State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

Abstract

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 neurofuzzy; 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.

Sustainable Banking; Evaluation of the European Business Models

“…On the other hand, the SBMs incorporate sustainability in the company's value proposition and value creation logic [55]. According to Nosratabadi et al [1], sustainable business models constitute components that the interrelation among the components creates and delivers sustainable values for the multi-stakeholders. The SBMs generate competitive advantages to the firms and incorporate sustainable value proposition, sustainable value creation, and sustainable value capturing processes to bring economic benefits.…”

“…Sustainable business models (SBMs) are tools allowing the businesses to meet the environmental, the social, and the economic benefits simultaneously [1]. Although the nature of the banking sector, as the backbone of the finance industry, requires fewer natural resources, carbon dioxide emissions, and waste management activities in comparison with the manufacturing companies, there is much evidence in the literature that sustainability and going green will lead the service sectors toward profitability [2,3].…”

“…The phrase of multistakeholder is similar to the triple bottom line where the stakeholders are the environment, society and business owners (shareholders) [34]. Therefore, Nosratabadi et al [1] argue that an SBM is designed in a way to meet the benefits of the environment, society and the business (economic benefit of a business) simultaneously. The SBMs provide solutions to the businesses to make money through decreasing the harmful effects of economic activities on the environment and the society.…”

Performance Analysis of Combine Harvester Using Hybrid Model of Artificial Neural Networks Particle Swarm Optimization

“…The hybridization of machine learning methods has shown to be an essential approach to improve the performance of the prediction models. For the future research, advancement of hybrid and ensemble machine learning models, e.g., [23][24][25][26][27][28], and comparative analysis with deep learning models, e.g., [29][30][31][32] are proposed to identify models with higher efficiency.…”

Comparative Analysis of Single and Hybrid Neuro-Fuzzy-Based Models for an Industrial Heating Ventilation and Air Conditioning Control System

“…The ANFIS-PSO model with the RMSE of 0.0065, MAE of 0.0028, and R2 equal to 0.9999, with a minimum deviation of 0.0691 (KJ/s), outperforms the ANFIS-GA and single ANFIS models. For the future research, advancement of hybrid and ensemble machine learning models, e.g., [47][48][49][50][51][52], and comparative analysis with deep learning models, e.g., [53][54][55][56] are proposed to identify models with higher efficiency.…”