检索结果-内蒙古大学图书馆

Prediction of pulmonary embolism by an explainable machine learning approach in the real world

SCIENTIFIC REPORTS 2025年第1期15卷 1-8页

作者： Zhou, Qiao Huang, Ruichen Xiong, Xingyu Liang, Zongan Zhang, Wei Second Mil Med Univ Changhai Hosp Dept Resp & Crit Care Med Shanghai Peoples R China Sichuan Univ Dept Resp & Crit Care Med West China Hosp Chengdu Sichuan Peoples R China

In recent years, large amounts of researches showed that pulmonary embolism (PE) has become a common disease, and PE remains a clinical challenge because of its high mortality, high disability, high missed and high misdiagnosed rates. To address this, we employed an artificial intelligence-based machine learning algorithm (MLA) to construct a robust predictive model for PE. We retrospectively analyzed 1480 suspected PE patients hospitalized in West China Hospital of Sichuan University between May 2015 and April 2020. 126 features were screened and diverse MLAs were utilized to craft predictive models for PE. Area under the receiver operating characteristic curves (AUC) were used to evaluate their performance and SHapley Additive exPlanation (SHAP) values were utilized to elucidate the prediction model. Regarding the efficacy of the single model that most accurately predicted the outcome, RF demonstrated the highest efficacy in predicting outcomes, with an AUC of 0.776 (95% CI 0.774-0.778). The SHAP summary plot delineated the positive and negative effects of features attributed to the RF prediction model, including D-dimer, activated partial thromboplastin time (APTT), fibrin and fibrinogen degradation products (FFDP), platelet count, albumin, cholesterol, and sodium. Furthermore, the SHAP dependence plot illustrated the impact of individual features on the RF prediction model. Finally, the MLA based PE predicting model was designed as a web page that can be applied to the platform of clinical management. In this study, PE prediction model was successfully established and designed as a web page, facilitating the optimization of early diagnosis and timely treatment strategies to enhance PE patient outcomes.

关键词： Pulmonary embolism machine learning algorithms Prediction model SHAP value

来源：评论

学校读者我要写书评

暂无评论

machine learning-Enabled Evolutionary Two-Stage Stochastic Programming

引用

IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年

作者： Pan, Jeng-Shyang Song, Pei-Cheng Chu, Shu-Chuan Snasel, Vaclav Watada, Junzo Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing Peoples R China Chaoyang Univ Technol Dept Informat Management Taichung Taiwan Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China VSB Tech Univ Ostrava Dept Comp Sci Ostrava Czech Republic Waseda Univ Grad Sch Informat Prod & Syst Kitakyushu Japan

Two-stage Stochastic Programming (2SP) is an effective framework for decision-making and modeling under uncertainty. Some 2SP problems are challenging due to their high dimensionality and nonlinearity. machine learning can assist in solving 2SP problems by providing data-driven insights and approximations. Evolutionary algorithms are more general and effective methods for handling various 2SP problems by exploiting their structures and features. However, there is still a research gap in combining machine learning and evolutionary algorithms for solving 2SP problems. Therefore, this paper proposes for the first time a machine learning-enabled Evolutionary 2SP framework (MLE2SP), which uses machine learning to construct surrogate-assisted evolutionary optimization frameworks for 2SP. It constructs a novel multi-output 2SP surrogate model that considers scenarios and decision variables of both stages for the first time and proposes a data conversion method to handle the high-dimensional decision variables and scenarios. It also proposes a machine learning-enabled Differential Evolution Sampling (MLDES) method to update candidate solutions, which extracts knowledge from dominant candidate solutions to guide the evolutionary direction. Moreover, this work provides open sources of linear and nonlinear two-stage stochastic mixed-integer programming problem instances as benchmark test functions. The effectiveness and generality of the proposed algorithm and framework are verified by the test results on the benchmark test functions and a disaster relief logistics problem, which provide a new research direction for designing general and effective two-stage stochastic programming solving frameworks.

关键词： Stochastic processes Programming machine learning Evolutionary computation machine learning algorithms Linear programming Random variables Metaheuristics Heuristic algorithms Decision making Stochastic programming machine learning evolutionary computation surrogate model uncertainty

来源：评论

学校读者我要写书评

暂无评论

DiGraph-Enabled Digital Twin and Label-Encoding machine learning for SCADA Network's Cyber Attack Analysis in Industry 5.0

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

引用

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2025年 6卷 3404-3416页

作者： Al-Qirim, Nabeel Bani-Hani, Anoud Majdalawieh, Munir Al Hamadi, Hussam Hasan, Mohammad Kamrul Zayed Univ Coll Technol Innovat Dubai U Arab Emirates Univ Colorado Business Sch Denver CO USA Univ Dubai Coll Engn & IT Dubai U Arab Emirates Univ Kebangsaan Malaysia Ctr Cyber Secur Informat Sci & Technol Bangi 43600 Malaysia

False-Data Injection Attack (FDIA), Remote-Tripping Command Injection (RTCI), and System Reconfiguration Attack (SRA) on SCADA (Supervisory Control and Data Acquisition) networks impact industry 5.0 enabled smart grid components such as intelligent-electronic-device (IED), circuit-breaker, network-switch, and power transmission lines. Since the SCADA-network-based cyber-attacking flow is not in digital-twin form, it is impossible to simulate the effects of the attack. Furthermore, the string nature of these affected components' data makes it challenging to incorporate into machine-learning-enabled intelligence (CTI) processes. To visualize the attacking flow of FDIA, RTCI, and SRA cyber-attacks on SCADA networks, this paper presents a novel "Digital Twin and machine learning empowered Cyber Attacking Flow Analysis (DT-ML-CAFA)" approach for grid CTI in Industry 5.0. To process digital twins and determine how the cyberattacks are impacting SCADA components, the directed-graph (DiGraph) algorithm-based knowledge-graph method is utilized. The overall digital-twin process is examined using machine learning techniques based on Extra-Trees, Random-Forest, Bootstrap-Aggregating (Bagging), XGBoost, and Logistic-Regression. Based on the experimental results of this study, this paper shows that the proposed method can simulate the flow of cyber-attacks on the SCADA network in the form of the digital twin, and the confusion metrics of the digital twin are obtained with high accuracy.

关键词： Measurement Power transmission lines machine learning algorithms Data visualization SCADA systems Digital twins Smart grids Fifth Industrial Revolution Cyberattack Bagging Smart grid cyber security digital twin SCADA knowledge graph

来源：评论

学校读者我要写书评

暂无评论

Early detection of feline chronic kidney disease via 3-hydroxykynurenine and machine learning

引用

SCIENTIFIC REPORTS 2025年第1期15卷 1-18页

作者： Vanden Broecke, Ellen Van Mulders, Laurens De Paepe, Ellen Paepe, Dominique Daminet, Sylvie Vanhaecke, Lynn Univ Ghent Fac Vet Med Dept Translat Physiol Infectiol & Publ Hlth Lab Integrat Metabol LIMET Salisburylaan 133 B-9820 Merelbeke Belgium Univ Ghent Fac Vet Med Small Anim Dept Salisburylaan 133 B-9820 Merelbeke Belgium Queens Univ Belfast Inst Global Food Secur Sch Biol Sci Chlorine Gardens 19 Belfast BT9 5DL North Ireland

Feline chronic kidney disease (CKD) is one of the most frequently encountered diseases in veterinary practice, and the leading cause of mortality in cats over five years of age. While diagnosing advanced CKD is straightforward, current routine tests fail to diagnose early CKD. Therefore, this study aimed to identify early metabolic biomarkers. First, cats were retrospectively divided into two populations to conduct a case-control study, comparing the urinary and serum metabolome of healthy (n = 61) and CKD IRIS stage 2 cats (CKD2, n = 63). Subsequently, longitudinal validation was conducted in an independent population comprising healthy cats that remained healthy (n = 26) and cats that developed CKD2 (n = 22) within one year. Univariate, multivariate, and machine learning-based (ML) approaches were compared. The serum-to-urine ratio of 3-hydroxykynurenine was identified as a single biomarker candidate, yielding a high AUC (0.844) and accuracy (0.804), while linear support vector machine-based modelling employing metabolites and clinical parameters enhanced AUC (0.929) and accuracy (0.862) six months before traditional diagnosis. Furthermore, analysis of variable importance indicated consistent key serum metabolites, namely creatinine, SDMA, 2-hydroxyethanesulfonate, and aconitic acid. By enabling accurate diagnosis at least six months earlier, the highlighted metabolites may pave the way for improved diagnostics, ultimately contributing to timely disease management.

关键词： Diagnostic panel Renal disease Targeted metabolomics Kynurenines machine learning algorithms Multivariate models

来源：评论

学校读者我要写书评

暂无评论

Blockchain-Based Crowdsourcing Framework for machine learning Ground Truth

引用

IEEE ACCESS 2025年 13卷 73041-73055页

作者： Alzahrani, Asma S. Alahmadi, Dimah H. Alharbi, Nesreen M. Almagrabi, Hana A. King Abdulaziz Univ Fac Comp & Informat Technol Jeddah 21589 Saudi Arabia

machine learning has evolved from a lab curiosity to a widely used technology that is fundamentally reliant on ground truth data for model training and evaluation. This research addresses the challenges in obtaining accurate ground truth data due to limited domain expertise, sparse and unrepresentative datasets, and the high costs associated with data acquisition. The quality of this data significantly influences the reliability of machine learning models, prompting research into methods to improve ground-truth reliability. This research proposes a framework that utilize blockchain-based crowdsourcing for ground-truth data annotation. Blockchain technology, with its decentralized immutable ledger system, offers a secure method for data verification and collection from decentralized entities. The proposed framework was implemented in an Ethereum network environment using blockchain technology and smart contracts. Next, we evaluated the collected ground truth by measuring the inter-rater agreement among the participants. The experimental results indicate that blockchain can enhance annotation consistency, showing a higher reliability of crowd-sourced data compared to expert opinions. Most annotator pairs demonstrated moderate to strong agreement, confirming the potential of blockchain technology in improving ground truth data annotation.

关键词： Crowdsourcing Annotations Reliability Blockchains machine learning Accuracy Data models machine learning algorithms Games Training Blockchain crowdsourcing ground truth inter-rater agreement reliability

来源：评论

学校读者我要写书评

暂无评论

Academic Performance Prediction Using machine learning Approaches: A Survey

引用

IEEE TRANSACTIONS ON learning TECHNOLOGIES 2025年 18卷 351-368页

作者： Pan, Jialun Zhao, Zhanzhan Han, Dongkun Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong Peoples R China Chinese Univ Hong Kong Shenzhen Sch Humanities & Social Sci Shenzhen Peoples R China

Properly predicting students'academic performance is crucial for elevating educational outcomes in various disciplines. Through precise performance prediction, schools can quickly pinpoint students facing challenges and provide customized educational materials suited to their specific learning needs. The reliance on teachers' experience to predict students' academic performance has proven to be less accurate and efficient than desired. Consequently, the past decade has witnessed a marked surge in employing machine learning and data mining techniques to forecast students' performance. However, the academic community has yet to agree on the most effective algorithm for predicting academic outcomes. Nonetheless, conducting an analysis and comparison of the existing algorithms in this field remains meaningful. Furthermore, recommendations for selecting an appropriate algorithm will be provided to interested researchers and educators based on their specific requirements. This article reviews the state-of-the-art literature on academic performance predictions using machine learning approaches in recent years. It details the variables analyzed, the algorithms implemented, the datasets utilized, and the evaluation metrics applied to assess model efficacy. What makes this work different is that relevant surveys in the past 10 years are also analyzed and compared, highlighting their contributions and review methods. In addition, we compared the accuracy of various machine learning models using popular open-access datasets and determined the best-performing algorithms among them. Our dataset and source codes are released for future algorithm comparisons and evaluations in this community.

关键词： Reviews Prediction algorithms Surveys machine learning Data mining machine learning algorithms Random forests Artificial intelligence Accuracy Logistic regression Artificial intelligence (AI) in education learning analytics machine learning neural networks (NNs) performance prediction students' performance supervised learning survey

来源：评论

学校读者我要写书评

暂无评论

Deep learning-Augmented Evolutionary Strategies for Intelligent Global Optimization

引用

IEEE ACCESS 2025年 13卷 70138-70178页

作者： Ezugwu, Absalom El-Shamir Oyelade, Olaide Nathaniel Agushaka, Jeffrey Ovre Saha, Apu Kumar North West Univ Unit Data Sci & Comp ZA-2520 Potchefstroom South Africa Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT9 5BN North Ireland Fed Univ Lafia Dept Comp Sci Lafia 950101 Nigeria Natl Inst Technol Agartala Dept Math Agartala 799046 Tripura India

This paper introduces the Susceptible-Infected-Removed Optimizer (SIRO), a novel learned heuristic inspired by biological systems and deep learning techniques. SIRO models its search process after the SIR epidemiological compartmental model, predicting the susceptibility, infection, and recovery dynamics of solutions. SIRO integrates deep learning into its initialization and parameter setting to enhance its efficiency, enabling intelligent and adaptive behavior. This hybridization improves solution quality, accelerates convergence, enhances robustness, and reduces computational costs. The algorithm's performance was evaluated using CEC 2017 benchmark functions, demonstrating superior results in hybrid functions (C1-C28) despite moderate performance on traditional CEC1-CEC14 functions. Friedman's test ranked SIRO 4th overall, with SSA as the top-performing algorithm. Additionally, SIRO was tested on real-world optimization problems, including mechanical engineering design, hyperparameter tuning, and feature selection for medical image classification. In the classification task, SIRO-enhanced CNN achieved an accuracy of 0.86 at the 5th epoch, outperforming CNN (0.66), CNN-GA (0.76), and CNN-WOA (0.75). Furthermore, SIRO reported a precision of 0.96, recall of 1.0, and F1-score of 0.98, highlighting its effectiveness. These results validate the benefits of integrating a learning mechanism into SIRO, yielding superior precision, computational efficiency, and performance over conventional optimization approaches.

关键词： Metaheuristics Optimization machine learning algorithms Search problems Heuristic algorithms Deep learning Prediction algorithms MIMICs Genetic algorithms Gravity Susceptible-infected-removed SIR-model optimization algorithms engineering design optimization metaheuristics machine learning deep learning

来源：评论

学校读者我要写书评

暂无评论

Integrative machine learning frameworks to uncover specific protein signature in neuroendocrine cervical carcinoma

引用

BMC CANCER 2025年第1期25卷 1-16页

作者： Shen, Tao Dong, Tingting Wang, Haiyang Ding, Yi Zhang, Jianuo Zhu, Xinyi Ding, Yeping Cai, Wen Wei, Yalan Wang, Qiao Wang, Sufen Jiang, Feiyun Tang, Bin Anhui Normal Univ Coll Life Sci Anhui Prov Engn Res Ctr Mol Detect & Diagnost Anhui Prov Key Lab Mol Enzymol & Mech Major Dis Wuhu Peoples R China East China Normal Univ Peoples Hosp Wuhu City 2 Dept Gynecol Wuhu Affiliated Hosp Wuhu 241000 Peoples R China East China Normal Univ Peoples Hosp Wuhu City 2 Dept Pathol Wuhu Affiliated Hosp Wuhu 241000 Peoples R China Wannan Med Coll Yijishan Hosp Dept Pathol Affiliated Hosp 1 Wuhu Peoples R China

ObjectiveNeuroendocrine cervical carcinoma (NECC) is a rare but highly aggressive tumor. The clinical management of NECC follows neuroendocrine neoplasms and cervical cancer in general. However, the diagnosis and prognosis of NECC remain dismal. The aim of this study was to identify a specific protein signature for the diagnosis of *** and gene expression data for NECC and other cervical cancers were retrieved or downloaded from self-collected samples or public resources. Eleven machine-learning algorithms were packaged into 66 combinations, of which we selected the optimal algorithm, including randomForest, SVM-RFE, and LASSO, to select key NECC specific dysregulated proteins (kNsDEPs). The diagnostic effect of kNsDEPs was validated by a set of predictive models and immunohistochemical staining method. The dysregulation patterns of kNsDEPs were further investigated in other neuroendocrine *** results showed that NECC displays distinctive biological characteristics, such as HPV18 infection, and exhibits unique molecular features, particularly an enrichment in cytoskeleton-related functions. Furthermore, secretagogin (SCGN), adenylyl cyclase-associated protein 2 (CAP2), and calcyclin-binding protein (CACYBP) were identified as kNsDEPs. These kNsDEPs play a central role in cytoskeleton protein binding and showcase robust diagnostic ability and specificity for NECC. Moreover, the concurrent upregulation of SCGN and CACYBP, along with the downregulation of CAP2, represents a unique feature of NECC, distinguishing it from other neuroendocrine *** study uncovers the significance of kNsDEPs and elucidates their regulated networks in the context of NECC. It highlights the pivotal role of kNsDEPs in NECC diagnosis, thus offering promising prospects for the development of diagnostic biomarkers for NECC.

关键词： Neuroendocrine cervical carcinoma Cervical cancer Proteomics machine learning algorithms Predictive model

来源：评论

学校读者我要写书评

暂无评论

A diagnostic model for sepsis using an integrated machine learning framework approach and its therapeutic drug discovery

引用

BMC INFECTIOUS DISEASES 2025年第1期25卷 1-13页

作者： Zhang, Wuping Shi, Hanping Peng, Jie Jiangxi Prov Peoples Hosp Nanchang Med Coll Affiliated Hosp 1 152 Aiguo Rd Nanchang 330006 Jiangxi Peoples R China Nanchang Univ Sch Publ Hlth Jiangxi Prov Key Lab Prevent Med Nanchang 330031 Jiangxi Peoples R China

BackgroundSepsis remains a life-threatening condition in intensive care units (ICU) with high morbidity and mortality rates. Some biomarkers commonly used in clinic do not have the characteristics of rapid and specific growth and rapid decline after effective treatment. machine learning has shown great potential in early diagnosis, subtype analysis, accurate treatment and prognosis evaluation of *** expression matrices from GSE13904 and GSE26440 were combined into a training model after quality control and standardization. Then, the intersection genes were obtained by crossing the screened differentially expressed genes (DEGs) and the module genes with the strongest correlation obtained by WGCNA analysis. 113 combined machine learning algorithms to build a diagnosis model. Then the CIBERSORT algorithm is used to analyze the relationship between the change of core gene expression and immune response in sepsis. Construct nomogram, DCA and CIC to further verify the reliability of the diagnosis model. The potential molecular compounds interacting with key genes were searched from the Traditional Chinese Medicine Active Compound Library (TCMACL).ResultsWe screened 405 DEGs, including 334 up-regulated and 71 down-regulated genes. The 308 potential genes were obtained by intersection of MEturquoise module genes in WGCNA analysis and DEGs for subsequent machine learning analysis. GO and KEGG enrichment analysis showed that sepsis was mainly related to immune response and bacterial infection. Then 113 combined machine learning algorithms are applied to construct a diagnosis model to screen 22 hub genes. Four four key genes (CD177, GNLY, ANKRD22, and IFIT1) are obtained through further analysis of PPI network constructed by 22 hub genes. Subsequently, the diagnostic model is proved to have good predictive value by nomogram, DCA and CIC. Finally, molecular compounds (Dieckol, Grosvenorine and Tellimagrandin II) were screened out as potential ***113

关键词： Sepsis machine learning algorithms WGCNA analysis CIBERSORT analysis Molecular docking

来源：评论

学校读者我要写书评

暂无评论

Combined Multiple machine learning Performance Measure

引用

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Kim, Song-Kyoo Macao Polytech Univ Fac Appl Sci Macau Peoples R China

The combined multiple machine learning performance measure (CMMLPM) offers a groundbreaking framework for evaluating machine learning (ML) systems by integrating multiple performance metrics into a single comprehensive measure. Traditional evaluation methods often treat performance metrics in isolation, complicating the assessment process when conflicting performance measures arise. CMMLPM effectively addresses this challenge by providing a unified metric that facilitates easier comparison and optimization of ML models across diverse applications. Through rigorous analysis and case studies, the robustness of CMMLPM in real-world scenarios has been demonstrated including credit card fraud detection and biometric authentication systems. Our findings reveal that CMMLPM not only streamlines the evaluation process but also enhances decision-making for selecting the most effective ML models. This innovative approach not only advances the field of performance measurement in ML but also sets the stage for future research to refine performance evaluation frameworks, ensuring they remain relevant in the increasingly complex landscape of ML applications.

关键词： machine learning Training Market research Measurement Accuracy Particle measurements machine learning algorithms Atmospheric measurements Time measurement Optimization Decision support system machine learning (ML) multivariable performance evaluation performance measure

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：