Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods

Ardabili, Sina Faizollahzadeh; Mosavi, Amir; Várkonyi-Kóczy, Annamária R.

doi:10.1007/978-3-030-36841-8_21

Cited by 115 publications

(82 citation statements)

References 73 publications

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“…This paper further identified future trends in the advancement of learning algorithms for smart cities. The trend in smart cities have shown to follow the trend in the overall trend which is a shift toward the advancement of the more sophisticated hybrid, ensemble and deep learning models, as also shown in [80][81][82][83][84][85][86][87][88].…”

Section: Discussionmentioning

confidence: 99%

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

Nosratabadi

Mosavi

Keivani

et al. 2020

Lecture Notes in Networks and Systems

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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.

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Section: Discussionmentioning

confidence: 99%

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

Nosratabadi

Mosavi

Keivani

et al. 2020

Lecture Notes in Networks and Systems

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“…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.…”

Section: Discussionmentioning

confidence: 99%

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

Nádai¹,

Felde²,

Ardabili³

et al. 2020

Preprint

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Novel applications of artificial intelligence for tuning the parameters of industrial machines for optimal performance are emerging at a fast pace. Tuning the combine harvesters and improving the machine performance can dramatically minimize the wastes during harvesting, and it is also beneficial to machine maintenance. Literature includes several soft computing, machine learning and optimization methods that had been used to model the function of harvesters of various crops. Due to the complexity of the problem, machine learning methods had been recently proposed to predict the optimal performance with promising results. In this paper, through proposing a novel hybrid machine learning model based on artificial neural networks integrated with particle swarm optimization (ANN-PSO), the performance analysis of a common combine harvester is presented. The hybridization of machine learning methods with soft computing techniques has recently shown promising results to improve the performance of the combine harvesters. This research aims at improving the results further by providing more stable models with higher accuracy. Keywords-Combine harvester, hybrid machine learning, artificial neural networks (ANN), particle swarm optimization (PSO), ANN-PSO

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“…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.…”

Section: Discussionmentioning

confidence: 99%

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

Ardabili

Beszedes

Nádai

et al. 2020

Preprint

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Hybridization of machine learning methods with soft computing techniques is an essential approach to improve the performance of the prediction models. Hybrid machine learning models, particularly, have gained popularity in the advancement of the high-performance control systems. Higher accuracy and better performance for prediction models of exergy destruction and energy consumption used in the control circuit of heating, ventilation, and air conditioning (HVAC) systems can be highly economical in the industrial scale to save energy. This research proposes two hybrid models of adaptive neuro-fuzzy inference system-particle swarm optimization (ANFIS-PSO), and adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) for HVAC. The results are further compared with the single ANFIS model. The ANFIS-PSO model with the RMSE of 0.0065, MAE of 0.0028, and R 2 equal to 0.9999, with a minimum deviation of 0.0691 (KJ/s), outperforms the ANFIS-GA and single ANFIS models.

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Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods

Cited by 115 publications

References 73 publications

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

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

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

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

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