6 research outputs found
Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks
The recent developments of computer and electronic systems have made the use
of intelligent systems for the automation of agricultural industries. In this
study, the temperature variation of the mushroom growing room was modeled by
multi-layered perceptron and radial basis function networks based on
independent parameters including ambient temperature, water temperature, fresh
air and circulation air dampers, and water tap. According to the obtained
results from the networks, the best network for MLP was in the second
repetition with 12 neurons in the hidden layer and in 20 neurons in the hidden
layer for radial basis function network. The obtained results from comparative
parameters for two networks showed the highest correlation coefficient (0.966),
the lowest root mean square error (RMSE) (0.787) and the lowest mean absolute
error (MAE) (0.02746) for radial basis function. Therefore, the neural network
with radial basis function was selected as a predictor of the behavior of the
system for the temperature of mushroom growing halls controlling system
Prediction of remaining service life of pavement using an optimized support vector machine (case study of Semnan-Firuzkuh road)
201903 bcrcVersion of RecordPublishe
An Automatic Image Processing Algorithm Based on Crack Pixel Density for Pavement Crack Detection and Classification
State of the art survey of deep learning and machine learning models for smart cities and urban sustainability
Deep learning (DL) and machine learning (ML) methods have recently contributed to the advancement of models in the various aspects of prediction, planning, and uncertainty analysis of smart cities and urban development. This paper presents the state of the art of DL and ML methods used in this realm. Through a novel taxonomy, the advances in model development and new application domains in urban sustainability and smart cities are presented. Findings reveal that five DL and ML methods have been most applied to address the different aspects of smart cities. These are artificial neural networks; support vector machines; decision trees; ensembles, Bayesians, hybrids, and neuro-fuzzy; and deep learning. It is also disclosed that energy, health, and urban transport are the main domains of smart cities that DL and ML methods contributed in to address their problems