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

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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

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.

本研究探讨了社交媒体情绪对使用机器学习模型、整合链上数据和应用多模态融合模型预测比特币价格的影响。使用2014年至2022年的历史加密市场、链上和Twitter数据来训练模型，包括k近邻、逻辑回归、高斯朴素贝叶斯、支持向量机、极端梯度增强和多模态融合。使用Twitter- roberta和VADAR模型分析了Twitter情绪数据，并对有无Twitter情绪数据进行了比较。情感数据的加入提高了模型的性能，基于twitter - roberta的模型的平均准确率得分为0.81。表现最好的模型是使用Twitter-roBERTa优化的Multi - Modal Fusion模型，准确率得分为0.90。这项研究强调了在财务预测中整合社交媒体情绪分析和链上数据的价值，为加密货币交易中的明智决策提供了一个强大的工具。

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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

In current times, after the rapid expansion and spread of the COVID-19 outbreak globally, people have experienced severe disruption to their daily lives. One idea to manage the out-break is to enforce people wear a face mask in public places. Therefore, automated and efficient face detection methods are essential for such enforcement. In this paper, a face mask detection model for images has been presented which classifies the images as “with mask” and “without mask”. The model is trained and evaluated using the three datasets Real-World Masked Face Dataset (RMFD), Simulated Masked Face Dataset (SMFD), and Labeled Faces in the Wild (LFW), and attained a performance accuracy rate of 99.72% for first dataset, and 100% for the second and third datasets. This work can be utilized as a digitized scanning tool in schools, hospitals, banks, and airports, and many other public or commercial locations.

当前，新冠肺炎疫情在全球范围内迅速扩大和蔓延，人们的日常生活受到严重干扰。控制疫情的一个想法是强制人们在公共场所戴口罩。因此，自动化和高效的人脸检测方法对于此类执法至关重要。本文提出了一种图像的人脸检测模型，将图像分为“带口罩”和“不带口罩”两类。该模型使用真实世界蒙面数据集(RMFD)、模拟蒙面数据集(SMFD)和野生标记脸(LFW)三个数据集进行训练和评估，第一个数据集的性能准确率为99.72%，第二和第三个数据集的性能准确率为100%。这项工作可以用作学校、医院、银行、机场和许多其他公共或商业场所的数字化扫描工具。

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

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.

当乳房中的细胞不受控制地扩张和分裂时，乳腺癌组织就会生长，导致通常被称为肿瘤的组织块。乳腺癌是女性中第二常见的癌症，仅次于皮肤癌。虽然它更常见于50岁及以上的女性，但它可以影响任何年龄的个体。虽然这种情况很少见，但男性也会患乳腺癌，占所有病例的比例不到1%，美国每年报告的病例约为2600例。早期发现乳腺肿瘤对于降低患乳腺癌的风险至关重要。利用包含乳腺肿瘤特征的公开数据集，使用机器学习和深度学习技术识别乳腺肿瘤。构建了多种预测模型，包括逻辑回归(LR)、决策树(DT)、随机森林(RF)、支持向量机(SVM)、梯度增强(GB)、极限梯度增强(XGB)、轻型GBM和递归神经网络(RNN)模型。这些模型经过训练，根据所提供的特征对乳腺肿瘤病例进行分类和预测。

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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

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.

在大多数现实世界的问题中都存在缺失数据，需要仔细处理以保持下游分析中的预测准确性和统计一致性。作为处理缺失数据的金标准，提出了多重插值方法来考虑插值的不确定性并提供适当的统计推断。在这项工作中，我们提出了通过生成对抗网络(MI-GAN)进行多重输入，这是一种基于深度学习(具体来说是基于gan)的多重输入方法，可以在随机缺失(MAR)机制下工作，并得到了理论支持。MI-GAN利用了条件生成对抗神经系统的最新进展，在输入误差方面，它在高维数据集上显示出与现有最先进的输入方法相匹配的强大性能。特别是，MI-GAN在统计推断和计算速度方面明显优于其他imputation方法。

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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

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.

在这项工作中，我们开发了一种有效的方法来计算实体之间具有成对加性能量相互作用的系统中的集成平均。涉及构型空间全枚举的方法导致指数复杂度。采样方法如马尔可夫链蒙特卡罗(MCMC)算法已经被提出来解决这些问题的指数复杂性;然而，在某些情况下，实体之间存在显著的能量耦合，这种算法的效率可能会降低。为了提高MCMC的效率，我们采用了一种策略，利用相互作用能量矩阵中的簇结构对采样进行偏置。我们对有偏差的MCMC运行采用了两种不同的方案，并证明它们是有效的MCMC方案。我们使用合成系统和实际系统来展示与常规MCMC方法相比，我们的有偏差MCMC方法的性能有所提高。特别地，我们将这些算法应用于估计蛋白质中残基的质子化系综平均值和滴定曲线的问题。

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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

This talk describes our efforts to abstract from the animal visual system the computational principles to explain images in video. We develop a hierarchical, distributed architecture of dynamical systems that self-organizes to explain the input imagery using an empirical Bayes criterion with sparseness constraints and dual state estimation. The interpretation of the images is mediated through causes that flow top down and change the priors for the bottom up processing. We will present preliminary results in several data sets.

这个演讲描述了我们从动物视觉系统中抽象出计算原理来解释视频图像的努力。我们开发了一种自组织的动态系统的分层分布式架构，使用具有稀疏约束和对偶状态估计的经验贝叶斯准则来解释输入图像。对图像的解释是通过自上而下流动的原因来调解的，并改变了自下而上处理的先验。我们将介绍几个数据集的初步结果。

引用次数: 0

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