Estimation of number of foreign visitors with ANFIS by using ABC algorithm

Karaboğa, Derviş; Kaya, Ebubekir

doi:10.1007/s00500-019-04386-5

Cited by 26 publications

(16 citation statements)

References 24 publications

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“…There are numerous optimization methodologies available, however, the most promising approach is the application of the swarm intelligence metaheuristics to reinforce the parameters and outputs of the ANFIS. In Karaboga and Kaya (2019b) , ABC algorithm was applied for ANFIS optimization, in order to estimate the number of foreign visitors coming to Turkey. The same authors proposed training ANFIS with and adaptive and hybrid ABC algorithm, as shown in Karaboga and Kaya (2019c) .…”

Section: Background and Related Workmentioning

confidence: 99%

COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach

Živković

Bačanin

Venkatachalam

et al. 2021

Sustainable Cities and Society

263

105

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Section: Background and Related Workmentioning

confidence: 99%

COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach

Živković

Bačanin

Venkatachalam

et al. 2021

Sustainable Cities and Society

263

105

View full text Add to dashboard Cite

“…Literature overview shows that, in [32], ABC algorithm was applied for the optimization process of ANFIS, with a goal to estimate the number of tourists coming to Turkey. The same authors also suggested training ANFIS model with a hybrid ABC algorithm, as stated in [33].…”

Section: Background and Related Workmentioning

confidence: 99%

Hybrid Genetic Algorithm and Machine Learning Method for COVID-19 Cases Prediction

Živković

Venkatachalam

Bačanin

et al. 2021

Lecture Notes in Networks and Systems

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A novel type of coronavirus, now known under the acronym COVID-19, was initially discovered in the city of Wuhan, China. Since then, it has spread across the globe and now it is affecting over 210 countries worldwide. The number of confirmed cases is rapidly increasing and has recently reached over 14 million on July 18, 2020, with over 600,000 confirmed deaths. In the research presented within this paper, a new forecasting model to predict the number of confirmed cases of COVID-19 disease is proposed. The model proposed in this paper is a hybrid between machine learning adaptive neuro-fuzzy inference system and enhanced genetic algorithm metaheuristics. The enhanced genetic algorithm is applied to determine the parameters of the adaptive neuro-fuzzy inference system and to enhance the overall quality and performances of the prediction model. Proposed hybrid method was tested by using realistic official dataset on the COVID-19 outbreak in the state of China. In this paper, proposed approach was compared against multiple existing state-of-the-art techniques that were tested in the same environment, on the same

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“…Adaptive Neuro Fuzzy Inference System (ANFIS) which is known as a machine learning technique consists of a combination of artificial neural networks and fuzzy inference system (Karaboga & Kaya, 2020) which has been recently adopted in the tourism and hospitality industry to predict various variables. For example, Sun et al (2019) used ML to accurately forecast tourist arrivals for popular destinations in China.…”

Section: The Complexity Of Human Behaviour (Peb)mentioning

confidence: 99%

Application of machine learning to predict visitors’ green behavior in marine protected areas: evidence from Cyprus

Rezapouraghdam¹,

Akshiq

Ramkissoon

2021

Journal of Sustainable Tourism

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Interpretive marine turtle tours in Cyprus yields an alluring ground to unfold the complex nature of pro-environmental behavior among travelers in nature-based destinations. Framing on Collins (2004) interaction ritual concept and the complexity theory, the current study proposes a configurational model and probes the interactional effect of visitors' memorable experiences with environmental passion and their demographics to identify the causal recipes leading to travelers' sustainable behaviors. Data was collected from tourists in the marine protected areas located in Cyprus. Such destinations are highly valuable not only for their function as an economic source for locals but also as a significant habitat for biodiversity preservation. Using fuzzy-set Qualitative Comparative Analysis (fsQCA), this empirical study revealed that three recipes predict the high score level of visitors' environmentally friendly behavior. Additionally, an adaptive neuro-fuzzy inference system (ANFIS) method was applied to train and test the patterns of visitors' proenvironmental behavior in a machine learning environment to come up with a model which can best predict the outcome variable. The unprecedented implications on the use of technology to simulate and encourage pro-environmental behaviors in sensitive protected areas are discussed accordingly.

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Estimation of number of foreign visitors with ANFIS by using ABC algorithm

Cited by 26 publications

References 24 publications

COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach

COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach

Hybrid Genetic Algorithm and Machine Learning Method for COVID-19 Cases Prediction

Application of machine learning to predict visitors’ green behavior in marine protected areas: evidence from Cyprus

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