Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Aggregates Classification01:29

Aggregates Classification

305
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...
305
Classification of Systems-I01:26

Classification of Systems-I

169
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:
169
Classification of Systems-II01:31

Classification of Systems-II

134
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,
134
Classification of Signals01:30

Classification of Signals

403
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.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
403
Classification of Leukocytes01:30

Classification of Leukocytes

1.7K
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...
1.7K
Reducing Line Loss01:18

Reducing Line Loss

144
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
144

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Community pharmacists' knowledge, attitudes, and practices toward self-medication for common cold and influenza: A COM-B model-based cross-sectional study.

Exploratory research in clinical and social pharmacy·2026
Same author

Development and Validation of a Novel Deep Learning-Based Model for Detection of Diabetic Kidney Disease from Retinal Imaging Using a Weighted Loss Method.

Clinical ophthalmology (Auckland, N.Z.)·2026
Same author

Bioimpedance-based evaluation of relative leaf age in mango twigs using electrical impedance spectroscopy.

Journal of electrical bioimpedance·2026
Same author

Mitigating distributed denial of service-based cyberattack in federated computing framework using deep reinforcement learning with frilled lizard algorithm.

Scientific reports·2025
Same author

An effective approach to improving photovoltaic defect detection using the new DCD-YOLOv8s model.

Scientific reports·2025
Same author

Weighted loss for imbalanced glaucoma detection: Insights from visual explanations.

Computers in biology and medicine·2025

相关实验视频

Updated: Jun 7, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

678

使用转移学习和卷积神经网络与加权损失的玉米疾病分类.

Krisnanda Ahadian1, Novanto Yudistira1, Bayu Rahayudi1

  • 1Informatics Department, Faculty of Computer Science, Brawijaya University, 65145, Malang, Indonesia.

Heliyon
|November 11, 2024
PubMed
概括

本研究使用卷积神经网络 (CNN) 准确分类玉米植物疾病. 像VGG16和EfficientNet这样的机器学习模型实现了高准确度,有助于及时进行作物管理和疾病干预.

关键词:
分类 分类 分类 分类.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.机器学习是机器学习.玉米病是玉米的疾病.

更多相关视频

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.5K

相关实验视频

Last Updated: Jun 7, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

678
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.5K

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:

  • 玉米是食品和动物料的重要作物,但由于疾病管理的延迟,由于作物失败,种植受到威胁.
  • 技术进步,特别是机器学习 (ML),为早期发现疾病和干预玉米种植提供了潜在的解决方案.

研究的目的:

  • 开发和评估卷积神经网络 (CNN) 模型,以准确分类玉米植物疾病.
  • 为了比较不同CNN架构 (ResNet-18,VGG16,EfficientNet) 和玉米疾病识别优化技术的性能.

主要方法:

  • 使用了两个不同的玉米植物图像数据集 (4,144张图像,4个类和5,155张图像,7-8个类).
  • 在第二个数据集中使用加权交叉损失解决了类不平衡.
  • 在ResNet-18,VGG16和EfficientNet架构中进行了实验,采用了随机梯度下降 (SGD) 优化.

主要成果:

  • 使用冷层的VGG16在第一个数据集上实现了97.146%的准确性.
  • 没有冷层的EfficientNet与加权损失相结合,在第二个不平衡数据集上获得了94.798%的准确性.
  • 随机梯度下降 (SGD) 证明是这两个数据集的最佳优化器.

结论:

  • CNN模型在对玉米植物疾病进行分类方面表现出高效,有助于农业管理.
  • 选择模型架构和处理数据不平衡对于实现玉米疾病识别的最佳性能至关重要.