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Titlebook: Artificial Intelligence in IoT and Cyborgization; Rajesh Kumar Dhanaraj,Bharat S. Rawal,Balamurugan Book 2023 The Editor(s) (if applicabl

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樓主: Glitch
21#
發(fā)表于 2025-3-25 05:16:16 | 只看該作者
22#
發(fā)表于 2025-3-25 09:36:44 | 只看該作者
23#
發(fā)表于 2025-3-25 14:47:53 | 只看該作者
,Advanced Human–Computer Interaction Technology in Digital Twins, Manufacturing (IM) and the HCI problem in human–computer assembly are explored; aiming at the Human Action Recognition (HAR) of machine perspective in human–computer assembly, it proposes the Human Pose Estimation (HPE) method based on improved HRNet and inroduces the attention mechanism to establi
24#
發(fā)表于 2025-3-25 16:33:02 | 只看該作者
CNN Architecture and Classification of Miosis and Mydriasis Clinical Conditions, or classification of a disease has now exponentially reduced with the help of certain state of the?art works of DL.?In this work,?we?have?proposed two types of classification procedures for the diseases Miosis and Mydriasis which unlike Anisocoria, extremely dilates or constricts both the pupils. T
25#
發(fā)表于 2025-3-25 21:58:35 | 只看該作者
Role of Object Detection for Brain Tumor Identification Using Magnetic Resonance Image Scans, are used to propose a solution to the Separation Problem. Various previously trained Architecture?in large databases such as VGG-16, V66-19, Inception V3, ResNet-50, DenseNet-201, etc. are available, which can be used, this technique being called as Transfer learning. The process of finding an obje
26#
發(fā)表于 2025-3-26 04:11:44 | 只看該作者
Deep Learning Model for Predicting Diabetes Disease Using SVM, the most common illnesses in the world. A high blood glucose level, which has a major impact on human organs, is a guarantee of diabetes. There are currently 382 million diabetics worldwide, and by 2035, the International Diabetes Federation (IDA) projects that number to reach 592 million. Building
27#
發(fā)表于 2025-3-26 07:41:59 | 只看該作者
28#
發(fā)表于 2025-3-26 11:51:44 | 只看該作者
29#
發(fā)表于 2025-3-26 14:38:44 | 只看該作者
Environment Twin Based Deep Learning Model Using Reconfigurable Holographic Surface for User Locatihich is called as Environment-Twin (Env-Twin). The objective of the Env-Twin framework is to empower mechanization of optimal control at various coarseness. Deep learning techniques such as Convolution Neural Network (CNN) and long short-term memory architecture (LSTM) are used to build our model an
30#
發(fā)表于 2025-3-26 19:41:19 | 只看該作者
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