Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications最新文献

Sentiment-Driven Cryptocurrency Price Prediction: A Machine Learning Approach Utilizing Historical Data and Social Media Sentiment Analysis 情绪驱动的加密货币价格预测:利用历史数据和社交媒体情绪分析的机器学习方法

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2023-09-28 DOI: 10.5121/mlaij.2023.10301

Saachin Bhatt, Mustansar Ghazanfar, Mohammad Hossein Amirhosseini

{"title":"Sentiment-Driven Cryptocurrency Price Prediction: A Machine Learning Approach Utilizing Historical Data and Social Media Sentiment Analysis","authors":"Saachin Bhatt, Mustansar Ghazanfar, Mohammad Hossein Amirhosseini","doi":"10.5121/mlaij.2023.10301","DOIUrl":"https://doi.org/10.5121/mlaij.2023.10301","url":null,"abstract":"This research explores the impact of social media sentiments on predicting Bitcoin prices using machine learning models, integrating on-chain data, and applying a Multi Modal Fusion Model. Historical crypto market, on-chain, and Twitter data from 2014 to 2022 were used to train models including K-Nearest Neighbors, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Extreme Gradient Boosting, and Multi Modal Fusion. Performance was compared with and without Twitter sentiment data which was analysed using the Twitter-roBERTa and VADAR models. Inclusion of sentiment data enhanced model performance, with Twitter-roBERTa-based models achieving an average accuracy score of 0.81. The best performing model was an optimised Multi Modal Fusion model using Twitter-roBERTa, with an accuracy score of 0.90. This research underscores the value of integrating social media sentiment analysis and onchain data in financial forecasting, providing a robust tool for informed decision-making in cryptocurrency trading.","PeriodicalId":74528,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135470323","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

Face Mask Detection Model Using Convolutional Neural Network 基于卷积神经网络的人脸检测模型

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2023-09-28 DOI: 10.5121/mlaij.2023.10303

Mamdouh M. Gomaa, Alaa Elnashar, Mahmoud M. Eelsherif, Alaa M. Zaki

引用次数: 0

Breast Tumor Detection Using Efficient Machine Learning and Deep Learning Techniques 利用高效机器学习和深度学习技术进行乳腺肿瘤检测

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2023-09-28 DOI: 10.5121/mlaij.2023.10302

Ankita Patra, Santi Kumari Behera, Prabira Kumar Sethy, Nalini Kanta Barpanda, Ipsa Mahapatra

{"title":"Breast Tumor Detection Using Efficient Machine Learning and Deep Learning Techniques","authors":"Ankita Patra, Santi Kumari Behera, Prabira Kumar Sethy, Nalini Kanta Barpanda, Ipsa Mahapatra","doi":"10.5121/mlaij.2023.10302","DOIUrl":"https://doi.org/10.5121/mlaij.2023.10302","url":null,"abstract":"Breast cancer tissues grow when cells in the breast expand and divide uncontrollably, resulting in a lump of tissue commonly called and named tumor. Breast cancer is the second most prevalent cancer among women, following skin cancer. While it is more commonly diagnosed in women aged 50 and above, it can affect individuals of any age. Although it is rare, men can also develop breast cancer, accounting for less than 1% of all cases, with approximately 2,600 cases reported annually in the United States. Early detection of breast tumors is crucial in reducing the risk of developing breast cancer. A publicly available dataset containing features of breast tumors was utilized to identify breast tumors using machine learning and deep learning techniques. Various prediction models were constructed, including logistic regression (LR), decision tree (DT), random forest (RF), support vector machine (SVM), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), Light GBM, and a recurrent neural network (RNN) model. These models were trained to classify and predict breast tumor cases based on the provided features.","PeriodicalId":74528,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135470329","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

Context-free Self-Conditioned GAN for Trajectory Forecasting 用于轨迹预测的无上下文自条件GAN

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2022-01-01 DOI: 10.1109/ICMLA55696.2022.00196

Tiago Rodrigues de Almeida, Eduardo Gutiérrez-Maestro, Óscar Martínez Mozos

引用次数: 0

Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problems. 基于生成对抗网络的高维块缺失值问题多重插值。

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2021-12-01 DOI: 10.1109/icmla52953.2021.00131

Zongyu Dai, Zhiqi Bu, Qi Long

{"title":"Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problems.","authors":"Zongyu Dai, Zhiqi Bu, Qi Long","doi":"10.1109/icmla52953.2021.00131","DOIUrl":"https://doi.org/10.1109/icmla52953.2021.00131","url":null,"abstract":"Missing data are present in most real world problems and need careful handling to preserve the prediction accuracy and statistical consistency in the downstream analysis. As the gold standard of handling missing data, multiple imputation (MI) methods are proposed to account for the imputation uncertainty and provide proper statistical inference. In this work, we propose Multiple Imputation via Generative Adversarial Network (MI-GAN), a deep learning-based (in specific, a GAN-based) multiple imputation method, that can work under missing at random (MAR) mechanism with theoretical support. MI-GAN leverages recent progress in conditional generative adversarial neural works and shows strong performance matching existing state-of-the-art imputation methods on high-dimensional datasets, in terms of imputation error. In particular, MI-GAN significantly outperforms other imputation methods in the sense of statistical inference and computational speed.","PeriodicalId":74528,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications","volume":"2021 ","pages":"791-798"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8841955/pdf/nihms-1776623.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10217351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12

A Data-Efficient Reinforcement Learning Method Based on Local Koopman Operators 基于局部Koopman算子的数据高效强化学习方法

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2021-01-01 DOI: 10.1109/ICMLA52953.2021.00086

Lixing Song, Junheng Wang, Junhong Xu

引用次数: 0

Predicting Real-time Scientific Experiments Using Transformer models and Reinforcement Learning 利用变压器模型和强化学习预测实时科学实验

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2021-01-01 DOI: 10.1109/ICMLA52953.2021.00084

Juan Manuel Parrilla Gutierrez

引用次数: 0

A clustering-based biased Monte Carlo approach to protein titration curve prediction. 基于聚类的偏向蒙特卡罗方法的蛋白质滴定曲线预测。

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2020-12-01 Epub Date: 2021-02-23 DOI: 10.1109/icmla51294.2020.00037

Arun V Sathanur, Nathan A Baker

{"title":"A clustering-based biased Monte Carlo approach to protein titration curve prediction.","authors":"Arun V Sathanur, Nathan A Baker","doi":"10.1109/icmla51294.2020.00037","DOIUrl":"https://doi.org/10.1109/icmla51294.2020.00037","url":null,"abstract":"In this work, we developed an efficient approach to compute ensemble averages in systems with pairwise-additive energetic interactions between the entities. Methods involving full enumeration of the configuration space result in exponential complexity. Sampling methods such as Markov Chain Monte Carlo (MCMC) algorithms have been proposed to tackle the exponential complexity of these problems; however, in certain scenarios where significant energetic coupling exists between the entities, the efficiency of the such algorithms can be diminished. We used a strategy to improve the efficiency of MCMC by taking advantage of the cluster structure in the interaction energy matrix to bias the sampling. We pursued two different schemes for the biased MCMC runs and show that they are valid MCMC schemes. We used both synthesized and real-world systems to show the improved performance of our biased MCMC methods when compared to the regular MCMC method. In particular, we applied these algorithms to the problem of estimating protonation ensemble averages and titration curves of residues in a protein.","PeriodicalId":74528,"journal":{"name":"Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications","volume":"2020 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/icmla51294.2020.00037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39530145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning with Unpaired Data 使用未配对数据学习

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2020-01-01 DOI: 10.1109/ICMLA51294.2020.00008

Jiebo Luo

引用次数: 0

A Cognitive Architecture for Object Recognition in Video 视频中对象识别的认知体系结构

Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications Pub Date : 2020-01-01 DOI: 10.1109/ICMLA51294.2020.00009

J. Príncipe

引用次数: 0