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检索条件"任意字段=IEEE 14th International Conference on Machine Learning and Applications ICMLA"

共 2699 条记录，以下是1-10 订阅

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Proceedings - 2015 ieee 14th international conference on machine learning and applications, icmla 2015

Proceedings - 2015 IEEE 14th International Conference on Mac...

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ieee 14th international conference on machine learning and applications, icmla 2015

ISBN: (纸本)9781509002870

the proceedings contain 220 papers. the topics discussed include: feature selection using Gustafson-Kessel fuzzy algorithm in high dimension data clustering;coordinate descent fuzzy twin support vector machine for classification;learning multi-valued biological models with delayed influence from time-series observations;a proposal of a methodological framework with experimental guidelines to investigate clustering stability on financial time series;a study of the use of complexity measures in the similarity search process adopted by kNN algorithm for time series prediction;the influence of sample reconstruction on stock trend prediction via NARX neural network;statistical fault localization based on importance sampling;topic novelty detection using infinite variational inverted Dirichlet mixture models;evaluating the uncertainty of a Bayesian network query response by using joint probability distribution;and performance analysis of majority vote combiner for multiple classifier systems.

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Proceedings - 16th ieee international conference on machine learning and applications, icmla 2017

Proceedings - 16th IEEE International Conference on Machine ...

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16th ieee international conference on machine learning and applications, icmla 2017

ISBN: (纸本)9781538614174

the proceedings contain 197 papers. the topics discussed include: anytime exploitation of stragglers in synchronous stochastic gradient descent;an evolutionary learning approach to self-configuring image pipelines in the context of carbon fiber fault detection;learning to coordinate with deep reinforcement learning in doubles pong game;anomaly prediction based on k-means clustering for memory-constrained embedded devices;clustering distributed short time series with dense patterns;attribute assisted interpretation confidence classification using machine learning;predictive modelling strategies to understand heterogeneous manifestations of asthma in early life;home appliance energy disaggregation using low frequency data and machine learning classifiers;on the impacts of noise from group-based label collection for visual classification;learning robust video synchronization without annotations;machine learning in appearance-based robot self-localization;learning antecedent structures for event coreference resolution;automatic generation and recommendation for API mashups;an evolutionary learning approach to self-configuring image pipelines in the context of carbon fiber fault detection;and machine learning approach to detecting sensor data modification intrusions in WBANs.

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Proceedings - 19th ieee international conference on machine learning and applications, icmla 2020

Proceedings - 19th IEEE International Conference on Machine ...

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19th ieee international conference on machine learning and applications, icmla 2020

ISBN: (纸本)9781728184708

the proceedings contain 228 papers. the topics discussed include: what are they reporting? examining student cybersecurity course surveys through the lens of machine learning;enhancing piano transcription by dilated convolution;MuSeM: detecting incongruent news headlines using mutual attentive semantic matching;predicting purchase probability of retail items using an ensemble learning approach and historical data;PENet: object detection using points estimation in high definition aerial images;graph neural networks for model recommendation using time series data;machine learning fairness in justice systems: base rates, false positives, and false negatives;MaskedFusion: mask-based 6d object pose estimation;DeepGamble: towards unlocking real-time player intelligence using multi-layer instance segmentation and attribute detection;and a new clustering-based technique for the acceleration of deep convolutional networks.

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Proceedings - 18th ieee international conference on machine learning and applications, icmla 2019

Proceedings - 18th IEEE International Conference on Machine ...

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18th ieee international conference on machine learning and applications, icmla 2019

ISBN: (纸本)9781728145495

the proceedings contain 304 papers. the topics discussed include: regularization learning for image recognition;through-wall pose imaging in real-time with a many-to-many encoder/decoder paradigm;domain mixture: an overlooked scenario in domain adaptation;particle detector simulation using generative adversarial networks with domain related constraints;leveraging semi-supervised learning for fairness using neural networks;a deep structural model for analyzing correlated multivariate time series;recurrent dilated densenets for a time-series segmentation task;state summarization of video streams for spatiotemporal query matching in complex event processing;brown planthopper damage detection using remote sensing and machine learning;generative feature models and robustness analysis for multimedia content classification;and an industry case of large-scale demand forecasting of hierarchical components.

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Proceedings - 17th ieee international conference on machine learning and applications, icmla 2018

Proceedings - 17th IEEE International Conference on Machine ...

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17th ieee international conference on machine learning and applications, icmla 2018

ISBN: (纸本)9781538668047

the proceedings contain 235 papers. the topics discussed include: inner attention based bi-LSTMs with indexing for non-factoid question answering;localized deep norm-CNN structure for face verification;dynamic analysis of executables to detect and characterize malware;actionable pattern mining - a scalable data distribution method based on information granules;recursive feature elimination by sensitivity testing;reinforcement learning algorithms for uncertain, dynamic, zero-sum games;exploring sentence vector spaces through automatic summarization;trademark design code identification using deep neural networks;a multi-objective rule optimizer with an application to risk management;learning to fingerprint the latent structure in question articulation;and time series prediction of agricultural products price based on time alignment of recurrent neural networks.

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Proceedings - 20th ieee international conference on machine learning and applications, icmla 2021

Proceedings - 20th IEEE International Conference on Machine ...

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20th ieee international conference on machine learning and applications, icmla 2021

ISBN: (纸本)9781665443371

the proceedings contain 276 papers. the topics discussed include: messing up 3D virtual environments: transferable adversarial 3D objects;character-level adversarial examples in Arabic;universal adversarial attack on deep learning based prognostics;feature popularity between different web attacks with supervised feature selection rankers;guided-generative network for noise detection in Monte-Carlo rendering;detection of endoscope withdrawal time in colonoscopy videos;contraband materials detection within volumetric 3D computed tomography baggage security screening imagery;disease prediction based on individual’s medical history using CNn;a novel convolutional neural network for pavement crack segmentation;recurrence plot Spacial pyramid pooling network for appliance identification in non-intrusive load monitoring;seed classification using synthetic image datasets generated from low-altitude UAV imagery;damage estimation and localization from sparse aerial imagery;and automated antenna testing using encoder-decoder-based anomaly detection.

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Proceedings - 2016 15th ieee international conference on machine learning and applications, icmla 2016

Proceedings - 2016 15th IEEE International Conference on Mac...

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15th ieee international conference on machine learning and applications, icmla 2016

ISBN: (纸本)9781509061662

the proceedings contain 186 papers. the topics discussed include: interaction network representations for human behavior prediction;demographic group prediction based on smart device user recognition gestures;cross-document knowledge discovery using semantic concept topic model;domain ontology induction using word embeddings;machine learning for plant disease incidence and severity measurements from leaf images;exposing in painting forgery in JPEG images under recompression attacks;recognition and analysis of the contours drawn during the Poppelreuter's test;automatic species recognition based on improved birdsong analysis;ECG biometric identification using wavelet analysis coupled with probabilistic random forest;toward an online anomaly intrusion detection system based on deep learning;investigating transfer learners for robustness to domain class imbalance;learning fairness under constraints: a decentralized resource allocation game;consensus clustering: a resampling-based method for building radiation hybrid maps;an led based indoor localization system using k-means clustering;phase identification in electric power distribution systems by clustering of smart meter data;identifying nontechnical power loss via spatial and temporal deep learning;a next-generation secure cloud-based deep learning license plate recognition for smart cities;using domain knowledge features for wind turbine diagnostics;and improving HSDPA traffic forecasting using ensemble of neural networks.

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Perceptual learning Technology Based on Computer Vision and machine learning Algorithms

Perceptual Learning Technology Based on Computer Vision and ...

引用

international conference on Communication Systems and Network Technologies (CSNT)

作者： Tianjian Wang School of Computer Science and Technology Wuhan Qingchuan University Wuhan China

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

To address the issue of low accuracy and poor robustness of perceptual learning in complex scenarios, a new method integrating computer vision and machine learning is adopted, that is, by applying deep neural networks, transfer learning and self-supervised learning, combined with multimodal data fusion strategy, to improve target recognition efficiency and learning ability. First, based on the preprocessing algorithm of improved Gaussian filtering and image segmentation, the quality of image features is improved. Data enhancement methods such as random rotation, flipping, and blurring are used to expand data distribution and improve model adaptability. Secondly, a convolutional neural network (CNN) is utilized in combination with an attention mechanism to extract multi-scale target features, and transfer learning is applied to transfer common features from pre-trained models to reduce dependence on large-scale labeled data. Finally, a contrastive learning framework is constructed to mine the correlation of unlabeled data. Transformer is used to realize the fusion of multimodal data of images and texts, and the model performance is optimized through multi-task learning. the mAP (Mean Average Precision) of the traditional method in dynamic occlusion scenarios is 60.5%, which is relatively weak. this may be because the traditional method cannot fully extract the effective features of the target under the occlusion of complex moving targets. the mAP of the method in this paper is 72.8%, which is higher than that of the traditional method. the perceptual learning method adopted in this paper effectively improves the accuracy and robustness in complex scenarios, and provides reliable technical support for intelligent applications. In the future, it can be combined with edge computing to further optimize the real-time processing capability and promote its application in the fields of unmanned driving and intelligent security.

关键词： Adaptation models Computer vision Image segmentation machine learning algorithms Computational modeling Transfer learning Feature extraction Data models Robustness Real-time systems

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A Bayesian Optimization Framework for Adaptive Node Deployment in Wireless Sensor Networks

A Bayesian Optimization Framework for Adaptive Node Deployme...

引用

international conference on Communication Systems and Network Technologies (CSNT)

作者： Rahul Priyadarshi Jayant Kumar Rout Anish Kumar Vishwakarma Nikhil Dhengre Rakesh Ranjan Amit Verma Faculty of Engineering and Technology ITER SOA Bhubaneswar India School of Computer Application KIIT Bhubaneswar India School of Computer Science UPES Dehradun Uttarakhand India Department of ECE IIIT Nagpur Maharashtra India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Wireless Sensor Networks (WSNs) require efficient node deployment strategies to optimize network longevity, energy consumption, and coverage. this paper introduces a novel approach for strategic node deployment in WSNs using Bayesian Optimization (BO). A BO-based framework that iteratively modifies node positions to maximize network performance metrics, such as energy efficiency and coverage ratio, while minimizing execution time is proposed. through extensive simulations across diverse deployment scenarios, the proposed method is evaluated and demonstrates superior performance compared to both traditional and machine learning (ML)-based techniques. the results indicate significant improvements in coverage and energy efficiency, alongside competitive execution times. these findings highlight the potential of BO to enhance WSN deployment strategies, offering a scalable and adaptive solution for real-world applications. Future research will focus on integrating BO with other optimization techniques and extending the approach to dynamic environments and varied network configurations.

关键词： learning systems Wireless sensor networks Energy consumption Adaptive systems machine learning Energy efficiency Real-time systems Bayes methods Reliability Optimization

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Deep learning for Aerial and Satellite Image Analysis: a CNN-Based Approach

Deep Learning for Aerial and Satellite Image Analysis: a CNN...

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international conference on Communication Systems and Network Technologies (CSNT)

作者： Sailesh Adda Hemanth Valeti Murali Krishna Enduri Gangireddy Salla Anupoju Tejaswi Department of CSE SRM University-AP India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

applications like disaster management, urban planning, and environmental monitoring rely on satellite image categorization. this project develops a machine learning pipeline using MobileNetV2, a CNN architecture, to classify high-resolution satellite images. It employs two convolutional layers (3x3 kernels) with ReLU activation, 2x2 max-pooling, a fully connected layer, and a SoftMax output for multi-class classification. Images are resized to 200x200 pixels (RGB) to balance detail and efficiency. MobileNetV2 was chosen for its low latency and high performance, using depth-wise separable convolutions and inverted residuals. the model, optimized with Adam and categorical crossentropy, achieved 98% validation accuracy and F1-scores above 0.96 across all classes, converging in 8 epochs. the architecture balances simplicity and performance for robust feature learning and generalization. this approach highlights CNNs' ability to classify satellite images effectively. Future work could explore transformer-based models or integrate temporal satellite data to enhance analysis. this work offers a scalable, automated solution for satellite image classification.

关键词： Representation learning Satellites Image recognition Urban planning Pipelines Transformers Satellite images Convolutional neural networks Low latency communication Kernel

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