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共 3357 条记录，以下是181-190 订阅

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Solar Flare Forecasting with Deep learning-based Time Series Classifiers 26

Solar Flare Forecasting with Deep Learning-based Time Series...

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

作者： Ji, Anli Wen, Junzhi Angryk, Rafal Aydin, Berkay Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Over the past two decades, machine learning and deep learning techniques for forecasting solar flares have generated great impact due to their ability to learn from a high dimensional data space. However, lack of high quality data from flaring phenomena becomes a constraining factor for such tasks. One of the methods to tackle this complex problem is utilizing trained classifiers with multivariate time series of magnetic field parameters. In this work, we compare the exceedingly popular multivariate time series classifiers applying deep learning techniques with commonly used machine learning classifiers (i.e., SVM). We intend to explore the role of data augmentation on time series oriented flare prediction techniques, specifically the deep learning-based ones. We utilize four time series data augmentation techniques and couple them with selected multivariate time series classifiers to understand how each of them affects the outcome. In the end, we show that the deep learning algorithms as well as augmentation techniques improve our classifiers performance. the resulting classifiers' performance after augmentation outplayed the traditional flare forecasting techniques.

关键词： solar flare prediction deep neural networks data augmentation

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Unsupervised Feature Selection via Feature-Grouping and Orthogonal Constraint 26

Unsupervised Feature Selection via Feature-Grouping and Orth...

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

作者： Yuan, Aihong Huang, Jiahao Wei, Chen Zhang, Wenjie Zhang, Naidan You, Mengbo Northwest A&F Univ Coll Informat Engn Yangling Shaanxi Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In the fields of machine learning and data mining, unsupervised feature selection plays an important role in processing large amounts of high-dimensional unlabeled data. this paper proposes an original and novel unsupervised feature selection based on feature grouping and orthogonal constraints. We consider the domain relationship in the original data and reconstruct the similarity matrix based on the correlation between the features. We use a generalized incoherent regression model based on orthogonal constraints. Furthermore, a graph regularization term with local structure preservation constraints is added to ensure that the feature subset does not lose local structural features in the original data space. Besides, an iterative algorithm is proposed to solve the optimization problem by iteratively updating the global similarity matrix, and constructing weight matrix, pseudo-label matrix and transformation matrix. through experiments on 6 benchmark datasets, the clustering performance of the proposed method outperforms state-of-the-art unsupervised feature selection methods. the source code is available at: https://***/misteru/FGOC.

关键词： Correlation Source coding machine learning Benchmark testing Feature extraction data structures data models

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Drugs Recommender System Based on Side Effects and Opinions Analysis 4th

Drugs Recommender System Based on Side Effects and Opinions ...

引用

4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Ziani, Amel Adouane, Mohamed Amine Delladj, Mohamed Ayoub Brahimi, Fatima Zahra Merouani, Hayet Farida Chadli Benjedid Univ Dept Comp Sci El Tarf Algeria Badji Mokhtar Univ LRI Lab Annaba Algeria Benyoucef Benkhedda Univ Dept Comp Sci Algiers Algeria Badji Mokhtar Univ Dept Comp Sci Annaba Algeria

ISBN: (纸本)9783031821493;9783031821509

Currently, a large amount of clinical data spread across various websites makes it difficult for users to find useful information to improve their wellbeing. Furthermore, the overload of medical information has caused many difficulties for healthcare specialists to make decisions in favor of patients. the choice of the most suitable drug is important for patients and requires specialists who have a lot of information about all drugs as well as medical data of patients. the importance of drug choice is not just in the effectiveness of the latter in the treatment, but there is another factor, which is the side effects presented by the drug. A side effect can range from mild disturbances to life-threatening problems. therefore, this project's objective is to create a drug recommendation system that addresses the challenges of scattered clinical data and medical information overload. It aims to recommend effective drugs with minimal side effects, collecting patients' data to overcome data scarcity. the system targets cases where patients experience side effects, offering alternative drug options. Its significance lays in providing personalized drug recommendations for patients and building a comprehensive patient profile database. Ultimately, this system can enhance healthcare decision-making and improve patient well-being.

关键词： Health recommendation system machine learning opinion analysis datasets (*** and SIDER) construction of a profile-based dataset

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Online Adaptive Metrics for Model Evaluation on Non-representative Offline Test data 26

Online Adaptive Metrics for Model Evaluation on Non-represen...

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

作者： Piovano, Enrico Le, Dieu-thu Chen, Bei Bradford, Melanie Amazon Com Inc Bellevue WA 98004 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

A major challenge encountered in the offline evaluation of machine learning models before being released to production is the discrepancy between the distributions of the offline test data and of the online data, due to, e.g., biased sampling scheme, data aging issues and occurrence(s) of regime shift. Consequently, the offline evaluation metrics often do not reflect the actual performance of the model online. In this paper, we propose online adaptive metrics, a computationally efficient method which re-weights the offline metrics based on calculating the joint distributions of the model hypotheses over the offline test data VS. the online data. It provides offline metrics which estimate the production performance of the model by taking into account the test data biases. the proposed method is demonstrated by real life examples on text classification and a commercial natural language understanding system. We show that the online adaptive metrics can provide accurate predictions of online recall and precision even with a small test dataset.

关键词： Measurement Adaptation models Computational modeling Text categorization Production machine learning data models

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Real-Time Water Quality Monitoring via Impedance Spectroscopy and machine learning 12

Real-Time Water Quality Monitoring via Impedance Spectroscop...

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12th international conference on Agro-Geoinformatics (Agro-Geoinformatics)

作者： Aybar, Oguzhan Kara, Zeynep Yucel, Meric Ustundag, Burak Berk Istanbul Tech Univ Comp & Informat Dept Istanbul Turkiye Istanbul Tech Univ Natl Software Certificat Res Ctr Istanbul Turkiye

ISBN: (纸本)9798350380613;9798350380606

Water quality is crucial for plant growth, with factors like salinity levels significantly impacting crop health. Variations in water quality, especially from wells, necessitate regular monitoring. Existing methods are often costly, timeconsuming, or unsuitable for continuous monitoring. this study utilizes impedance spectroscopy for real-time water quality monitoring in irrigation systems and machine learning methods for prediction. the proposed method captures spectral features and employs a compact machine-learning model for efficient and accurate pattern recognition, outperforming traditional electric conductivity measurements. Experiments measured various spectral features from water with different concentrations of NaCl, MgSO4, and their mixtures, across 1kHz to 1MHz using an Analog Discovery 2 device. data from these experiments were used to estimate solute concentrations. machine learning methods, including Random Forest and Multilayer Perceptron, were employed to predict NaCl and MgSO4 concentrations in mixed samples. Results demonstrate significant improvements over existing methodologies, supporting continuous monitoring. With 55.4 ppm MAE for NaCl and 121.5 ppm MAE for MgSO4, the prediction results are promising for real-time water quality monitoring. Implementing farmer-specific devices could enhance agricultural automation by setting thresholds, warnings, and enabling automatic

关键词： Water quality IoT neural networks Impedance spectroscopy machine learning irrigation monitoring agricultural monitoring

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Enhancing Tomato Crop Health: Leveraging Modified InceptionResNetV2 for Disease Detection 4th

Enhancing Tomato Crop Health: Leveraging Modified InceptionR...

引用

4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Zubair, Fida Saleh, Moutaz Qatar Univ Comp Sci & Engn Dept Doha Qatar

ISBN: (纸本)9783031821523;9783031821530

the proposed research aims to address the critical need for robust disease detection in tomato crops in the Middle East, given the essential role of tomatoes in the region's food security. Recognizing the significance of plant disease detection becomes important for securing a stable supply of this essential crop. this research aims to employ transfer learning and A modified InceptionResNetV2 architecture for the purpose of disease detection. the study evaluates the efficiency of this model with two datasets: Plant Village and Noulam. the Proposed Model Surpasses Prior Works: Achieving 99.8% and 99.7% Accuracies on Respective datasets for Tomato Disease Detection. the primary objective of this research is to enhance the early detection of tomato diseases, contributing to the efficiency and sustainability of tomato cultivation.

关键词： Transfer learning InceptionResNetV2 tomato data augmentation Softmax Adam Optimizer

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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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data Enhancement Strategy Using Reinforcement learning and Xgboost with BlockChain Security 4

Data Enhancement Strategy Using Reinforcement Learning and X...

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4th international conference on Intelligent Technologies, CONIT 2024

作者： Selvapriya, S. Manvizhi, N. Ajay, S. Kavya, V.K. Gilmary, Rosario Ifet College of Engineering Dept. of Information Technology Tamilnadu Villupuram India

ISBN: (纸本)9798350349900

the enormous volumes of data generated by Next Generation Sequencing (NGS) technology have transformed genomics and made efficient data analysis techniques necessary. To create data analytics applications, the extraction, transformation, and loading (ETL) process is necessary. In understanding data features, representation, storage, I/O operations, intermediate findings, in-memory analysis, and total processing time, the data format is crucial, as this study emphasizes. data conformance is required for effective analysis since different machine learning and data mining algorithms require different types and forms of data. In this paper, we explore data format-centric optimization of the ETL process for NGS data analysis. We investigate how computing efficiency and analytical correctness are affected by the choice of data type. By using distributed computing and parallel processing, we evaluate the effectiveness of several data formats in NGS data analysis processes, such as FASTQ, BAM, and VCF. Our study offers suggestions for developing data analytics applications in the genomics field by clarifying the relationship between data format, ETL, and algorithmic needs. We also look at the trade-offs between resource usage and data format choice. In the end, our study contributes to more accurate and efficient genomics research by addressing the urgent need for well-informed data format options in NGS data analysis. © 2024 IEEE.

关键词： Reinforcement learning

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Self-Supervised learning for Point Clouds through Multi-crop Mutual Prediction 4

Self-Supervised Learning for Point Clouds through Multi-crop...

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4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Zeng, Changyu Wang, Wei Xiao, Jimin Nguyen, Anh Yue, Yutao Institute of Deep Perception Technology JITRI Wuxi China Xi'An Jiaotong-Liverpool University School of Advanced Technology Suzhou China University of Liverpool Department of Computer Science Liverpool United Kingdom

ISBN: (纸本)9798350324303

As the demand for high-level autonomous driving continues to grow, efficient processing of large amount of point cloud data from LiDAR sensors has become a severe issue. Despite its great success, it is extremely difficult, if not impossible, to apply supervised learning in many real-world applications with natural scenes lacking annotations. In addition, removing label dependency and improving algorithm generalization ability are the current key research challenges in 3D deep learning. We propose a crop-based self-supervised learning framework for point clouds called Multi-Crop Mutual Prediction (MCMP). By cropping the original point clouds into different number of multiscale patches and making patches from the same object as close as possible through dynamic updates of online prototypes, the encoder is well able to maintain consistency between both local and global features. MCMP framework is model-agnostic so it is convenient to integrate it into encoders of any architectures. We pre-train three commonly used point cloud feature extraction networks on a single object-level dataset MondelNet40 and evaluate MCMP on three downstream tasks, namely, object classification, partial segmentation and semantic segmentation. Experimental results show that MCMP outperforms the previous crop-based self-supervised approaches in the object classification and semantic segmentation tasks and produces comparable results with the supervised benchmark methods. © 2023 IEEE.

关键词： Semantic Segmentation

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the Implementation of Non-negative Matrix Decomorization and Its Optimization Algorithm in Face recognition 4

The Implementation of Non-negative Matrix Decomorization and...

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4th international conference on Computer Vision and pattern Analysis, ICCPA 2024

作者： Wang, Youzuo Wuhan Guanggu Vocational College Wuhan China

ISBN: (纸本)9781510682528

With the popularization of mobile devices and the explosion of data in this era, more and more images, voice, text and other information need to use machine learning to improve the processing efficiency. In order to reduce the dimension of original processing, improve the learning rate and reduce the storage cost, researchers have studied a large number of data dimension reduction algorithms, which have made great achievements in the past hundred years, including principal component analysis, linear discriminant analysis, independent component analysis and so on. In the matrix decomposition of images, speech and text, the negative value obtained by decomposition often has no practical significance. therefore, Lee et al. proposed non-negative matrix decomposition in 1999, adding non-negative constraints in the decomposition process to decompose the base matrix and coefficient matrix. Generally, low-dimensional base matrix is replaced by the original data matrix to learn classification. Since non-negative matrix decomposition does not produce enough sparsity in the actual operation process and its nature is unsupervised learning cannot, some researchers propose non-negative matrix decomposition for adding sparsity and matrix decomposition for adding discriminative information, which can reduce the spatial storage and learning time more effectively. Due to the slow convergence rate, some researchers propose local decomposition and two-dimensional non-negative matrix decomposition, which effectively improve the convergence rate. In order to reduce the influence of each type on the same face portrait, the product of each matrix in the same angle can be used as the type limit item to the target function. With two constraint terms, more multi-angle data. In order to obtain face data in different dimensions, this paper also combines the multi-dimensional technology with Multi-view NMF technology, designs a multi-dimensional multi-view face recognition method, and provides a

关键词： Unsupervised learning

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