DRILL
发表于 2025-3-25 05:13:04
Financial Statement Fraud Detection Using Optimized Deep Neural Network,raction. The simulation results show that the proposed approach provides high detection accuracy (95%) with the feature selection process on testing data as compared to other standard classifiers. This method also gives better results on training and testing data using feature selection and without
潜伏期
发表于 2025-3-25 10:22:58
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连接
发表于 2025-3-25 13:31:57
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ingestion
发表于 2025-3-25 18:24:07
Identification of Plant Leaf Disease Using Synthetic Data Augmentation ProGAN to Improve the Perforrate realistic infected leaf images of Grape for effective data augmentation. By combining the original dataset with these created images, VGG16 deep-learning model, and through extensive analysis, it has been demonstrated that the method greatly improves plant disease identification accuracy. With
Banister
发表于 2025-3-25 21:42:36
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Inscrutable
发表于 2025-3-26 03:55:22
,XGBoost Tuned by Hybridized SCA Metaheuristics for Intrusion Detection in Healthcare 4.0 IoT Systemsystems are highly valuable in domains like healthcare, where prompt actions can significantly impact outcomes. However, despite the widespread adoption of IoT, a crucial obstacle hinders its broader integration. For IoT to support sustainable healthcare, it must deliver well-organized healthcare se
HEW
发表于 2025-3-26 05:23:18
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起皱纹
发表于 2025-3-26 09:25:45
,IoT and Satellite Image Driven Water Quality Monitoring and Assessment Method in Coastal Region,n, climatic changes, pollution, high financial demands, and excessive salt extraction from the sea, safe water is becoming threat to get from natural resource. Some unambiguous and comprehensive coordination of planning and sustainable action must take to ensure access to freshwater for urban and ru
他去就结束
发表于 2025-3-26 15:50:55
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鼓掌
发表于 2025-3-26 18:23:22
Model Accuracy Test for Early Stage of Diabetes Risk Prediction with Data Science Approach,nology that is currently being preferred to perform early-stage disease detection. Although various machine learning models are available, the most accurate model for forecasting the risk of developing early-stage diabetes is still unknown. This research study compares 14 machine learning models—neu