检索结果-内蒙古大学图书馆

25th IEEE international conference on Information Reuse and Integration for data science (IEEE IRI)

作者： Afolabi, Ayomide Aygun, Ramazan Tran, Truong X. Kennesaw State Univ Sch Data Sci & Analyt Kennesaw GA 30144 USA Kennesaw State Univ Comp Sci Dept Kennesaw GA 30144 USA Penn State Univ Penn State Harrisburg Middletown PA USA

ISBN: (纸本)9798350351194;9798350351187

data difficulty level measurement is a critical aspect of machine learning performance evaluation. Several measures have been used to assess the difficulty level of classifying data points in binary classification. However, these measures typically involve building a machine learning model first, which is then used to assess the data difficulty level. In this paper, we propose a novel model agnostic measure named as polarized K-entropy to evaluate the difficulty of classifying a data instance. Our measure leverages the computation of entropy based on the nearest neighbors of a data point. We conducted experiments to evaluate the effectiveness of our proposed method by analyzing how the accuracy of machine learning models change with respect to data difficulty. We used Spearman's rank correlation coefficient to analyze this relationship for neural network, support vector machine, and random forest. Our results show that our measure outperformed the non-conformity measure in all the experiments conducted for six datasets using the selected machine learning models.

关键词： data difficulty polarized K-entropy non-conformity

来源：评论

学校读者我要写书评

暂无评论

Heart Disease Prediction Using machine learning Techniques 5th

Heart Disease Prediction Using Machine Learning Techniques

引用

5th international conference on data science, machine learning and Applications

作者： Sadar, Uzama Agarwal, Parul Parveen, Suraiya Jain, Sapna Obaid, Ahmed J. Jamia Hamdard Dept Comp Sci & Engn New Delhi India Univ Kufa Fac Comp Sci & Math Kufa Iraq Al Ayen Univ Dept Comp Tech Engn Thi Qar Iraq

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Heart disease, also called cardiovascular disease, is considered one of the deadliest diseases that cause high mortality worldwide. Early detection or prediction is a challenging task in the medical field. There is a massive amount of data in the healthcare industry, and processing this amount of data is a tedious task. A computer-aided system that predicts cardiac disease can save time and money. Researchers have researched several computer-assisted diagnoses for disease prediction and prognosis. In this paper, the authors provide an extensive literature survey of various classification approaches such as machine learning, Feature Selection, Hybrid, Ensemble, and Deep learning used by researchers in the last decade for Heart Disease prediction. Furthermore, as the paper focuses on machine learning techniques, comparative analysis of the performance and accuracy of various machine learning techniques are summarized in tabular form. Additionally, this work critically assesses earlier methods and outlines their shortcomings. Finally, the article offers some potential future research direction in machine learning-based automated heart disease prediction.

关键词： machine learning Heart Disease Prediction model Hybrid method Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

A Systematic Review on Application of Multimodal learning and Explainable AI in Tuberculosis Detection

引用

IEEE ACCESS 2025年 13卷 62198-62221页

作者： Nansamba, Barbara Nakatumba-Nabende, Joyce Katumba, Andrew Kateete, David Patrick Makerere Univ Dept Comp Sci Kampala Uganda Makerere Univ Dept Elect & Comp Engn Kampala Uganda Makerere Univ Dept Immunol & Mol Biol Kampala Uganda

Physicians rely on various data sources when diagnosing Tuberculosis (TB). This includes the patient's historical data, demographic data, clinical laboratory results, and imaging data. Traditionally, the application of machine learning and deep learning in detecting TB has focused more on using single modes of data. This constrains the capabilities of the artificial intelligence (AI) techniques to replicate the clinical practice of incorporating multiple sources of information in decision-making. Recent advancements in deep learning and machine learning have enabled the integration of multimodal data which has led to the development of applications that more accurately reflect the clinician's approach. However, the operations of deep learning techniques are still blackbox in nature, which makes it hard to understand their internal work mechanisms. As a result, it is necessary to incorporate explainable AI techniques to assist AI model users understand how the models make decisions. In this paper, we carried out a systematic review of two areas: First, we reviewed recent studies on the application of multimodal learning in TB detection. Here we have provided a summary of the public datasets used in the studies, data modalities used, the fusion techniques, and finally identified AI techniques that can be used with multimodal data. Then we looked at papers that used explainable AI techniques in TB diagnosis and prognosis. This study followed PRISMA guidelines to ensure replicability and accurate reporting of the main findings of the reviewed studies. To stay up-to-date with the state of the art, we specifically examined papers published between 2019 and June 2024. We reviewed thirty-one journal and conference papers we found using Web of science, Scopus and Pubmed databases. The review indicated that models trained on multiple data modalities outperformed those trained on single data modalities. This is due to the additional information extracted from each data modalit

关键词： Tuberculosis Artificial intelligence Explainable AI Deep learning Systematic literature review databases Prognostics and health management Accuracy Guidelines Decision making explainable AI multimodal learning machine learning tuberculosis detection

来源：评论

学校读者我要写书评

暂无评论

Mechanical Quantities Prediction of Metal Cutting by machine learning and Simulation data

引用

international JOURNAL OF applied MECHANICS 2024年第7期16卷

作者： Cheng, Yijin Li, Yan Cong, Yu Joli, Pierre Feng, Zhiqiang Southwest Jiaotong Univ Sch Mech & Aerosp Engn Chengdu 611756 Peoples R China Univ Paris Saclay Univ Evry LMEE F-91020 Evry France

Metal cutting is an important process in industrial manufacturing. Using the mechanical quantities of metal cutting to optimize process design is helpful to improve productivity. However, it is expensive to obtain these quantities due to the complexity of the cutting process, including material nonlinearity, geometric nonlinearity, state nonlinearity and their interactions. In this paper, a prediction model is constructed by combining machine learning (ML) and simulation data to quickly acquire multi-difficult-to-obtain metal cutting mechanical quantities to solve this problem. First, Adaptive Smoothed Particle Hydrodynamics (ASPH) is used to generate a simulation dataset of 2000 metal cutting cases. Based on the simulation data, six machine learning (ML) methods are employed to establish two prediction models, single-task learning and multi-task learning, to predict the mechanical quantities of metal cutting. The experimental results demonstrate that the ML method can predict abundant reference data efficiently after understanding the relationship between simulation parameters and mechanical quantities from simulation data, which is expected to replace some similar and repetitive simulation work. The Multilayer Perceptron (MLP) model under the multi-task setting provides the best prediction performance, fastest prediction time efficiency, and stable model behavior. Additionally, input erasure experiments reveal that the prediction of maximum equivalent plastic strain is significantly affected by particle spacing, and cutting speed plays a vital role in predicting maximum velocity. This work highlights the promotion of the data-driven ML method in quickly obtaining abundant reference data for the metal cutting process, and provides an auxiliary means for process optimization.

关键词： machine learning metal cutting mechanical quantities prediction adaptive smoothed particle hydrodynamics

来源：评论

学校读者我要写书评

暂无评论

machine learning Classification for Intrusion Detection on Computer Networks 9

Machine Learning Classification for Intrusion Detection on C...

引用

9th IEEE international conference on Computational Intelligence and Applications, ICCIA 2024

作者： Tachaapornchai, Abhibhu Kosolsombat, Somkiat Ratanavilisagul, Chiabwoot Data Science and Innovation College of Interdisciplinary Studies Thammasat University Pathum Thani Thailand King Mongkut's University of Technology Faculty of Applied Science Department of Computer and Information Science North Bangkok Thailand

ISBN: (纸本)9798350352214

To build an intelligent intrusion detection system, it is essential to have a suitable and high-quality dataset with a sufficiently large quantity to simulate real-world scenarios. The NSL-KDD dataset is an improved version derived from the previous KDD 99 dataset. In this article, an analysis of the NSL-KDD dataset was conducted to study the efficiency of classification machine learning in detecting abnormalities in network data transmission patterns. The study achieved the highest accuracy by XGBoost method using 14 features important and grid search for hyperparameter tuning and revealing many interesting insights. This research has significant implications and can be further applied in the field of cybersecurity by leveraging machine learning for network intrusion detection systems. © 2024 IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Investigation variable star classification through light curve analysis using machine learning approach

Investigation variable star classification through light cur...

引用

2024 international conference on Photonics Solutions, ICPS 2024

作者： Tongleak, Chutipon Thongsuwan, Setthanun Srithongtae, Kewalee Kitrattana, Borirak Tanirat, Purin Channumsin, Sittiporn Buranasiri, Prathan Artificial Intelligence Photonics Advanced Research Laboratory Department of Physics School of Science King Mongkut's Institute of Technology Thailand Electronic and Optoelectronic Device Research Unit School of Science King Mongkut's Institute of Technology Ladkrabang Thailand Department of Information Technology Sriracha Faculty of Science Kasetsart University Sriracha Campus Thailand Thailand

ISBN: (数字)9781510688308

ISBN: (纸本)9781510688292

With the development of space technology, wide-field sky surveys using telescopes have expanded the range of new data available for time-domain astronomical research. Traditional data analysis methods can no longer respond quickly and accurately enough to the growing volume of data. Thus, classifying time-series data, such as light curves, has become a significant challenge in the era of big data. In modern times, analyzing light curves has become essential for using machine learning techniques to handle and filter through massive amounts of data. machine learning algorithms can be divided into two categories: shallow learning and deep learning. Numerous researchers have proposed and developed a variety of algorithms for light curve classification. In this study, we experimented with Support Vector machine (SVM) and XGBoost, which are shallow machine learning algorithms, as well as 1D-CNN and Long Short-Term Memory (LSTM), which are deep learning algorithms, which are branches of deep machine learning, to classify variable stars. The training and testing data used in this study were from the Optical Gravitational Lensing Experiment-III (OGLE-III), consisting of variable star data from the Large Magellanic Cloud (LMC), categorized into five main classes: Classical Cepheids, δ Scutis, eclipsing binaries, RR Lyrae stars, and Long-period variables. The results demonstrate the performance analysis of each machine learning algorithm type applied to light curve data, while also highlighting the accuracy and statistical metrics of the algorithms used in the experiments. © 2025 SPIE.

关键词： Contrastive learning

来源：评论

学校读者我要写书评

暂无评论

Who Did What to Succeed? Individual Differences in Which learning Behaviors Are Linked to Achievement 25

Who Did What to Succeed? Individual Differences in Which Lea...

引用

15th international conference on learning Analytics and Knowledge

作者： Deininger, Hannah Parrisius, Cora Lavelle-Hill, Rosa Meurers, Detmar Trautwein, Ulrich Nagengast, Benjamin Kasneci, Gjergji Univ Tubingen Hector Res Inst Educ Sci & Psychol Tubingen Germany Karlsruhe Univ Educ Karlsruhe Germany Univ Copenhagen Dept Psychol Copenhagen Denmark Univ Copenhagen Copenhagen Ctr Social Data Sci SODAS Copenhagen Denmark Leibniz Inst Wissensmedien IWM Language & AI Educ Lab Tubingen Germany Tech Univ Munich Responsible Data Sci Munich Germany

ISBN: (纸本)9798400707018

It is commonly assumed that digital learning environments such as intelligent tutoring systems facilitate learning and positively impact achievement. This study explores how different groups of students exhibit distinct relationships between learning behaviors and academic achievement in an intelligent tutoring system for English as a foreign language. We examined whether these differences are linked to students' prior knowledge, personality traits, and motivation. We collected behavioral trace data from 507 German seventh-grade students during the 2021/22 school year and applied machine learning models to predict English performance based on learning behaviors ( best-performing model's R-2 =.41). To understand the impact of specific behaviors, we applied the explainable AI method SHAP and identified three student clusters with distinct learning behavior patterns. Subsequent analyses revealed that these clusters also varied in prior knowledge and motivation: one with high prior knowledge and average motivation, another with low prior knowledge and average motivation, and a third with both low prior knowledge and low motivation. Our findings suggest that learning behaviors are linked differently to academic success across students and are closely tied to their prior knowledge and motivation. This hints towards the importance of personalizing learning systems to support individual learning needs better.

关键词： learning Analytics Behavioral Trace data Academic Performance Interindividual Differences

来源：评论

学校读者我要写书评

暂无评论

AI Fairness-From machine learning to Federated learning

引用

Computer Modeling in Engineering & sciences 2024年第5期139卷 1203-1215页

作者： Lalit Mohan Patnaik Wenfeng Wang Consciousness Studies Program School of HumanitiesNational Institute of Advanced StudiesBangalore560012India Research Institute of Intelligent Engineering and Data Applications Shanghai Institute of TechnologyShanghai201418China Research Center of Ecology and Environment of Central Asia Chinese Academy of SciencesUrumqi830011China Applied Nonlinear Science Lab Anand International College of EngineeringJaipur391320India London Institute of Technology The ASE-London CTI of SCOLondonCR26EQUK Sino-Indian Joint Research Center of AI and Robotics The IMT InstituteBhubaneswar752054India

This article reviews the theory of fairness in AI-frommachine learning to federated learning,where the constraints on precision AI fairness and perspective solutions are also *** a reliable and quantitative evaluation of AI fairness,many associated concepts have been proposed,formulated and ***,the inexplicability of machine learning systems makes it almost impossible to include all necessary details in the modelling stage to ensure *** privacy worries induce the data unfairness and hence,the biases in the datasets for evaluating AI fairness are *** imbalance between algorithms’utility and humanization has further reinforced *** for federated learning systems,these constraints on precision AI fairness still *** solution is to reconcile the federated learning processes and reduce biases and imbalances accordingly.

关键词： Formulation evaluation classification constraints imbalance biases

来源：评论

学校读者我要写书评

暂无评论

Evaluating Uber Customers' Perception Through machine learning Techniques: A Case Study in Ecuador

Evaluating Uber Customers' Perception Through Machine Learni...

引用

international conference on Computational science and Computational Intelligence (CSCI)

作者： Becerra-Salas, Maria Roa, Henry N. Ponliticia Univ Catolica Ecuador Fac Ingn Quito Ecuador

ISBN: (纸本)9798350361513;9798350372304

This study analyzes the perception of Uber users through Twitter, currently known as X, using the CRISP-DIM methodology in Python. We collected data from the last twelve years to accomplish this study. The data set is divided into training and testing, processing them using natural language processing and classifying them as neutral, positive, and hostile. Classification algorithms such as Logistic Regression, Support Vector machines (SVM), and Naive Bayes are applied, with SVM being the most effective in predicting user sentiments. This approach leverages Twitter accessibility and data analytics to understand the public perception of Uber.

关键词： Sentiment analysis Artificial intelligence machine learning Twitter (X) CRISP-DM

来源：评论

学校读者我要写书评

暂无评论

Can Synthetic data be Fair and Private? A Comparative Study of Synthetic data Generation and Fairness Algorithms 25

Can Synthetic Data be Fair and Private? A Comparative Study ...

引用

15th international conference on learning Analytics and Knowledge

作者： Liu, Qinyi Deho, Oscar Vadiee, Farhad Khalil, Mohammad Joksimovic, Srecko Siemens, George Univ Bergen Ctr Sci Learning & Technol SLATE Bergen Norway Univ South Australia Adelaide SA Australia

ISBN: (纸本)9798400707018

The increasing use of machine learning in learning analytics (LA) has raised significant concerns around algorithmic fairness and privacy. Synthetic data has emerged as a dual-purpose tool, enhancing privacy and improving fairness in LA models. However, prior research suggests an inverse relationship between fairness and privacy, making it challenging to optimize both. This study investigates which synthetic data generators can best balance privacy and fairness, and whether pre-processing fairness algorithms, typically applied to real datasets, are effective on synthetic data. Our results highlight that the DEbiasing CAusal Fairness (DECAF) algorithm achieves the best balance between privacy and fairness. However, DECAF suffers in utility, as reflected in its predictive accuracy. Notably, we found that applying pre-processing fairness algorithms to synthetic data improves fairness even more than when applied to real data. These findings suggest that combining synthetic data generation with fairness pre-processing offers a promising approach to creating fairer LA models.

关键词： Privacy Synthetic data Generation Algorithmic Fairness Fairness Metrics Classifiers

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：