Medicaid
发表于 2025-3-23 09:46:01
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低三下四之人
发表于 2025-3-23 17:16:04
Optimized Detection, Classification, and Tracking with YOLOV5, HSV Color Thresholding, and KCF Traco outperform the results. Multithreading is used to concurrently detect and track the arrows in consecutive frames, producing a computationally efficient approach as compared to standalone detection with YOLOv5. This paper also describes an approach to effectively get depth information on an object using an Intel RealSense D435i depth camera.
frugal
发表于 2025-3-23 19:40:26
https://doi.org/10.1007/978-3-319-00894-3d users’ choice. This paper is all about creating an environment for smart farm technology, recording various parameters in terms of temperature, humidity, and moisture and automating a system for operating the motor pump along with farm gate opening and closing along with providing crop suggestions to the farmer.
淡紫色花
发表于 2025-3-23 23:58:32
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量被毁坏
发表于 2025-3-24 03:18:42
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浮雕
发表于 2025-3-24 09:25:01
https://doi.org/10.1007/978-3-031-36566-9rmalized probability of test data does not cross threshold value of the corresponding class, we will classify test data into unknown class. This TSM layer with neural network is evaluated on three UCI benchmark dataset (Glass, Yeast and Wine quality) and successfully handles the unknown class problem with reduced misclassification error.
exclusice
发表于 2025-3-24 11:04:19
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carotenoids
发表于 2025-3-24 17:16:16
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讲个故事逗他
发表于 2025-3-24 21:22:17
Multitask Learning-Based Simultaneous Facial Gender and Age Recognition with a Weighted Loss Functinctions that balances the loss of each task. We train our method for the recognition of gender and age on the publicly available Adience benchmark dataset. Finally, we experiment our method on VGGFace and FaceNet architectures and evaluate on the Adience test set to achieve better performance than previous architectures.
infantile
发表于 2025-3-25 01:06:26
Ensemble of Deep Learning Enabled Tamil Handwritten Character Recognition Model,apsNet) and VGGNet models take place for feature extraction process. Finally, softmax layer is employed to classify the Tamil characters in an effective way. A comprehensive experimental analysis is carried out on benchmark dataset, and the results portrayed the better performance of the EDL-THCR technique.