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2018 International Conference on Machine Learning and Cybernetics (ICMLC)最新文献

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Research On Attribute Reduction Algorithm In Covering Granular Computing Model 覆盖颗粒计算模型中的属性约简算法研究
2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8527018
Jiaqing Zhou, H. Nie
{"title":"Research On Attribute Reduction Algorithm In Covering Granular Computing Model","authors":"Jiaqing Zhou, H. Nie","doi":"10.1109/ICMLC.2018.8527018","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8527018","url":null,"abstract":"The granular computing model is a very important model in data mining. Seven kinds of models have been proposed for the covering granular computing model. Among these models, the third kind of models was considered to be more reasonable model. However, there are not many solutions to attribute reduction based on the third kind of covering models. Aiming at the third kind of covering granular computing models, this paper discusses the concepts of covering related families and simplified covering related families, and gives a simplified calculation method for covering related families, and then calculates all attribute reductions from the simplified covering related families. Then, a heuristic method of attribute reduction is discussed. The feasibility of the proposed method is verified by an example, which provides a new method for attribute reduction in data mining.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126145122","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
Image Segmentation Algorithm Based On Clustering 基于聚类的图像分割算法
2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8527000
Hao Wu, Gongfa Li, Ying Sun, Bo Tao, Jianyi Kong, Shuang Xu
{"title":"Image Segmentation Algorithm Based On Clustering","authors":"Hao Wu, Gongfa Li, Ying Sun, Bo Tao, Jianyi Kong, Shuang Xu","doi":"10.1109/ICMLC.2018.8527000","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8527000","url":null,"abstract":"Image segmentation plays an important role in image processing. Image segmentation algorithms have been proposed as early as the last century, and constantly find and optimize various algorithms. The quality of the image segmentation algorithm determines the result of image analysis and image understanding. The principle, advantages and disadvantages of traditional image segmentation algorithms are briefly introduced in this paper. The variety of image segmentation algorithms is determined by the complexity of the image itself. In recent years, scholars continue to improve a variety of image segmentation algorithms, the paper introduces the improvement of fuzzy C-means algorithm and mean-shift algorithm. The fuzzy C-means algorithm does not consider the spatial information of the image. Put forward an fuzzy C-means algorithm based on membership correction is proposed, taking into account the high correlation of pixels in image segmentation. The mean shift algorithm converges slowly, and mean shift algorithm based on conjugate gradient method is proposed to improve the convergence speed of the algorithm.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126545822","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
Rough Period Estimation And Peak Prediction Of Stock Market Based On Multiple Sine Functions Extraction 基于多重正弦函数提取的股票市场粗周期估计与峰值预测
2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526942
Yunong Zhang, Zhongxian Xue, Tao Jing, Yingbiao Ling, Chengxu Ye
{"title":"Rough Period Estimation And Peak Prediction Of Stock Market Based On Multiple Sine Functions Extraction","authors":"Yunong Zhang, Zhongxian Xue, Tao Jing, Yingbiao Ling, Chengxu Ye","doi":"10.1109/ICMLC.2018.8526942","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8526942","url":null,"abstract":"With the development of economy, the stock market of China is booming rapidly. In recent years, a method called multiple sine functions extraction (MSFE) has been proposed, e.g., to predict the potential stock market. In this paper, we use the MSFE to roughly forecast the peak-point of Chinese stock market, in which the inputs are stock data of Shanghai Security Exchange (SSE) Composite Index from 1992 to 2015, and the outputs are potential peak-points of the stock market in the next ten years. Eventually, even though the stock market has cyclical fluctuations, it is corresponding to the rising economy development in China. In addition, we make the conclusion that the peak-point of the Chinese stock market in the next ten years may be around 11/10/2024 in the form of Month/Day/Year.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123657025","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
Establish A Predictive Model of Hypertension Complications 建立高血压并发症预测模型
2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526951
Sinkuo Chai, Lyu-Yi Wu, Sue-Ting Chang, Chun-Jung Lin, Yuh-Tzong Liu
{"title":"Establish A Predictive Model of Hypertension Complications","authors":"Sinkuo Chai, Lyu-Yi Wu, Sue-Ting Chang, Chun-Jung Lin, Yuh-Tzong Liu","doi":"10.1109/ICMLC.2018.8526951","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8526951","url":null,"abstract":"Health promotion and prevention of diseases have become the current trend. Health promotion includes health education and health policies, and the environment. It is about healthy behaviors [1]. Nowadays, society has changed from a pattern of mainly infectious diseases to a pattern of chronic diseases. The WHO has analyzed the death causes of people in the country recently. The top few diseases were caused by chronic diseases, cerebrovascular disease, diabetes, coronary heart disease, etc., are the common disease. These diseases have a wide spread, long duration, high cost and high health risk.The Health Promotion Administration in Taiwan, Ministry of Health and Welfare pointed out that hypertension is a major risk factor for heart disease, stroke, kidney disease, eye diseases and other diseases. According to statistics, the prevalence of hypertension in Taiwan over the age of 18 years is 24.1%, and about 4% people suffer from hypertension. The severity of hypertension affects the occurrence of complications. If the timing of complications can be known immediately or in advance, conducting early health education can enable patients to receive early treatment and can also reduce the medical resources used afterward.In this study, data mining technology was used to establish a predictive model for the complications of hypertension, and the data of subjective, objective, assessment and planning (SOAP) in the medical records was extracted, and the main influence factors were obtained through the decision tree and Bayesian classification method.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131830496","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
Probabilistic-Based Image Categorization Using Novel Visual Vocabulary 基于概率的新型视觉词汇图像分类
2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8527009
C. Kuo, C. Hsieh, Nai-Chung Yang, Yao-Sheng Hsieh
{"title":"Probabilistic-Based Image Categorization Using Novel Visual Vocabulary","authors":"C. Kuo, C. Hsieh, Nai-Chung Yang, Yao-Sheng Hsieh","doi":"10.1109/ICMLC.2018.8527009","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8527009","url":null,"abstract":"The visual words representation is widely applied to many multimedia applications. In this paper, we use a novel visual vocabulary developed in our previous work and propose a category-specific visual model for image categorization. The category-specific visual model is composed by macro and micro visual words description, respectively. We can categorize image by considering macro or micro content in a flexible way. Meanwhile, we will propose a probabilistic-based method to achieve effective and excellent image categorization. The performance evaluation for the proposed systems indicates that the new categorization scheme achieves promising results.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129942746","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
Research On Image Compression Technology Based On Bp Neural Network 基于Bp神经网络的图像压缩技术研究
2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8527016
Chengcheng Li, Gongfa Li, Ying Sun, Guozhang Jiang, Jianyi Kong, Shuang Xu
{"title":"Research On Image Compression Technology Based On Bp Neural Network","authors":"Chengcheng Li, Gongfa Li, Ying Sun, Guozhang Jiang, Jianyi Kong, Shuang Xu","doi":"10.1109/ICMLC.2018.8527016","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8527016","url":null,"abstract":"The traditional image compression technique uses the redundancy of the image data to compress the image without lossless coding by the corresponding coding techniques. Different compression coding techniques are redundant for different image data. Under the background of many new applications, the traditional image compression technology can no longer meet the requirement of further improving the quality of compressed images. In recent years, with the continuous development of artificial neural network technology, Some good properties of neural network, such as nonlinear, fault-tolerant, self-organizing and adaptive, has been widely applied in image processing technology, which greatly simplifies the complexity of image processing. This paper mainly introduces the basic principles of BP neural network, studies the application of neural network in image compression, carries out simulation experiments through MATLAB, and analyzes the feasibility and advantages of BP neural network applied to image compression.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"295 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134100475","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
Variable Neighborhood Programming For Evolving Discriminent Functions With Dynamic Thresholds 具有动态阈值的演化判别函数的变邻域规划
2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526925
Souhir Elleuch, B. Jarboui
{"title":"Variable Neighborhood Programming For Evolving Discriminent Functions With Dynamic Thresholds","authors":"Souhir Elleuch, B. Jarboui","doi":"10.1109/ICMLC.2018.8526925","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8526925","url":null,"abstract":"This paper aims to develop a new automatic programming system to solve multi-class classification problem. Our method is based on a recently proposed algorithm called variable neighborhood programming (VNP). VNP is a solution-based metaheuristic which evolves programs and classifiers. Numeric expression is one of classifier representation in automatic programming algorithms. In the case of binary classification, a translation can simply performed according to the sign of the output (negative: class 1, positive: class 2). However, when the number of classes is higher, setting the appropriate boundary values and fixing the optimal class order is more complicated. To solve this problem, we introduce a new local search which aims to find dynamically the more adequate thresholds for each classifier. To include the developed local search within VNP (and also within any automatic programming algorithm), a multidimensional schema is proposed to ensure the simultaneous optimization of the discriminant function (tree),the class order, and the corresponding boundaries. Experimental results on three real data sets show that the proposed approach is a good automatic programming tool, able to build a full classifier with high accuracy in a single run.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134423820","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
A Deep Learning Load Forcasting Method Based on Load Type Rcognition 基于负荷类型识别的深度学习负荷预测方法
2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8527022
Jingjie Yang, Qiang Wang
{"title":"A Deep Learning Load Forcasting Method Based on Load Type Rcognition","authors":"Jingjie Yang, Qiang Wang","doi":"10.1109/ICMLC.2018.8527022","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8527022","url":null,"abstract":"In this paper, a short-term load forecasting model based on load type recognition is established. According to the characteristics of electric power load, the load data can be divided into three types: peak load, valley load and normal load. During the forecasting process, the support vector machine recognition model is used to determine load type on the predicted time point. After then, load samples of the same type are selected as the training sample to establish deep learning forecasting model. Finally, the trained model is applied to forecasting the load value. The experimental comparison shows that its prediction accuracy is higher than that of other models.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132647153","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: 5
Javascript Malware Detection Using A High-Level Fuzzy Petri Net Javascript恶意软件检测使用高级模糊Petri网
2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8527036
V. Shen, Chin-Shan Wei, T. Juang
{"title":"Javascript Malware Detection Using A High-Level Fuzzy Petri Net","authors":"V. Shen, Chin-Shan Wei, T. Juang","doi":"10.1109/ICMLC.2018.8527036","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8527036","url":null,"abstract":"JavaScript is a scripting language for web pages that allows website creators to run any code they want when users visit their website. Therefore, malicious JavaScript is the greatest threats within the cyber world. This paper proposes a novel method of detecting the JavaScript malware by using a high-level fuzzy Petri net (HLFPN). Firstly, the web pages are crawled to get JavaScript files. Secondly, the malicious features are extracted from JavaScript code. Finally, an HLFPN is used to determine the malicious code. The experimental results can support the effectiveness of our approach.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132063905","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: 8
Real-Time Ear Detection Based On Embedded Systems 基于嵌入式系统的实时耳朵检测
2018 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2018-07-01 DOI: 10.1109/ICMLC.2018.8526987
Li Yuan, Fei Lu
{"title":"Real-Time Ear Detection Based On Embedded Systems","authors":"Li Yuan, Fei Lu","doi":"10.1109/ICMLC.2018.8526987","DOIUrl":"https://doi.org/10.1109/ICMLC.2018.8526987","url":null,"abstract":"Ear detection is the first step of ear recognition. This paper proposes a real-time ear detection system based on embedded systems. An improved YOLO network is proposed for ear detection. With the same network depth, the width of the improved yolov2-tiny network in YOLO has been reduced to a quarter of the conventional network. By introducing batch processing and reducing the regularization coefficient of the improved yolov2-tiny network, the detection accuracy has been improved and the detection time has been reduced. The proposed model is more applicable for embedded implementation. On Nvidia Jetson tx2 real-time detection of the ear has been realized. The image is captured by an external camera and the ear will be marked when the ear appears in the image. On Nvidia Jetson tx2, the real-time detection frame rate is 30 frames per second which can meet the real-application requirements.","PeriodicalId":309312,"journal":{"name":"2018 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132218601","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: 4
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