PAD416
发表于 2025-3-30 11:09:21
1865-0929 23..The 22 full papers and the 4 short papers included in this volume were carefully reviewed and selected from 70 submissions. This volume focuses on ideas, methodologies, and cutting-edge research that can drive progress and foster interdisciplinary collaboration in the fields of data science and
JAUNT
发表于 2025-3-30 15:46:03
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ARY
发表于 2025-3-30 18:23:06
Hybridization of Modified Grey Wolf Optimizer and Dragonfly for Feature Selectionrtinent features. Our experimental results showcase robust model performance, achieving an F1-score of 90% on our experimental dataset, surpassing other approaches. Further results and discussions are provided in this paper, .
incision
发表于 2025-3-30 23:21:08
Deep-Learning-Based LSTM Model for Predicting a Tidal River’s Water Levels: A Case Study of the Kapusize, the LSTM model consistently outperforms GRU and RNN models in comparative assessments. These findings offer not only valuable insights into water level prediction in the study area but also the potential of deep learning to enhance flood and disaster management in similar river systems globally.
暖昧关系
发表于 2025-3-31 02:44:39
SecureQNN: Introducing a Privacy-Preserving Framework for QNNs at the Deep Edgehe number of epochs an attacker requires to build a model with the same accuracy as the target with the information disclosed. The set of layers whose information makes the attacker spend less training effort than the owner training from scratch is protected in an isolated environment, i.e., the sec
过份
发表于 2025-3-31 05:56:29
Chaotic Mountain Gazelle Optimizer (CMGO): A Robust Optimization Algorithm for K-Means Clustering of outperforms the original MGO and other tested algorithms in clustering pure numeric and categorical data, securing first place, and third for mixed data. Thus, CMGO emerges as a robust, efficient K-means optimizing method for complex, diverse datasets.