2020 International Conference on Machine Learning and Cybernetics (ICMLC)最新文献_第3页

Video-Based Vehicle Flow Detection Algorithm 基于视频的车辆流量检测算法

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469561

Cheng Xu, G. Ji, Bin Zhao

引用次数: 0

Semantic Segmentation of Brain Tumor from 3D Structural MRI Using U-Net Autoencoder 基于U-Net自编码器的三维结构MRI脑肿瘤语义分割

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469580

Maisha Farzana, Md. Jahid Hossain Any, Md. Tanzim Reza, M. Parvez

{"title":"Semantic Segmentation of Brain Tumor from 3D Structural MRI Using U-Net Autoencoder","authors":"Maisha Farzana, Md. Jahid Hossain Any, Md. Tanzim Reza, M. Parvez","doi":"10.1109/ICMLC51923.2020.9469580","DOIUrl":"https://doi.org/10.1109/ICMLC51923.2020.9469580","url":null,"abstract":"Automated semantic segmentation of brain tumors from 3D MRI images plays a significant role in medical image processing, monitoring and diagnosis. Early detection of these brain tumors is highly requisite for the treatment, diagnosis and surgical pre-planning of the anomalies. The physicians normally follow the manual way of delineation for diagnosis of tumors which is time consuming and requires too much knowledge of anatomy. To resolve these limitations, convolutional neural network (CNN) based U-Net autoencoder model is proposed which performs automated segmentation of brain tumors from 3D MRI brain images by extracting the key features of the tumor. Additionally, Image normalization, image augmentation, image binarization etc. are applied for data pre-processing. Later on, the model is applied to the new 3D MRI brain images to test the accuracy of it. Applying the proposed method, the accuracy is obtained upto 96.06% considering the 18 subjects. Finally, this approach is a well-structured model for segmenting the tumor region from MRI brain images as compare to the other existing models which may assist the physicians for better diagnosis and therefore, opening the door for more precise therapy and better treatment to the patient.","PeriodicalId":170815,"journal":{"name":"2020 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132706135","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

Novel Class Detection Using Hybrid Ensemble 基于混合集成的新型类检测

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469587

Diptangshu Pandit, Li Zhang, Kamlesh Mistry, Richard M. Jiang

{"title":"Novel Class Detection Using Hybrid Ensemble","authors":"Diptangshu Pandit, Li Zhang, Kamlesh Mistry, Richard M. Jiang","doi":"10.1109/ICMLC51923.2020.9469587","DOIUrl":"https://doi.org/10.1109/ICMLC51923.2020.9469587","url":null,"abstract":"In this research, we propose a hybrid meta-classifier for novel class detection. It is able to efficiently detect the arrival of novel unseen classes as well as tackle real-time data stream classification. Specifically, the proposed hybrid meta-classifler includes three ensemble models, i.e. class-specific, cluster-specific and complementary boosting ensemble classifiers. Distinctive training strategies are also proposed for the generation of effective and diversified ensemble classifiers. The weights of the above ensemble models and the threshold of the novel class confidence are subsequently optimized using a modified Firefly Algorithm, to enhance performance. The above proposed ensemble and optimization algorithms cooperate with each other to conduct the detection of novel unseen classes. Several UCI databases are employed for evaluation, i.e. The KDD Cup, Image Segmentation, Soybean Large, Glass and Iris databases. In comparison with the baseline meta-algorithms such as Boosting, Bagging and Stacking, our approach shows significantly enhanced performance with the increment of the number of novel classes for all the test data sets, which poses great challenges to the existing baseline ensemble methods.","PeriodicalId":170815,"journal":{"name":"2020 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133644214","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

Applying a Genetic Algorithm to Determine Premium Rate of Occupational Accident Insurance 应用遗传算法确定职业意外保险费率

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469570

Jia-Ching Ying, Chi-Kai Chan, Yen-Ting Chang

{"title":"Applying a Genetic Algorithm to Determine Premium Rate of Occupational Accident Insurance","authors":"Jia-Ching Ying, Chi-Kai Chan, Yen-Ting Chang","doi":"10.1109/ICMLC51923.2020.9469570","DOIUrl":"https://doi.org/10.1109/ICMLC51923.2020.9469570","url":null,"abstract":"At present, the Occupational Accident Labor Insurance premium rate is calculated based on the business categories in Taiwan. The premium rate is calculated as a combination of the experience rate and the manual rate for each business category. The traditional actuarial methods are based on many hypotheses to calculate future actual claims and adjust the rate for each business category. Unfortunately, with such adjustments, the risk level of the insured in the business category will be affected. To accurately estimate the size of actual losses for specific industries, we propose a genetic algorithm applied grouping to determine the premium rate for occupational accidents. The proposed approach has been evaluated using the real-world dataset from the Bureau of Labor Insurance in Taiwan that includes occupational accident insurance data from 2009 to 2015. The results demonstrate that the method is practicable at predicting the applicable premium. The proposed method differs from Taiwan's prevailing occupational accident premium rate calculation method. Moreover, it is efficient at selecting the best group of the Standard Industrial Classification from the genetic algorithm. Lastly, the accuracy of the estimates of the total claim amounts are analyzed.","PeriodicalId":170815,"journal":{"name":"2020 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124364689","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

Development of a Drawing Assistant System for Blind Users Using an Object-Oriented Graphic Description Language 用面向对象的图形描述语言开发盲人绘图辅助系统

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469041

N. Takagi, Takashi Suzuki, Tomoyuki Araki

{"title":"Development of a Drawing Assistant System for Blind Users Using an Object-Oriented Graphic Description Language","authors":"N. Takagi, Takashi Suzuki, Tomoyuki Araki","doi":"10.1109/ICMLC51923.2020.9469041","DOIUrl":"https://doi.org/10.1109/ICMLC51923.2020.9469041","url":null,"abstract":"With the development of information processing systems such as screen readers, the accessibility of various information to visually impaired people has been dramatically improved in recent years. In addition, InftyReader, an OCR system for mathematical expression recognition, is making it easier to access scientific documents with mathematical expressions. However, there are still large barriers for blind people to access visual information such as graphs and diagrams. In particular, it is almost impossible for blind people to generate precise figures without the assistance of sighted people. Therefore, we are developing a graphic description language available for the blind. In conventional graphic description languages, it was necessary to accurately specify the coordinates of parameters when drawing elementary shapes. By introducing an object-oriented idea, our language enables users not to specify many coordinates. We have produced an experimental drawing assistant system using our language. In this paper, we outline our language and system, and show the results of experiments which were conducted to verify the effectiveness of our system.","PeriodicalId":170815,"journal":{"name":"2020 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126138443","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

Bert-Pair-Networks for Sentiment Classification 情感分类的bert - pair网络

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469534

Ziwen Wang, Haiming Wu, Han Liu, Qianhua Cai

引用次数: 2

A Novel Chinese Reading Comprehension Model Based on Attention Mechanism and Convolutional Neural Networks 一种基于注意机制和卷积神经网络的汉语阅读理解模型

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469533

C. Fahn, Yi-Lun Wang, Chu-Ping Lee

{"title":"A Novel Chinese Reading Comprehension Model Based on Attention Mechanism and Convolutional Neural Networks","authors":"C. Fahn, Yi-Lun Wang, Chu-Ping Lee","doi":"10.1109/ICMLC51923.2020.9469533","DOIUrl":"https://doi.org/10.1109/ICMLC51923.2020.9469533","url":null,"abstract":"This paper presents a novel machine reading comprehension model based on deep learning techniques in Chinese environment. In our manner, the training process can be performed using a general-level GPU, and the convergence of the training process can be accelerated for a shorter period of time. In the architectural design, two main constituting parts include Self-Attention Mechanism and Convolutional Neural Networks. To enhance the interaction between an article and questions, we carry out the operation of Context-Query Attention twice, so that our model is more effectively for acquiring the information of the questions related to the article and converges faster while training. In the experiment, we adopt the Delta Reading Comprehension Dataset for model evaluation in Chinese environment. The experimental results reveal that our model is able to reach the accuracy of 64.9% for EM and 79.0% for Fl. The convergence time is less than 1 hour using the Titan XP GPU, and the memory usage is comparatively lower. The training performance is about 3 times faster than other models with state- of-the-art architecture.","PeriodicalId":170815,"journal":{"name":"2020 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129317782","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

Hyperspectral Image Classification Approach Based on Wasserstein Generative Adversarial Networks 基于Wasserstein生成对抗网络的高光谱图像分类方法

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469586

Naigeng Chen, Chenming Li

{"title":"Hyperspectral Image Classification Approach Based on Wasserstein Generative Adversarial Networks","authors":"Naigeng Chen, Chenming Li","doi":"10.1109/ICMLC51923.2020.9469586","DOIUrl":"https://doi.org/10.1109/ICMLC51923.2020.9469586","url":null,"abstract":"Hyperspectral image classification is an important research direction in the application of remote sensing technology. In the process of labeling different types of objects based on spectral information and geometric spatial characteristics, noise interference often exists in continuous multi-band spectral information, which brings great troubles to spectral feature extraction. Besides, far from enough spectral samples will restrict the classification performance of the algorithm to some extent. In order to solve the problem of small amount of original spectral sample data and noisy signal, Wasserstein generative adversarial networks (WGAN) is used to generate samples similar to the original spectrum, and spectral features are extracted from the samples. In the case of small samples, the original materials are provided for the classification of hyperspectral images and a semi-supervised classification model WGAN-CNN for hyperspectral images based on Wasserstein generation antagonistic network is proposed in this paper. This model combines with CNN classifier and completes the classification of terrain objects according to the label for the synthesized samples. The proposed method is compared with several classical hyperspectral image classification methods in classification accuracy. WGAN-CNN can achieve higher classification accuracy in the case of small sample size, which proves the effectiveness of the proposed method.","PeriodicalId":170815,"journal":{"name":"2020 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"302 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132096272","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 Multiple Deep Learner Approach for X-Ray Image-Based Pneumonia Detection 基于x射线图像的肺炎检测的多重深度学习方法

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/ICMLC51923.2020.9469043

Zonglin Yang, Qiang Zhao

引用次数: 4

[Copyright notice] (版权)

2020 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2020-12-02 DOI: 10.1109/icmlc51923.2020.9469563

引用次数: 0