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相关概念视频

Classification of Systems-II01:31

Classification of Systems-II

137
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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Light Acquisition02:16

Light Acquisition

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In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
177
Aggregates Classification01:29

Aggregates Classification

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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.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
306
Classification of Leukocytes01:30

Classification of Leukocytes

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Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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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.
In the absence...
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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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贝叶斯优化多式混合深度学习方法用于番茄叶疾病分类的贝叶斯优化多式混合深度学习方法

Bodruzzaman Khan1, Subhabrata Das2, Nafis Shahid Fahim3

  • 1Department of Agricultural Construction and Environmental Engineering, Sylhet Agricultural University, Sylhet, 3100, Bangladesh. bodruzzamankhan.sau@gmail.com.

Scientific reports
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PubMed
概括

使用混合深度学习模型自动识别番茄叶病,显著提高了准确性和速度. 该CNN-堆叠模型的准确度超过98%,为农民提供了一个计算成本低廉的工具.

关键词:
贝叶斯的优化是贝叶斯的优化.波鲁塔 (Boruta) 是一个波鲁塔 (Boruta)在美国,CNN是CNN.深度学习是一种深度学习.混合学习是一种混合学习.机器学习 机器学习番茄叶病是一种番茄叶病.树结构的帕森估计器

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科学领域:

  • 农业科学 农业科学
  • 计算机科学 计算机科学
  • 机器学习 机器学习

背景情况:

  • 手动识别番茄叶病是劳动密集型的,容易出现不准确的情况.
  • 自动化系统对于早期疾病检测和及时干预至关重要,以保持作物产量和质量.

研究的目的:

  • 开发和评估强大的贝叶斯优化深度混合学习模型,用于自动化番茄叶疾病分类.
  • 为了比较七个混合模型的性能,包括卷积神经网络 (CNN) 和各种机器学习分类器,用于疾病识别.

主要方法:

  • 提出了七个深度混合学习模型,将CNN与随机森林,XGBoost,SVM等分类器结合起来进行特征提取.
  • 整合了一个Boruta特征过层,用于统计学上显著的特征选择.
  • 利用PlantVillage数据集进行培训和测试,使用各种统计分类指标.

主要成果:

  • 在七种混合模型中,CNN-堆叠模型表现出最高的分类性能.
  • 在一个看不见的数据集上实现了平均精度,回忆,f1分数,MCC和精度超过98%的精度.
  • 这些模型表现出高时间效率,测试时间低至0.174秒,并且在具有挑战性的图像条件下展示了可概括性.

结论:

  • 开发的混合深度学习方法在计算上提供了一种廉价且优越的替代方案,与现有的番茄叶病诊断方法相比.
  • CNN-Stacking模型的高精度和效率支持其在农民实时智能手机应用程序中集成的潜力.
  • 这项技术可以为农民提供及时的疾病诊断和管理策略,从而提高番茄产量.