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共 2812 条记录，以下是141-150 订阅

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FedGait: A Benchmark for Federated Gait recognition 26

FedGait: A Benchmark for Federated Gait Recognition

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Li, Ziqiong Li, Yan-ran Yu, Shiqi Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Gait recognition has been greatly improved by deep learning and can achieve a relative high accuracy. the advances depend on the data size of gait. However, due to public concerns on privacy and regulations and laws from different countries, it is very difficult and almost impossible to collect a huge centralized gait database for algorithm training. Federated learning is a distributed machine learning technique for privacy-preserving, and can help to solve the problem. We propose a federated gait recognition benchmark, FedGait, to train algorithms using distributed gait data. It is the first benchmark on gait recognition to the best of our knowledge. FedGait can utilizes the gait videos available on multiple clients to learn a robust and generalized model. Based on the real-world gait scenarios, we introduce two federated gait recognition scenarios: institution-based scenario (IBS) and device-based scenario (DBS). Compared with centralized training, federated learning will encounter more serious heterogeneous data and data imbalance problems. We employ four popular databases for experiments, CASIA-B, CASIA-E, ReSGait and OU-MVLP, are involved in FedGait to investigate the problems in federated learning. We hope FedGait is a good start to solve data privacy problem in gait recognition.

关键词： Training Privacy data privacy machine learning algorithms Federated learning Satellite broadcasting Benchmark testing

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Using Facial Expressions to Predict Process mining Task Performance

Using Facial Expressions to Predict Process Mining Task Perf...

引用

international Workshops which were held in conjunction with the 6th international conference on Process mining, ICPM 2024

作者： Shalev, Lital Hadar, Irit Dror, Rotem Solomon, Adir Sorokina, Elizaveta Raymond, Michal Weisman Soffer, Pnina University of Haifa Haifa Israel Ben Gurion University of the Negev Beer Sheva Beersheba Israel

ISBN: (纸本)9783031822247

Process mining analysis is a complex task that presents significant challenges to human analysts. To aid along this process, it is essential to identify difficulties as they occur. this study takes an initial step in this direction, by predicting the quality of task performance based on analysts’ facial expressions while they are engaged in a process mining task. data were collected using participants’ webcams and the iMotions™ cloud application while they performed a process mining task. the data were then utilized to train and evaluate several machine learning classifiers, which classified participants based on the grade given to their task outcome. Our results show the high performance of these classifiers in predicting participants’ success based on facial expressions. We further showed that the chosen outcome classifier could accurately classify additional participants, demonstrating its generalizability. Notably, the classifier was able to predict participants’ success within a very short time frame. these findings could pave the way for developing a near-real-time support system to detect when analysts engaged in process mining may benefit from assistance. © the Author(s) 2025.

关键词： machine learning

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A Xgboost Optimized Ensemble Model for Remaining useful Life Prediction of Aircraft Turbofan Engines 6

A Xgboost Optimized Ensemble Model for Remaining useful Life...

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6th IEEE international conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Sharma, Vaasudev Dagar, Riyansh Sharanya, S. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India

ISBN: (纸本)9798350306613

Proactive maintenance for the optimized lifecycle of equipment is now effortless via condition monitoring and fault diagnosis. recognition and avoidance of a state of failure help to achieve solidity and reliability. Artificial Intelligence and machine learning (ML) forecast such situations and aid in the prevention of economic and safety emergencies. this work reviews such ML algorithms and ML models that help diagnose faults in Turbofan engines via measurement of the Remaining Useful Life parameter. this proposed an ensemble machine-learning model optimized by Extreme Gradient Boosting. the approach is then evaluated via different metrics and their performance is measured to current innovative methods. the system is useful for all processes that require high reliability quality and efficiency and reduces unplanned downtime, maintenance costs and asset quality. © 2024 IEEE.

关键词： CMAPSS Ensemble Model machine learning Predictive Maintenance Remaining useful life (RUL) Turbofan Engine

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machine learning-Based Phishing Website Detection: A Comparative Analysis and Web Application Development 6th

Machine Learning-Based Phishing Website Detection: A Compara...

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6th international conference on Soft Computing and data mining (SCDM)

作者： Yau, Jia Xin Chia, Kai Lin Univ Tunku Abdul Rahman Lee Kong Chian Fac Engn & Sci Kajang Selangor Malaysia

ISBN: (纸本)9783031669644;9783031669651

Phishing, a cybercrime that uses sociotechnical and technical deception, targets identifiable information and financial credentials and poses a high risk according to the IBM Cost of a data Breach Report 2022 which shows that on average precisely, the cost per transaction is $4.91 million, phishing attacks are on the rise, challenging the ability of traditional scanning systems to adapt to trends this study examines and it compares the effectiveness of three antiphishing methods: Autoencoder, Extreme Gradient Boost (XGBoost), and Random Forest (RF). through feature selection and robust machine learning (ML) algorithms, including Random Forest achieving a remarkable 97.03% accuracy, the proposed solution integrates list-based methods with ML models for two-tier security. the wrapper method is employed to extract crucial features, facilitating precise phishing detection. Specific algorithms such as Random Forest and XGBoost are chosen for their proven effectiveness in handling complex data and class imbalances. However, potential limitations include the need for continuous adaptation to new phishing methods and exploring ensemble techniques for enhanced model robustness. Benchmarking against existing methods highlights the superiority of Random Forest in achieving balanced recall and precision. this study contributes to advancing phishing detection systems by leveraging machine learning and proposing strategies for improved performance and accuracy, which are then applied to a web application for countering phishing attacks.

关键词： Phishing detection machine learning Internet security Google Safe Browsing Web application

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Semi-Supervised learning for Pupil Segmentation in TCM 6

Semi-Supervised Learning for Pupil Segmentation in TCM

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6th IEEE international conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Niu, Ning Peng, Hong Liu, Qinkai Zeng, Penghui Yao, Qiaoyi Wang, Lu Wang, Yixuan Wang, Guanjun Huang, Mengxing Xue, Chengyang Hainan University School of Information and Communication Engineering Haikou China Hainan University School of Information and Communication Engineering Haikou China North University of China School of Instrument and Electronics Taiyuan China

ISBN: (纸本)9798350325485

Accurately locating and segmenting objects in medical images is crucial for providing precise data support. Traditional Chinese medicine diagnosis relies on pupillary features to reflect the condition of internal organs and pathologies. Combining image segmentation technology with pupil features can offer practical tools for medical treatment. However, obtaining and sharing medical image data is challenging, posing a hurdle for supervised learning approaches. Semi-supervised learning, which can use labeled and unlabeled data, can improve accuracy and generalization and is suitable for medical image processing. this method combines deep learning and traditional Chinese medicine to achieve medical image pupil segmentation with limited data, paving the way for disease discrimination. It effectively addresses the scarcity of data and the inadequacy of supervised learning methods for traditional TCM medical images. this approach provides more accurate and practical TCM image analysis and diagnosis tools. © 2023 IEEE.

关键词： Image enhancement

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Enhancing Rule learning on Knowledge Graphs through Joint Ontology and Instance Guidance 6th

Enhancing Rule Learning on Knowledge Graphs Through Joint On...

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6th Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Bao, Xianglong Wang, Zhe Wang, Kewen Zhang, Xiaowang Wu, Hutong Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Griffith Univ Sch Informat & Commun Technol Brisbane Qld Australia

ISBN: (纸本)9789819984343;9789819984350

Rule learning is a machine learning method that extracts implicit rules and patterns from data, enabling symbol-based reasoning in artificial intelligence. Unlike data-driven approaches such as deep learning, using rules for inference allows for interpretability. Many studies have attempted to automatically learn first-order rules from knowledge graphs. However, existing methods lack attention to the hierarchical information between ontology and instance and require a separate rule evaluation stage for rule filtering. To address these issues, this paper proposes a Ontology and Instance Guided Rule learning (OIRL) approach to enhance rule learning on knowledge graphs. Our method treats rules as sequences composed of relations and utilizes semantic information from both ontology and instances to guide path generation, ensuring that paths contain as much pattern information as possible. We also develop an end-to-end rule generator that directly infers rule heads and incorporates an attention mechanism to output confidence scores, eliminating the need for rule instance-based rule evaluation. We evaluate OIRL using the knowledge graph completion task, and experimental results demonstrate its superiority over existing rule learning methods, confirming the effectiveness of the mined rules.

关键词： Knowledge graph Rule learning First-order Logic

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17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th Workshop on Intelligent learning Environments, WILE 2024 and 6th Workshop on New Trends in Computational Intelligence and Applications, CIAPP 2024, held in conjunction with Mexican international conference on Artificial Intelligence, MICAI 2024

17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th ...

引用

17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th Workshop on Intelligent learning Environments, WILE 2024 and 6th Workshop on New Trends in Computational Intelligence and Applications, CIAPP 2024, held in conjunction with Mexican international conference on Artificial Intelligence, MICAI 2024

ISBN: (纸本)9783031838781

the proceedings contain 38 papers. the special focus in this conference is on Hybrid Intelligent Systems. the topics include: Integration of Alexa and Social IoT for Generation X: A Study on the Optimization Use of Smart Treadmill to Improve Physical Health;logarithmic Weighted Random Selector Algorithm: A Novel Approach for Biasing Selection Based on Positional Order Without Hyperparameters;design and Implementation of a machine learning Model for Soccer Match Prediction Based on Player Statistics;electric Scooters and Renewable Energy Integration Associated with Tourist Parks: A Dijkstra-Based Model for Smart Mobility Optimization;use of Convolutional Neural Networks for the recognition of Bird Species in Risk Categories in the State of Chihuahua;exploring Deep learning Applications in Neurodegenerative Diseases: A State-of-the-Art Review;Automated Insights: LLMs in Neurodegenerative Disease Research and Comparison;convolutional Neural Network Models for Classifying of Peach (Prunus persica L);Cleaning Binary Distortion on MNIST dataset;rule-Based Expert System with Bayesian theory and Fuzzy Inference for Vocational Guidance: A Tool to Prevent School Dropouts;detection of Basic Motorcycle Faults Using a Fuzzy Bayesian Expert System;intervention Model with data mining Techniques to Work with Dating Violence Victims Using a Social Support Network;enhanced Pest Detection Using Quaternion-Based Image Segmentation in Yellow Sticky Trap Samples for Precision Agriculture;Optimization of a Treatment Plant through the Incorporation of New Waste Separation Components: A TOPSIS-Focused Multi-criteria Analysis Approach;multicriteria Analysis Applied to the Selection of Shopping Centers for a Family-Owned Restaurant Business in Smart Cities: A Case Study in Ciudad Juárez.

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Detecting Feet's Bending patterns With Smart Insoles 5

Detecting Feet's Bending Patterns With Smart Insoles

引用

5th IEEE international conference on pattern recognition and machine learning, PRML 2024

作者： Zhong, Yuchen Ying, Qijun Cai, Xiaohui dept. of Computer Science and Technology Hefei China dept. of Data Science Hefei China

ISBN: (纸本)9798350355925

Foot bending is a basic component of walking. Although bending is important for foot-ground interaction and ensures the speed and balance of the whole walking cycle, there is still no practical method for recording and analyzing foot bending under everyday scenarios. In this study we propose to use a smart insole, with two embedded Inertial Measurement Units, to acquire the movements of the fore-foot and the hind-foot and deduce the bending, represented by the bending point's position. We use the deep learning approach to estimate the bending position and its changes during different walking patterns. On the dataset containing 8 test subjects and 6 walking patterns, our method can achieve an overall mean absolute error accuracy of 2.58 cm. the smart insole can be with the subject all the day, bring in a new method for continuous foot bending monitoring. © 2024 IEEE.

关键词： Wearable technology

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17th Workshop of Hybrid Intelligent Systems, HIS 2024, 17th ...

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ISBN: (纸本)9783031838811

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EngageDat-vL: A Multimodal Engagement dataset Comprising of Emotional, Cognitive, and Behavioral Cues in Virtual learning Environment 10th

EngageDat-vL: A Multimodal Engagement Dataset Comprising of ...

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10th Biennial international conference on pattern recognition and machine Intelligence (PReMI)

作者： Akre, Sarthak Palandurkar, Nilesh Iyengar, Akshat Chayande, Gourav Kumar, Praveen Visvesvaraya Natl Inst Technol South Ambazari Rd Nagpur 440010 Maharashtra India

ISBN: (纸本)9783031451690;9783031451706

To assess student engagement in an e-learning environment, this work proposes a novel learning analytics dataset collection that combines emotional, cognitive, and behavioral data. the dataset includes facial expressions recorded by a webcam during e-learning sessions and log data such as play, pause, seek, course views, course material views, lecture views, quiz responses, etc. the procedure for gathering the data and the difficulties encountered are covered in the study. Combining emotional, cognitive, and behavioral cues for engagement detection in e-learning settings is made possible by this dataset, which represents a significant advancement in the field of learning analytics.

关键词： learning analytics Facial emotion recognition Engagement detection

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