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Titlebook: Cyber Warfare, Security and Space Computing; Second International Sandeep Joshi,Amit Kumar Bairwa,Anton Pljonkin Conference proceedings 202

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樓主: Jurisdiction
31#
發(fā)表于 2025-3-26 23:47:32 | 只看該作者
32#
發(fā)表于 2025-3-27 02:01:02 | 只看該作者
33#
發(fā)表于 2025-3-27 08:34:19 | 只看該作者
Secure Data Management Using BlockChain,rading their understanding of the capabilities and imperatives of blockchain-related information administration frameworks. It moreover advocates for the advancement of combination frameworks custom fitted to a differing cluster of viable needs.
34#
發(fā)表于 2025-3-27 12:41:11 | 只看該作者
35#
發(fā)表于 2025-3-27 16:07:46 | 只看該作者
An Empirical Analysis of Neighborhood-Based Approaches for Trustworthy Recommendations with Apache F methods as implemented in the Mahout. Finally, the accuracy of UBCF methods is measured in terms of two metrics: Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Empirically obtained results demonstrate that an RS can provide trustworthy recommendations to its end users by increasing
36#
發(fā)表于 2025-3-27 19:47:20 | 只看該作者
Multilingual Sentiment Analysis over Real-Time Voice, of this paper include developing a flexible system that can accommodate multiple languages – namely – English, Hindi and Telugu. Additionally, it seeks to offer real-time sentiment feedback, which is a useful function in several fields, such as social media monitoring, voice assistant technologies,
37#
發(fā)表于 2025-3-27 23:18:13 | 只看該作者
Identity Verification: A Decentralized KYC Approach Using Blockchain,the AES encryption algorithm, and the hash of the encrypted data is provided to the user and stored on the blockchain. This decentralized and secure KYC system minimizes redundancy, enhances user control, and fortifies data security.
38#
發(fā)表于 2025-3-28 05:09:13 | 只看該作者
S-Defender: A Smishing Detection Approach in Mobile Environment, content and employs a Naive Bayesian classifier to categorize the message as either smishing or ham. Additionally, our approach normalizes and converts Lingo language text messages into their standard form to improve classification accuracy. The model’s validation, conducted using the English SMS dataset, resulted in an overall accuracy of 94.72%.
39#
發(fā)表于 2025-3-28 08:54:47 | 只看該作者
40#
發(fā)表于 2025-3-28 14:26:49 | 只看該作者
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