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4th International Conference on Artificial Intelligence, Automation and algorithms

作者： Liu, Tianyue Guo, Binghui Mi, Zhilong Yin, Ziqiao Yang, Xiaobo He, Qingcai Beihang Univ Beijing Adv Innovat Ctr Future Blockchain & Priva Sch Artificial Intelligence Beijing Peoples R China Zhongguancun Lab Beijing Peoples R China Beijing Technol & Business Univ Sch Food & Hlth Beijing Peoples R China Beihang Univ Sch Math Sci LMIB & NLSDE Beijing Peoples R China Beihang Univ Shen Yuan Honors Coll Beijing Peoples R China

ISBN: (纸本)9798400717840

Medical image processing requires learning meaningful features from diverse datasets, which are often private and scattered across different institutions. This leads to image data heterogeneity, including feature skew and quantity skew. Federated learning (FL) can guarantees data analysis without exposing privacy, making it ideal for healthcare. However, there is no metric available to measure the data heterogeneity, determining the appropriate FL algorithm for different scenarios is unworkable. To address this, a FL algorithms evaluation system for heterogeneous medical image is proposed, where entropy is used to quantify the data heterogeneity among clients. After normalization and segmentation of the entropy values across clients, the evaluation system is constructed for candidate algorithms, providing selection strategies incorporating the performance and efficiency. Experiments on the distributed X-ray datasets for breast cancer samples figure it out that, the proposed evaluation system can effectively screen out the most suitable FL algorithm for the current data distribution.

关键词： Federated learning Information entropy Heterogeneous medical images algorithm evaluation

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Comprehensive algorithm Portfolio evaluation using Item Response Theory

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JOURNAL OF MACHINE LEARNING RESEARCH 2023年第1期24卷 1-52页

作者： Kandanaarachchi, Sevvandi Smith-Miles, Kate CSIROs Data61 Res Way Clayton Vic 3168 Australia Univ Melbourne Sch Math & Stat Parkville Vic 3010 Australia

Item Response Theory (IRT) has been proposed within the field of Educational Psychometrics to assess student ability as well as test question difficulty and discrimination power. More recently, IRT has been applied to evaluate machine learning algorithm performance on a single classification dataset, where the student is now an algorithm, and the test question is an observation to be classified by the algorithm. In this paper we present a modified IRT-based framework for evaluating a portfolio of algorithms across a repository of datasets, while simultaneously eliciting a richer suite of characteristics - such as algorithm consistency and anomalousness - that describe important aspects of algorithm performance. These characteristics arise from a novel inversion and reinterpretation of the traditional IRT model without requiring additional dataset feature computations. We test this framework on algorithm portfolios for a wide range of applications, demonstrating the broad applicability of this method as an insightful algorithm evaluation tool. Furthermore, the explainable nature of IRT parameters yield an increased understanding of algorithm portfolios.

关键词： Item Response Theory algorithm evaluation algorithm portfolios classification ma chine learning algorithm selection instance space analysis explainable algorithm evaluation.

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Applicability of algorithm evaluation Metrics for Predictive Maintenance in Production Systems 6

Applicability of Algorithm Evaluation Metrics for Predictive...

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6th IEEE International Congress on Information Science and Technology (IEEE CiSt)

作者： Engbers, Hendrik Alla, Abderrahim Ait Kreutz, Markus Freitag, Michael Univ Bremen BIBA Bremer Inst Prod & Logist GmbH Planning & Control Prod & Logist Syst Bremen Germany Univ Bremen Planning & Control Prod & Logist Syst BIBA Bremen Germany Univ Bremen Fac Prod Engn Bremen Germany

ISBN: (纸本)9781728166469

algorithm evaluation metrics are used to measure and compare the performance of diagnostic and prognostic algorithms. However, there is no consensus on which of the various metrics are the most suitable for classifiers' performance evaluation. This paper examines the applicability of common evaluation metrics for predictive maintenance applications in production systems. It is intended to clarify (1) which metrics are well suited for this use case, and (2) whether they are sufficient as a sole decision criterion for algorithm selection. Further, (3) the significance of evaluation metrics concerning the practical impact on the production system's performance is investigated. Moreover, we analyze (4) how increasing the production system's complexity affects the correlation between algorithm evaluation metrics and performance. We conducted 960 simulation runs of a flexible flow shop production system to examine the impact of machine breakdowns and different failure distributions with varying confusion matrices. In addition, two different configurations of the production system, which differ in system complexity, were investigated. Eventually, we determined the product-moment correlation coefficient between common evaluation metrics and the production system's performance. The simulation results reveal that metrics that are sensitive to false negative values (FN), like the False Omission Rate (FOR), are very well suited. Nevertheless, a detailed analysis shows that even (FOR) can only provide reliable statements combined with other metrics. We found that as the system becomes more complex, the informative value of the metrics in terms of their impact on the production system's performance decreases. Finally, we propose parameters that could be relevant for developing new metrics considering the current system configuration.

关键词： Fault detection and diagnostics Predictive maintenance and prognostics algorithm evaluation Performance evaluation

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Comprehensive algorithm portfolio evaluation using item response theory

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 8395-8446页

作者： Sevvandi Kandanaarachchi Kate Smith-Miles CSIRO ' s Data61 ClaytonVIC Australia School of Mathematics and Statistics University of Melbourne Parkville VIC Australia

Item Response Theory (IRT) has been proposed within the field of Educational Psychometrics to assess student ability as well as test question difficulty and discrimination power. More recently, IRT has been applied to evaluate machine learning algorithm performance on a single classification dataset, where the student is now an algorithm, and the test question is an observation to be classified by the algorithm. In this paperwe present a modified IRT-based framework for evaluating a portfolio of algorithms across a repository of datasets, while simultaneously eliciting a richer suite of characteristics - such as algorithm consistency and anomalousness - that describe important aspects of algorithm performance. These characteristics arise from a novel inversion and reinterpretation of the traditional IRT model without requiring additional dataset feature computations. We test this framework on algorithm portfolios for a wide range of applications, demonstrating the broad applicability of this method as an insightful algorithm evaluation tool. Furthermore, the explainable nature of IRT parameters yield an increased understanding of algorithm portfolios.

关键词： item response theory algorithm evaluation algorithm portfolios classification machine learning algorithm selection instance space analysis explainable algorithm evaluation

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Study on algorithm evaluation of Image Fusion Based on Multi-hierarchical Synthetic Analysis

Study on Algorithm Evaluation of Image Fusion Based on Multi...

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IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： He, Guiqing Liang, Fan Xing, Siyuan Dong, DanDan Feng, Xiaoyi Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Shaanxi Peoples R China

ISBN: (纸本)9781509027088

To resolve the algorithm evaluation issue of multi-sensor image fusion, we propose a novel synthetic evaluation method using multi-hierarchical gray relational analysis mechanism, which has the merit of using small-sized samples and of allowing unitary comparison. The proposed method combines a priori knowledge and quantization evaluation. In this paper we first outline a basic three-step procedure in order toperform the gray relational analysis for a single-hierarchy evaluation system, and then give a four-step procedure to perform multi-hierarchical evaluation system. Therefore, we obtain a synthetic evaluation result that is more quantitative and comprehensive than conventional subjective and objective measures such as correlation coefficient and average gradient. The novel evaluation method can give not only overall performance evaluation for image fusion algorithm but also specific performance evaluation. Extensive experimental analysis shows that the proposed method generates better evaluation result with respect to quantization, precision, objectivity, reliability, and real-time evaluation. These advantages make it applicable to fusion systems with feedback capability, and can enrich and perfect the image fusion system.

关键词： image fusion algorithm evaluation multi-hierarchical gray relational analysis

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Performance evaluation of computerized antepartum fetal heart rate monitoring: Dawes-Redman algorithm at term

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ULTRASOUND IN OBSTETRICS & GYNECOLOGY 2025年第2期65卷 191-197页

作者： Jones, G. Davis Albert, B. Cooke, W. Vatish, M. Univ Oxford John Radcliffe Hosp Womens Ctr Nuffield Dept Womens & Reprod HlthOxford Digital Oxford OX3 9DU England Alan Turing Inst London England

Objectives To assess the effectiveness of the Dawes-Redman algorithm in identifying fetal wellbeing at term by analyzing 30 years of retrospective clinical data, comparing normal and adverse pregnancy outcomes, evaluating key metrics and testing its performance when used 0-48 h before delivery. Methods Antepartum fetal heart rate (FHR) traces from term singleton pregnancies at 37 + 0 to 41 + 6 weeks' gestation obtained between 1991 and 2024 were extracted from the Oxford University Hospitals database. Traces with > 30% of their signal information missing or with incomplete Dawes-Redman analyses were excluded. Only traces performed within 48 h prior to delivery were considered. A cohort of pregnancies with subsequent normal pregnancy outcome (NPO) was established using rigorous inclusion and exclusion criteria. Another cohort of pregnancies with adverse pregnancy outcome (APO) was developed if the neonate experienced at least one of seven APOs after delivery. Propensity score matching (PSM) facilitated a balanced comparison between NPO and APO cohorts using six factors: gestational age at FHR monitoring, fetal sex, maternal body mass index at presentation, maternal age at delivery, parity and time interval between FHR trace and delivery. FHR traces were categorized as either 'criteria met' (indicating fetal wellbeing) or 'criteria not met' (indicating a need for further evaluation) according to the Dawes-Redman algorithm, which informed the evaluation of predictive performance metrics. Performance was assessed using accuracy, sensitivity, specificity, positive predictive value, and negative predictive value (NPV) adjusted for various population risk prevalences of APO. Results A balanced dataset of 3316 antepartum FHR traces was developed with PSM (standardized mean difference < 0.10). The Dawes-Redman algorithm showed a high specificity of 90.7% (95% CI, 89.2-92.0%) for ruling out APO. Sensitivity was 18.2% (95% CI, 16.3-20.0%). The NPV varied with the population pr

关键词： algorithm evaluation antepartum cardiotocography clinical decision-making electronic fetal monitoring high-risk pregnancy non-stress test obstetrics predictive value of tests prevalence effect

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evaluation on algorithm reliability and efficiency for an image flame detection technology

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JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY 2023年第11期148卷 5063-5070页

作者： Yang, Yi Wang, Xue-Feng Pan, Meng-Yi Li, Pu Tsai, Yun-Ting Xian Univ Sci & Technol Coll Safety Sci & Engn Xian 710054 Peoples R China Zhengzhou Airport Econ Zone Fire Brigade Zhengzhou 450000 Peoples R China Nanjing Tech Univ Coll Safety Sci & Engn Nanjing 211816 Peoples R China

Image-based flame detection (IBFD) algorithms are effective in flame detection, and their advantages and disadvantages directly affect the accuracy and timeliness of flame detection. However, based on the existing research in China, the IBFD algorithm still lacks a clear standard for distinguishing advantages and disadvantages. This study adopted a fuzzy comprehensive hierarchical analysis method to establish an evaluation method for IBFD and to define the rating standards for 29 third-level evaluation indexes. A three-level index system was installed for algorithm evaluation, and the masses of needles at all levels were determined. An index system was used to assign indexes to the two flame identification algorithms in the YOLOv3 series. The algorithm evaluation was completed according to the mass of the algorithm index, which indicated the reliability and validity of the algorithm evaluation method. Therefore, this study established a set of methods for the performance evaluation of IBFD and thus laid a theoretical foundation for improving relevant standards for image-based flame detectors.

关键词： IBFD algorithm Fuzzy comprehensive hierarchical analysis method evaluation indexes algorithm evaluation Image-based flame detectors

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Misinformation detection: datasets, models and performance

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ONLINE INFORMATION REVIEW 2025年第3期49卷 570-584页

作者： Chung, Hsin-Hsuan Chen, Jiangping Univ North Texas Dept Informat Sci Denton TX USA Univ Illinois Urbana Champaigns Sch Informat Sci Champaign IL 61820 USA

Purpose - This paper aims to understand the characteristics of current misinformation detection studies, including the datasets used by researchers, the computational models or algorithms being developed or applied, and the performance of misinformation detection models or algorithms. Design/methodology/approach - We first identified articles from the Scopus database with inclusion and exclusion criteria. Then a coding scheme was derived from the articles based on research questions. Next, datasets, models, and performance were coded. The paper concluded with answers to research questions and future research directions. Findings - From 115 relevant articles published during 2019-2023 on misinformation detection. We found that most studies used previously existing datasets. Twitter (now X) has been the most widely used source for collecting social media misinformation data. The ten most frequently used datasets are identified. Most studies (96.1%) developed or applied machine learning, especially deep learning models. The most advanced current misinformation detection models could achieve pretty high performance. For example, among 104 studies reporting performance with accuracy, 44.2% achieved an accuracy of 0.95 or higher, and 24.0% achieved 0.90-0.94 on accuracy. Research limitations/implications - Our study only reviewed English articles from 2019-2023 that are included in the Scopus database. Articles that are not included in the Scopus database are not reviewed. Practical implications - The high performance of misinformation detection indicates that social media should be able to detect most misinformation if they are willing to do it. However, no system or algorithm could achieve 100% misinformation on performance. Due to the complexity of misinformation, users of social media still need to improve their capabilities of evaluating information on the Internet. Social implications - This study provides evidence to policymakers that social media platforms have th

关键词： Misinformation detection Datasets Machine learning Deep learning algorithm evaluation

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Intelligent recognition and measurement for fatigue cracks in orthotropic steel decks: A comparative study of algorithms

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MEASUREMENT 2025年 247卷

作者： Zhang, Lexin Jie, Zhiyu Li, Zhong-Xian Lu, Wei Zheng, Hao Wang, Wanzhen Ningbo Univ Dept Civil Engn Ningbo 315211 Peoples R China Tianjin Univ Sch Civil Engn Tianjin 300350 Peoples R China

Orthotropic steel bridge decks are highly susceptible to fatigue cracking under cyclic loading. To overcome the limitations in precision and efficiency of existing crack identification technologies, this study systematically analyzed the performance of four traditional machine learning algorithms, twelve deep learning algorithms, and forty-eight hybrid algorithms with various combinations in the task of fatigue crack image recognition. An innovative comprehensive evaluation method was proposed to assess these algorithms. It integrated an intelligent crack image recognition method with an automatic crack measurement technology to design an application. The results indicate that the combination of deep learning algorithms with K-Nearest Neighbors (KNN) or Support Vector Machines (SVM) algorithms significantly enhances recognition precision and efficiency. Among all the algorithms tested, the ResNet-101+SVM hybrid algorithm achieved the highest score of 98.88, with a final test set accuracy of 98.95 %, a transfer recognition rate of 95.97 %, and a training time of only 20.15 s, demonstrating excellent stability. Furthermore, it has successfully developed a low-cost and high-precision crack measurement technology, capable of controlling measurement errors within 2 %. It offers an efficient and reliable tool for intelligent detection and assessment of fatigue cracks in orthotropic steel bridge decks for engineers and researchers.

关键词： Crack identification Machine learning Deep learning Hybrid algorithms algorithm evaluation Crack measurement

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An efficient iterative algorithm for laser ranging signals extraction based on multi-frame overlaying

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OPTICS COMMUNICATIONS 2025年 583卷

作者： Zhou, Chengkai Qian, Qiang Li, Hui Han, Xida Wu, Xianlin Li, Ming Sun Yat Sen Univ Tianqin Res Ctr Gravitat Phys Frontiers Sci Ctr Tianqin Gravitat Wave Res Ctr CNSAMOE Key Lab Tianqin Mis Zhuhai Campus Zhuhai 519082 Peoples R China Sun Yat Sen Univ Frontiers Sci Ctr Tianqin Gravitat Wave Res Ctr CNSA Sch Phys & Astron Zhuhai Campus Zhuhai 519082 Peoples R China

At present, the commonly used signal extraction algorithms face limitations in low signal-to-noise ratio and prediction data deviation, leading to restricted interpretability. This paper presents an algorithm for extracting laser ranging signals based on the concept of multi-frame overlaying. The statistical dimension is enhanced from 1D to 2D by considering the cumulative effect of laser ranging signals over time and utilizing a portion of the ranging data to create a template. The physical significance of the algorithm parameters is examined by an elucidation of their calculation method. The algorithm is utilized for processing the observation data from Tianqin laser ranging station, and the results demonstrate its effective signal extraction capability. In the simulation data, our algorithm demonstrates higher accuracy across various signal-to-noise ratios. Thus, our algorithm offers a solution to mitigate the low signal-to-noise ratio and prediction data deviation.

关键词： Laser ranging Signal extraction Multi-frame overlaying algorithm evaluation

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