The latest advancements in computer vision and deep learning(DL)techniques pave the way to design novel tools for the detection and monitoring of forestfires.In this view,this paper presents an intelligent wild forestfi...The latest advancements in computer vision and deep learning(DL)techniques pave the way to design novel tools for the detection and monitoring of forestfires.In this view,this paper presents an intelligent wild forestfire detec-tion and alarming system using deep learning(IWFFDA-DL)model.The pro-posed IWFFDA-DL technique aims to identify forestfires at earlier stages through integrated sensors.The proposed IWFFDA-DL system includes an Inte-grated sensor system(ISS)combining an array of sensors that acts as the major input source that helps to forecast thefire.Then,the attention based convolution neural network with bidirectional long short term memory(ACNN-BLSTM)model is applied to examine and identify the existence of danger.For hyperpara-meter tuning of the ACNN-BLSTM model,the bacterial foraging optimization(BFO)algorithm is employed and thereby enhances the detection performance.Finally,when thefire is detected,the Global System for Mobiles(GSM)modem transmits messages to the authorities to take required actions.An extensive set of simulations were performed and the results are investigated interms of several aspects.The obtained results highlight the betterment of the IWFFDA-DL techni-que interms of various measures.展开更多
Conventional analysis methods cannot fully meet the business needs of power grids.At present,several artificial intelligence (AI) projects in a single business field are competing with each other,and the interfaces be...Conventional analysis methods cannot fully meet the business needs of power grids.At present,several artificial intelligence (AI) projects in a single business field are competing with each other,and the interfaces between the systems lack unified specifications.Therefore,it is imperative to establish a comprehensive service platform.In this paper,an AI platform framework for power fields is proposed;it adopts the deep learning technology to support natural language processing and computer vision services.On one hand,it can provide an algorithm,a model,and service support for power-enterprise applications,and on the other hand,it can provide a large number of heterogeneous data processing,algorithm libraries,intelligent services,model managements,typical application scenarios,and other services for different levels of business personnel.The establishment of the platform framework could break data barrier,improve portability of technology,avoid the investment waste caused by repeated constructions,and lay the foundation for the construction of "platform + application + service" ecological chain.展开更多
背景:基于机器学习的不同算法,如何借助各种算法模型开展腰椎间盘突出症的临床研究已成为目前智能化医学发展的趋势和热点。目的:综述机器学习不同算法模型在腰椎间盘突出症诊治中的特点,归纳相同用途的算法模型各自优势和应用策略。方...背景:基于机器学习的不同算法,如何借助各种算法模型开展腰椎间盘突出症的临床研究已成为目前智能化医学发展的趋势和热点。目的:综述机器学习不同算法模型在腰椎间盘突出症诊治中的特点,归纳相同用途的算法模型各自优势和应用策略。方法:计算机检索PubMed、Web of Science、EMBASE、中国知网、万方数据、维普及中国生物医学数据库中与机器学习在腰椎间盘突出症诊治中的相关应用文献,按入组标准筛选后最终纳入96篇文献进行综述。结果与结论:①机器学习的不同算法模型为腰椎间盘突出症的临床诊治提供了智能化、精准化的应用策略。②监督学习中的传统统计学方法和决策树在探究危险因素,制定诊断、预后模型方面简单高效;支持向量机适用于高维特征的小数据集,作为非线性分类器可应用于正常或退变椎间盘的识别、分割、分类,制定诊断、预后模型;集成学习可相互弥补单一模型的不足,具有处理高维数据的能力,提高临床预测模型的精度和准确性;人工神经网络提高了模型的学习能力,可应用于椎间盘识别和分类,制作临床预测模型;深度学习在具有以上用途的基础上,还能优化图像,辅助手术操作,是目前腰椎间盘突出症诊治中应用最广泛、性能最佳的模型;无监督学习中的聚类算法主要用于椎间盘分割和不同突出节段的分类;而半监督学习方式临床应用相对较少。③目前,机器学习在腰椎间盘的识别、分割,退变椎间盘的分类和分级,自动化临床诊断和分类,构建临床预测模型以及辅助术中操作方面具有一定临床优势。④近年来,机器学习的研究策略已向神经网络和深度学习方向转变,具有更强学习能力的深度学习算法将会是未来实现智能化医疗的关键。展开更多
文摘The latest advancements in computer vision and deep learning(DL)techniques pave the way to design novel tools for the detection and monitoring of forestfires.In this view,this paper presents an intelligent wild forestfire detec-tion and alarming system using deep learning(IWFFDA-DL)model.The pro-posed IWFFDA-DL technique aims to identify forestfires at earlier stages through integrated sensors.The proposed IWFFDA-DL system includes an Inte-grated sensor system(ISS)combining an array of sensors that acts as the major input source that helps to forecast thefire.Then,the attention based convolution neural network with bidirectional long short term memory(ACNN-BLSTM)model is applied to examine and identify the existence of danger.For hyperpara-meter tuning of the ACNN-BLSTM model,the bacterial foraging optimization(BFO)algorithm is employed and thereby enhances the detection performance.Finally,when thefire is detected,the Global System for Mobiles(GSM)modem transmits messages to the authorities to take required actions.An extensive set of simulations were performed and the results are investigated interms of several aspects.The obtained results highlight the betterment of the IWFFDA-DL techni-que interms of various measures.
基金supported by National Key Research and Development Project(2017YFE0112600)Science and Technology Project of China Electric Power Research Institute(Research on the Key Technologies and Typical Application Scenarios of the Artificial Intelligence Basic Framework for Integrated Energy)
文摘Conventional analysis methods cannot fully meet the business needs of power grids.At present,several artificial intelligence (AI) projects in a single business field are competing with each other,and the interfaces between the systems lack unified specifications.Therefore,it is imperative to establish a comprehensive service platform.In this paper,an AI platform framework for power fields is proposed;it adopts the deep learning technology to support natural language processing and computer vision services.On one hand,it can provide an algorithm,a model,and service support for power-enterprise applications,and on the other hand,it can provide a large number of heterogeneous data processing,algorithm libraries,intelligent services,model managements,typical application scenarios,and other services for different levels of business personnel.The establishment of the platform framework could break data barrier,improve portability of technology,avoid the investment waste caused by repeated constructions,and lay the foundation for the construction of "platform + application + service" ecological chain.
文摘背景:基于机器学习的不同算法,如何借助各种算法模型开展腰椎间盘突出症的临床研究已成为目前智能化医学发展的趋势和热点。目的:综述机器学习不同算法模型在腰椎间盘突出症诊治中的特点,归纳相同用途的算法模型各自优势和应用策略。方法:计算机检索PubMed、Web of Science、EMBASE、中国知网、万方数据、维普及中国生物医学数据库中与机器学习在腰椎间盘突出症诊治中的相关应用文献,按入组标准筛选后最终纳入96篇文献进行综述。结果与结论:①机器学习的不同算法模型为腰椎间盘突出症的临床诊治提供了智能化、精准化的应用策略。②监督学习中的传统统计学方法和决策树在探究危险因素,制定诊断、预后模型方面简单高效;支持向量机适用于高维特征的小数据集,作为非线性分类器可应用于正常或退变椎间盘的识别、分割、分类,制定诊断、预后模型;集成学习可相互弥补单一模型的不足,具有处理高维数据的能力,提高临床预测模型的精度和准确性;人工神经网络提高了模型的学习能力,可应用于椎间盘识别和分类,制作临床预测模型;深度学习在具有以上用途的基础上,还能优化图像,辅助手术操作,是目前腰椎间盘突出症诊治中应用最广泛、性能最佳的模型;无监督学习中的聚类算法主要用于椎间盘分割和不同突出节段的分类;而半监督学习方式临床应用相对较少。③目前,机器学习在腰椎间盘的识别、分割,退变椎间盘的分类和分级,自动化临床诊断和分类,构建临床预测模型以及辅助术中操作方面具有一定临床优势。④近年来,机器学习的研究策略已向神经网络和深度学习方向转变,具有更强学习能力的深度学习算法将会是未来实现智能化医疗的关键。
