Proceedings of the 2019 4th International Conference on Machine Learning Technologies最新文献

Asynchronous Distributed Clustering Algorithm for Wireless Sensor Networks 无线传感器网络的异步分布式聚类算法

Proceedings of the 2019 4th International Conference on Machine Learning Technologies Pub Date : 2019-06-21 DOI: 10.1145/3340997.3341007

Cheng Qiao, Kenneth N. Brown

引用次数: 4

Diagnosis of Methylmalonic Acidemia using Machine Learning Methods 甲基丙二酸血症的机器学习诊断

Proceedings of the 2019 4th International Conference on Machine Learning Technologies Pub Date : 2019-06-21 DOI: 10.1145/3340997.3341000

Xin Li, Xiaoxing Yang, Wushao Wen

{"title":"Diagnosis of Methylmalonic Acidemia using Machine Learning Methods","authors":"Xin Li, Xiaoxing Yang, Wushao Wen","doi":"10.1145/3340997.3341000","DOIUrl":"https://doi.org/10.1145/3340997.3341000","url":null,"abstract":"Methylmalonic acidemia (MMA) is an autosomal recessive metabolic disorder. Traditional diagnosis needs physicians' personal level of professional medical knowledge and clinical experience. In this paper, we employ machine learning methods to diagnose MMA based on patients' laboratory blood tests and laboratory urine tests, in order to make a timely diagnosis and reduce dependence on physicians' personal level of professional medical knowledge and clinical experience. By comparing different machine learning algorithms for diagnosing MMA, we obtain the following conclusions: (a) machine learning methods can perform well for diagnosing MMA (all established predictive models obtain high accuracies and AUC values which are greater than 0.85 over all data sets, and some of these results are even more than 0.98); (b) random forest algorithm performs best among the compared algorithms; and (c) diagnosis based on the data combining both urine tests and blood tests is better than diagnosis based on single test alone in general. The conclusions show that applying machine learning algorithms to the diagnosis of MMA can achieve good performance. Thus, it is credible to build machine learning models to give an initial diagnosis without professional medical knowledge.","PeriodicalId":409906,"journal":{"name":"Proceedings of the 2019 4th International Conference on Machine Learning Technologies","volume":"518 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133634373","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

Design and Application of University Evaluation Performance Intelligent Analysis Based on Data Mining 基于数据挖掘的高校评价绩效智能分析的设计与应用

Proceedings of the 2019 4th International Conference on Machine Learning Technologies Pub Date : 2019-06-21 DOI: 10.1145/3340997.3341014

Cheng Wei

引用次数: 0

Aspect-based Sentiment Analysis on mobile phone reviews with LDA 基于方面的LDA手机评论情感分析

Proceedings of the 2019 4th International Conference on Machine Learning Technologies Pub Date : 2019-06-21 DOI: 10.1145/3340997.3341012

Ye Yiran, S. Srivastava

{"title":"Aspect-based Sentiment Analysis on mobile phone reviews with LDA","authors":"Ye Yiran, S. Srivastava","doi":"10.1145/3340997.3341012","DOIUrl":"https://doi.org/10.1145/3340997.3341012","url":null,"abstract":"With the maturation of e-commerce platform, online shopping has become an easy and preferable mode of shopping. As one of the largest e-commerce platforms worldwide, Amazon enjoy numerous user communities. Volumes of user-generated data of users' preferences and opinions towards products, usually for specific aspects of a commodity, popped up every day. Although loaded with information, these texts are often unstructured data that requires a thorough analysis for both consumers and manufactures to extract meaningful and relevant information. Traditional lexicon-based sentiment analysis considers polarity score of words but ignores the differences among aspects. Document level topic modeling help overcome these lacunae. In this paper, we claim that the aspects should also be weighted for highlighting significance of various aspects appropriate to a domain. Thus, manufacturers can understand what potential consumers may want as improvement in the forthcoming products. To showcase our framework, more than 400,000 Amazon unlocked phone reviews were collected as training data. LDA models were used to cluster topic words with their corresponding probability values. Based on the machine learning framework results, a corpus of nearly 1,000 Amazon reviews of a new mobile phone mode, iPhone X, was tested using this framework to perform topic labeling and sentiment analysis. Performance analysis was done using Confuse Matrix and F-measure.","PeriodicalId":409906,"journal":{"name":"Proceedings of the 2019 4th International Conference on Machine Learning Technologies","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115108209","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: 19

An Anomaly Detection Method for Cloud Service Platform 一种云服务平台的异常检测方法

Proceedings of the 2019 4th International Conference on Machine Learning Technologies Pub Date : 2019-06-21 DOI: 10.1145/3340997.3341005

P. Lou, Yun Yang, Junwei Yan

{"title":"An Anomaly Detection Method for Cloud Service Platform","authors":"P. Lou, Yun Yang, Junwei Yan","doi":"10.1145/3340997.3341005","DOIUrl":"https://doi.org/10.1145/3340997.3341005","url":null,"abstract":"The cloud service platform is an open platform designed to provide various users with application services. The reliability of the platform is threatened by anomalous access behaviors such as resource abuse, DDoS attacks etc. Detecting anomalous behaviors to access the cloud service platform is an essential task. In this paper, an anomaly detection method based on Max-min distance and Support vector data description (MMD-SVDD) is proposed. The method identifies anomalous user access behaviors using CPU/memory/disk/network related system resource metrics. It firstly uses MMD to divide servers in the cloud service platform into multi-clusters. The servers in each of the clusters have similar running environment and can share an anomaly detection model. This process can effectively reduce the detection scale and system resource consumption. Then, aiming at the problem of incomplete abnormal data samples, the anomaly detection models are built based on SVDD algorithm, which utilizes normal data samples to construct a hypersphere for each cluster. Finally, the anomalous behavior is identified via judging whether the target data falls outside the hypersphere. The method is applied in cloud service platform and the result shows that it can accurately identity anomalies with lower system resource consumption.","PeriodicalId":409906,"journal":{"name":"Proceedings of the 2019 4th International Conference on Machine Learning Technologies","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115250760","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: 1

Education and Digital Economy: Trends, Opportunities and Challenges 教育与数字经济:趋势、机遇与挑战

Proceedings of the 2019 4th International Conference on Machine Learning Technologies Pub Date : 2019-06-21 DOI: 10.1145/3340997.3341013

Gaurav Gupta

引用次数: 4

The Advantages of Blockchain Technology in Commercial Bank Operation and Management 区块链技术在商业银行运营管理中的优势

Proceedings of the 2019 4th International Conference on Machine Learning Technologies Pub Date : 2019-06-21 DOI: 10.1145/3340997.3341009

Binghui Wu, Tingting Duan

引用次数: 14

Opioid Abuse Prediction Based on Multi-output Support Vector Regression 基于多输出支持向量回归的阿片类药物滥用预测

Proceedings of the 2019 4th International Conference on Machine Learning Technologies Pub Date : 2019-06-21 DOI: 10.1145/3340997.3341006

Haifan Gong, C. Qian, Yue Wang, Jian-Ye Yang, Sheng Yi, Zichen Xu

引用次数: 6

Detecting Atrial Fibrillation from Single-Lead ECG Using Unbalanced Multi-classification Support Vector Machine 基于非平衡多分类支持向量机的单导联心房颤动检测

Proceedings of the 2019 4th International Conference on Machine Learning Technologies Pub Date : 2019-06-21 DOI: 10.1145/3340997.3341004

Xinming Mei, N. Rao, Quanchi Li, Cheng-Si Luo, Kipkurui Felix Biwott, Hongxiu Jiang

{"title":"Detecting Atrial Fibrillation from Single-Lead ECG Using Unbalanced Multi-classification Support Vector Machine","authors":"Xinming Mei, N. Rao, Quanchi Li, Cheng-Si Luo, Kipkurui Felix Biwott, Hongxiu Jiang","doi":"10.1145/3340997.3341004","DOIUrl":"https://doi.org/10.1145/3340997.3341004","url":null,"abstract":"Atrial fibrillation (AF) is an common arrhythmia. The incidence of AF has been increasing with the acceleration of urbanization and social aging. Therefore, the wearable ECG acquisition devices with single-lead ECG came out for early diagnosis, monitoring and management of AF. However, it is still great challenge to accurately detect AF from massive ECG data. This study proposed a method detecting AF from single-lead ECG signals based on unbalanced multi-classification support vector machine(SVM). The novel method first screened 73 effective features by correlation analysis from 110 candidate features, which have been confirmed to be associated with AF in literature. Then, an unbalanced four-class SVM classifier was designed to detect four types of ECG signals (including AF, other arrhythmia, artifactual and normal) based on the distribution of different types of ECG data. Finally, the data provided by the PhysioNet/Computing in Cardiology Challenge 2017 confirmed that the proposed method had a overall good performance compared with five other related methods. Also, the data from MIT Arrhythmia Database and the MIT Atrial Fibrillation Database confirmed the robustness of proposed method with AF detection score of > 0.97 and with the scores of > 0.9 in other arrhythmia, artifactual and normal. The proposed method has a good application prospect in AF aided diagnosis, monitoring and management of AF.","PeriodicalId":409906,"journal":{"name":"Proceedings of the 2019 4th International Conference on Machine Learning Technologies","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116491925","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

Detection of Crop Pests and Diseases Based on Deep Convolutional Neural Network and Improved Algorithm 基于深度卷积神经网络及改进算法的农作物病虫害检测

Proceedings of the 2019 4th International Conference on Machine Learning Technologies Pub Date : 2019-06-21 DOI: 10.1145/3340997.3341010

Jianyu Wu, Bo Li, Zhilu Wu

{"title":"Detection of Crop Pests and Diseases Based on Deep Convolutional Neural Network and Improved Algorithm","authors":"Jianyu Wu, Bo Li, Zhilu Wu","doi":"10.1145/3340997.3341010","DOIUrl":"https://doi.org/10.1145/3340997.3341010","url":null,"abstract":"Agriculture is not only China's primary industry but also the foundation of the national economy. The amount and quality of agricultural products are inextricably linked to people's daily life. The outbreak of pests and diseases in the field has a great impact on agricultural production, so it can be seen that the prevention and control of pests and diseases are very important. In order to control crop diseases and pests, this paper combines emerging machine learning techniques based on a large number of crop pest and disease pictures, and introduces two kinds of convolutional neural networks------AlexNet and GoogleNet to detect crop pests and diseases. Much work has been done to improve the algorithm, including proposing an improved network based on migration learning and data expansion, which greatly improves the accuracy of detection. Compared with the manual detection method and the traditional algorithm, the improved detection algorithm based on the deep convolutional neural network has the highest detection accuracy rate of 98.48% for 38 pests and diseases, which has higher efficiency, practicability, and accuracy.","PeriodicalId":409906,"journal":{"name":"Proceedings of the 2019 4th International Conference on Machine Learning Technologies","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125227475","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: 6