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Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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使用基于梯度的优化卷积神经网络与BERT嵌入式进行灾难推特分类的高效方法.

Deepak Dharrao1, Aadithyanarayanan Mr1, Rewaa Mital1

  • 1Department of Computer Science and Engineering, Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.

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这项研究引入了基于BERT嵌入式的卷积神经网络 (CNN) 模型与RMSProp Optimizer,以准确地分类与灾难相关的推文,改进了传统的机器学习方法,用于实时信息传播.

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在美国,CNN是CNN.深度学习,BERT,深度学习使用CNN与BERT嵌入和RMS-Prop优化进行灾难推文分类,使用基于梯度的优化卷积神经网络与BERT嵌入进行灾难推文分类.发生灾难的推特.自然语言处理自然语言处理.推特分类 推文分类

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科学领域:

  • 自然语言处理自然语言处理.
  • 机器学习 机器学习
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 像Twitter这样的微博平台对于灾难沟通至关重要.
  • 分类真实与假的灾难相关推特对于防止混乱至关重要.
  • 传统的机器学习模型在推特分类中显示出有限的准确性.

研究的目的:

  • 提出一个高效的深度学习模型来分类与灾难相关的推文.
  • 通过将BERT嵌入与CNN集成来提高推特分类准确性.
  • 为了优化CNN模型,使用基于梯度的优化器,特别是RMSprop.prop.

主要方法:

  • 一个卷积神经网络 (CNN) 被选为主要分类模型.
  • 为了捕捉上下文语义,纳入了BERT嵌入.
  • 该模型使用各种基于梯度的优化器进行了优化,RMSprop产生了最佳结果.

主要成果:

  • 拟议的基于BERT嵌入式的CNN模型与RMSProp优化器实现了F1得分0.80.
  • 该模型在对灾难推特进行分类时表现出0.83的高精度.
  • 对比分析证实了深度学习方法的优越性,而不是传统方法.

结论:

  • 基于BERT嵌入式的CNN模型与RMSProp优化器是分类灾难相关推文的有效方法.
  • 利用BERT嵌入式显著提高了模型在灾难场景中理解细微语言的能力.
  • 这项研究为社交媒体上的实时灾害信息管理提供了一个强大的解决方案.