reptile
发表于 2025-3-28 17:31:21
Ontology Integration for Cultural Landscape Management Using ML and Assistive Artificial Intelligen with differential thresholds, enhancing reasoning infrastructure. The model achieves a high precision of 95.06%, a low False Discovery Rate of 0.05, and an impressive F-measure of 96.06%. This research makes a significant contribution to automatic ontology conception, particularly in rare domains like cultural landscape management.
FRET
发表于 2025-3-28 20:25:29
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维持
发表于 2025-3-29 01:53:37
Conference proceedings 2024y the Department of Data Science, CHRIST (Deemed to be University), Pune Lavasa Campus, India, from 02–04 November, 2023. The proceeding targets the current research works in the areas of data science, data security, data analytics, artificial intelligence, machine learning, computer vision, algorit
不爱防注射
发表于 2025-3-29 05:07:54
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fringe
发表于 2025-3-29 10:27:30
OGIA: Ontology Integration and Generation Using Archaeology as a Domain,, and evolutionary algorithm help in optimizing initial solution sets to a much more optimal solution set for generating ontologies. Overall, a highest average precision percentage of 94.09%, highest average accuracy percentage of 95.09%, highest average recall percentage of 96.09%, and highest aver
Locale
发表于 2025-3-29 11:38:56
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绑架
发表于 2025-3-29 16:16:39
,Extracting Network Structures from Corporate Organization Charts Using Heuristic Image Processing,Diagram Handbook” published by Diamond, Inc., from 2008 to 2011. Out of the 10,008 organization chart PDF files, our method was able to reconstruct 4,606 organization networks (data acquisition success rate: 46%). For each reconstructed organization network, we measured several network diagnostics,
indicate
发表于 2025-3-29 23:39:25
Generating Equations for Mathematical Word Problems Using Multi-head Attention Transformer,cal equations for word problems, outperforming other models that use traditional sequence-to-sequence approaches. The analysis of the attention mechanism of our model is also mentioned, which sheds light on how it learns to attend to relevant parts of the input sequence to generate the correct mathe
indemnify
发表于 2025-3-30 03:02:30
Early Phase Detection of Bacterial Blight in Pomegranate Using GAN Versus Ensemble Learning, traditional methods. Without the use of paired training data, the suggested method of Cycle-GAN is a sort of Generative Adversarial Network (GAN) that can learn to translate images from one domain to another. Cycle-GAN has demonstrated its efficacy in various image-to-image translation problems by
游行
发表于 2025-3-30 07:16:20
Pioneering Image Analysis with Hybrid Convolutional Neural Networks and Generative Adversarial Netw of the standout features of the hybrid G-CNN model is its exceptional loss metrics. It achieves the lowest loss at an astonishingly low value of 0.030, and concurrently, it records the lowest validation loss at 0.031. These results clearly establish the superiority of the hybrid G-CNN model in comp