检索结果-内蒙古大学图书馆

57th Hawaii international conference on System Sciences (HICSS)

作者： Kari, Hemanth Kumar Texas A&M Univ Commerce Commerce TX 75428 USA

ISBN: (纸本)9780998133171

Sentiment analysis stands as a prominent tool within microblogging platforms, gaining substantial traction as a means to discern public opinion and sentiment across various topics, including movie tweet reviews. In response to this demand, the study introduces a robust system architecture that incorporates an array of algorithms, ranging from Multinomial Naive Bayes and Support Vector machine (SVM) to K-Nearest Neighbors (KNN), Bernoulli's Naive Bayes, and Random Forest. This architecture is meticulously trained using annotated Twitter data, methodically excluding non-opinionated content while precisely identifying sentiment. Thorough experimentation underscores the effectiveness of our methodology. To accomplish this, we curate an extensive data set of movie-related tweets, each carefully labeled with sentiments spanning positive, negative, or neutral tones. The methodological framework involves intricate text preprocessing steps, encompassing tokenization, stemming, and the removal of extraneous stop words. This facilitates the extraction of essential features and the conversion of raw text into numerical representations suitable for machine learning. Our sentiment classification modeling employs a diverse ensemble of machine learning algorithms, including Naive Bayes, Support Vector machines, and Recurrent Neural Networks. The assessment involves a range of metrics such as accuracy, precision, recall, and F1-score, supported by rigorous techniques like cross-validation to enhance the dependability and robustness of results. Our unique contribution lies in the strategic deployment of algorithms and a resilient system architecture adept at surmounting the challenges inherent to microblogs. We emphasize the utmost importance of preprocessing in augmenting the precision of sentiment classification. This research substantiates the system's aptitude in extracting valuable insights for informed decision-making through the scrutiny of microblog sentiments.

关键词： sentiment analysis microblogging machine learning system architecture experimental results feature selection accuracy metrics Confusion Matrix

来源：评论

学校读者我要写书评

暂无评论

A Unified Framework for learning with Nonlinear Model Classes from Arbitrary Linear Samples 41

A Unified Framework for Learning with Nonlinear Model Classe...

引用

41st international conference on machine learning, ICML 2024

作者： Adcock, Ben Cardenas, Juan M. Dexter, Nick Department of Mathematics Simon Fraser University BurnabyBC Canada Ann and H. J. Smead Department of Aerospace Engineering Sciences University of Colorado Boulder BoulderCO United States Department of Scientific Computing Florida State University TallahasseeFL United States

This work considers the fundamental problem of learning an unknown object from training data using a given model class. We introduce a framework that allows for objects in arbitrary Hilbert spaces, general types of (random) linear measurements as training data and general types of nonlinear model classes. We establish a series of learning guarantees for this framework, which provide explicit relations between the amount of training data and the model class to ensure near-best generalization bounds. In doing so, we introduce the key notion of the variation of a model class with respect to a distribution of sampling operators. We show that this framework can accommodate many different types of well-known problems of interest, such as matrix sketching by random sampling, compressed sensing with isotropic vectors, active learning in regression and compressed sensing with generative models. In all cases, known results become straightforward corollaries of our general theory. Hence, this work provides a powerful framework for studying and analyzing many different types of learning problems. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

The Application of machine learning on Concrete Samples 1st

The Application of Machine Learning on Concrete Samples

引用

Science, engineering Management and Information Technology First international conference, SEMIT 2022

作者： Çiftçioğlu, Aybike Özyüksel Department of Civil Engineering Faculty of Engineering Manisa Celal Bayar University Manisa Turkey

ISBN: (纸本)9783031403972

machine learning is a branch of artificial intelligence that helps computers to learn from data and make predictions based on patterns identified in big data. The purpose of this study is to explore the applicability of machine learning models in classifying the compressive strength of concrete specimens with different types of ingredients. Despite the investigations in the literature about estimating concrete density, there is no relevant study on categorizing compressive strength. To address this gap, in this study, three machine learning classification algorithms (Decision Tree, Naive Bayes Classifier, and K-Nearest Neighbors) are employed to classify concrete samples. The performance of each algorithm is evaluated and compared. The results show that the Decision Tree classifier provides the best performance with an average precision and recall of 99%, f1-score of 0.99, and accuracy of 99%. Moreover, the study provides insights into the application of ML algorithms in a real-world dataset. This study demonstrates that machine learning is a powerful tool that can be used to improve the accuracy of concrete strength classification. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

HybridFL: Hybrid Approach Toward Privacy-Preserving Federated learning 6th

HybridFL: Hybrid Approach Toward Privacy-Preserving Federate...

引用

6th international conference on Security and Privacy in New Computing Environments, SPNCE 2023

作者： Ali, Sheraz Mamoon, Saqib Usman, Areeba Abidin, Zain ul Zhao, Chuan School of Information Science and Engineering University of Jinan Jinan250022 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China University of Sargodha Sargodha Pakistan Shandong Provincial Key Laboratory of Network-Based Intelligent Computing University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Software Engineering University of Jinan Jinan250022 China

ISBN: (纸本)9783031736988

In this study, we introduce a novel Hybrid Federated learning (HybridFL) approach aimed at enhancing privacy and accuracy in collaborative machine learning scenarios. Our methodology integrates Differential Privacy (DP) and secret sharing techniques to address inference risks during training and protect against information leakage in the output model. Drawing inspiration from recent advances, we present a HybridFL framework that combines the strengths of Homomorphic Encryption (HE) and Multi-Party Computation (MPC) to achieve secure computation without the computational overhead of pure HE methods. Our contributions include a privacy-preserving design for Federated learning (FL) that ensures local data privacy through secret sharing while leveraging DP mechanisms for noise addition. The system offers resilience against unreliable participants and is evaluated using various machine learning models, including Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), and linear regression. Furthermore, we address potential external threats by deploying predictive model outputs as robust services against inference attacks. Experimental results demonstrate improved accuracy and convergence speed, establishing the viability of HybridFL as an effective solution for collaborative machine learning with enhanced privacy guarantees. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Board #442: data-driven Approach to Problem Solving in Renewable Energy and engineering Education

Board #442: Data-driven Approach to Problem Solving in Renew...

引用

2024 ASEE Annual conference and Exposition

作者： Biswas, Mohammad Abu Rafe Khanal, Aaditya Sundaravadivel, Prabha Department of Mechanical Engineering The University of Texas at Tyler TylerTX75701 United States Jasper Department of Chemical Engineering The University of Texas at Tyler TylerTX75701 United States Department of Electrical and Computer Engineering The University of Texas at Tyler TylerTX75701 United States

Courses based on experiential learning provide an excellent avenue to promote problem-solving and collaborative skills among the students in STEM. However, the current engineering curriculum does not have sufficient project-based learning emphasizing collaborative research on renewable energy to support the government's goal of Net Zero emissions by 2050. So, this work-in-progress presents the results from our recent implementation of project-based learning assignments to existing courses to model and analyze renewable energy systems while introducing machine learning methods. We used assignments and selected projects to introduce concepts related to reducing GHG emissions through renewable energy sources, improvement in efficiency, or novel technologies. The courses included modules covering two supervised machine learning algorithms, video lectures, and tutorials for analyzing and evaluating data sets of each technology using computational tools. Finally, indirect and direct assessments were carried out, including pre- and post-course qualitative surveys completed by students to assess the impact of the modules and course. Based on the results from the current work, future implementation will aim to (1) expand and implement additional machine learning instructional and training modules for sustainability projects using experiential learning, (2) promote a data-driven approach to solving engineering problems for complex problems in science and engineering, and (3) train future leaders on topics related to sustainability. These modules are to enable students to acquire the relevant knowledge and skills for data-driven modeling. © American Society for engineering Education, 2024.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

machine learning BASED PREDICTION OF WAVE-INDUCED VESSEL RESPONSE 41

MACHINE LEARNING BASED PREDICTION OF WAVE-INDUCED VESSEL RES...

引用

ASME 41st international conference on Ocean, Offshore and Arctic engineering (OMAE)

作者： Cetin, Ali Solum, Vegard R. Evans, Cristina M. 4Subsea AS Asker Norway Subsea 7 Westhill England

ISBN: (纸本)9780791885895

"Waiting on weather" is a costly restraint on offshore vessel operability. Vessel operating windows are determined based on the relationships between the weather and vessel movement, and uncertainties in these predictions may result in vessel operations being ceased prematurely. To improve the efficiency of offshore operations, existing assumptions and calculations based on conventional response amplitude operators (RAOs) should be challenged and improved. A machine learning approach is presented as a means of enriching these conventional RAOs with data. The machine learning model uses sea state forecasts to predict vessel response spectra. The model is cleverly formulated to use any existing RAO as a fallback solution in the absence of sufficient data. When applied to a comprehensive real-world scenario, the model predominantly outperforms the "best" available existing RAO. The results can be used not only to improve wave-vessel response predictions, but also to improve our understanding of existing RAOs and their shortcomings. Ultimately, the work can contribute to reducing overconservatism in weather-based restrictions on offshore vessel operability.

关键词： Waves Vessels machine learning Ocean engineering Seas Uncertainty Spectra (Spectroscopy)

来源：评论

学校读者我要写书评

暂无评论

Temporal Adaptive Clustering for Heterogeneous Clients in Federated learning 38

Temporal Adaptive Clustering for Heterogeneous Clients in Fe...

引用

38th international conference on Information Networking (ICOIN)

作者： Ali, Syed Saqib Kumar, Ajit Ali, Mazhar Singh, Ankit Kumar Choi, Bong Jun Soongsil Univ Sch Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9798350330946;9798350330953

Federated learning has emerged as a highly promising approach for training machine learning models across a decentralized network of clients, with a key focus on maintaining the privacy of data. Nevertheless, the management of system heterogeneity and the handling of time-varying interests continue to pose hurdles for conventional federated learning methodologies. This work presents temporal-based adaptive clustered federated learning as a viable solution to the difficulties mentioned above. The evaluation of clusterability is conducted by calculating the Silhouette score following each iteration of federated training. The process of model aggregation is performed at the cluster level, resulting in enhanced convergence efficiency and improved accuracy of predictions. The inclusion of temporal-based adaptiveness in clustered federated learning for time-varying environments enables the system to dynamically modify cluster configurations in response to clients joining or leaving the network. The experimental results on a real-world dataset of an electric vehicle charging station network illustrate the efficacy of the suggested approach in terms of model correctness, convergence, and adaptability. The temporal-based adaptive clustered federated learning framework has demonstrated significant advancements compared to the current state-of-the-art clustered federated learning approaches.

关键词： Federated learning Energy Demand Prediction Clustered Federated learning Adaptive Clustered Federated learning Temporal based CFL

来源：评论

学校读者我要写书评

暂无评论

Generation of global 1-km daily top-of-atmosphere outgoing longwave radiation product from 2000 to 2021 using machine learning

引用

international Journal of Digital Earth 2023年第1期16卷 2002-2012页

作者： Chuan Zhan Shunlin Liang School of Resources and Environmental Engineering Wuhan University of Science and TechnologyWuhanPeople's Republic of China Department of Geography The University of Hong KongPokfulamPeople's Republic of China

Top-of-atmosphere(TOA)outgoing longwave radiation(OLR),a key component of the Earth’s energy budget,serves as a diagnostic of the Earth’s climate system response to incoming solar ***,existing products are typically estimated using broadband sensors with coarse spatial *** paper presents a machine learning method to estimate TOA OLR by directly linking Moderate Resolution Imaging Spectroradiometer(MODIS)TOA radiances with TOA OLR determined by Clouds and the Earth’s Radiant Energy System(CERES)and other information,such as the viewing geometry,land surface temperature and cloud top temperature determined by Modern-Era Retrospective analysis for Research and Applications,Version 2(MERRA-2).Models are built separately under clear-and cloudy-sky conditions using a gradient boosting regression *** test results show that the root mean square errors(RMSEs)of the clear-sky and cloudy-sky models for estimating instantaneous values are 4.1 and 7.8 W/m^(2),***-time conversion ratios derived from CERES daily and hourly OLR data are used to convert the instantaneous MODIS OLR to daily ***-comparisons of the daily results show that the RMSE of the estimated MODIS OLR is 8.9 W/m^(2) in East *** developed high resolution dataset will be beneficial in analyzing the regional energy budget.

关键词： TOA outgoing longwave radiation MODIS CERES machine learning Earth’s energy budget

来源：评论

学校读者我要写书评

暂无评论

machine learning MODELS FOR PREDICTING DEFLECTION AND SHAPE OF 2D CANTILEVER BEAMS

MACHINE LEARNING MODELS FOR PREDICTING DEFLECTION AND SHAPE ...

引用

ASME international Design engineering Technical conferences / 42nd Annual Computers and Information in engineering conference (IDETC-CIE) / 46th Mechanisms and Robotics conference (MR)

作者： Jin, Yi Su, Hai-Jun Ohio State Univ Dept Mech & Aerosp Engn Columbus OH 43210 USA

ISBN: (纸本)9780791886281

Efficient predictive models for large deflection of beams are critical for the design and analysis of compliant mechanisms. machine learning (ML) models for predicting large tip deflection and shape of 2D cantilever beams are proposed for the analysis of compliant mechanisms in this paper. First, the training data is collected by running massive finite element simulations of cantilever beams subject to various loading conditions. Second, these training data are fed into several nonlinear regression learners. The result is a generalized predictive model for any 2D beams subject to a wide range of tip loads. The evaluation results show the maximum error is about 3.8%. The machine learning models with high prediction speed and accuracy are obtained. Two extreme loading conditions are studied to evaluate our machine learning model of deflections. Our results show that the error of our ML model is less than that of the conventional PRB 3R model, and the speed is 98 times that of the FEM in these two studies. A numerical example of 4-bar linkages with a 2D beam is applied to demonstrate the use of the machine learning model of the beam shape. The result shows the very close shape and high efficiency of our model.

关键词： Compliant mechanisms machine learning large deflection 2D cantilever beams

来源：评论

学校读者我要写书评

暂无评论

Exploring Character-Level Deep learning Models for POS Tagging in Assamese Language 2

Exploring Character-Level Deep Learning Models for POS Taggi...

引用

2nd international conference on machine learning and data engineering, icmlde 2023

作者： Phukan, Rituraj Baruah, Nomi Sarma, Shikhar Kr. Konwar, Darpanjit Dibrugarh University Dibrugarh786004 India Gauhati University Guwahati781014 India

The proposed research investigates a novel approach of character-level Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) for part-of-speech (POS) tagging in the Assamese language. The proposed work contributes to Natural Language Processing (NLP) by exploring these models' ability to assign grammatical labels (POS tags) to individual words within Assamese sentences. The corpus encompasses 60,000 Assamese words and utilizes the LDCIL Assamese tagset. For training and testing, the corpus is divided into an 80:20 ratio where 80% of the corpus is used for training the models, and the rest of 20% is used for evaluation. The character-level LSTM model achieves an accuracy of 92.80%, while the character-level Bi-LSTM model surpasses it by achieving an accuracy of 93.36%. The performance of the proposed research outperforms the existing research works in the Assamese language. The results of this work broaden the understanding of POS tagging in Assamese, offering valuable findings that could be applied to other languages with similar characteristics. © 2024 Elsevier B.V.. All rights reserved.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：