2019 International Conference on Machine Learning and Cybernetics (ICMLC)最新文献

Intelligent Robot Navigation Based on Human Emotional Model in Human-Aware Environment 基于人类感知环境下人类情感模型的智能机器人导航

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949247

T. Obo, Yuto Nakamura

引用次数: 3

A Quantification Approach of Uterine Peristalsis Propagated From the Cervix and the Fundus 子宫蠕动从子宫颈和眼底扩散的量化方法

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949268

Naziah Tasnim, Fahad Parvez Mahdi, S. Alam, N. Yagi, A. Nakashima, I. Komesu, Yoshimitsu Tokunaga, T. Sakumoto, S. Kobashi

引用次数: 1

An Accelerometer Based Gait Analysis System to Detect Gait Abnormalities in Cerebralspinal Meningitis Patients 基于加速度计的步态分析系统检测脑脊膜炎患者步态异常

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949256

Tung-Hua Yu, Chao-Cheng Wu

{"title":"An Accelerometer Based Gait Analysis System to Detect Gait Abnormalities in Cerebralspinal Meningitis Patients","authors":"Tung-Hua Yu, Chao-Cheng Wu","doi":"10.1109/ICMLC48188.2019.8949256","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949256","url":null,"abstract":"This paper proposed a gait analysis system to detect abnormal gaits based on each gait cycle. The proposed system took advantage of a tri-axial accelerometer to collect the gait signals in three dimensions. The collected signals were divided into four intervals for each gait cycle, including the step, swing, stance phase, and stride. The time domain and time-frequency domain features were generated for each interval. Later, Fisher score was calculated to determine discrimination ability for each feature. Support Vector Machine would be trained for classification of normal and abnormal gaits based on selected features with the highest Fisher scores. Cerebralspinal Meningitis (CSM) patients with/without spinal cord edema were used as samples to conduct the experiments. The results demonstrated that the proposed gait analysis system could provide 90% accuracy. The feature subset with the best accuracy includes kurtosis, crest factor, and mean of lateral acceleration data in stride interval. It implied the force to make the body left and right in stride interval is an critical indicator for diagnosis of spinal cord edema. The proposed gait analysis system could further be extended to more symptoms if other sets of training samples are available in the future.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124260561","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

Discovering Emotional Logic Rules From Physiological Data of Individuals 从个体生理数据中发现情感逻辑规律

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949274

N. Costadopoulos, M. Islam, D. Tien

引用次数: 3

A Car Navigation Map Equipped With Speed Recommendation 配备速度推荐的汽车导航地图

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949266

Yue Xu, Zhimin He, Haozhen Situ, Junjian Su, Yan Zhou

引用次数: 0

Real Life Image Recognition of Panama Disease by an Effective Deep Learning Approach 一种有效的深度学习方法用于巴拿马病的真实生活图像识别

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949269

Cheng-Fa Tsai, Yu-Chieh Chen, Chia-En Tsai

引用次数: 1

Stress Assessment for Work Proficiency Analysis by Heart Rate Variability 心率变异性对工作能力分析的压力评估

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949285

Momoka Fujimoto, H. Nakajima, Yasuyo Kotake, Danni Wang, Y. Hata

引用次数: 0

An Experimental Study on the Effectiveness of Artificial Neural Network-Based Stock Index Prediction 基于人工神经网络的股指预测有效性实验研究

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949282

Y. Tsai, Qiangfu Zhao

引用次数: 5

Human Face Sentiment Classification Using Synthetic Sentiment Images with Deep Convolutional Neural Networks 基于深度卷积神经网络合成情感图像的人脸情感分类

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949240

Chen-Chun Huang, Yi-Leh Wu, Cheng-Yuan Tang

{"title":"Human Face Sentiment Classification Using Synthetic Sentiment Images with Deep Convolutional Neural Networks","authors":"Chen-Chun Huang, Yi-Leh Wu, Cheng-Yuan Tang","doi":"10.1109/ICMLC48188.2019.8949240","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949240","url":null,"abstract":"Image is one of the most important ways for users to express their emotions on social networks. In this paper, we use the deep convolutional neural networks to solve the problem of image sentiment analysis from visual content. Because training a neural network requires a large number of data sets to provide good training performance, we cannot obtain such a real human emotion training set, because emotions are subjective, and multiple people need to provide annotations for the images, which requires a lot of manpower. This study proposes to incorporate synthetic face images into the training set to substantially increase the size of the training set. We use only synthetic face images, real facial images, and mixtures of synthetic and real facial images in the training set. Our experiments show that by using only 4026 real images, where each image is supplemented by the synthetic image to the same data set size (Anger: 1063 + 937 true, Disgust: 1857 + 143 true, Fear: 1802 + 198 true, Happy: 2000 true, Sad: 1252 + 748 true) total of 10,000 images, can reach 87.79%, 74.19%, 86.99%, 79.80% average testing accuracy in each testing set in human face sentiment classification.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133109680","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 Study on Machine Learning-Based Image Identification Towards Assitive Automation of Commentary on Animation Characters 基于机器学习的图像识别面向动画人物评论的被动自动化研究

2019 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2019-07-01 DOI: 10.1109/ICMLC48188.2019.8949258

Yutaka Yoshino, Kazuki Nakada, M. Kobayashi, H. Tatsumi

{"title":"A Study on Machine Learning-Based Image Identification Towards Assitive Automation of Commentary on Animation Characters","authors":"Yutaka Yoshino, Kazuki Nakada, M. Kobayashi, H. Tatsumi","doi":"10.1109/ICMLC48188.2019.8949258","DOIUrl":"https://doi.org/10.1109/ICMLC48188.2019.8949258","url":null,"abstract":"This study aims to assist visually impaired people as well as animation novices by focusing on problems that arise at the time of viewing animation videos and images. We focus on the following problems: (1) difficulty of understanding behaviors and situations, (2) difficulty of discriminating animation characters, and (3) confusion caused by animation characters with similarities. We use deep neural networks to identify animation characters as preliminary verification by training a customized convolutional neural network (CNN) from scratch on a small class of data based on the original database of animation characters. The results show that some combinations of characters are difficult to discriminate in cross validation. To resolve this problem, we performed transfer learning based on the CNN variants pre-trained on the natural image database ImageNet. We confirmed that the learning proceeded steadily with a gradual learning curve, resulting in high accuracy. The results indicate that the bottleneck features of the CNN variants pre-trained on ImageNet are effective in identifying animation characters. Furthermore, we verified the operation speed of the inference of our trained CNN on a microcomputer board with a machine learning accelerator Intel Movidius and confirmed that the speed is sufficient in real-time execution.","PeriodicalId":221349,"journal":{"name":"2019 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131869643","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