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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...
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有效的基于卷积神经网络的按键动态,以促进用户身份验证.

Hussien AbdelRaouf1, Samia Allaoua Chelloug2, Ammar Muthanna3

  • 1Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shebin El-Kom 32511, Menoufia, Egypt.

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概括
此摘要是机器生成的。

本研究介绍了一种优化的卷积神经网络,用于使用键盘动态增强用户身份验证. 该方法通过打字模式来验证用户合法性,从而提高在线安全性.

关键词:
资本市场统一 (CMU) 是一个在美国,CNN是CNN.提升技术的提升技术.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.按键的动态 按键的动态量子的转换是量子的转换.用户身份验证用户身份验证

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相关实验视频

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科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 机器学习 机器学习

背景情况:

  • 用户身份验证对于在线服务安全至关重要.
  • 多因素身份验证增强了安全性,但可能是复杂的.
  • 按键动态通过分析打字模式提供了一个无的身份验证方法.

研究的目的:

  • 提出一个优化的卷积神经网络 (CNN),以改善按键动态中的特征提取.
  • 提高用户身份验证系统的准确性和效率.
  • 利用数据合成和量子转换来最大限度地提高认证结果.

主要方法:

  • 使用了优化的卷积神经网络 (CNN) 架构.
  • 采用数据合成和量子转换来增强功能.
  • 应用集体学习技术进行模型培训和测试.
  • 在公开可用的卡内基梅隆大学 (CMU) 数据集上评估了该方法.

主要成果:

  • 实现了99.95%的平均准确性.
  • 达到0.65%的平均等错率 (EER).
  • 获得了 99.99% 的曲线下的平均面积 (AUC).
  • 超越了最近在资本市场联盟数据集上的进展.

结论:

  • 拟议的优化CNN与集体学习展示了基于按键动态身份验证的卓越性能.
  • 该方法为保护在线服务提供了高度准确和高效的解决方案.
  • 数据合成和量子转换显著改善了行为生物识别的特征提取.