A session-based recommendation model combining star graph and dynamic perception is proposed to solve the problem of remote information, ignoring the dynamic change of users' interests and the inaccurate expressio...
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The application of synthetic Intelligence (AI) to records science has spread out a massive array of new possibilities. Via the combination of advanced evaluation strategies, effective understanding-pushed system gaini...
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To enhance the real-time monitoring and early-warning capabilities for dust disasters in underground coal mine, this paper presents a novel WGAN-CNN-based prediction approach to predict the dust concentration at under...
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With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices,crowdsensing systems in the Internet of Things(IoT)are now conducting complicated video analy...
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With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices,crowdsensing systems in the Internet of Things(IoT)are now conducting complicated video analysis tasks such as behaviour *** applications have dramatically increased the diversity of IoT ***,behaviour recognition in videos usually requires a combinatorial analysis of the spatial information about objects and information about their dynamic actions in the temporal *** recognition may even rely more on the modeling of temporal information containing short-range and long-range motions,in contrast to computer vision tasks involving images that focus on understanding spatial ***,current solutions fail to jointly and comprehensively analyse short-range motions between adjacent frames and long-range temporal aggregations at large scales in *** this paper,we propose a novel behaviour recognition method based on the integration of multigranular(IMG)motion features,which can provide support for deploying video analysis in multimedia IoT crowdsensing *** particular,we achieve reliable motion information modeling by integrating a channel attention-based short-term motion feature enhancement module(CSEM)and a cascaded long-term motion feature integration module(CLIM).We evaluate our model on several action recognition benchmarks,such as HMDB51,Something-Something and *** experimental results demonstrate that our approach outperforms the previous state-of-the-art methods,which confirms its effective-ness and efficiency.
The present research endeavors to introduce a groundbreaking web application, which serves as a unified billing management system specifically designed for pharmacies. The current state of pharmacy management systems ...
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Digital Image Processing (DIP) is the ability to manipulate digital photographs via algorithms for pattern detection, segmentation, enhancement, and noise reduction. In addition, the Internet of Things (IoT) acts as t...
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This research explores the application of deep learning techniques for soil analysis in precision agriculture, focusing on the classification of soil types using convolutional neural networks (CNNs). The study address...
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Autism Spectrum Disorder (ASD) is a neurodevelopment-based disability caused by variations in the brain. This may cause impact on social skills and communication of an individual. Autism is a highly challenging issue ...
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Aortic Dissection (AD) is a life-threatening disease that can be rapidly screened by using deep learning methods. However, deep learning model training requires a large amount of manual annotation of data. To improve ...
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3D object detection based on point cloud has an important application prospect in automatic driving technology. Aiming at the low precision of 3D object detection based on point cloud and the poor real-time performanc...
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3D object detection based on point cloud has an important application prospect in automatic driving technology. Aiming at the low precision of 3D object detection based on point cloud and the poor real-time performance caused by large numbers of 3D convolutions, a novel end-to-end real-time object detection algorithm named GridNet-3D is proposed. In the work, 2D gridmapping is used to preprocess the original point *** a novel structure grid encoding layer is adopted to encode point cloud features and is gotten grid feature maps in bird's eye view which is connected to region proposal network module to generate detections. Despite only using point clouds, the results on the KITTI 3D detection benchmark show that our algorithm has higher detection precision and better real-time performance on the detection of cars, pedestrians and cyclists, which has high practical value.
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