2022 International Conference on Machine Learning and Cybernetics (ICMLC)最新文献_第2页

Non-Invasive Deep Temperature Measurement Based on the Long Short Term Memory for Hyperthermia Therapy 基于长短期记忆的无创深度体温测量在热疗中的应用

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941284

K. Mori, Y. Tange

{"title":"Non-Invasive Deep Temperature Measurement Based on the Long Short Term Memory for Hyperthermia Therapy","authors":"K. Mori, Y. Tange","doi":"10.1109/ICMLC56445.2022.9941284","DOIUrl":"https://doi.org/10.1109/ICMLC56445.2022.9941284","url":null,"abstract":"In this study, we developed the model predicted the deep temperatures from the surface temperature information in order to realize non-invasive measurement for the hyperthermia therapy. The deep temperatures were predicted based on the surface temperature, surface temperature change, initial surface temperature, and lapsed time by using deep learning method based on long short term memory. The model was learned by using temperature characteristics measured by biological phantoms composed by agar. Errors of the model’s prediction accuracies for the phantoms were around 0.45 degree at the largest point. We measured the temperature characteristics of the pork-based phantom as a material similar to human tissue and used the model to make predictions. Errors of the prediction accuracies for the phantom were around 5.0 degree at the largest point. In this study, we used two type heat sources. The model does not enough learn temperature characteristics for each heat source. We confirmed that the system was able to achieve a prediction accuracy of less than 0.3 degree for data using a heat pack as a heat source","PeriodicalId":117829,"journal":{"name":"2022 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131766725","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

On The Development of a Legal Penalty Prediction System for Drunk Driving Cases 论醉酒驾驶案件刑罚预测系统的开发

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941286

Meng-Luen Wu, Chen Lin, Po-Cheng Yu

{"title":"On The Development of a Legal Penalty Prediction System for Drunk Driving Cases","authors":"Meng-Luen Wu, Chen Lin, Po-Cheng Yu","doi":"10.1109/ICMLC56445.2022.9941286","DOIUrl":"https://doi.org/10.1109/ICMLC56445.2022.9941286","url":null,"abstract":"Recent years, computer-aided penalty prediction have been promoted to gain people's trust to the judicial systems, especially in developing Chinese region. In this paper, we propose machine learning based models to predict the legal penalty of criminal cases. Particularly, we focus on drunk driving cases as they are frequent, and the regulations are clear. Unlike western text which words are separated by spaces, words in Chinese text are continuum. In our proposed method, we first use a word segmentation method to separate the Chinese words in text and apply a pre-trained model to convert words into vectors. In the vector space, words with similar meanings have short distance with each other. As the amount of each penalty varies greatly, resulting a data imbalance problem. Therefore, we adapt the Synthetic Minority Oversampling Technique (SMOTE) algorithm as a solution. Finally, we apply deep learning-based models, including Bi-GRU and TextCNN to perform penalty prediction, and compare their advantages and disadvantages.In the experimental result, for drunk driving case penalty prediction, our propose SMOTE + TextCNN solution can reach 73.96% of accuracy. If we allow the prediction to be plus or minus one month from the actual, the accuracy is 95.60%. As for the computation time, our proposed method can predict the penalty of 1,524 drunk driving cases per second.","PeriodicalId":117829,"journal":{"name":"2022 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"7 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133939360","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 Development of Kohs Block Design Test in Virtual Reality with Eye Tracking and Hand Tracking 基于眼动跟踪和手动跟踪的虚拟现实Kohs块设计测试的开发

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941285

Kensuke Shigenaga, K. Nagamune

引用次数: 2

Investigation of Inspection Methods in Acoustic Analysis Using Pronunciation Feature Extraction 语音特征提取声学分析检测方法研究

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941300

N. Yagi, Yutaka Hata, Y. Sakai

引用次数: 0

Deep Learning for Automatic Road Marking Detection with Yolov5 基于Yolov5的深度学习道路标记自动检测

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941313

Rung-Ching Chen, Yong-Cun Zhuang, Jeang-Kuo Chen, Christine Dewi

引用次数: 1

Alternative Methods of Translational Gains by Viewpoint Manipulation in the Pitch Direction 在俯仰方向上通过视点操纵获得平移增益的替代方法

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941339

Yudai Ishikawa, K. Tagawa, Hideaki Touyama

{"title":"Alternative Methods of Translational Gains by Viewpoint Manipulation in the Pitch Direction","authors":"Yudai Ishikawa, K. Tagawa, Hideaki Touyama","doi":"10.1109/ICMLC56445.2022.9941339","DOIUrl":"https://doi.org/10.1109/ICMLC56445.2022.9941339","url":null,"abstract":"This paper proposes a new method of translational manipulation for redirected walking through pitch-oriented viewpoint manipulation, which is known to change the walking speed of a user. Pitch viewpoint manipulation adds a slope to the virtual reality environment. In reality, walking on a slope increases or decreases the walking speed, and this study conducts an investigation based on the assumption that similar fluctuations would occur in a virtual reality environment. Through an experiment, the variation in walking speed by dynamically manipulating the viewpoint in the pitch direction for a 5 m walking section, and the perception threshold, which is the range in which the viewpoint can be manipulated without being noticed by the user, were investigated. The results showed that the walking speed significantly decreased with pitch gains of -0.5° in the downward direction, and 3° and 5° in the upward direction. It was discovered that perceptual thresholds of 0.17° and -1.12° in the upward and downward directions, respectively, were imperceptible, indicating that the viewpoint could be manipulated without being perceived.","PeriodicalId":117829,"journal":{"name":"2022 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116293886","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 Dynamic Immune Strategy for Blocking the Spreading of Worms in Vanets 一种动态免疫策略阻止昆虫在叶片中的传播

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941292

Yuxin Ding, Huang Ningxin, Wenting Xu

{"title":"A Dynamic Immune Strategy for Blocking the Spreading of Worms in Vanets","authors":"Yuxin Ding, Huang Ningxin, Wenting Xu","doi":"10.1109/ICMLC56445.2022.9941292","DOIUrl":"https://doi.org/10.1109/ICMLC56445.2022.9941292","url":null,"abstract":"Currently VANETs still face many serious security issues. One of which is attacks from worms. To prevent the propagation of worms, different immune strategies have been proposed. One problem with these strategies is that they adopt a greedy strategy or random strategy to select immune nodes. These strategies do not consider the dynamic changes of the network topology caused by vehicle movement, which means that the strategies cannot effectively prevent a worm from spreading. In this paper, we propose a dynamic immune strategy. Considering the dynamic changes of VANETs, we use machine learning methods to predict vehicle positions at the next moment and combine the position information of vehicles at different times to evaluate the influence of a vehicle. We provide a method for computing the influence of vehicles. The vehicles with a large influence are selected as immune nodes. We compare the proposed immune strategy with several typical strategies, preemptive immunization, interactive immunization, blacklist isolation and degree immunization. The results show that the proposed method can prevent the spread of worms more effectively than existing techniques.","PeriodicalId":117829,"journal":{"name":"2022 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116451823","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

Toward Prediction of Traffic Accidents Using Formal Concept Analysis of Actual Accidents and Related Data 利用实际事故及相关数据的形式概念分析预测交通事故

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941304

Shogo Kotani, Masaki Nakamura, K. Sakakibara, Tatsuo Motoyoshi, Keisuke Hoshikawa

引用次数: 0

Gold Investment Model on RNN and Finding Best Investment Strategy on PSO 基于RNN的黄金投资模型及PSO的最佳投资策略研究

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941321

Pakamas Kanchanakantikul, S. Nootyaskool

引用次数: 0

Negative Hesitation Soft Fuzzy Sets and its Application on Decision Making Problems 负犹豫软模糊集及其在决策问题中的应用

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941306

Youpeng Yang, Sanhgyuk Lee, Haolan Zhang, W. Pedrycz

引用次数: 0