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International Conference on Probabilistic Graphical Models

作者： Kwisthout, Johan Radboud Univ Nijmegen Donders Inst Brain Cognit & Behav Nijmegen Netherlands

The MAP problem in Bayesian networks is notoriously intractable, even when approximated. In an earlier paper we introduced the Most Frugal Explanation heuristic approach to solving MAP, by partitioning the set of intermediate variables (neither observed nor part of the MAP variables) into a set of relevant variables, which are marginalized out, and irrelevant variables, which will be assigned a sampled value from their domain. In this study we explore whether knowledge about which variables are relevant for a particular query (i.e., domain knowledge) speeds up computation sufficiently to beat both exact MAP as well as approximate MAP while giving reasonably accurate results. Our results are inconclusive, but also show that this probably depends on the specifics of the MAP query, most prominently the number of MAP variables.

关键词： MAP problem approximate MAP heuristic algorithm comparison background knowledge

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A comprehensive comparison of accuracy and practicality of different types of algorithms for pre-impact fall detection using both young and old adults

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MEASUREMENT 2022年 201卷

作者： Yu, Xiaoqun Koo, Bummo Jang, Jaehyuk Kim, Youngho Xiong, Shuping Korea Adv Inst Sci & Technol KAIST Dept Ind & Syst Engn Daejeon 34141 South Korea Yonsei Univ Dept Biomed Engn Wonju 26493 South Korea

This study aims to comprehensively compare the accuracy and practicality of three different types of algorithms for pre-impact fall detection using both young and old subjects. Threshold-based, conventional machine learning (SVM) and deep learning (ConvLSTM) algorithms were compared. Results showed that ConvLSTM had an ac-curacy of 99.16 % (sensitivity: 99.32 %, specificity: 99.01 %) and an averaged lead time of 403 ms on young subjects, which outperformed SVM (97.16 %, 385 ms) and much superior to the threshold-based algorithm (89.06 %, 333 ms). In addition, latency tests on an embedded device showed that the Lite model of ConvLSTM had a low latency of 2.1 ms, which was comparable to the threshold-based algorithm (<1 ms) but much lower than SVM (86.9 ms). The feasibility and effectiveness of applying algorithms trained on young subjects to old subjects were also validated. These findings suggested that ConvLSTM has great potential for detecting pre -impact falls and preventing fall-related injuries.

关键词： algorithm comparison Fall risk Inertial sensor Machine learning Pre -impact fall detection

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Effects of Medical Clothing on Person Re-Identification algorithms 17

Effects of Medical Clothing on Person Re-Identification Algo...

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17th IEEE International Symposium on Medical Measurements and Applications (IEEE MeMeA)

作者： Kohout, Lukas Scheerer, Jan Zimmermann, Christoph Stork, Wilhelm FZI Reaserch Ctr Informat Technol Embedded Syst & Sensors Engn Karlsruhe Germany Karlsruhe Inst Technol KIT Inst Informat Proc Technol Karlsruhe Germany

ISBN: (数字)9781665482998

ISBN: (纸本)9781665482998

Accurate camera-based human action recognition over longer periods of time or in different camera views requires re-identification of individuals to correctly associate the actions. This is especially important if you want to track people's actions over time. Most work in person re-identification currently focuses on improving the performance of re-identification models for images of people wearing everyday clothing. This becomes a problem when the re-identification scenario changes, and with it the typical appearance of people in that specific environment. Therefore, this work examines the effects of medical clothing on five different person re-identification algorithms. As artificial intelligence and computer vision find more and more applications in the medical field, the question arises to what extent current implementations of person re-identification algorithms are able to generalize from non-medical data, so that the algorithms can be applied in a medical scenario. Since person re-identification is a well-studied topic in the computer vision community, and can also be used in medical settings, this work focuses on the impact of medical clothing on such algorithms. This becomes relevant because the medical clothing is highly uniform and covers many features of a person's characteristics. In addition to examining the effects of clothing as described, ways to overcome the resulting limitations are discussed. In the absence of medical datasets for person re-identification, a suitable dataset was generated containing images of people in medical clothing and the required annotations. Five different existing re-identification models were trained on a non-medical dataset and then tested with the medical data created for this study. The results show a general drop in performance when subjects are wearing medical clothing instead of normal cloths. By additionally marking all people with individual colored hairnets, the re-identification performance can be improved comp

关键词： person re-identification medical clothing medical environment uniform clothing individual markers ReID generalization computer vision algorithm comparison OR monitoring

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Gaussian Distance Weighted algorithm for Geometric Characteristics of Three-Dimensional Discrete Curves

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Journal of Applied Mathematics and Physics 2024年第10期12卷 3599-3612页

作者： Liyan Zhang Haiyi Ai Shaohong Yan Haili Chen Jiali Zou Junqing Zhang College of Science North China University of Science and Technology Tangshan China Hebei Provincial Key Laboratory of Data Science and Applications North China University of Science and Technology Tangshan China Key Laboratory of Data Science in Tangshan Tangshan China Tangshan Zhongyu Technology Development Co. Ltd. Tangshan China

Discrete curves are composed of a set of ordered discrete points distributed at the intersection of the scanning plane and the surface of the object. In order to accurately calculate the geometric characteristics of any point on the discrete curve, a distance-based Gaussian weighted algorithm is proposed to estimate the geometric characteristics of three-dimensional space discrete curves. According to the definition of discrete derivatives, the algorithm fully considers the relative position difference between a specific point and its neighboring points, introduces the distance weighting idea, and integrates the smoothing strategy. The experiment uses two spatial discrete curves for uniform and non-uniform sampling, and compares them with two commonly used estimation algorithms. The comparative analysis is carried out in terms of sampling density, neighborhood radius and noise resistance. The experimental results show that the Gaussian distance weighted algorithm is effective and provides an efficient algorithm for underground pipeline safety detection.

关键词： Discrete Curve Angle Weight algorithm comparison Underground Pipeline Inspection

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Unsupervised anomaly detection algorithms on real-world data: how many do we need?

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 5199-5232页

作者： Roel Bouman Zaharah Bukhsh Tom Heskes Institute for Computing and Information Sciences Radboud University EC Nijmegen The Netherlands Information Systems Industrial Engineering and Innovation Sciences Eindhoven University of Technology AE Eindhoven The Netherlands

In this study we evaluate 33 unsupervised anomaly detection algorithms on 52 real-world multivariate tabular data sets, performing the largest comparison of unsupervised anomaly detection algorithms to date. On this collection of data sets, the EIF (Extended Isolation Forest) algorithm significantly outperforms the most other algorithms. Visualizing and then clustering the relative performance of the considered algorithms on all data sets, we identify two clear clusters: one with "local" data sets, and another with "global" data sets. "Local" anomalies occupy a region with low density when compared to nearby samples, while "global" occupy an overall low density region in the feature space. On the local data sets the kNN (k-nearest neighbor) algorithm comes out on top. On the global data sets, the EIF (extended isolation forest) algorithm performs the best. Also taking into consideration the algorithms' computational complexity, a toolbox with these two unsupervised anomaly detection algorithms suffices for finding anomalies in this representative collection of multivariate data sets. By providing access to code and data sets, our study can be easily reproduced and extended with more algorithms and/or data sets.

关键词： unsupervised anomaly detection anomaly analysis algorithm comparison outlier detection outlier analysis

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algorithm analysis of defect depth for PVC plate with flat-bottomed holes in active pulsed infrared thermography

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MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第8期34卷 085404-085404页

作者： Gou, Yi Li, Qingying Yao, Rao Chen, Jianing Bai, Tian Shanghai Univ Engn Sci Sch Air Transportat Shanghai 201620 Peoples R China Beijing Aerosp Inst Metrol & Measurement Technol Beijing 100048 Peoples R China

Active infrared thermography (IRT) has been extensively employed in non-destructive testing in a wide variety of fields. It is capable of extracting defect information of tested object based on the infrared thermal image sequence. However, conventional infrared thermal images are often subjected to defect information with low pixel resolution, and defects are difficult to quantitatively analyze. By exploiting flat-bottomed holes in a PVC plate as defect specimens, a method for quantitative defect depth recognition based on the fusion principal component analysis (PCA) algorithm with sliding-window mechanism and the one dimensional-residual neural network-convolutional block attention module (1DResnet50_CBAM) model was proposed for the reconstructed image sequence of active IRT to address the above-described issues in this study. First, defect information and location were extracted from the original infrared sequence thermal image of the specimen using PCA algorithm with sliding-window mechanism. Then, the dimension of the defect data was reduced using the temporal characteristic of the infrared temperature field. That is, the three-dimensional defect data were transformed into 1D temporal infrared thermal signal. Moreover, the 1D infrared signal time series corresponding to the defect pixel points in the infrared sequence image served as the input to the network, and the defect depth served as the output for automatic defect detection and depth quantification. As indicated by the results, the proposed method based on the fusion PCA algorithm with sliding-window mechanism and 1DResnet50_CBAM model is capable of accurately detecting and quantifying defects. Compared with conventional prediction algorithms, the proposed model can more effectively extract defect information from the infrared detection images, with the defect depth relative prediction error less than 1.5%. Thus, the proposed model was confirmed as an effective method and model for defect recognition and

关键词： infrared thermography flat-bottomed hole defect depth defect extraction 1DResnet50_CBAM model algorithm comparison

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Comprehensive Analysis and Integration Strategies of Modular and End-To-End Solutions in Autonomous Driving Systems: Prospects and Challenges for Long-Term Coexistence 12th

Comprehensive Analysis and Integration Strategies of Modular...

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12th International Conference on Signal and Information Processing, Networking and Computers, ICSINC 2024

作者： Hu, Qinyu Li, Xiaofeng Shang, Weirui University of Hong Kong Hong Kong Beijing Institute of Control Engineering China Academy of Space Technology Beijing China

ISBN: (纸本)9789819649662

This paper explores two system-level architecture approaches for Autonomous Driving algorithms: Modular and End-to-end solutions. Modular solutions employ a Modular design, breaking down complex autonomous driving tasks into manageable modules such as perception, decision and planning, and control, to achieve more effective system debugging and optimization. However, these Modular systems often suffer from information loss and response time delays due to extensive inter-module communication. End-to-end solutions, by using advanced machine learning techniques, map raw sensor data directly to driving behavior, simplifying the system architecture, improving overall system efficiency, and reducing latency. Nevertheless, End-to-end solutions still face challenges such as high training data requirements and difficulties in simulation validation and closed-loop evaluation. Through a systematic comparison, this study highlights the strengths and weaknesses of both studies and proposes a gradual integration strategy. This strategy involves initially integrating modularly and then progressively expanding their application. This allows Modular and End-to-end solutions to run in parallel, complementing each other and eventually leading to a fully integrated system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： algorithm comparison Autonomous driving Data requirements End-to-end solutions Foundation models Modular solutions

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Statistical comparisons of classifiers by generalized stochastic dominance

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 10872-10908页

作者： Christoph Jansen Malte Nalenz Georg Schollmeyer Thomas Augustin Department of Statistics Ludwig-Maximilians-Universität Munich Germany

Although being a crucial question for the development of machine learning algorithms, there is still no consensus on how to compare classifiers over multiple data sets with respect to several criteria. Every comparison framework is confronted with (at least) three fundamental challenges: the multiplicity of quality criteria, the multiplicity of data sets and the randomness of the selection of data sets. In this paper, we add a fresh view to the vivid debate by adopting recent developments in decision theory. Based on so-called preference systems, our framework ranks classifiers by a generalized concept of stochastic dominance, which powerfully circumvents the cumbersome, and often even self-contradictory, reliance on aggregates. Moreover, we show that generalized stochastic dominance can be operationalized by solving easy-to-handle linear programs and moreover statistically tested employing an adapted two-sample observation-randomization test. This yields indeed a powerful framework for the statistical comparison of classifiers over multiple data sets with respect to multiple quality criteria simultaneously. We illustrate and investigate our framework in a simulation study and with a set of standard benchmark data sets.

关键词： algorithm comparison statistical test generalized stochastic dominance preference system decision theory

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Comparative analysis of the KL-UCB and UCB algorithms:Delving into complexity and performance

Comparative analysis of the KL-UCB and UCB algorithms:Delvin...

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作者： Chenyue Wu School of Information Science and Technology School of Cyber Security Guangdong University of Foreign Studies

This paper embarks on a meticulous comparative exploration of two venerable algorithms often invoked in multi-armed bandit problems: the Kullback-Leibler Upper Confidence Bound(KL-UCB) and the generic Upper Confidence Bound(UCB) ***, a comprehensive discourse is presented, elucidating the definition, evolution, and realworld applications of both algorithms. The crux of the study then shifts to a side-by-side comparison, weighing the regret performance and time complexities when applied to a quintessential movie rating dataset. In the trenches of practical implementations, addressing multi-armed bandit problems invariably demands extensive training. Consequently, even seemingly minor variations in algorithmic complexity can usher in pronounced differences in computational durations and resource utilization. This inherent intricacy prompts introspection:Is the potency of a given algorithm in addressing diverse practical quandaries commensurate with its inherent complexity. By juxtaposing the KL-UCB and UCB algorithms, this study not only highlights their relative merits and demerits but also furnishes insights that could serve as catalysts for further refinement and optimization. The overarching aim is to cultivate an informed perspective, guiding practitioners in choosing or fine-tuning algorithms tailored to specific applications without incurring undue computational overheads.

关键词： Multi-armed bandit algorithm comparison algorithm complexity regret performance algorithm improvements

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Comparative Breast Cancer Detection with Artificial Neural Networks and Machine Learning Methods 29

Comparative Breast Cancer Detection with Artificial Neural N...

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29th IEEE Conference on Signal Processing and Communications Applications (SIU)

作者： Irmak, Muhammed Coskun Tas, Mehmet Bilge Han Turan, Sedat Hasiloglu, Abdulsamet Van Yuzuncu Yil Univ Bilgisayar Muhendisligi Van Turkey Erzincan Binali Yildirim Univ Bilgisayar Muhendisligi Erzincan Turkey Erzincan Binali Yildirim Univ Elekt & Otomasyon Erzincan Turkey Ataturk Univ Bilgisayar Muhendisligi Erzurum Turkey

ISBN: (纸本)9781665436496

The number of cancer patients is increasing all over the world due to reasons such as environmental pollution, excess of technological and biological wastes, climate change, and increased consumption of unnatural foods. According to the data of the World Health Organization in 2020, there are over 19 million cancer patients in the world. The most common type of cancer is breast cancer with 11.7%. Breast cancer is as high as 24.5% in women and 15.5% of these cases result in death. Therefore, it is important to detect cancer at an early stage. The purpose of this study is to detect cancer in the fastest way and with the highest accuracy rates. In the study, high performance results were obtained by comparing artificial neural networks and traditional machine learning methods. In the study;Logistic Regression (LR), k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Xgboost (XGB) and Artificial Neural Network (ANN) were used. The highest result obtained using traditional machine learning methods is 98.08% with LR. In addition, using ANN, the highest value was obtained with an accuracy rate of 99.36%.

关键词： artificial neural network machine learning algorithm comparison breast cancer detection logistic regression

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