International Conference on Machine Learning and Computing最新文献_第9页

Fuzzy Adaptive Internal Model Control for a Pneumatic Muscle Actuator 气动肌肉执行器的模糊自适应内模控制

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318360

Xiong Zhang, Jiwei Hu, Zemin Liu

引用次数: 1

Text Deduplication with Minimum Loss Ratio 具有最小丢失率的文本重复数据删除

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318369

Youming Ge, Jiefeng Wu, Genan Dai, Yubao Liu

引用次数: 1

A Moderately Deep Convolutional Neural Network for Relation Extraction 一种用于关系提取的中等深度卷积神经网络

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318326

Xinyang Bing, Liu Shen, Liying Zheng

引用次数: 1

Deep Residual Network with Self Attention Improves Person Re-Identification Accuracy 基于自关注的深度残差网络提高了人再识别的准确性

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318324

Jean-Paul Ainam, Ke Qin, Guisong Liu, Guangchun Luo

{"title":"Deep Residual Network with Self Attention Improves Person Re-Identification Accuracy","authors":"Jean-Paul Ainam, Ke Qin, Guisong Liu, Guangchun Luo","doi":"10.1145/3318299.3318324","DOIUrl":"https://doi.org/10.1145/3318299.3318324","url":null,"abstract":"In this paper, we present an attention mechanism scheme to improve the person re-identification task. Inspired by biology, we propose Self Attention Grid (SAG) to discover the most informative parts from a high-resolution image using its internal representation. In particular, given an input image, the proposed model is fed with two copies of the same image and consists of two branches. The upper branch processes the high-resolution image and learns high dimensional feature representation while the lower branch processes the low-resolution image and learns a filtering attention grid. We apply a max filter operation to non-overlapping sub-regions on the high feature representation before element-wise multiplied with the output of the second branch. The feature maps of the second branch are subsequently weighted to reflect the importance of each patch of the grid using a softmax operation. Our attention module helps the network to learn the most discriminative visual features of multiple image regions and is specifically optimized to attend feature representation at different levels. Extensive experiments on three large-scale datasets show that our self-attention mechanism significantly improves the baseline model and outperforms various state-of-art models by a large margin.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"16 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127559437","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

Application of Machine Learning Methods in Pork Price Forecast 机器学习方法在猪肉价格预测中的应用

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318364

Zaixin Ma, Zhongmin Chen, Taotao Chen, Mingwei Du

{"title":"Application of Machine Learning Methods in Pork Price Forecast","authors":"Zaixin Ma, Zhongmin Chen, Taotao Chen, Mingwei Du","doi":"10.1145/3318299.3318364","DOIUrl":"https://doi.org/10.1145/3318299.3318364","url":null,"abstract":"With the improvement of people's living standards, people's consumption of meat is getting higher and higher, and pork has become the core of Chinese meat production and consumption structure. Among pig farmers, retail investors account for more than half, their risk resistance capacity is weak, and they are vulnerable to price shocks. The price of live pigs showed significant seasonal changes, and violent fluctuations not only affected the interests of various links in the pig industry chain and the welfare of consumers, but also affected the development of the entire Chinese pig industry. Effective hog price forecast which is conducive to social stability and unity can not only ensure the income of farmers, but also ensure relationship between supply and demand. The article synthesizes the main indicators related to pork prices in the Chinese pork market, applying DBN (Dynamic Bayesian network) method and the SVM (support vector machine) method, the BP neural network method, these Machine Learning methods, and compare with traditional methods of the ARIMA method, to establish a predictive model of pork prices. The experiment was conducted in R and Bayes Server using 2001-2016 price data from the National Bureau of Statistics. The price is forecasted and analysed, the prediction effects of the four models are compared in this paper. The results show that the accuracy of predicting the pork price based on DBN model is better than other methods, RMSE=1.200822, MAPE=1.137312, TIC=0.0351875, all belong to a minimum.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"327 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124301196","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

Human Emotion Recognition in Video Using Subtraction Pre-Processing 基于减法预处理的视频人类情感识别

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318321

Zhihao He, Tian Jin, Amlan Basu, J. Soraghan, G. D. Caterina, L. Petropoulakis

{"title":"Human Emotion Recognition in Video Using Subtraction Pre-Processing","authors":"Zhihao He, Tian Jin, Amlan Basu, J. Soraghan, G. D. Caterina, L. Petropoulakis","doi":"10.1145/3318299.3318321","DOIUrl":"https://doi.org/10.1145/3318299.3318321","url":null,"abstract":"In this paper, we describe a new image pre-processing method, which can show features or important information clearly. Deep learning methods have grown rapidly in the last ten years and have better performance than the traditional machine learning methods in many domains. Deep learning shows its powerful ability particular in difficult multi-classes classification challenges. Video Facial expression recognition is one of the most popular classification topics and will become essential in robotics and auto-motion fields. The new system presented is a combination of new video pre-processing and Convolutional Neural Network (CNN). The new pre-processing method is proposed because we believe individual emotions are dynamic, which means the change of the face is the key feature. RAVDESS is the video set used, to train and test the neural network. From RAVDESS dataset the video songs without audio are taken for focusing on video frames differences. The chosen video set has six different classes of emotions. Each video presents a sentence in a melodious way. Based on the chosen video set, the new system with a new pre-processing method has been designed and trained. Later, the classification result of the new method has been compared with others in which the same dataset for video emotion recognition was used.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126293858","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: 12

A Systematic Literature Review of Continuous Blood Glucose Monitoring and Suggesting the Quantity of Insulin or Artificial Pancreas (AP) for Diabetic Type 1 Patients 1型糖尿病患者持续血糖监测及建议胰岛素或人工胰腺(AP)用量的系统文献综述

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318352

Muhammad Asad, Usman Qamar, Aimal Khan, Rahmat Ullah Safdar

{"title":"A Systematic Literature Review of Continuous Blood Glucose Monitoring and Suggesting the Quantity of Insulin or Artificial Pancreas (AP) for Diabetic Type 1 Patients","authors":"Muhammad Asad, Usman Qamar, Aimal Khan, Rahmat Ullah Safdar","doi":"10.1145/3318299.3318352","DOIUrl":"https://doi.org/10.1145/3318299.3318352","url":null,"abstract":"Background: Diabetes Mellitus is one of the most common diseases, which is rapidly increasing worldwide. Early detection of Blood Glucose Level not only helps in better management of Diabetes Mellitus but also decreases the cost of treatment. In the recent past, numerous researches have been carried out to monitor blood glucose level which suggests the quantity of insulin i.e. artificial pancreas. Method: In this paper, we summarize and analyze the past work of continuous blood glucose monitoring and automatic insulin suggestion, in a systematic way. Particularly, 24 journal studies from 2015 to 2018 are identified and analyzed. The paper provided a dynamic study of insulin-glucose regulators by identifying some research questions and answering from the literature. Moreover, it provides brief of the methodology of each study and how it contributes towards this field. It also underlines the advantages of the methods used in past and how they lack in determining other aspects for achieving a completely autonomous, adaptive and individualized model. Results: A comprehensive investigation of the selected studies leads to identify four major areas i.e. Machine learning techniques (8 studies), MPC (6 studies), PID (2 studies), mixed (6) and others (2 studies).Conclusion: This study is helpful in opening a gateway for new researchers to have an overview of the past work on continuous glucose monitoring and insulin suggestion. It identifies the challenges in this particular domain in order to lay the foundation for future research. The survey discovers the most popular techniques used for blood glucose monitoring and insulin suggestion, exogenous or intravenous (Subcutaneous) or artificial pancreas. For future work, the nonlinear autoregressive neural network based model predictive controller is suggested.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126328141","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

A Comparative Study on Machine Learning Approaches to Thunderstorm Gale Identification 雷暴大风识别的机器学习方法比较研究

International Conference on Machine Learning and Computing Pub Date : 2019-02-22 DOI: 10.1145/3318299.3318317

Haifeng Li, Yan Li, Xutao Li, Yunming Ye, Xian Li, Pengfei Xie

{"title":"A Comparative Study on Machine Learning Approaches to Thunderstorm Gale Identification","authors":"Haifeng Li, Yan Li, Xutao Li, Yunming Ye, Xian Li, Pengfei Xie","doi":"10.1145/3318299.3318317","DOIUrl":"https://doi.org/10.1145/3318299.3318317","url":null,"abstract":"In this paper, we make a comparative study to examine the performance of different machine learning approaches for the thunderstorm gale identification. To this end, a thunderstorm gale benchmark dataset is constructed, which comprises radar images in Guangdong from 2015 to 2017. The corresponding wind velocities recorded by the automatic meteorological observation stations are utilized to offer the ground-truth. Based on the dataset, we evaluate the performance of Decision Tree Regressor (DT), Linear Regression (LR), Ridge regression, Lasso regression, Random Forest Regressor (RFR), K-nearest Neighbor Regressor (KNNR), Bayesian Ridge Regressor (BR), Adaboost Regressor (AR), Support Vector Regressor (SVR), Gradient Boosting Regressor (GBR), and Convolutional Neural Network (CNN). Ten important features are extracted to apply these approaches, except CNN, which include radar echo intensity, radar reflectivity factor, radar combined reflectivity, vertical integrated liquid, echo tops and their changes with respect to (w.r.t.) time. Experimental results demonstrate the machine learning approaches can effectively identify the thunderstorm gale, and the CNN model performs the best. Finally, a thunderstorm system is developed based on CNN model, which help meteorologists to identify thunderstorm gales in terms of radar images.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127158003","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

The power study about three statistics of alignment-free comparison based on AT-RICH model 基于AT-RICH模型的三种无对准比较统计量的幂函数研究

International Conference on Machine Learning and Computing Pub Date : 2015-07-12 DOI: 10.1109/ICMLC.2015.7340613

Meiliu Xue, Binhe Rui, Dongliu Bo, Xiangzang, Xiazhang Yu, Yaoliang Wen

{"title":"The power study about three statistics of alignment-free comparison based on AT-RICH model","authors":"Meiliu Xue, Binhe Rui, Dongliu Bo, Xiangzang, Xiazhang Yu, Yaoliang Wen","doi":"10.1109/ICMLC.2015.7340613","DOIUrl":"https://doi.org/10.1109/ICMLC.2015.7340613","url":null,"abstract":"Similarity comparison between two biological sequences is one of the main problems in computational biology research. A powerful statistical method D2 which depends on the joint k-tuples content in the two sequences, has been applied to the alignment-free sequences comparison. Two mutually independent random sequences under the null model have been produced, which is composed by AT-rich (P a =P t =0.33, P c =P g =0.17) distribution, and based on the null model, we got two foreground sequences with Bernoulli variables by a pattern transfer model. For the foreground sequences, by comparing local sequences pairs and then summing over all the local sequences pairs of certain length, and the local alignment-free of two sequences has been tested by statistics D2, D2star, D2shepp, then from the power of the three statistics, we can find the optimal parameters. The simulation results show that D2star is better than D2shepp, and D2 is relatively weak. We also analyze the power value distribution under different parameters, including Bernoulli variable g and tuple sizek and type I Error. At the same time by comparing the proposed local with globalalignment-freeabout D2star, and D2shepp under the same parameters, it showed that the power of local alignment-free based on D2star tends to 1 quickly with the increase of the length of the sequence, faster and more accurate than the global alignment.","PeriodicalId":164987,"journal":{"name":"International Conference on Machine Learning and Computing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132978619","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

Robust H∞ control for uncertain switched nonlinear singular systems using LMI approach 不确定切换非线性奇异系统的LMI鲁棒H∞控制

International Conference on Machine Learning and Computing Pub Date : 2015-07-12 DOI: 10.1109/ICMLC.2015.7340638

Na Liu, J. Qiu, Li-Jun Zhang

引用次数: 0