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2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)最新文献

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Pneumonia X-ray Imaging Classification Based on an Interpretable Machine Learning Model 基于可解释机器学习模型的肺炎x射线成像分类
2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00067
Luyu Zeng, Zhong Zheng, Rui Zhang
{"title":"Pneumonia X-ray Imaging Classification Based on an Interpretable Machine Learning Model","authors":"Luyu Zeng, Zhong Zheng, Rui Zhang","doi":"10.1109/CONF-SPML54095.2021.00067","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00067","url":null,"abstract":"The outbreak of Covld-19 has put tremendous pressure on medical systems around the world. The highly infectious nature of this respiratory disease challenges advanced diagnostic technology to achieve rapid, scalable, affordable, and high-precision testing. In previous studies, Tsiknakis used Convolutional Neural Network (CNN) and transfer learning to achieved high accuracy in distinguishing the lung X-ray images of Covid-19 infectors and healthy people. However, its accuracy is not so high in quaternary classification (Bacterial Pneumonia, Covidl9, Normal, and Viral Pneumonia). It can hardly distinguish between bacterial pneumonia and viral pneumonia. Based on CNN, transfer learning, and interpretable machine learning methods, this work precisely implements data processing and augmentation and adds a second binary classifier following a confidence level. In this way, the accuracy and recall rate of the quaternary classification are significantly improved, especially for bacterial pneumonia and viral pneumonia, and the model also becomes more interpretable.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126496752","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
Optimized Methods for Online Monitoring of L-Glutamic Acid Crystallization l -谷氨酸结晶在线监测方法的优化
2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00027
Timing Yang, Chen Jiang, Qi Meng
{"title":"Optimized Methods for Online Monitoring of L-Glutamic Acid Crystallization","authors":"Timing Yang, Chen Jiang, Qi Meng","doi":"10.1109/CONF-SPML54095.2021.00027","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00027","url":null,"abstract":"In order to monitor the crystallization process of L-glutamic acid online, a real-time detection method based on non-invasive image analysis has been proposed to obtain in-situ images, and a deep-learning based network Mask R-CNN is applied to detect target crystals in images. Considering deep-learning network requires an enormous amount of dataset with labelled region of interest (RoI) samples, this paper proposes semi-automatic labelling methods to reduce human work when generating the dataset. By applying another Mask R-CNN for labelling the dataset, human work can be reduced from labelling the whole dataset to filtering the detection results of the labeller Mask R-CNN. The final detection results prove the feasibility of this method. The proposed method is also proved to be more feasible and reliable than transfer learning.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122761439","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
An Overview on Remote Sensing Image Classification Methods with a Focus on Support Vector Machine 基于支持向量机的遥感图像分类方法综述
2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00019
Hao Li
{"title":"An Overview on Remote Sensing Image Classification Methods with a Focus on Support Vector Machine","authors":"Hao Li","doi":"10.1109/CONF-SPML54095.2021.00019","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00019","url":null,"abstract":"With the growing demand for better performance of remote sensing (RS) image classification, a variety of methods have been proposed in RS image classification field in recent years. In general, there are two categories of RS image classification methods: pixel-based (PB) approach and object-based (OB) approach. In this paper, RS image classification methods are reviewed from the perspective of PB approach and OB approach and, specifically, the development and characteristics of a promising methodology for RS image classification named support vector machine (SVM) are surveyed. SVM is particularly popular in the RS field since it can deal with small-sized training dataset and provide higher classification accuracy than some traditional methods like maximum likelihood classifier. Besides, SVM has advantages of high memory-efficiency and strong generalization. However, SVM-based approaches also suffer from some problems. For instance, SVM-based methods tend to overfit due to inappropriate choice of kernel functions and it is inefficient for them to determine the optimum kernel function parameters as well as to process hyperspectral images. This paper also proposes the improvement of SVM-based methods aiming to address the limitations and improve the performance of SVM in RS image classification field. Moreover, future directions for SVM in RS image classification field are presented, expecting to help researchers to find possible research focuses in the future.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127730353","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
Big Data Classification and Machine Learning Using Zillow Estimates 使用Zillow估计的大数据分类和机器学习
2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00056
Si-Hao Du, Yi. Gu, Yuewei Zhu
{"title":"Big Data Classification and Machine Learning Using Zillow Estimates","authors":"Si-Hao Du, Yi. Gu, Yuewei Zhu","doi":"10.1109/CONF-SPML54095.2021.00056","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00056","url":null,"abstract":"Zillow’s is a real estate company that relies on the estimated costs of a house to set its price. The log error of prediction is calculated by the log difference between the prediction and the actual sale price. Thusly, the goal of this work is trying to minimize this error in order to improve accuracy. Due to the fact that real estate dataset has multiple feature blanks, preprocessing methods of the data show large significance in this work. On the other hand, particularly important features are selected, and several machine learning models— Decision Tree, Random Forest, Linear Regression— are applied to predict. In conclusion, Linear Regression performs better than the other two models. Some future work, like feature engineering methods, can be done to further improve the accuracy.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132798521","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
A CNN-based Traffic Sign Detection and Classification Method Using Priori Knowledge 基于cnn的基于先验知识的交通标志检测与分类方法
2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00057
Linze Shi, Yuting Zhou
{"title":"A CNN-based Traffic Sign Detection and Classification Method Using Priori Knowledge","authors":"Linze Shi, Yuting Zhou","doi":"10.1109/CONF-SPML54095.2021.00057","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00057","url":null,"abstract":"Traffic sign detection and classification is one of the main tasks of the advanced driving assistance system (ADAS). It is an integral part of the automatic driving vehicle. How to improve the accuracy and detection speed of traffic sign recognition has always been the focus of research. To solve the above problems, a fast three-stage traffic sign detection and classification method is proposed in this paper to improve the algorithm accuracy. In the first stage, we develop a probability distribution model based on the color, location, and type of traffic signs as a priori information, which can drastically minimize the search range of signs and enhance detection efficiency. In the second stage, this paper proposes an image color segmentation method based on Gaussian mixture model (GMM) as the detection module, uses the YCbCr color model for image segmentation. The morphological closure is then performed to refine the segmented image. In the third stage, the classification module classifies the extracted target areas through deep convolutional neural network (CNN).","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133647463","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
Decision Making for Autonomous Vehicle at Single-Lane Road Under Uncertainties 不确定条件下单车道自动驾驶车辆的决策研究
2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00055
Yuyang Wang
{"title":"Decision Making for Autonomous Vehicle at Single-Lane Road Under Uncertainties","authors":"Yuyang Wang","doi":"10.1109/CONF-SPML54095.2021.00055","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00055","url":null,"abstract":"Every year, the negligence of drivers leads to many accidents. According to World Health Organization, approximately 1.3 million people die each year due to road traffic crashes. Safety is the main factor driving the growth of demand for autonomous vehicles. When vehicles go on the road, decision-making plays a crucial role in the autonomous driving system. This paper proposes an approach based on the value-iteration for Markov Decision Process to train the autonomous car to drive appropriately on the single-track road. By following the optimal policy from value-iteration, the simulation on CARLO shows the results of decision-making for autonomous vehicles under a single-track road scenario. This work makes a contribution on decision-making for cars at single-lane road.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117296060","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
A Method for Generating PSF Based on 2-D Fast Fourier Transform 基于二维快速傅里叶变换的PSF生成方法
2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00051
Chixun Zhang
{"title":"A Method for Generating PSF Based on 2-D Fast Fourier Transform","authors":"Chixun Zhang","doi":"10.1109/CONF-SPML54095.2021.00051","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00051","url":null,"abstract":"In recent years, high-resolution optical microscopy has developed rapidly and its resolution has been increasing, and the point spread function directly affects the resolution. In this paper I generate a point spread function for an aperture-based imaging system (a lens with a shaped aperture). I also generate a flat-top signal (uniformly illuminated circular unobstructed aperture) and a pupil-masked two-dimensional Fourier transform and pass through an inverse oscillation filter, and compare them by analyzing the centrality of the spectrum, frequency distribution, and energy distribution.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"30 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124817137","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 Optimizing Facial Expression Recognition Based on Convolutional Neural Network 基于卷积神经网络的面部表情识别优化研究
2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00066
Zirui Leng
{"title":"Research on Optimizing Facial Expression Recognition Based on Convolutional Neural Network","authors":"Zirui Leng","doi":"10.1109/CONF-SPML54095.2021.00066","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00066","url":null,"abstract":"With the development of deep learning in recent years, artificial intelligence has been widely applied in daily lives, industries, and services, which has attracted widespread attention. Based on the above application, this paper studies the typical application technology of artificial intelligence, and builds an “emotional intelligence” model using traditional facial emotion recognition as an example, accelerating the response of the model as much as possible while ensuring correct recognition.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122115262","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 Comprehensive Review of Deep Learning-Based COVID-19 Detection Mechanisms Using CT Images 基于CT图像的深度学习COVID-19检测机制综述
2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00029
Bohao Zhang
{"title":"A Comprehensive Review of Deep Learning-Based COVID-19 Detection Mechanisms Using CT Images","authors":"Bohao Zhang","doi":"10.1109/CONF-SPML54095.2021.00029","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00029","url":null,"abstract":"The diagnosis of COVID-19 has become a highly focused research area that captures researchers’ attention worldwide. Although the results of RT-PCR have been regarded as the golden standard for diagnosing COVID-19, CT-based diagnostic systems also have their unique advantages, attracting numerous researchers continuously into the area of developing deep learning-based diagnostic systems that utilize CT images. This paper is committed to presenting a comprehensive review, including current dynamics, generalized framework and useful resources. To capture the pattern of the developed methods, this paper introduces a generalized framework containing two stages: segmentation and classification. Furthermore, various valuable online resources have also been collected to provide more datasets, existing implementations of diagnostic systems, and commonly adopted evaluation metrics to researchers that are new to this area for their better adaptation and contribution to this meaningful, life-changing field.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129663423","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
A Lightweight Phishing Website Detection Algorithm by Machine Learning 基于机器学习的轻量级网络钓鱼网站检测算法
2021 International Conference on Signal Processing and Machine Learning (CONF-SPML) Pub Date : 2021-11-01 DOI: 10.1109/CONF-SPML54095.2021.00054
Chenyu Gu
{"title":"A Lightweight Phishing Website Detection Algorithm by Machine Learning","authors":"Chenyu Gu","doi":"10.1109/CONF-SPML54095.2021.00054","DOIUrl":"https://doi.org/10.1109/CONF-SPML54095.2021.00054","url":null,"abstract":"With the rapid development of the Internet, phishing websites now show the characteristics of short life cycle and low construction cost, which leads to a large amount of data brought by the detection of phishing websites for URL (uniform resource locator). It will also lead to increased retrieval time and decreased detection speed. In order to deal with diverse, complex and hidden phishing websites, this paper proposes a lightweight framework for detecting phishing websites. We first choose the faster Minhash signature to match URLs. On one hand, similarity detection is employed if the websites is suspicious. On the other hand, based on machine learning, the phishing websites can be finally determined by intention detection without similar sites.","PeriodicalId":415094,"journal":{"name":"2021 International Conference on Signal Processing and Machine Learning (CONF-SPML)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128900608","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
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