transdermal 发表于 2025-3-28 17:39:59
MaaSim: A Liveability Simulation for Improving the Quality of Life in Citiess an optimisation agent that could help in decision making. This paper describes the creation of an open-source simulation based on an existing Dutch liveability score with a built-in AI module. Features are selected using feature engineering and Random Forests. Then, a modified scoring function is广口瓶 发表于 2025-3-28 19:43:44
Designing Data-Driven Solutions to Societal Problems: Challenges and Approachesex, affect the bottom of the socio-economic pyramid, and have wider spread across populations and geographies with resource constraints. In this paper we identify challenges and propose certain design considerations to guide development of machine learning based data-driven applications for addressiEpithelium 发表于 2025-3-29 01:50:39
Host Based Intrusion Detection System with Combined CNN/RNN Model avoid the impact of cybercrimes. Host based intrusion detection systems help to protect systems from various kinds of malicious cyber attacks. One approach is to determine normal behaviour of a system based on sequences of system calls made by processes in the system [.]. This paper describes a com内行 发表于 2025-3-29 03:13:31
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Pseudo-Random Number Generation Using Generative Adversarial Networksy proposing the use of generative adversarial networks (GAN) to train a neural network to behave as a PRNG. Furthermore, we showcase a number of interesting modifications to the standard GAN architecture. The most significant is partially concealing the output of the GAN’s generator, and training thFLACK 发表于 2025-3-29 17:17:04
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0302-9743 .SoGood 2018: Third Workshop on Data Science for Social Good..IWAISe 2018: Second International Workshop on Artificial Intelligence in Security.Green Data Mining 2018: First International Workshop on Energy Efficient Data Mining and Knowledge Discovery.978-3-030-13452-5978-3-030-13453-2Series ISSN 0302-9743 Series E-ISSN 1611-3349Veneer 发表于 2025-3-30 01:07:28
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Thyago S. P. C. Duque,David E. Goldbergonnected neural network to produce pseudo-random number sequences with good statistical properties. At best, subjected to the NIST test suite, the trained generator passed around 99% of test instances and 98% of overall tests, outperforming a number of standard non-cryptographic PRNGs.