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IFSM: An Iterative Feature Selection Mechanism for Few-Shot Image Classification 25

IFSM: An Iterative Feature Selection Mechanism for Few-Shot ...

引用

25th international conference on pattern recognition (ICPR)

作者： Cai, Chunhao Yuan, Minglei Lu, Tong Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9781728188089

Nowadays many deep learning algorithms have been employed to solve different types of problems in the area of computer vision;however, most of them require a great amount of training data and tend to struggle in few-shot learning tasks. On the other hand, those methods designed for few-shot learning usually face the difficulty that once one or more samples show a relatively large bias, the predicted result may be much less reliable due to the fact that the sample will cause a large shift of class-level features during few-shot learning. To solve this problem, this paper presents a novel and Iterative Feature Selection Mechanism (IFSM) for few-shot image classification, which can be applied to lots of metric-based few-shot learners. IFSM learns to construct a more feasible class-level feature which is less affected by samples with relatively large biases, using an iterative approach. the proposed mechanism is tested on three previous state-of-the-art few-shot learning methods, and the experimental results show that the proposed mechanism considerably improves (by 1% to 2%) the image classification accuracies of both methods on the miniImageNet, tieredImageNet or CUB benchmarks in 5-way 5-shot tasks. this approves the effectiveness and generality of the proposed mechanism.

关键词： few-shot learning metric learning feature selection

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Research on User Electricity Behavior Analysis Based on Clustering and Deep learning

Research on User Electricity Behavior Analysis Based on Clus...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Jiaqi Zhang Xin Tong Haitao Song Song Zhang State Grid Shenyang Electric Power Supply Company Shenyang China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

With the development of smart grids and the increasing importance of power big data, accurate analysis of user electricity consumption behavior has become particularly important. this paper addresses the demand for big data analysis of electricity in smart grids. the K-means algorithm and LSTM model are used to cluster analyze residential electricity consumption behavior and predict electricity consumption patterns. data decomposition and random matrix theory are used to detect abnormal situations in the electricity consumption data of smart meter users. the research results indicate that the proposed LSTM model performs well in prediction accuracy and can effectively support power consumption pattern recognition and anomaly detection, providing strong support for policy formulation and economic benefits improvement in the power system.

关键词： Deep learning Analytical models Accuracy Electricity Clustering algorithms Predictive models Prediction algorithms Smart meters Smart grids Long short term memory

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Collaborative Computation Offloading in Multi-UAV-MEC Networks: A Reinforcement learning Approach 5

Collaborative Computation Offloading in Multi-UAV-MEC Networ...

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5th international conference on Artificial Intelligence and pattern recognition, AIPR 2022

作者： Zeng, Yaoping Yang, Ting Hu, Yanwei Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

To cope with the unprecedented surge in demand for data computing, the promising unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) has been proposed to enable the network edges to provide closer data processing for users. Hence, data offloading from user to the MEC server will require more efficient. the integration of nonorthogonal multiple access (NOMA) technique with MEC has been shown to provide applications with lower latency and higher energy efficiency. To further enhance offloading performance, in this work, we propose an offloading scheme based on the data division and fusion reinforcement learning (DF-RL) algorithm to handle tasks through multi-user and multi-UAV collaboration. We formulate the optimization problem to minimize the delay and energy consumption of the system, and optimize the offloading strategy through the DF-RL algorithm. Firstly, the data fusion module is used to reduce the processing of repetitive tasks. Secondly, the task is divided into sub-tasks by task segmentation module to better complete the cooperation between UAVs. Finally, reinforcement learning (RL) is used to solve the problem and the optimal offloading strategy decision is obtained. Simulation results show that our algorithm not only has great superiority, but also improves the successful rate of the tasks. © 2022 ACM.

关键词： Reinforcement learning

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Analysis of active decision model based on big data and pattern recognition 23

Analysis of active decision model based on big data and patt...

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Proceedings of the 2023 5th international conference on Internet of things, Automation and Artificial Intelligence

作者： Xiandong Meng Tingting Zhang Qixuan Wang Minghao Liao Yongjian Sun Liaoning Institute of Science and Technology China

ISBN: (纸本)9798400716485

In order to further improve the generalization ability of the active decision model, a new active decision model based on big data and pattern recognition was constructed by taking agricultural decision making as an example. the characteristics of land remote sensing big data are obtained from two aspects of spectrum and shape. the features of remote sensing big data were input into support vector machine to realize the classification of farmland. Based on this, combined with vector description method, pattern recognition of agricultural environment is completed. Taking grain production, land ecological compatibility minimization and social and economic development as objective functions, an active decision-making model was constructed. the experimental results show that when using the constructed model to make active decisions on farmland, the recall and precision rates can both reach over 95%, and the time consumption is always less than 20ms.

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A General Modeling Process Framework for Building Bayesian Network to Mine the Influencing Factors of Diabetes 25

A General Modeling Process Framework for Building Bayesian N...

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2025 5th international conference on Bioinformatics and Intelligent Computing, BIC 2025

作者： Wu, Ting Li, Yanan Department of Big Data Baoshan University Yunnan Baoshan China Department of Pathology The First People's Hospital of Yunnan Province Yunnan Kunming China

ISBN: (纸本)9798400712203

Diabetes is a metabolic syndrome, and its annual incidence is rising sharply. Using machine learning methods could help in the early detection of diabetes and early treatment to prevent the condition from worsening. Bayesian network is a type of machine learning algorithm based on the probabilistic graphical model (PGM) and the strength of the model is its ability to combine qualitative visual representations with quantitative reasoning. In the study, a Bayesian network general modelling process framework (including network structure learning, parameter learning, model cross-validation, and inference) is proposed for diagnosing the probability of developing diabetes. For the Pima Indians Diabetes dataset, Bayesian networks built by the framework were used to interpret and visualize the interactions between the influencing factors of diabetes. At the same time, We use the thresholds which are statistical averages for the diabetic population as a benchmark to get the probability values for different combinations, several types of high-risk groups are listed. the study can draw the following conclusions: Glucose is the most direct and important judgment index for measuring diabetes, the higher the blood glucose concentration (>145 mg/dL), the higher the risk of the disease (0.6170) may be. Overweight middle-aged people have a high risk of diabetes (0.6527), and if there is a problem with high blood sugar on this basis, the risk of disease (0.7969) will increase by about 15%. Furthermore, the probability of diabetes can be estimated under any given prior conditions, providing a reference for medical diagnosis. © 2025 Copyright held by the owner/author(s).

关键词： Bayesian network data mining Diabetes Influencing factors Process Framework

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Enhancing Education 4.0 with Artificial Intelligence

Enhancing Education 4.0 with Artificial Intelligence

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Recent Trends in Computer Science and Technology (ICRTCST), international conference on

作者： Debjyoti Ghosh Rajesh Kumar Tiwari Pravin Ramesh Gundalwar Department of Computer Applications Vinoba Bhave University Hazaribag Jharkhand India RVS College of Engineering and Technology Jamshedpur Jharkhand India Department of Information Technology Anurag University Hyderabad

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

this study advocates for self-regulated learning in Higher Education 4.0, integrating smart technologies like sensors, wearables, analytics, and machine learning. It introduces an Early recognition System driven by predictive analytics, inspired by N. A. Crowder's Auto Tutor theory. Personalized assessments and adaptive feedback empower students to steer their learning journey independently. this framework benefits students, professors, and administrators alike, fostering a dynamic educational environment. Enhanced data-driven decision-making leads to optimized resource allocation and program effectiveness, culminating in empowerment, engagement, and success for all stakeholders.

关键词： Education Decision making machine learning Market research Stakeholders Resource management Wearable devices

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Investigating the Effects of Two-Class Categorical Emotion Classification through Electrodermal Activity and machine learning

Investigating the Effects of Two-Class Categorical Emotion C...

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Cybernetics, Cognition and machine learning Applications (ICCCMLA), IEEE international conference on

作者： Sriram KumarP Jac Fredo Agastinose Ronickom School of Bio-medical Engineering Indian Institute of Technology (BHU) Varanasi Uttar Pradesh India

Wearable emotion recognition using physiological signals gains attention due to its non-intrusive nature, yet effectively using physiological data remains a challenge. In our study, we crafted machine learning models for two-class categorical emotion recognition systems employing Electrodermal Activity (EDA). Initially, EDA signals were obtained from the continuously annotated signals of emotion dataset then pre-processed and decomposed the EDA into phasic signals using the convex optimization approach to EDA. We further extracted temporal features from the phasic signal. Finally, all extracted features and selected features through recursive feature elimination with cross-validation (RFECV) were applied to machine learning algorithms, such as logistic regression, support vector machine, random forest (RF), extreme gradient boosting, and multi-layer perceptron, to classify various two-class categorical emotions. the results of our process pipeline, encompassing phasic signals, RFECV-selected features, and RF, showcased impressive classification performance between the scary and boring emotion classes with an accuracy of 84.17%. Our findings suggest that the models we've proposed hold potential for application in various healthcare settings, effectively detecting both positive and negative emotions and contributing to the enhancement of individual well-being.

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CAD-RADS Scoring using Deep learning and Task-Specific Centerline Labeling 5

CAD-RADS Scoring using Deep Learning and Task-Specific Cente...

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5th international conference on Medical Imaging with Deep learning (MIDL)

作者： Denzinger, Felix Wels, Michael Taubmann, Oliver Guelsuen, Mehmet A. Schoebinger, Max Andre, Florian Buss, Sebastian J. Goerich, Johannes Suehling, Michael Maier, Andreas Breininger, Katharina Friedrich Alexander Univ Erlangen Nurnberg Pattern Recognit Lab Erlangen Germany Siemens Healthcare GmbH Computed Tomog Forchheim Germany Sinsheim Eberbach Erbach Walldorf Heidelberg Radiol Zentrum Radiol Ctr Heidelberg Germany Friedrich Alexander Univ Erlangen Nurnberg Dept Artificial Intelligence Biomed Engn Erlangen Germany

With coronary artery disease (CAD) persisting to be one of the leading causes of death worldwide, interest in supporting physicians with algorithms to speed up and improve diagnosis is high. In clinical practice, the severeness of CAD is often assessed with a coronary CT angiography (CCTA) scan and manually graded with the CAD-Reporting and data System (CAD-RADS) score. the clinical questions this score assesses are whether patients have CAD or not (rule-out) and whether they have severe CAD or not (hold-out). In this work, we reach new state-of-the-art performance for automatic CAD-RADS scoring. We propose using severity-based label encoding, test time augmentation (TTA) and model ensembling for a task-specific deep learning architecture. Furthermore, we introduce a novel task- and model-specific, heuristic coronary segment labeling, which subdivides coronary trees into consistent parts across patients. It is fast, robust, and easy to implement. We were able to raise the previously reported area under the receiver operating characteristic curve (AUC) from 0.914 to 0.942 in the rule-out and from 0.921 to 0.950 in the hold-out task respectively.

关键词： Coronary Artery Disease Coronary CT Angiography Deep learning Ensembling CAD-RADS Coronary Artery Labeling

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Self-Training for Domain Adaptive Scene Text Detection 25

Self-Training for Domain Adaptive Scene Text Detection

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25th international conference on pattern recognition (ICPR)

作者： Chen, Yudi Wang, Wei Zhou, Yu Yang, Fei Yang, Dongbao Wang, Weiping Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China TAL Educ Grp Beijing Peoples R China

ISBN: (纸本)9781728188089

though deep learning based scene text detection has achieved great progress, well-trained detectors suffer from severe performance degradation for different domains. In general, a tremendous amount of data is indispensable to train the detector in the target domain. However, data collection and annotation are expensive and time-consuming. To address this problem, we propose a self-training framework to automatically mine hard examples with pseudo-labels from unannotated videos or images. To reduce the noise of hard examples, a novel text mining module is implemented based on the fusion of detection and tracking results. then, an image-to-video generation method is designed for the tasks that videos are unavailable and only images can be used. Experimental results on standard benchmarks, including ICDAR2015, MSRA-TD500, ICDAR2017 MLT, demonstrate the effectiveness of our self-training method. the simple Mask RCNN adapted with self-training and fine-tuned on real data can achieve comparable or even superior results with the state-of-the-art methods.

关键词： Deep learning

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Opinion mining Algorithm Based on the Evaluation of Online Mathematics Course with Python 5

Opinion Mining Algorithm Based on the Evaluation of Online M...

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5th international conference on Electronics, Communication and Aerospace Technology, ICECA 2021

作者： Huang, Yunpeng Shanxi Xueqian Normal University Shanxi Xi'an710061 China

ISBN: (纸本)9781665435246

Opinion mining algorithm based on the evaluation of the online mathematics course with Python is designed and implemented in this study. Natural language is the language used by humans to communicate. When using natural language, it can more or less always leave the user's subjective view of people, things and things. therefore, texts with subjective content are widely present in our lives. Hence, in this paper, the data mining and the pattern recognition models are used to construct the model. the Python is used and optimized for the systematic design. the framework is simulated on different datasets, the performance is well validated. © 2021 IEEE.

关键词： data mining

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