Multivariate air quality forecasting with nested long short term memory neural network
Artificial intelligence-based air quality index (AQI) forecasting is a hot research topic in the
fields of sustainable and smart industrial environment design. There are mainly two …
fields of sustainable and smart industrial environment design. There are mainly two …
Air quality forecasting with hybrid LSTM and extended stationary wavelet transform
Y Zeng, J Chen, N Jin, X Jin, Y Du - Building and Environment, 2022 - Elsevier
Air quality measurements and forecasting is one of the most popular research topics in the
field of sustainable intelligent environmental design, urban area development and pollution …
field of sustainable intelligent environmental design, urban area development and pollution …
Predicting housing sales in Turkey using ARIMA, LSTM and hybrid models
Having forecast of real estate sales done correctly is very important for balancing supply and
demand in the housing market. However, it is very difficult for housing companies or real …
demand in the housing market. However, it is very difficult for housing companies or real …
Collaborative deep learning framework on IoT data with bidirectional NLSTM neural networks for energy consumption forecasting
Energy consumption forecasting based on IoT data and deep learning algorithm inheriting
distributed and collaborative learning is a widely studied topic both in engineering and …
distributed and collaborative learning is a widely studied topic both in engineering and …
A novel hybrid model on the prediction of time series and its application for the gold price analysis and forecasting
E Jianwei, J Ye, H Jin - Physica A: Statistical Mechanics and its …, 2019 - Elsevier
Gold, as a dominant ingredient in financial market, has gripped a large quantities of the
financiers and scholars to research the formation mechanism of its price. Academic circles …
financiers and scholars to research the formation mechanism of its price. Academic circles …
基于多尺度特征的网络流量异常检测方法
段雪源， 付钰， 王坤， 刘涛涛， 李彬 - 通信学报, 2022 - infocomm-journal.com
针对传统的网络流量异常检测方法大都只关注流量数据的细粒度特征, 对多尺度特征信息利用不
充分, 可能导致异常检测结果准确率不高的问题, 提出了一种基于多尺度特征的网络流量异常 …
充分, 可能导致异常检测结果准确率不高的问题, 提出了一种基于多尺度特征的网络流量异常 …
Multi-scale LSTM model for BGP anomaly classification
As a policy-based routing protocol, the primary purpose of Border Gateway Protocol (BGP) is
to exchange routing reachability information to provide sufficient end-to-end Quality-of …
to exchange routing reachability information to provide sufficient end-to-end Quality-of …
A Novel Method for Regional NO2 Concentration Prediction Using Discrete Wavelet Transform and an LSTM Network
B Liu, L Zhang, Q Wang, J Chen - Computational Intelligence …, 2021 - Wiley Online Library
Achieving accurate predictions of urban NO2 concentration is essential for effectively control
of air pollution. This paper selected the concentration of NO2 in Tianjin as the research …
of air pollution. This paper selected the concentration of NO2 in Tianjin as the research …
Forecasting building energy consumption using ensemble empirical mode decomposition, wavelet transformation, and long short-term memory algorithms
SY Chou, A Dewabharata, FE Zulvia, M Fadil - Energies, 2022 - mdpi.com
A building, a central location of human activities, is equipped with many devices that
consume a lot of electricity. Therefore, predicting the energy consumption of a building is …
consume a lot of electricity. Therefore, predicting the energy consumption of a building is …
Using LSTM GRU and hybrid models for streamflow forecasting
AU Muhammad, X Li, J Feng - … , MLICOM 2019, Nanjing, China, August 24 …, 2019 - Springer
Forecasting streamflow discharge have economic impact as well as reducing the effects of
floods in flood prone regimes by presenting early warning. To minimize it's effects in these …
floods in flood prone regimes by presenting early warning. To minimize it's effects in these …