2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)最新文献_第8页

Physics-Informed Neural Networks for Modeling Cellulose Degradation in Power Transformers 基于物理信息的神经网络模拟电力变压器中纤维素的降解

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00216

F. Bragone, Khaoula Oueslati, T. Laneryd, Michele Luvisotto, Kateryna Morozovska

{"title":"Physics-Informed Neural Networks for Modeling Cellulose Degradation in Power Transformers","authors":"F. Bragone, Khaoula Oueslati, T. Laneryd, Michele Luvisotto, Kateryna Morozovska","doi":"10.1109/ICMLA55696.2022.00216","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00216","url":null,"abstract":"Insulation is an essential part of power transformers, which guarantees an efficient and reliable operational life. It mainly consists of mineral oil and insulation paper. Most of the major failures of power transformers originate from internal insulation failures. Monitoring aging and thermal behaviour of the transformer’s insulation paper is achieved by different techniques, which consider the Degree of Polymerization (DP) to evaluate the cellulose degradation and other chemical factors accumulated in mineral oil. Given the physical and chemical nature of the problem of degradation, we couple it with machine learning models to predict the desired parameters for considered equations. In particular, the equation used applies the Arrhenius relation, which comprises parameters like the pre-exponential factor, which depends on the cellulose’s contamination content, and the activation energy, which is connected to the temperature dependence; both of the factors need to be estimated for our problem. For this reason, Physics-Informed Neural Networks (PINNs) are considered for solving the data-driven discovery of the DP equation.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126583833","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

Increasing Accuracy in Predicting Student Test Scores with Neural Networks using Domain Reduction Technique of Principal Component Analysis 利用主成分分析的域约简技术提高神经网络预测学生考试成绩的准确性

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00241

M. S. Brown, Bhavana Rajashekar, Nastaran Davudi Pahnehkolaee

{"title":"Increasing Accuracy in Predicting Student Test Scores with Neural Networks using Domain Reduction Technique of Principal Component Analysis","authors":"M. S. Brown, Bhavana Rajashekar, Nastaran Davudi Pahnehkolaee","doi":"10.1109/ICMLA55696.2022.00241","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00241","url":null,"abstract":"This research uses Principal Component Analysis (PCA) in conjunction with a Neural Network to increase the accuracy of predicting student test scores. Much research has been conducted attempting to predict student test scores using a standard, well-known dataset. The dataset includes student demographic and educational data and test scores for Mathematics and Language. Multiple predictive algorithms have been used with a Neural Network being the most common.In this research PCA was used to reduce the domain space size using varying sizes. This began with just 1 attribute and increased to the full size of the original set’s domain values. The reduced domain values and the original domain values were independently used to train a Neural Network and the Mean Absolute errors were compared. Because results may vary depending upon which records in the dataset are training versus testing, 50 trials were conducted for each reduction size. Results were average and statistical tests were applied. Results show that using PCA prior to training the Neural Network can decrease the mean absolute error by up to 15%.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121678710","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

Few-Shot Link Prediction with Domain-Agnostic Graph Embedding 基于域不可知图嵌入的少镜头链接预测

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00109

Hao Zhu, Mahashweta Das, M. Bendre, Fei Wang, Hao Yang, S. Hassoun

{"title":"Few-Shot Link Prediction with Domain-Agnostic Graph Embedding","authors":"Hao Zhu, Mahashweta Das, M. Bendre, Fei Wang, Hao Yang, S. Hassoun","doi":"10.1109/ICMLA55696.2022.00109","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00109","url":null,"abstract":"Real-world link prediction problems often deal with data from multiple domains, where data may be highly skewed and imbalanced. Computer vision research has studied similar issues under the Few-Shot Learning (FSL) umbrella. However, this problem has rarely been addressed and explored in the graph domain, specifically for link prediction. In this work, we propose an adversarial training-based framework that aims at improving link prediction for highly skewed and imbalanced graphs from different domains by generating domain agnostic embedding. We introduce a domain discriminator on pairs of graph-level embedding. We then use the discriminator to improve the model in an adversarial way, such that the graph embedding generated by the model are domain agnostic. We test our ideas on one large real-world user-business-review dataset and three benchmark datasets. Our results demonstrate that when domain differences exist, our method creates better graph embedding that are more evenly distributed across domains and generate better prediction outcomes. In the absence of domain differences, our method is on par with state-of-the-art.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125294216","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

Impact of Labeling Noise on Machine Learning: A Cost-aware Empirical Study 标签噪声对机器学习的影响:一个成本意识的实证研究

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00156

A. Gharawi, Jumana Alsubhi, Lakshmish Ramaswamy

引用次数: 0

Dealing with Distribution Shift in Acoustic Mosquito Datasets 声学蚊子数据集分布移位的处理

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00246

H. Y. Nkouanga, Suresh Singh

引用次数: 0

Fine-grained analysis of the transformer model for efficient pruning 对变压器模型进行细粒度分析，以实现高效修剪

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00149

Leila Ben Letaifa, Jean-Luc Rouas

引用次数: 0

Deformable Registration of Low-overlapping Medical Images 低重叠医学图像的可变形配准

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00157

Bertram Sabrowsky-Hirsch, Bernhard Schenkenfelder, Christoph Klug, G. Reishofer, Josef Scharinger

{"title":"Deformable Registration of Low-overlapping Medical Images","authors":"Bertram Sabrowsky-Hirsch, Bernhard Schenkenfelder, Christoph Klug, G. Reishofer, Josef Scharinger","doi":"10.1109/ICMLA55696.2022.00157","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00157","url":null,"abstract":"Even though whole-body MRI becomes more accessible, its use is still restricted by technical limitations such as field of view and resolution. To minimize artifacts caused by respiratory motion, the acquisition time can be reduced to a feasible breath-hold by decreasing the image size. Conversely, a series of acquisitions is required to cover a larger extent. While the method is effective for individual acquisitions, different respiratory states introduce artifacts when a composite image is reconstructed from the series. In this paper, we propose a deformable registration method for low-overlapping MRI to compensate for such artifacts and facilitate seamless mosaicing. Based on an unsupervised learning-based model, our method generalizes well to different modalities and target anatomies. We demonstrate this on a dataset of 16 abdominal MRI series from a medical use case as well as synthetic image pairs generated from a large heterogeneous dataset, with 13% to 24% overlap. The evaluation shows an improved Dice Similarity Coefficient (DSC) for target structures in the overlap region by +0.14 (from 0.73) for real and +0.21 (from 0.68) for synthetic image pairs. Our method is fast and robust and may be applied to various mosaicing tasks.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129768525","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

Predicting ME/CFS After Infectious Mononucleosis Using Cytokine Network Correlations 利用细胞因子网络相关性预测传染性单核细胞增多症后ME/CFS

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00091

Jennifer Schwabe, Chelsea Hua, Emma M. Allen, Leonard A. Jason, Jacob Furst, Daniela Raciu

{"title":"Predicting ME/CFS After Infectious Mononucleosis Using Cytokine Network Correlations","authors":"Jennifer Schwabe, Chelsea Hua, Emma M. Allen, Leonard A. Jason, Jacob Furst, Daniela Raciu","doi":"10.1109/ICMLA55696.2022.00091","DOIUrl":"https://doi.org/10.1109/ICMLA55696.2022.00091","url":null,"abstract":"We investigated if a predictive modeling strategy based on the interdependence of the cytokine network could accurately predict if a patient would develop Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) after contracting infectious mononucleosis (IM). We analyzed previously collected data from Northwestern University (NU) students in a three-stage experiment, following them from the start of the school year (Stage 1), to development of IM (Stage 2), to six months post development of IM (Stage 3). At all three stages, blood was stored from participants for cytokine measurement and analysis. Additionally, eight psychological and behavioral scales were used to identify participants as healthy controls or as ME/CFS. Using participants’ measured cytokine expression levels, we built a predictive model based on the inherent correlations within the cytokine network. We found that we could predict ME/CFS in patients 6 months after IM with 86.84% accuracy using correlation matrices made from cytokines taken during IM infection. These results suggest that there may be potential in using an approach that is based on the interdependence of the cytokine network to predict ME/CFS post IM. Future work may explore the validity of these findings and if such an approach could have applications in other diseases.","PeriodicalId":128160,"journal":{"name":"2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127097374","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

Math Chunking and Function Recognition using Deep Learning 使用深度学习的数学分块和函数识别

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00067

Fatimah Alshamari, Abdou Youssef

引用次数: 1

Evolutionary Neural Architecture Search for Traffic Forecasting 交通预测的进化神经结构搜索

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2022-12-01 DOI: 10.1109/ICMLA55696.2022.00198

Daniel Klosa, C. Büskens

引用次数: 0