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检索条件"任意字段=Conference on Applications of Neural Networks and Machine Learning in Image Processing IX"

共 6128 条记录，以下是221-230 订阅

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Vehicle-Based Evolutionary Travel Time Estimation with Deep Meta learning 33rd

Vehicle-Based Evolutionary Travel Time Estimation with Deep ...

引用

33rd International conference on Artificial neural networks and machine learning (ICANN)

作者： Wang, Chenxing Zhao, Fang Luo, Haiyong Fang, Yuchen Zhang, Haichao Xiong, Haoyu Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9783031723551;9783031723568

Vehicle-based travel time estimation is crucial for many travel scheduling and city planning applications in intelligent transportation systems. Since trajectories in different trips are affected by evolutionary Spatio-temporal dynamics (e.g., evolving travel patterns for different days of the week and varying road networks affected by traffic accidents or temporary restrictions, etc.), it is substantial to investigate these dynamics for accurate estimation. In this paper, we propose a novel deep learning model which fuses location features, distance features, and temporal features with meta learning-based neural networks, to implicitly learn path representations for evolving travel patterns in different days of week and road networks. Specifically, we utilize the meta learning-based optimization method to transfer the shared meta knowledge across trajectories in distinct Evolving-Tasks (i.e., a limited amount of trajectory data on different days of the week), which facilitates generalizing rapidly on evolving travel patterns for different days of the week. In addition, road network information is obviated in our model, which makes it a natural solution to tolerate evolving road networks while mitigating the computation burden contemporaneously. Comprehensive experiments on three real-world datasets demonstrate the superiority of our proposed model.

关键词： Travel time estimation Meta-learning Spatio-temporal data mining

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23rd International conference on Intelligent Systems Design and applications, ISDA 2023

23rd International Conference on Intelligent Systems Design ...

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23rd International conference on Intelligent Systems Design and applications, ISDA 2023

ISBN: (纸本)9783031646492

The proceedings contain 47 papers. The special focus in this conference is on Intelligent Systems Design and applications. The topics include: machine learning Techniques for Pancreatic Cancer Detection;integrating Artificial Intelligence and Data Analytics for Enhanced Healthcare Management: Innovations and Challenges;an Intelligent Model for Post Covid Hearing Loss;medical Reports Simplification Using Large Language Models;analysis of Magnetic Resonance Imaging for Parkinson's Disease;study on Health Issue Identification Using Deep learning and Convolutional neural networks;early-Stage Lung Cancer Prediction: A machine learning Approach;convolutional neural Network-Based Brain Tumor Segmentation Using Detectron2;Deep learning-Based Histopathological Analysis for Colon Cancer Diagnosis: A Comparative Study of CNN and Transformer Models with image Preprocessing Techniques;Detecting Parkinson's Disease at an Early Stage Through machine learning Analysis of Brain MRI images;early-Stage Cervical Cancer Detection via Ensemble learning and image Feature Integration;comprehensive Comparative Analysis of Breast Cancer Forecasting Using machine learning Algorithms and Feature Selection Methods;classification of Arrhythmia Using Deep learning;an Integrated machine learning and IoT Based Approach for Enhanced Healthcare Efficiency and Personalized Treatment;ceLaTis: A Large Scale Multimodal Dataset with Deep Region Network to Diagnose Cervical Cancer;a Deep learning Approach With Sparse Autoencoder for Alzheimers Disease Classification;an Improved Gradient Based Joint Histogram Equalization Technique for Mammogram image Contrast Enhancement;comparative Performance Analysis of Deep learning Models in Cervical Cancer Detection;comparative Analysis for Feature Selection Approaches for Parkinson’s Disease Prediction;autism Spectrum Disorder Prediction: A machine learning Approach;arterial Disease Prediction in Inflammatory Bowel Disease Patients.

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Real-Time Scratched image Restoration Using neural networks 2

Real-Time Scratched Image Restoration Using Neural Networks

引用

2nd International conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Sharma, Harsh Pradhan, Rajeshwari Mogha, Harsh Singh, Shivpratap Kumar, Rupesh Computer Science and Engineering Chandigarh University Punjab India

ISBN: (纸本)9798331519056

Real-time image restoration is a cutting-edge tech-nology that accelerates or rebuilds images as soon as they are captured, processed, or released to correct issues such as noise, blur, and compression. This field is of great importance in applications such as medical imaging, surveillance, autonomous vehicles. and live video streaming where sharpness and image quality are fundamental The main challenge lies in developing algorithms that can quickly and efficiently restore image quality. It balances the need for high-speed processing with the need for precision and adaptability to different conditions. Advanced techniques Including deep learning and convolutional neural networks (CNN) are often used to achieve these goals. Moreover, it is essential to optimize these algorithms for real-time pro-cessing. Real-time performance in constrained hardware such as mobile devices or embedded systems As real-time image recovery technology continues to develop, It thus promises to significantly improve the quality of image data in a variety of domains. They provide sharper, more reliable images for critical applications. © 2024 IEEE.

关键词： Convolutional neural networks

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23rd International conference on Intelligent Systems Design and applications, ISDA 2023

23rd International Conference on Intelligent Systems Design ...

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23rd International conference on Intelligent Systems Design and applications, ISDA 2023

ISBN: (纸本)9783031648359

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Can Deep learning Outperform Traditional Methods in Upscaling IR images for Solar Irradiance Estimation? 26

Can Deep Learning Outperform Traditional Methods in Upscalin...

引用

26th Irish machine Vision and image processing conference, IMVIP 2024

作者： Hossain, Md Yearat Nijhum, Ifran Rahman Rakib, Md Mahbub Hasan Dev, Soumyabrata The ADAPT SFI Research Centre Ireland School of Computer Science University College Dublin Ireland Department of Electrical and Computer Engineering North South University Bangladesh

ISBN: (纸本)9781837242672

Infrared imaging provides crucial information in the infrared spectrum, invisible to standard visible-light imaging systems. However, high-resolution infrared imaging is challenging and expensive, limiting its applications. Deep learning, particularly Convolutional neural networks (CNNs), has significantly advanced image processing but relies on high-resolution images for optimal performance. To address the difficulty of acquiring high-resolution infrared images, image upscaling techniques, including traditional interpolation and modern deep learning methods, can be used. This study compares these methods using the GIRASOL dataset for solar irradiance estimation. We evaluated bicubic, bilinear, and nearest neighbor interpolation against deep learning image upscaling models SwinIR and RealESRGAN. Despite their success in upscaling visible spectrum images, these models did not effectively enhance low-resolution infrared images, resulting in less accurate CNN predictions for solar irradiance compared to traditional methods. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Convolutional neural networks

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23rd International conference on Intelligent Systems Design and applications, ISDA 2023

23rd International Conference on Intelligent Systems Design ...

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23rd International conference on Intelligent Systems Design and applications, ISDA 2023

ISBN: (纸本)9783031648120