Titlebook: Engineering Applications of Neural Networks; 20th International C John Macintyre,Lazaros Iliadis,Chrisina Jayne Conference proceedings 2019

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书目名称Engineering Applications of Neural Networks影响因子(影响力)




书目名称Engineering Applications of Neural Networks影响因子(影响力)学科排名




书目名称Engineering Applications of Neural Networks网络公开度




书目名称Engineering Applications of Neural Networks网络公开度学科排名




书目名称Engineering Applications of Neural Networks被引频次




书目名称Engineering Applications of Neural Networks被引频次学科排名




书目名称Engineering Applications of Neural Networks年度引用




书目名称Engineering Applications of Neural Networks年度引用学科排名




书目名称Engineering Applications of Neural Networks读者反馈




书目名称Engineering Applications of Neural Networks读者反馈学科排名

荒唐

茁壮成长

Application of Deep Learning Long Short-Term Memory in Energy Demand Forecastinglected by smart meter per day provides a huge potential for analytics to support the operation of a smart grid, an example of which is energy demand forecasting. Short term energy forecasting can be used by utilities to assess if any forecasted peak energy demand would have an adverse effect on the

饰带

Modelling of Compressors in an Industrial CO,-Based Operational Cooling System Using ANN for Energy tion and power requirements through intelligent energy and power management, such as utilizing excess heat, thermal energy storage and local renewable energy sources. Intelligent energy and power management in an operational setting is only possible if the time-varying performance of the individual

Corral

Outlier Detection in Temporal Spatial Log Data Using Autoencoder for Industry 4.0ny relationships and dependencies in the data. Outlier detection and problem solving is difficult in such an environment. We present an unsupervised outlier detection method to find outliers in temporal spatial log data without domain-specific knowledge. Our method is evaluated with real-world unlab

强制令

Reservoir Computing Approaches Applied to Energy Management in Industryithful reproduction of the behavior of the system to model with a usually limited computational burden for a training phase. This aspect favors the deployment of Echo-State Neural networks in the industrial field. In this paper, a novel application of such approach is proposed for the modelling of i

强制令

泥瓦匠

Classification of Sounds Indicative of Respiratory Diseasesas asthma, COPD, and pneumonia. We designed a feature set based on wavelet packet analysis characterizing data coming from four sound classes, i.e. ., ., ., .. Subsequently, the captured temporal patterns are learned by hidden Markov models (HMMs). Finally, classification is achieved via a directed

definition

Eye Disease Prediction from Optical Coherence Tomography Images with Transfer Learningl Neovascularization, Drusen (CNV), Diabetic Macular Odeama (DME), Drusen. Detecting these diseases are quite challenging and requires hours of analysis by experts, as their symptoms are somewhat similar. We have used transfer learning with VGG16 and Inception V3 models which are state of the art CN

Exposure