Shu‐Ching Chen, M. Shyu, S. Rubin

引用次数: 4