Deep learning for time series forecasting: a survey
Time series forecasting has become a very intensive field of research, which is even
increasing in recent years. Deep neural networks have proved to be powerful and are …
increasing in recent years. Deep neural networks have proved to be powerful and are …
Effects of hidden layers on the efficiency of neural networks
M Uzair, N Jamil - 2020 IEEE 23rd international multitopic …, 2020 - ieeexplore.ieee.org
Hidden layers play a vital role in the performance of Neural network especially in the case of
complex problems where the accuracy and the time complexity are the main constraints. The …
complex problems where the accuracy and the time complexity are the main constraints. The …
Neural networks in wireless networks: Techniques, applications and guidelines
The design of modern wireless networks, which involves decision making and parameter
optimization, is quite challenging due to the highly dynamic, and often unknown …
optimization, is quite challenging due to the highly dynamic, and often unknown …
Artificial intelligence-based prediction of strengths of slag-ash-based geopolymer concrete using deep neural networks
S Oyebisi, T Alomayri - Construction and Building Materials, 2023 - Elsevier
The construction and building industry, one of the greatest emitters of greenhouse gases, is
under tremendous pressure because of the growing concern about global climate change …
under tremendous pressure because of the growing concern about global climate change …
Predicting the impacts of urban development on urban thermal environment using machine learning algorithms in Nanjing, China
M Zhang, S Tan, J Liang, C Zhang, E Chen - Journal of Environmental …, 2024 - Elsevier
The urban thermal environment undergoes significant influences from changes in land
use/land cover (LULC). This article uses CA-ANN and ANN algorithms to forecast LULC and …
use/land cover (LULC). This article uses CA-ANN and ANN algorithms to forecast LULC and …
Effect of number of neurons and layers in an artificial neural network for generalized concrete mix design
Selection of the number of neurons in different layers of an artificial neural network (ANN) is
a key decision-making step involved in its successful training. Although the number of …
a key decision-making step involved in its successful training. Although the number of …
Solar energy prediction model based on artificial neural networks and open data
With climate change driving an increasingly stronger influence over governments and
municipalities, sustainable development, and renewable energy are gaining traction across …
municipalities, sustainable development, and renewable energy are gaining traction across …
The influence of deep learning algorithms factors in software fault prediction
The discovery of software faults at early stages plays an important role in improving software
quality; reduce the costs, time, and effort that should be spent on software development …
quality; reduce the costs, time, and effort that should be spent on software development …
An indoor Wi-Fi localization algorithm using ranging model constructed with transformed RSSI and BP neural network
This paper focuses on improving indoor Wi-Fi localization by mitigating the effect of
fluctuation of received signal strength indication (RSSI). The RSSI data collected at each …
fluctuation of received signal strength indication (RSSI). The RSSI data collected at each …
Artificial neural network modeling to evaluate polyvinylchloride composites' properties
S Altarazi, M Ammouri, A Hijazi - Computational Materials Science, 2018 - Elsevier
The mechanical properties of extruded Polyvinylchloride (PVC) composites cannot be easily
predicted due to the nonlinear nature of the relationship between the composite's …
predicted due to the nonlinear nature of the relationship between the composite's …