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Titlebook: Big Data, Machine Learning, and Applications; Proceedings of the 2 Malaya Dutta Borah,Dolendro Singh Laiphrakpam,Vale Conference proceeding

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11#
發(fā)表于 2025-3-23 09:46:01 | 只看該作者
12#
發(fā)表于 2025-3-23 17:16:04 | 只看該作者
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.
13#
發(fā)表于 2025-3-23 19:40:26 | 只看該作者
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.
14#
發(fā)表于 2025-3-23 23:58:32 | 只看該作者
15#
發(fā)表于 2025-3-24 03:18:42 | 只看該作者
16#
發(fā)表于 2025-3-24 09:25:01 | 只看該作者
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.
17#
發(fā)表于 2025-3-24 11:04:19 | 只看該作者
18#
發(fā)表于 2025-3-24 17:16:16 | 只看該作者
19#
發(fā)表于 2025-3-24 21:22:17 | 只看該作者
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.
20#
發(fā)表于 2025-3-25 01:06:26 | 只看該作者
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.
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