2022 International Conference on Machine Learning and Cybernetics (ICMLC)最新文献_第4页

Portfolio Trading of Financial Products Based on Machine Learning 基于机器学习的金融产品组合交易

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941281

Yifan Zhang, Qian Shen, Jian Guo, Yiwen Jia

引用次数: 0

Prediction of the Stock Adjusted Closing Price Based On Improved PSO-LSTM Neural Network 基于改进PSO-LSTM神经网络的股票调整收盘价预测

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941330

Yulan Luo, Yi Ji

{"title":"Prediction of the Stock Adjusted Closing Price Based On Improved PSO-LSTM Neural Network","authors":"Yulan Luo, Yi Ji","doi":"10.1109/ICMLC56445.2022.9941330","DOIUrl":"https://doi.org/10.1109/ICMLC56445.2022.9941330","url":null,"abstract":"Volatility in the stock market has a significant impact on all finance-related fields. As an important part of stock data, the adjusted closing price often reflects the attention of market funds to a stock, helping predict the market movement of the next trading day, especially for short-term investors. With the development of artificial intelligence technology, the machine learning algorithms are widely applied to predict stock trends. However, the noisy, nonlinear, and chaotic nature of stock price changes makes the prediction not accurate enough. Hence, we proposed a hybrid prediction model combining improved particle swarm optimization (IPSO) and long short-term memory (LSTM) neural network to predict the adjusted closing price of the stock. In this paper, nonlinear methods are presented to optimize the velocity inertia weight and learning factors of traditional particle swarm optimization (PSO). Meanwhile, IPSO is used to optimize the hyperparameters of LSTM neural network to improve its prediction accuracy. The experiments proved that the proposed IPSO-LSTM outperformed the Autoregressive Integrated Moving Average model (ARIMA), LSTM, and PSO-LSTM on the prediction of the S&P 500 Index. Furthermore, the Dow Jones Industrial Average Index (DJI) and Nasdaq Composite Index (IXIC) were chosen to verify the accuracy and robustness of the model we put forward.","PeriodicalId":117829,"journal":{"name":"2022 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"02 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":"130590038","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

Domain-Robust Pre-Training Method for the Sensor-Based Human Activity Recognition 基于传感器的人体活动识别的域鲁棒预训练方法

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941291

Zhongkai Zhao, Tatsuhito Hasegawa

{"title":"Domain-Robust Pre-Training Method for the Sensor-Based Human Activity Recognition","authors":"Zhongkai Zhao, Tatsuhito Hasegawa","doi":"10.1109/ICMLC56445.2022.9941291","DOIUrl":"https://doi.org/10.1109/ICMLC56445.2022.9941291","url":null,"abstract":"Transfer learning improves problem-solving efficiency by transferring the learned knowledge from the source domain to the target domain. In transfer learning, using a large amount of data for pre-training is beneficial to improve the robustness of the model. Data differ significantly when the domain changes in Sensor-Based human activity recognition (HAR). Currently, in HAR, data usage is relatively independent, lacking source domains with massive data and rich labels. This paper proposes a new pre-training method using multiple domain datasets to construct a domain-robust pre-training model. We divide the pre-training dataset into basic and complex activities scenarios by considering the difference in activity classification. We evaluate the classification scenarios that are most beneficial for sensor-based HAR based on the constituted dataset and using deep convolutional networks. We show that our method verified the influence of the source domain on transfer learning in sensor-based HAR. By constructing a sizeable correlated source domain, our method can enhance the generalization ability of the network model. This paper also demonstrated that large-scale and basic activity classification datasets can be better used as pre-training models to participate in HAR classification tasks.","PeriodicalId":117829,"journal":{"name":"2022 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"200 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":"133724422","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 Study of Breath Alcohol Concentration Fluctuations and Cognitive Decline Due to Low-Impact Drinking 低冲击饮酒引起的呼吸酒精浓度波动和认知能力下降的研究

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941327

Yuichi Sato, Kosuke Nagano, Fumiya Kinoshita, Hideaki Touyama

引用次数: 0

Fast Semantic Segmentation for Vectorization of Line Drawings Based on Deep Neural Networks 基于深度神经网络的线形图矢量化快速语义分割

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941326

Shodai Ito, Noboru Takagi, K. Sawai, H. Masuta, T. Motoyoshi

引用次数: 0

Fingerprint Liveness Detection Using Denoised-Bayes Shrink Wavelet and Aggregated Local Spatial and Frequency Features 基于去噪贝叶斯收缩小波和聚合局部空间和频率特征的指纹活跃度检测

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941303

Farchan Hakim Raswa, Indra Yusuf Kinarta, Reza Pulungan, A. Harjoko, Chung-Ting Lee, Yung-Hui Li, Jia-Ching Wang

{"title":"Fingerprint Liveness Detection Using Denoised-Bayes Shrink Wavelet and Aggregated Local Spatial and Frequency Features","authors":"Farchan Hakim Raswa, Indra Yusuf Kinarta, Reza Pulungan, A. Harjoko, Chung-Ting Lee, Yung-Hui Li, Jia-Ching Wang","doi":"10.1109/ICMLC56445.2022.9941303","DOIUrl":"https://doi.org/10.1109/ICMLC56445.2022.9941303","url":null,"abstract":"Fingerprint has a competent level of uniqueness because the various features can form different patterns in humans. It is a verification requirement in various aspects, such as mobile phone, banking accounts, attendance, etc. One of the preventive measures in maintaining performance is liveness detection. We deep exploited the handcrafted method to achieve adequate performance. To encapsulate the noise possibility, we added the Bayes shrink-wavelet transform as the noise removal. So, the noise obtained in the fingerprint image can be minimized but keep the quality of the fingerprint image is in good condition. Then, we conjugated the spatial and frequency domain in pixel neighborhood distribution using the local binary pattern (LBP) and local phase quantization (LPQ) feature. Finally, we mapped the learning stage using a prominent classifier, i.e., a support vector machine (SVM). Our experiment was evaluated with LivDet 2015 dataset. The proposed method has achieved sustainable results regarding average error rate (AER).","PeriodicalId":117829,"journal":{"name":"2022 International Conference on Machine Learning and Cybernetics (ICMLC)","volume":"37 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":"116327576","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

Real-Time Vehicle Counting by Deep-Learning Networks 基于深度学习网络的实时车辆计数

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941299

Chun-Ming Tsai, F. Shih, J. Hsieh

引用次数: 0

Unsupervised Representation Learning Method In Sensor Based Human Activity Recognition 基于传感器的人体活动识别中的无监督表示学习方法

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941334

Koki Takenaka, Tatsuhito Hasegawa

引用次数: 1

Automatic Digit Hand Sign Detection With Hand Landmark 自动数字手势检测与手地标

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941325

Rung-Ching Chen, William Eric Manongga, Christine Dewi, Hung-Yi Chen

引用次数: 0

Transfer Learning and LSTM to Predict Stock Price 迁移学习与LSTM预测股票价格

2022 International Conference on Machine Learning and Cybernetics (ICMLC) Pub Date : 2022-09-09 DOI: 10.1109/ICMLC56445.2022.9941296

R. Chen, Wanjun Yang, Kuei-Chien Chiu

引用次数: 2