2021 the 5th International Conference on Information System and Data Mining最新文献_第2页

On Biclique Connectivity in Bipartite Graphs and Recommendation Systems 二部图的Biclique连通性与推荐系统

2021 the 5th International Conference on Information System and Data Mining Pub Date : 2021-05-27 DOI: 10.1145/3471287.3471302

Cristina Maier, D. Simovici

引用次数: 2

Actor-Critic Neural Network Based Finite-time Control for Uncertain Robotic Systems 基于Actor-Critic神经网络的不确定机器人系统有限时间控制

2021 the 5th International Conference on Information System and Data Mining Pub Date : 2021-05-27 DOI: 10.1145/3471287.3471288

Changyi Lei

引用次数: 0

Use of Efficient Machine Learning Techniques in the Identification of Patients with Heart Diseases 高效机器学习技术在心脏病患者识别中的应用

2021 the 5th International Conference on Information System and Data Mining Pub Date : 2021-05-27 DOI: 10.1145/3471287.3471297

Pronab Ghosh, S. Azam, Asif Karim, M. Jonkman, Md. Zahid Hasan

{"title":"Use of Efficient Machine Learning Techniques in the Identification of Patients with Heart Diseases","authors":"Pronab Ghosh, S. Azam, Asif Karim, M. Jonkman, Md. Zahid Hasan","doi":"10.1145/3471287.3471297","DOIUrl":"https://doi.org/10.1145/3471287.3471297","url":null,"abstract":"Cardiovascular disease has become one of the world's major causes of death. Accurate and timely diagnosis is of crucial importance. We constructed an intelligent diagnostic framework for prediction of heart disease, using the Cleveland Heart disease dataset. We have used three machine learning approaches, Decision Tree (DT), K- Nearest Neighbor (KNN), and Random Forest (RF) in combination with different sets of features. We have applied the three techniques to the full set of features, to a set of ten features selected by “Pearson's Correlation” technique and to a set of six features selected by the Relief algorithm. Results were evaluated based on accuracy, precision, sensitivity, and several other indices. The best results were obtained with the combination of the RF classifier and the features selected by Relief achieving an accuracy of 98.36%. This could even further be improved by employing a 5-fold Cross Validation (CV) approach, resulting in an accuracy of 99.337%. CCS CONCEPTS • Applied computing • Life and medical sciences • Health informatics","PeriodicalId":306474,"journal":{"name":"2021 the 5th International Conference on Information System and Data Mining","volume":"56 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126126126","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

SF-U-Net: Using Accurate Shape Estimation and Feature Restoration to Improve Retinal Vessel Segmentation SF-U-Net:利用精确形状估计和特征恢复改进视网膜血管分割

2021 the 5th International Conference on Information System and Data Mining Pub Date : 2021-05-27 DOI: 10.1145/3471287.3471377

Wen-Chun Yang

{"title":"SF-U-Net: Using Accurate Shape Estimation and Feature Restoration to Improve Retinal Vessel Segmentation","authors":"Wen-Chun Yang","doi":"10.1145/3471287.3471377","DOIUrl":"https://doi.org/10.1145/3471287.3471377","url":null,"abstract":"The features of retinal blood vessels are very essential indicators playing an important part in the process of judging and diagnosing the eye diseases for doctors. Sometimes, these features can also be the indicators for the examination of hypertension, coronary heart disease and diabetes. However, retinal blood vessels are often very small and complex in distribution, which brings toughness to the doctors when doing the operations of the segmentation of retinal blood vessels. Although the deep learning manners represented by U-Net has performed very well in the field of the segmentation of the images of the retinal blood vessel in recent years, the above-mentioned inconvenience still cannot be effectively settled. For the purpose of improving the correct rate of the segmentation and settling the above-mentioned inconvenience we propose a network called SF-U-Net, which uses accurate shape estimation and feature restoration to achieve the improvement of the accuracy. We follow the structure of Fully Convolutional Networks (FCN) and Skip Connection of U-Net and use deformable convolution to accurately capture the shape of blood vessels when extracting features at the coding layer to overcome the problem of complex blood vessel distribution. At the decoding layer, we adopt a novel dual-stream up-sampling method to achieve accurate feature restoration. Experimental results show that our SF-U-Net is capable of improving the segmentation results of retinal blood vessels conspicuously. In the experiment, we use both fundus image datasets called DRIVE and CHASE-DB1 and the experimental results of multiple indicators on them surpass other deep-learning methods significantly. The experimental results of the SF-U-Net model on a variety of indicators on DRIVE dataset exceed the experimental performances of the currently most advanced methods. The mean accuracy is 0.9602 the area under the curve (AUC) is 0.9848 and the sensitivity is 0.8567.","PeriodicalId":306474,"journal":{"name":"2021 the 5th International Conference on Information System and Data Mining","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126413715","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

Rumor Remove Order Strategy on Social Networks 谣言移除社会网络上的订单策略

2021 the 5th International Conference on Information System and Data Mining Pub Date : 2021-05-27 DOI: 10.1145/3471287.3471294

Yuanda Wang, Haibo Wang, Shigang Chen, Ye Xia

{"title":"Rumor Remove Order Strategy on Social Networks","authors":"Yuanda Wang, Haibo Wang, Shigang Chen, Ye Xia","doi":"10.1145/3471287.3471294","DOIUrl":"https://doi.org/10.1145/3471287.3471294","url":null,"abstract":"Rumors are defined as widely spread talk with no reliable source to back it up. In modern society, the rumors are widely spreading on the social network. The spread of rumors poses great challenges for the society. A ”fake news” story can rile up your emotions and change your mood. Some rumors can even cause social panic and economic losses. As such, the influence of rumors can be far-reaching and long-lasting. Efficient and intelligent rumor control strategies are necessary to constrain the spread of rumors. Existing rumor control strategies are designed for controlling a single rumor. However, there are usually many rumors existing on social networks and only limited rumors can be removed at a time due to the limited detection capacity and CPU performance. Consequently, when dealing with multiple rumors, we should remove rumors in a certain order. We argue that the order of removing rumors matters as different rumors possess different properties, e.g., acceptance rate, propagation speed, etc. Unfortunately, to the best of our knowledge, there is no prior work on removing multiple rumors and the order of removing rumors. To this end, this paper proposes two novel rumor control strategies to remove the multiple rumors. We also extends the classical Susceptible Infected Recovered (SIR) model to simulate the dynamics of rumor propagation in a more practical manner. We evaluate the performance of strategies. The experiments show that our proposed rumor control strategies obviously outperform than benchmark strategy.","PeriodicalId":306474,"journal":{"name":"2021 the 5th International Conference on Information System and Data Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129067616","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

FOB-Net: A Semantic Segmentation Method Focusing on Boundary Information FOB-Net:一种基于边界信息的语义分割方法

2021 the 5th International Conference on Information System and Data Mining Pub Date : 2021-05-27 DOI: 10.1145/3471287.3471290

Jiayu Wang

引用次数: 1

A Survey of CNN and Facial Recognition Methods in the Age of COVID-19∗ CNN和人脸识别方法在COVID-19时代的研究*

2021 the 5th International Conference on Information System and Data Mining Pub Date : 2021-05-27 DOI: 10.1145/3471287.3471292

Adinma Chidumije, Fatima Gowher, Ehsan Kamalinejad, Justine Mercado, Jiwanjot Soni, Jiaofei Zhong

引用次数: 0

Ethnicity Based Consumer Buying Behavior Analysis and Prediction on Online Clothing Platforms in Sri Lanka 基于种族的斯里兰卡在线服装平台消费者购买行为分析与预测

2021 the 5th International Conference on Information System and Data Mining Pub Date : 2021-05-27 DOI: 10.1145/3471287.3471291

T. Ginige, K. Mahima

{"title":"Ethnicity Based Consumer Buying Behavior Analysis and Prediction on Online Clothing Platforms in Sri Lanka","authors":"T. Ginige, K. Mahima","doi":"10.1145/3471287.3471291","DOIUrl":"https://doi.org/10.1145/3471287.3471291","url":null,"abstract":"With the busy lives, people feel uncomfortable and have less time to go to a shop and buy things nowadays. E-Shopping is rapidly growing during the last decade simply because people are able to purchase items from these online platforms 24x7. Online shopping is a process where consumers buy goods and services from a shop or a seller over the internet. Popular websites like AlieExpress, eBay are good examples of that. Presently this Online-shopping concept is also very popular in Sri Lanka. In this research, the authors mainly concentrate on the Sri Lankan Online Clothing stores and try to examine the online consumers buying behavior based on ethnicity. The authors chose Sri Lanka since Sri Lanka is a multi-ethnic country. There are mainly four ethnicities live in Sri Lanka. They are Sinhalese, Tamils, Muslims, and Burghers. For this research mainly Sinhalese and Tamils, the two main ethnicities are considered. In this research, the study authors analyze the buying behavior of those two ethnicities such as cloth types, favorite colors in online clothing shopping platforms. Moreover, in this research, authors implement classification models that can predict the cloth type and the color of the clothes based on consumers' ethnicities. The main benefit of this research would go to Sri Lankan online sellers. They would be able to get a clear understanding of the buying behaviors and the expectations of the consumers based on their ethnicities. Moreover, from this, sellers could improve their sales and online shopping users can get an attractive and good online shopping experience.","PeriodicalId":306474,"journal":{"name":"2021 the 5th International Conference on Information System and Data Mining","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114451720","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

Predicting Water Quality Parameters in Lake Pontchartrain using Machine Learning: A comparison on K-Nearest Neighbors, Decision Trees, and Neural Networks to Predict Water Quality 使用机器学习预测庞恰特雷恩湖水质参数:k近邻、决策树和神经网络预测水质的比较

2021 the 5th International Conference on Information System and Data Mining Pub Date : 2021-05-27 DOI: 10.1145/3471287.3471308

A. Daniels, C. Koutsougeras

{"title":"Predicting Water Quality Parameters in Lake Pontchartrain using Machine Learning: A comparison on K-Nearest Neighbors, Decision Trees, and Neural Networks to Predict Water Quality","authors":"A. Daniels, C. Koutsougeras","doi":"10.1145/3471287.3471308","DOIUrl":"https://doi.org/10.1145/3471287.3471308","url":null,"abstract":"This work is about the use of machine learning methods to improve the monitoring of water quality. The work aims to use machine learning to predict the normal values of a quality indicator (pH, salinity, etc.). Upon significant deviation from actual measurements, monitoring scientists would be alerted to the need to inspect the water way more closely thereby reducing the possibility of missing a problem and speeding up determinations of issues regarding water quality. This study compares methods to predict water quality parameters using water data from Lake Pontchartrain in Southeast Louisiana. K-Nearest neighbors, decision trees, and an artificial neural network have been used to determine which method most accurately predicted water quality parameters such as pH, temperature, salinity, specific conductance, and dissolved oxygen. The decision tree and k-nearest neighbors algorithms produced similar results which were only slightly below the standard deviation of the data. However, a neural network was able to predict the values with a much higher accuracy.","PeriodicalId":306474,"journal":{"name":"2021 the 5th International Conference on Information System and Data Mining","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127262038","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

Data Mining Techniques in Direct Marketing on Imbalanced Data using Tomek Link Combined with Random Under-sampling 基于Tomek Link和随机欠抽样的不平衡直销数据挖掘技术

2021 the 5th International Conference on Information System and Data Mining Pub Date : 2021-05-27 DOI: 10.1145/3471287.3471299

Ümit Yılmaz, C. Gezer, Z. Aydın, V. C. Gungor

{"title":"Data Mining Techniques in Direct Marketing on Imbalanced Data using Tomek Link Combined with Random Under-sampling","authors":"Ümit Yılmaz, C. Gezer, Z. Aydın, V. C. Gungor","doi":"10.1145/3471287.3471299","DOIUrl":"https://doi.org/10.1145/3471287.3471299","url":null,"abstract":"Determining the potential customers is very important in direct marketing. Data mining techniques are one of the most important methods for companies to determine potential customers. However, since the number of potential customers is very low compared to the number of non-potential customers, there is a class imbalance problem that significantly affects the performance of data mining techniques. In this paper, different combinations of basic and advanced resampling techniques such as Synthetic Minority Oversampling Technique (SMOTE), Tomek Link, RUS, and ROS were evaluated to improve the performance of customer classification. Different feature selection techniques are used in order the decrease the number of non-informative features from the data such as Information Gain, Gain Ratio, Chi-squared, and Relief. Classification performance was compared and utilized using several data mining techniques, such as LightGBM, XGBoost, Gradient Boost, Random Forest, AdaBoost, ANN, Logistic Regression, Decision Trees, SVC, Bagging Classifier based on ROC AUC and sensitivity metrics. A combination of Tomek Link and Random Under-Sampling as a resampling technique and Chi-squared method as feature selection algorithm showed superior performance among the other combinations. Detailed performance evaluations demonstrated that with the proposed approach, LightGBM, which is a gradient boosting algorithm based on decision tree, gave the best results among the other classifiers with 0.947 sensitivity and 0.896 ROC AUC value.","PeriodicalId":306474,"journal":{"name":"2021 the 5th International Conference on Information System and Data Mining","volume":"IM-36 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132899537","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