In today39;s digital landscape, a vast number of users engage with product network websites, leading to the registration of thousands of new accounts daily. These platforms facilitate constant interaction among user...
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The combination of Artificial intelligence (AI) and the Internet of Things (IoT) has improved the capabilities of location trackers, especially in disaster management. This brief explores the collaboration between AI ...
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This study is devoted to the design, implementation and evaluation of an Artificial intelligence (AI)-based hotel energy management optimization system (EMOS), aiming at improving energy efficiency, reducing operating...
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How to effectively mine information on geological structures, soil mechanics, groundwater, etc. becomes a topic worth studying. This article combines data mining and artificial intelligence technology to solve the pro...
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Development of Artificial intelligence and machine Learning (AI-ML) based automated tools for melody extraction from audio music files are severely handicapped due to the scarcity of proper musical annotation to act a...
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An SaaS-based conference management system that implements facial recognition technology has been designed to address the requirement for intelligent conference management. The system leverages the SaaS model to achie...
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The proceedings contain 19 papers. The topics discussed include: is Mamba capable of in-context learning?;HPOD: hyperparameter optimization for unsupervised outlier detection;speeding up NAS with adaptive subset selec...
The proceedings contain 19 papers. The topics discussed include: is Mamba capable of in-context learning?;HPOD: hyperparameter optimization for unsupervised outlier detection;speeding up NAS with adaptive subset selection;confidence interval estimation of predictive performance in the context of AutoML;analyzing few-shot neural architecture search in a metric-driven framework;FLIQS: one-shot mixed-precision floating-point and integer quantization search;improving transfer learning by means of ensemble learning and swarm intelligence-based neuroevolution;sequence alignment-based similarity metric in evolutionary neural architecture search;and don’t waste your time: early stopping cross-validation.
In this paper, the method of deep learning is applied for plant species identification, with a special emphasis on Convolutional Neural Network VGG16 for feature extraction and classification. Identification of plants...
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In the healthcare industry, machine Learning (ML) plays a crucial role in disease prediction. A patient must go through a series of tests before a condition can be diagnosed. However, using machine learning techniques...
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Traffic signs play a critical role in the transportation infrastructure, reducing accident risks by informing drivers, pedestrians, and other road users about roadway conditions. With rapid advancements in computer vi...
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ISBN:
(纸本)9798400718212
Traffic signs play a critical role in the transportation infrastructure, reducing accident risks by informing drivers, pedestrians, and other road users about roadway conditions. With rapid advancements in computer vision and artificial intelligence, traffic sign recognition systems have become increasingly integrated into driver assistance and autonomous driving systems. However, recognizing small traffic signs in real-world applications poses a considerable challenge due to information loss in feature extraction, limited available information, and large scale variations This paper presents a network structure, TSR-YOLO, that efficiently recognizes small-sized traffic signs. Initially, our research found that FPN and its variants place too much emphasis on the interaction between different feature maps and overlook their individual processing capabilities. We design a multi-stage perceptual self-processing module and a new FPN structure to boost the spatial and contextual information of features. To improve fusion of semantically and scale-inconsistent features, we suggest a multi-scale attention module that effectively resolves the issue of merging different features. Experiments on the challenging TT100K dataset show that our model outperforms popular object detection models by 4.2% when compared to the original YOLOV5, while preserving real-time speed.
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