Implementation of solar energy in smart cities using an integration of artificial neural network, photovoltaic system and classical Delphi methods

Ghadami, Nasim; Gheibi, Mohammad; Kian, Zahra; Faramarz, M. G.; Naghedi, Reza; Eftekhari, Mohammad; Fathollahi-Fard, Amir M.; Dulebenets, Maxim A.; Tian, Guangdong

doi:10.1016/j.scs.2021.103149

Cited by 134 publications

(57 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We followed the advice of several reliable sources, including [ 76 , 77 , 78 , 79 , 80 , 81 ], when putting up our comparisons. This method is also applicable in various sectors, such as other healthcare management services as well as the energy sector [ 82 , 83 , 84 ].…”

Section: Resultsmentioning

confidence: 99%

A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic

et al. 2021

View full text Add to dashboard Cite

The COVID-19 pandemic has had a significant impact on hospitals and healthcare systems around the world. The cost of business disruption combined with lingering COVID-19 costs has placed many public hospitals on a course to insolvency. To quickly return to financial stability, hospitals should implement efficiency measure. An average technical efficiency (ATE) model made up of data envelopment analysis (DEA) and stochastic frontier analysis (SFA) for assessing efficiency in public hospitals during and after the COVID-19 pandemic is offered. The DEA method is a non-parametric method that requires no information other than the input and output quantities. SFA is a parametric method that considers stochastic noise in data and allows statistical testing of hypotheses about production structure and degree of inefficiency. The rationale for using these two competing approaches is to balance each method’s strengths, weaknesses and introduce a novel integrated approach. To show the applicability and efficacy of the proposed hybrid VRS-CRS-SFA (VCS) model, a case study is presented.

show abstract

Section: Resultsmentioning

confidence: 99%

A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Our independent proposed model does not require predetermined preference information, and this is one of the main advantages of the suggested method, which cannot be seen in recent studies. This method is also applicable in various sectors, such as other healthcare management services as well as energy sector [ 79 , 80 , 81 ].…”

Section: Numerical Example In Hospitals That Provide Stroke Care Servicesmentioning

confidence: 99%

Developing a Novel Integrated Generalised Data Envelopment Analysis (DEA) to Evaluate Hospitals Providing Stroke Care Services

et al. 2021

View full text Add to dashboard Cite

Stroke is the biggest cause of adult disability and the third biggest cause of death in the US. Stroke is a medical emergency, and the treatment given in the early hours is important in shaping the patient’s long-term recovery and prognosis. Despite the fact that substantial attention has been dedicated to this complex and difficult issue in healthcare, novel strategies such as operation research-based approaches have hardly been used to deal with the difficult challenges associated with stroke. This study proposes a novel approach with data envelopment analysis (DEA) and multi-objective linear programming (MOLP) in hospitals that provide stroke care services to select the most efficient approach, which will be a new experiment in literature perception. DEA and MOLP are widely used for performance evaluation and efficiency measurement. Despite their similarities and common concepts, the two disciplines have evolved separately. The generalised DEA (GDEA) cannot incorporate the preferences of decision-makers (DMs) preferences and historical efficiency data. In contrast, MOLP can incorporate the DM’s preferences into the decision-making process. We transform the GDEA model into MOLP through the max-ordering approach to (i) solve the problem interactively; (ii) use the step method (STEM) and consider DM’s preferences; (iii) eliminate the need for predetermined preference information; and (iv) apply the most preferred solution (MPS) to identify the most efficient approach. A case study of hospitals that provide stroke care services is taken as an example to illustrate the potential application of the proposed approach method.

show abstract

“…Recently, many studies in which artificial intelligence (AI) technologies are combined have been conducted to further develop the various fields [3,4], as well as IDS. In early studies, intrusion detection research used machine learning models, such as the decision tree, support vector machine (SVM) and artificial neural network (ANN) [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Hyperparameter Control Method for a Network Intrusion Detection System Based on Proximal Policy Optimization

Han¹,

Kim²,

Kim³

2022

Symmetry

View full text Add to dashboard Cite

The complexity of network intrusion detection systems (IDSs) is increasing due to the continuous increases in network traffic, various attacks and the ever-changing network environment. In addition, network traffic is asymmetric with few attack data, but the attack data are so complex that it is difficult to detect one. Many studies on improving intrusion detection performance using feature engineering have been conducted. These studies work well in the dataset environment; however, it is challenging to cope with a changing network environment. This paper proposes an intrusion detection hyperparameter control system (IDHCS) that controls and trains a deep neural network (DNN) feature extractor and k-means clustering module as a reinforcement learning model based on proximal policy optimization (PPO). An IDHCS controls the DNN feature extractor to extract the most valuable features in the network environment, and identifies intrusion through k-means clustering. Through iterative learning using the PPO-based reinforcement learning model, the system is optimized to improve performance automatically according to the network environment, where the IDHCS is used. Experiments were conducted to evaluate the system performance using the CICIDS2017 and UNSW-NB15 datasets. In CICIDS2017, an F1-score of 0.96552 was achieved and UNSW-NB15 achieved an F1-score of 0.94268. An experiment was conducted by merging the two datasets to build a more extensive and complex test environment. By merging datasets, the attack types in the experiment became more diverse and their patterns became more complex. An F1-score of 0.93567 was achieved in the merged dataset, indicating 97% to 99% performance compared with CICIDS2017 and UNSW-NB15. The results reveal that the proposed IDHCS improved the performance of the IDS by automating learning new types of attacks by managing intrusion detection features regardless of the network environment changes through continuous learning.

show abstract

Implementation of solar energy in smart cities using an integration of artificial neural network, photovoltaic system and classical Delphi methods

Cited by 134 publications

References 27 publications

A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic

A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic

Developing a Novel Integrated Generalised Data Envelopment Analysis (DEA) to Evaluate Hospitals Providing Stroke Care Services

An Efficient Hyperparameter Control Method for a Network Intrusion Detection System Based on Proximal Policy Optimization

Contact Info

Product

Resources

About