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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 34017 条记录，以下是1241-1250 订阅

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Gradient Saliency-aware CutMix for Semi-Supervised Medical Image Segmentation

Gradient Saliency-aware CutMix for Semi-Supervised Medical I...

引用

IEEE international conference on Multimedia and Expo (ICME)

作者： Jiang, Yuxuan Zhu, Guobin Ding, Yi Qin, Zhen Pang, Minghui Univ Elect Sci & Technol China Sch Informat & Software Engn Network & Data Secur Key Lab Sichuan Prov Chengdu Peoples R China Yibin GREAT Technol Co Ltd Yibin 644000 Peoples R China Sichuan Prov Peoples Hosp Dept Geriatr Gen Surg Chengdu 610054 Peoples R China

ISBN: (纸本)9798350390155;9798350390162

In semi-supervised medical image segmentation, the use of CutMix in the Mean Teacher architecture is considered an effective strong data augmentation strategy. However, we believe that randomly selecting patches from the source image might mislead the model into learning unexpected feature representations. Therefore, we propose Gradient Saliency-aware CutMix for semi-supervised medical image segmentation (GSC-Seg). Utilizing the gradient from pre-trained models to detect salient regions and then copies and pastes the large gradient areas from labeled data into corresponding areas of unlabeled data based on the gradient, and vice versa, guiding the model to learn more appropriate feature representations. Furthermore, we propose a gradient augmentation strategy, which generates disruptions in the gradient through the network itself and enhances the gradient representation abilities of the network. Experiment results show that our approach achieves the state-of-the-art performance on three medical image segmentation datasets. Code is available at https://***/UESTC-Med424-JYX/GSC-Seg.

关键词： Semi-Supervised learning Medical Image Segmentation CutMix Saliency-aware Gradient Augmentation

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A Knowledge-Based Deep learning Approach for Automatic Fake News Detection using BERT on Twitter 2

A Knowledge-Based Deep Learning Approach for Automatic Fake ...

引用

2nd international conference on machine learning and data engineering, icmlde 2023

作者： Nair, Vinita Pareek, Dr. Jyoti Bhatt, Sanskruti Department of Computer Science Gujarat University 380009 India

Fake news generation and propagation is a major challenge of the digital age, resulting in various social impacts namely bandwagon, validity, echo chamber effects, deceiving the public with spams, misinformation, malicious content and many more. The widespread proliferation of fake news not only fosters misinformation but also undermines the credibility of news sources. The veracity of the information is a major concern at all the stages of generation, publication, and propagation. To comprehend the critical need for addressing this pervasive problem, this research paper presents a framework for automatic detection of fake news using a knowledge-based approach. An automatic fact checking mechanism is applied using concepts of Information Retrieval (IR), Natural Language Processing (NLP) and Graph theory. The knowledge base is generated using Twitter dataset, which basically contains four attributes: Subject-Predicate-Object (SPO) triplet, SPO sentiment polarity, SPO occurrence, and topic modeling. These attributes serve as pivotal indicators for the development of a knowledge base, subsequently employed to detect prevalent patterns and traits linked to deceptive or false information. We have employed Named Entity Recognition (NER) model to extract SPO triples and Latent Dirichlet Allocation (LDA) for topic modeling, thereby contributing to knowledge base generation. To evaluate the efficacy and efficiency of our proposed model, we utilize deep learning algorithms like RNN, GRU, LSTM, GPT-3 and BERT Transformer providing an acceptable level of accuracy. This research paper delivers valuable insights into addressing the proliferation of fake news on Twitter, employing data-driven approaches and advanced deep learning algorithms. © 2024 Elsevier B.V.. All rights reserved.

关键词： learning algorithms

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A Congruence-based Approach to Active Automata learning from Neural Language Models 16

A Congruence-based Approach to Active Automata Learning from...

引用

16th international conference on Grammatical Inference (ICGI)

作者： Mayr, Franz Yovine, Sergio Carrasco, Matias Pan, Federico Vilensky, Federico Univ ORT Uruguay Fac Ingn Montevideo Uruguay

The paper proposes an approach for probably approximately correct active learning of probabilistic automata (PDFA) from neural language models. It is based on a congruence over strings which is parameterized by an equivalence relation over probability distributions. The learning algorithm is implemented using a tree data structure of arbitrary (possibly unbounded) degree. The implementation is evaluated with several equivalences on LSTM and Transformer-based neural language models from different application domains.

关键词： MAT-PAC active learning Neural language models PDFA

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RoGER: Few-shot learning Based Robust Gesture Recognition with Multi-modal Wi-Fi CSI Measurements 1

RoGER: Few-shot Learning Based Robust Gesture Recognition wi...

引用

1st IEEE international conference on machine learning for Communication and Networking (IEEE ICMLCN)

作者： Bi, Suzhi Chen, Xinyu Zheng, Lili Hou, Huawei Lin, Xiaohui Shenzhen Univ State Key Lab Radio Frequency Heterogeneous Integ Coll Elect & Informat Engn Shenzhen 518060 Guangdong Peoples R China

ISBN: (纸本)9798350343205;9798350343199

Wi-Fi based gesture recognition has raised emerging interest recently due to its high identification accuracy and low-hardware cost. However, its application is impeded by the severe performance degradation in cross-domain sensing, where the change of sensing target or sensing distance/orientation would significantly reduce the sensing accuracy. In this paper, we propose a domain-Robust GEsture Recognition (RoGER) system based on few-shot learning (FSL) and multi-modal Wi-Fi channel state information (CSI) measurement fusion. In particular, the proposed RoGER system combines the features derived from the CSI data received from multiple pairs of transceiver devices and uses FSL to achieve stable cross-scenario sensing performance with the minimum number of labeled samples. The proposed RoGER consists of two major stages: the source domain meta-training stage and the target domain meta-testing stage. In the meta-training stage, we design and train separate convolutional neural network (CNN) modules using the CSI data captured from different transceiver links to extract and fuse the implicit multi-modal gesture features. In the meta-testing stage, we use the source-domain CNN module as the embedding model to generate the feature map and train a domain-specific classifier using minimum labeled samples of the target domain. Experiment results show that the proposed RoGER can achieve on average 94.84% - 99.71% cross-domain recognition accuracy in a 6-class gesture classification task using only 1 labeled sample per class, which significantly outperforms the considered benchmark methods.

关键词： Wi-Fi

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Power Quality Forecasting of Microgrids Using Adaptive Privacy-Preserving machine learning

Power Quality Forecasting of Microgrids Using Adaptive Priva...

引用

22nd international conference on Applied Cryptography and Network Security (ACNS)

作者： Ali, Mazhar Kumar, Ajit Choi, Bong Jun Soongsil Univ Sch Comp Sci & Engn Seoul 06978 South Korea

ISBN: (纸本)9783031614859;9783031614866

Microgrids face challenges in monitoring and controlling the power quality (PQ) of integrated electrical systems to make timely decisions. Inverter-based technologies handle small-scale smart grids' power quality parameters (PQPs) and play an important role in condition monitoring. Accurate forecasting of such parameters is difficult due to the stochastic nature of demand, distributed generation, and weather conditions. Moreover, energy clients have concerns over growing privacy and security breaches for collaboration involving data exchanges. This study aims to predict PQPs indices of home microgrids using ANN, LSTM, and CNN-LSTM models. To preserve users' privacy, federated learning has been applied with some adaptive differential privacy on the global model and clients' data. Comparative analysis of the ML model and DP parameters shows that the LSTM model gives better results with adequate privacy parameters to predict the PQPs of five distributed microgrids. LSTM model gives the least MAE of 0.2323 for FL without privacy and 0.3256 test loss for appropriate DP level.

关键词： machine learning Microgrid Federated learning Power Quality

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At-Risk Students Identification based on machine learning Approach: A Case Study of Computer Science Bachelor Student in Tunisia 20

At-Risk Students Identification based on Machine Learning Ap...

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20th ACS/IEEE international conference on Computer Systems and Applications (AICCSA)

作者： Khalifa, Amani BenSaid, Fatma Kacem, Yessine Hadj Jridi, Zouhaier Univ Gafsa Fac Sci Gafsa Gafsa Tunisia Univ Sfax REGIM Lab Res Grp Intelligent Machines Sfax Tunisia Univ Sfax Natl Engn Sch Sfax CES Lab Sfax Tunisia

ISBN: (纸本)9798350319439

Various data mining techniques, like prediction and clustering, can be applied on educational data in order to study the student's performance and behavior. Predicting academic results is one of the methods that aim at monitoring student progress and anticipating students that are at risk of failure in their academic career. In this paper, we propose a machine learning (ML) based Educational data Mining (EDM) approach, named ARSITUN, for the identification of at-risk students. Using ARSITUN, an early intervention can be performed for the detected students in order to lower the risk of their failure. The proposed approach was developed and tested using student's data that were collected from the Tunisian administration system for bachelors and masters called "Salima". We created a new dataset, named GCSD, that concerns 358 students from the faculty of Sciences of Gafsa during the school years period 2014-2022. The experimental results showed that our EDM model reaches an accuracy of 90.44% for computer science bachelors' grade prediction (Tunisian case study).

关键词： Educational data mining At-risk student identification machine learning Student's performance Bachelors grade prediction

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data Valuation in machine learning: "Ingredients", Strategies, and Open Challenges 31

Data Valuation in Machine Learning: "Ingredients", Strategie...

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31st international Joint conference on Artificial Intelligence (IJCAI)

作者： Sim, Rachael Hwee Ling Xu, Xinyi Low, Bryan Kian Hsiang Natl Univ Singapore Dept Comp Sci Singapore Singapore

ISBN: (纸本)9781956792003

data valuation in machine learning (ML) is an emerging research area that studies the worth of data in ML. data valuation is used in collaborative ML to determine a fair compensation for every data owner and in interpretable ML to identify the most responsible, noisy, or misleading training examples. This paper presents a comprehensive technical survey that provides a new formal study of data valuation in ML through its "ingredients" and the corresponding properties, grounds the discussion of common desiderata satisfied by existing data valuation strategies on our proposed ingredients, and identifies open research challenges for designing new ingredients, data valuation strategies, and cost reduction techniques.

关键词： machine learning

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Automatic Text Document Classification by Using Semantic Analysis and Lion Optimization Algorithm 2nd

Automatic Text Document Classification by Using Semantic Ana...

引用

2nd international conference on Computational Intelligence in machine learning, ICCIML 2022

作者： Ranjan, Nihar M. Prasad, Rajesh S. Mane, Deepak T. Information Technology JSPM RSCOE Pune India Computer Engineering MIT Art Design and Technology University Pune India Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9789819979530

Text mining is a popular research area in the field of computer science and engineering that enables the processing of natural language which has applications in the area of aerospace, biomedical, and so on. Text mining unsheathes the unknown information present in the data such that the extraction of the data seems to be effective. Text classification is a subdomain of the text mining that plays a major role in labelling the documents based on their semantic meaning and context. Different machine learning algorithms are available to classify the available text documents. The main contribution of this paper is use of semantic analysis with Lion Optimization Algorithm and Neural Network architecture. The semantic analysis technique is used for the text classification through semantic keywords rather than using independent features of keywords in the documents. Lion Optimization Algorithm is used to adjust the weight of the Neural Network to maximize the efficiency of the classifier. Two well-known open source dataset namely 20 Newsgroups and Reuters-21578 are used for the experimentation and evaluate the performance of the classification algorithms. Significant improvement in all three performance parameters in terms of accuracy, specificity, and sensitivity is observed. The maximum values observed with our proposed algorithm are 91.86, 95.54, and 84.96 for accuracy, sensitivity, and specificity, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： machine learning

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Routing network traffic predict based on firewall logs using machine learning 4

Routing network traffic predict based on firewall logs using...

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4th international conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2022

作者： Li, Yafei Wang, Liejun School of Information Science and Engineering Xinjiang University China

ISBN: (纸本)9781450398343

This paper employs firewall system log data to address the internet traffic issue caused by misconfigured firewall policies. The UCI machine learning library provided the data set. data sets are processed using feature engineering to produce classifiable results. machine learning techniques are used to predict permissible base traffic for specific network traffic based on log data. The machine learning model should be able to predict actions that should be taken quickly in order to avoid unnecessary network latency, which can be detrimental to network performance. The test time must be short, regardless of the complexity of the training. This article tested several machine learning approaches and discovered that LBGM is the most effective. © 2022 ACM.

关键词： Forecasting

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Deep learning Approaches for Image Classification Techniques

Deep Learning Approaches for Image Classification Techniques

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IEEE international conference on Electrical engineering, Big data and Algorithms (EEBDA)

作者： Guan, Youyou Han, Yuxuan Liu, Siqi Shandong Univ Dept Comp Sci & Technol Jinan Shandong Peoples R China China Agr Univ Int Coll Beijing Beijing Peoples R China Shanghai Univ Engn Sci Elect & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781665416061

Neural network is a popular and significant research direction in machine learning, which is widely used in classification. regression, pattern recognition and other fields. Based on the current direction of academic research, the application of neural network in image classification has great research value. However, due to a large number of such articles, involving too wide a range of aspects, it is difficult to grasp the main idea quickly when quoting. We have selected and sorted out some representative basic articles and innovative cuttingedge articles. After systematic analysis and integration, we give each a better algorithm for some of the previous algorithms. This upgrade is reflected in performance, efficiency and other aspects.

关键词： Image classification Computer vision Deep learning CNN FNN RNN

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