Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

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

Green synthesis of nickel oxide nanoparticles using <i>Calotropis procera</i> leaf extract for visible-light photocatalytic degradation of RBV-5R dye.

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering·2026

Same author

A decision-making framework based on complex fuzzy bipolar soft sets for investment strategy optimization.

Scientific reports·2026

Same author

Metal(loid)s Contamination in the Soil-Rice System: Health Risk Assessment Based on Source Analysis and Monte Carlo Simulation.

Journal of applied toxicology : JAT·2026

Same author

Novel Variants in PUS7 Associated With Intellectual Disability and Growth Retardation: Expanding the Clinical Spectrum in 13 Patients.

Clinical genetics·2026

Same author

Evolutionary divergence in sympatric populations of the fungal pathogen Alternaria alternata across wild tomato hosts.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences·2026

Same author

Dynamic light scattering-assisted design of an optimized NiS-ZnS nanocomposite for efficient photocatalytic dye degradation: experimental and theoretical insights.

RSC advances·2026

Search research articles

相关实验视频

Updated: Jul 8, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

基于群体情报的数据包调度,用于未来的智能网络.

Arif Husen^1,2, Muhammad Hasanain Chaudary¹, Farooq Ahmad¹

¹Department of Computer Science, COMSATS University Islamabad, Lahore, Punjab, Pakistan.

PeerJ. Computer science

|December 11, 2023

概括

此摘要是机器生成的。

本研究介绍了一种机器学习方法,用于网络中的智能数据包调度. 拟议的深度学习模型自动化决策,提高网络性能和成本效益.

关键词:

数据挖掘是一种数据挖掘.新兴技术新兴技术机器学习是机器学习.提示提示 (TIPS) 是一个提醒.

更多相关视频

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

相关实验视频

Last Updated: Jul 8, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Published on: December 25, 2017

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

科学领域:

计算机科学计算机科学
人工智能的人工智能
网络工程网络工程

背景情况:

网络运营依赖于去中心化决策,用于交通安排和政策执行等功能.
人类干预网络决策导致高成本,延误和错误.
机器学习 (ML) 和人工智能 (AI) 为网络功能提供智能自动化.

研究的目的:

为了研究最先进的数据包调度方法.
提出一种基于ML的新方法,以实现敏捷且具有成本效益的数据包调度.
解决当前网络决策过程中的挑战.

主要方法:

对当前的数据包调度和决策流程进行文献综述.
开发和实施基于深度学习的数据包调度模型.
对拟议模型的性能进行实验分析.

主要成果:

拟议的深度学习模型有效地自动化了数据包调度决策.
该模型实现了高网络性能指标:99.95%的吞吐量,99.97%的延迟和99.94%的动.
性能明显超过了传统的静态流量配置配置.

结论:

基于ML的数据包调度为智能网络提供了敏捷且具有成本效益的解决方案.
提出的深度学习方法成功地解决了网络运营方面的挑战.
通过ML/AI进行智能自动化对于未来的网络进步至关重要.