检索结果-内蒙古大学图书馆

20th international conference on Intelligent Computing (ICIC)

作者： Yu, Xingyu Li, Xiang Tong, Jijun Yang, Bin Zhejiang Sci Tech Univ Sch Informat Sci & Engn Hangzhou 310018 Peoples R China Qingdao Eighth Peoples Hosp Qingdao 266121 Peoples R China Zaozhuang Univ Sch Informat Sci & Engn Zaozhuang 277160 Peoples R China

ISBN: (纸本)9789819756919;9789819756926

The continuous advancement of next-generation sequencing (NGS) technology enables researchers to detect somatic mutations, significantly enhancing the accuracy of identifying somatic mutations from NGS data. With the continuous advancement of machine learning (ML) technology, researchers have gained more confidence in utilizing this technology for data prediction. This article proposes the combination of the Tree-structured Parzen Estimator (TPE) algorithm with the Support Vector machines (SVM) algorithm for detecting somatic mutations in matched tumor and normal paired sequencing data. The method is applied to real biological data from exome capture data and whole-genome shotgun data. The results indicate a significant improvement in the detectability of somatic mutations using the proposed integrated approach compared to the conventional methods.

关键词： next-generation sequencing technology somatic mutations machine learning SVM

来源：评论

学校读者我要写书评

暂无评论

Position: Explain to Question not to Justify 41

Position: Explain to Question not to Justify

引用

41st international conference on machine learning (ICML)

作者： Biecek, Przemyslaw Samek, Wojciech Univ Warsaw MI2 AI Warsaw Poland Warsaw Univ Technol MI2 AI Warsaw Poland Fraunhofer Heinrich Hertz Inst Dept Artificial Intelligence Berlin Germany Tech Univ Berlin Dept Elect Engn & Comp Sci Berlin Germany BIFOLD Berlin Inst Fdn Learning & Data Berlin Germany

Explainable Artificial Intelligence (XAI) is a young but very promising field of research. Unfortunately, the progress in this field is currently slowed down by divergent and incompatible goals. We separate various threads tangled within the area of XAI into two complementary cultures of human/value-oriented explanations (BLUE XAI) and model/validation-oriented explanations (RED XAI). This position paper argues that the area of RED XAI is currently under-explored, i.e., more methods for explainability are desperately needed to question models (e.g., extract knowledge from well-performing models as well as spotting and fixing bugs in faulty models), and the area of RED XAI hides great opportunities and potential for important research necessary to ensure the safety of AI systems. We conclude this paper by presenting promising challenges in this area.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Heart Disease Prediction Using Novel Ensemble and Blending Based Cardiovascular Disease Detection Networks: EnsCVDD-Net and BlCVDD-Net

引用

IEEE Access 2024年 12卷 109230-109254页

作者： Khan, Hira Javaid, Nadeem Bashir, Tariq Akbar, Mariam Alrajeh, Nabil Aslam, Sheraz Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Islamabad44000 Pakistan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Douliu64002 Taiwan Department of Biomedical Technology College of Applied Medical Sciences King Saud University Riyadh11633 Saudi Arabia Cyprus University of Technology Limassol3036 Cyprus

Cardiovascular Diseases (CVDs) have emerged as a significant physiological condition, being a primary contributor to mortality. Timely and precise diagnosis of heart disease is crucial to safeguard patients from additional harm. Recent studies show that the usage of data driven approaches, such as Deep learning (DL) and machine learning (ML) techniques, in the field of medical science is highly useful in accurately diagnosing heart disease in less time. However, statistical learning and traditional ML approaches require feature engineering to generate robust and effective features from data, which are then used in the prediction models. In the case of large complex data, both processes pose many challenges. Whereas, DL techniques are capable of learning features automatically from the data and are effective at handling large and intricate datasets while outperforming the ML models. This study focuses on the accurate prediction of CVDs, considering the patient’s health and socio-economic conditions while mitigating the challenges presented by imbalanced data. The Adaptive Synthetic Sampling Technique is used for data balancing, while the Point Biserial Correlation Coefficient is used as a feature selection technique. In this study, two DL models, Ensemble based Cardiovascular Disease Detection Network (EnsCVDD-Net) and Blending based Cardiovascular Disease Detection Network (BlCVDD-Net), are proposed for accurate prediction and classification of CVDs. EnsCVDD-Net is made by applying an ensemble technique to LeNet and Gated Recurrent Unit (GRU), and BlCVDD-Net is made by blending LeNet, GRU and Multilayer Perceptron. SHapley Additive exPlanations is used to provide a clear understanding of the influence different factors have on CVD diagnosis. The network’s performance is evaluated on the basis of various performance metrics. The results indicate that the EnsCVDD-Net outperforms all base models with 88% accuracy, 88% F1-score, 91% precision, 85% recall, and 777s execu

关键词： Heart

来源：评论

学校读者我要写书评

暂无评论

Flight delay prediction based on machine learning method

Flight delay prediction based on machine learning method

引用

2024 international conference on Cloud Computing, Performance Computing, and Deep learning, CCPCDL 2024

作者： Fan, Yilu Nanjing University of Information Science & Technology Jiangsu Province Nanjing210044 China

ISBN: (纸本)9781510683044

The increasing growth of the aviation industry has been accompanied by increasing problems of flight delays. This research aims to predict flight delays by employing machine learning models to enhance the effectiveness of the aviation transportation system. Domestic flight takeoff and landing data from the United States in January 2019 and 2020 were used for the research, and data preprocessing was carried out. Four machine learning models (XGBoost, Random Forest, LightGBM, and Gradient Boosting Tree) were used for model training, followed by an assessment of their respective performances on a designated test set. The outcomes indicate that the XGBoost model outperforms others in accuracy and the AUC of the test set, followed by the LightGBM model, while the Random Forest model shows overfitting on the training set. This research offers a comprehensive analysis and resolution to the issue of flight delay prediction, which helps airlines and passengers to make rational decisions. © 2024 SPIE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Performance prediction of co-rotating disk cavity with finned vortex reducer based on machine learning

引用

international JOURNAL OF THERMAL scienceS 2024年 205卷

作者： Zhang, Minhui Wang, Chunhua Zhang, Jingzhou Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing 210016 Peoples R China

machine learning method was applied for rapid and precise prediction of flow and heat transfer characteristics within co-rotating disk cavity with a finned vortex reducer. A new data preprocessing scheme was developed to reduce modeling cost. By using this scheme, classical radial basis function neural network (RBFNN) shows better prediction performance compared with deconvolutional neural network. Furthermore, RBFNN has a simpler topological structure and fewer hyperparameters. By testing, the relative root mean square error for total pressure, total temperature, and swirl ratio is 0.51 %, 0.32 %, and 1.99 %, and corresponding coefficient of determination reaches 99.68 %, 96.55 %, and 99.16%. The effects of input parameters on the outputs of RBFNN were analyzed, and a sensitivity analysis was conducted. Within the investigated parameter range, the total pressure loss increases as dimensionless mass flow rate, rotational Reynolds number, and radial position of the fin increases. The total temperature drop increases with the increase of dimensionless mass flow rate and rotational Reynolds number, but the decrease of radial position of the fin. Additionally, an increase in dimensionless mass flow rate or a decrease in rotational Reynolds number causes the increase of swirl ratio. This research provides an efficient modeling method for components in secondary air system in gas turbines.

关键词： Gas turbine Co-rotating disk cavity Finned vortex reducer Radial basis function neural network Secondary air system

来源：评论

学校读者我要写书评

暂无评论

Predicting System Performance and Resource Exhaustion with machine learning: Tackling Class Imbalance 1

Predicting System Performance and Resource Exhaustion with M...

引用

1st international conference on Advances in Computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Swati, Shipra Kumar, Naween Dubey, Ankit Saini, Vaibhav Sharma, Subham Pranav, Ayushman Bennett University SCSET U.P. Greater Noida India

ISBN: (纸本)9798331518578

This study introduces a robust machine learning pipeline for event classification, utilizing a large-scale dataset from eight Google Borg compute clusters (May 2019) available via Google Big- Query. The dataset includes detailed job submissions, scheduling decisions, and resource usage data, with enhancements such as 5-minute CPU usage histograms, shared resource reservations, and job-parent relationships critical for MapReduce task analysis. data preprocessing involves imputation for missing values, label encoding for categorical data, and SMOTE to address the class imbalance. Feature scaling using StandardScaler and dimensionality reduction via Principal Component Analysis (PCA) retained 95% of the variance. The dataset was divided into training and testing sets, preserving class distribution. Seven machine learning models were evaluated, spanning linear, non-linear, ensemble, and neural network approaches. The Support Vector machine (SVM) achieved exceptional performance, with 98.0% accuracy, 97.5% cross-validation score, 98.2% precision, 97.8% recall, and 98.0% F1-score. This study highlights the evolution of workloads and resource usage patterns, contributing to developing scalable and efficient event classification frameworks in cloud computing. © 2025 IEEE.

关键词： Cloud computing Event classification Google Borg clusters machine learning Principal component analysis (PCA) Resource usage patterns Synthetic minority oversampling technique (SMOTE)

来源：评论

学校读者我要写书评

暂无评论

Multi-modal Contextual Prompt learning for Multi-label Classification with Partial Labels 24

Multi-modal Contextual Prompt Learning for Multi-label Class...

引用

16th international conference on machine learning and Computing (ICMLC)

作者： Wang, Rui Pan, Zhengxin Wu, Fangyu Lv, Yifan Zhang, Bailing Zhejiang Sci Tech Univ Hangzhou Peoples R China Zhejiang Univ Hangzhou Peoples R China Xian Jiaotong Liverpool Univ Suzhou Peoples R China NingboTech Univ Ningbo Peoples R China

ISBN: (纸本)9798400709234

Multi-label classification is a task with diverse applications, but current algorithms heavily rely on accurately labeled data, leading to time-consuming and labor-intensive data collection. However, multi-label classification with partial labels presents significant challenges. In this study, we propose Multi-modal Contextual Prompt learning (MCPL), a novel approach that leverages large-scale visual-language models and exploits the strong image-text alignment in CLIP to address the scarcity of label annotations. We pre-train the visual language model's encoder on a large number of imagetext pairs.. We introduce multi-modal contextual prompt learning in both images and labeled text to better utilize the image-label correspondence within CLIP, resulting in enhanced multi-label classification performance, even when faced with partial labels. We also use the coupling function to couple the two modes and realize the interactive connection of the two modal prompts. Extensive experiments on the MS-COCO and VOC2007 datasets, demonstrating its superiority and achieving competitive performance.

关键词： Multi-label classification Prompt learning Partial label

来源：评论

学校读者我要写书评

暂无评论

Blockchain-based data Quality Assessment to Improve Distributed machine learning

Blockchain-based Data Quality Assessment to Improve Distribu...

引用

international conference on Computing, Networking and Communications (ICNC)

作者： Du, Yao Wang, Zehua Leung, Cyril Leung, Victor C. M. Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China

ISBN: (纸本)9781665457194

data quality assessment is critical for distributed machine learning (DML). data collected from heterogeneous Internet of things (IoT) devices may contain biased information that decreases the prediction accuracy of DML models. To address these challenges, we propose a blockchain-based approach to assess the quality of data that are not independent and identically distributed (non-IID). A blockchain running atop mobile edge computing (MEC) is helpful to protect privacy, security, and integrity of healthcare data when IoT devices are connected to MEC servers. Therefore, it is critical to integrate data quality assessment module on blockchain when building a blockchain-enabled DML system. In this paper, we jointly consider information loss and marginal utility of non-IID data samples. Specifically, we use Kullback-Leibler (KL) divergence to evaluate the information loss between IID and non-IID data samples and apply the reciprocal of data quantity to model the marginal utility of data samples. Human activities and handwritten digit recognition data sets are used for performance evaluations. Experiments show that our proposed scheme out-performs benchmarks regarding model test accuracy on various non-IID data samples.

关键词： Blockchain distributed machine learning data quality edge computing Internet of things

来源：评论

学校读者我要写书评

暂无评论

Scalable and Interpretable learning Tool Development within Game-based learning Environment: A Case Study of Mission HydroSci 25

Scalable and Interpretable Learning Tool Development within ...

引用

2025 ACM international conference on Supporting Group Work, GROUP Companion 2025

作者： Lu, Wenyi Computer Science University of Missouri ColumbiaMO United States

ISBN: (纸本)9798400711879

This research develops scalable and interpretable learning tools—stealth assessment and a teacher dashboard—using a sophisticated logging system within the Mission HydroSci game-based learning environment. By integrating existing frameworks with customized approaches, the system captures in-game behaviors critical to understanding learning processes. A stealth assessment pipeline, combined with advanced machine learning, and a data-enriched dashboard, is being developed to enhance teacher-student communication. This study contributes a scalable framework for learning tools across diverse game contexts, offering valuable insights for educators, researchers, and game designers to improve both learning and educational game design in classroom settings. © 2025 Copyright held by the owner/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

Product Order data Analysis and Demand Forecasting based on machine learning

Product Order Data Analysis and Demand Forecasting based on ...

引用

international conference on Internet of Things and machine learning, IoTML 2023

作者： Lu, Yue Faculty of Computer Science China University of Geosciences Wuhan China

ISBN: (纸本)9781510671805

In recent years, the external environment of enterprises has been characterized by significant uncertainty;and the increasingly complex situation has also posed numerous challenges to their supply chains. Demand forecasting, as a critical component of the enterprise supply chain, directly affects market performance. However, demand forecasting is influenced by multiple factors, leading to generally low prediction accuracy, with substantial deviations from actual outcomes. To enhance forecasting precision, this study utilizes shipment data directed towards distributors from a large domestic manufacturing enterprise during the period from September 1, 2015, to December 20, 2018. The data undergoes preliminary processing, followed by exploratory analysis and feature engineering. Conventional machine learning models are applied for prediction. By comparing the Random Forest and LGBM models, the LGBM model is selected as the best-performing one for data prediction. Forecasts are conducted at daily, weekly, and monthly granularities for the product demand in January, February, and March 2019. © 2023 SPIE.

关键词： Supply chains

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：