Predicting the Number of Visitors with Artificial Neural Networks to Support Strategic Decision-Making for Science Centers

Ali Çetinkaya; Havva Kırgız; Ferzan Kara

Makale detayları

Predicting the Number of Visitors with Artificial Neural Networks to Support Strategic Decision-Making for Science Centers

Yazarlar:

Ali Çetinkaya | Havva Kırgız | Ferzan Kara

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Artificial Intelligence (Other)

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.13 Issue 3, pp.836-843

Belge Tipi:

Makale

Yayın Tarihi:

9/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001327666

pISSN:

2147-3129

Anahtar kelimeler:

Özet:

 Accurately predicting visitor attendance has become increasingly vital for science centers to optimize operations, improve visitor experiences, and stay competitive in attracting and engaging global audiences. As the demand for advanced predictive analytics grows, this study explores the use of artificial neural networks ANNs to forecast visitor numbers at science centers. In order to achieve this objective, data pertaining to the number of visitors to the Konya Science Centre was utilized. By analyzing a dataset of ten input factors, such as weather conditions and past visitor behavior, the study develops predictive models capable of accurately estimating future attendance patterns. The best-performing model, utilizing Bayesian regularization, 0.91444 for the training set, 0.25119 for the test set, and 0.91342 overall. These findings underscore the transformative potential of predictive analytics in science center management. Leveraging machine learning techniques, the study provides valuable insights into visitor preferences and behavior. This knowledge can empower science centers to make data-driven decisions, optimize resource allocation, and adapt their offerings to meet the evolving needs of their target audience. Ultimately, the study highlights how predictive analytics can enhance the long-term sustainability and global competitiveness of science center operations.

 Accurately predicting visitor attendance has become increasingly vital for science centers to optimize operations, improve visitor experiences, and stay competitive in attracting and engaging global audiences. As the demand for advanced predictive analytics grows, this study explores the use of artificial neural networks ANNs to forecast visitor numbers at science centers. In order to achieve this objective, data pertaining to the number of visitors to the Konya Science Centre was utilized. By analyzing a dataset of ten input factors, such as weather conditions and past visitor behavior, the study develops predictive models capable of accurately estimating future attendance patterns. The best-performing model, utilizing Bayesian regularization, 0.91444 for the training set, 0.25119 for the test set, and 0.91342 overall. These findings underscore the transformative potential of predictive analytics in science center management. Leveraging machine learning techniques, the study provides valuable insights into visitor preferences and behavior. This knowledge can empower science centers to make data-driven decisions, optimize resource allocation, and adapt their offerings to meet the evolving needs of their target audience. Ultimately, the study highlights how predictive analytics can enhance the long-term sustainability and global competitiveness of science center operations.

 Accurately predicting visitor attendance has become increasingly vital for science centers to optimize operations, improve visitor experiences, and stay competitive in attracting and engaging global audiences. As the demand for advanced predictive analytics grows, this study explores the use of artificial neural networks ANNs to forecast visitor numbers at science centers. In order to achieve this objective, data pertaining to the number of visitors to the Konya Science Centre was utilized. By analyzing a dataset of ten input factors, such as weather conditions and past visitor behavior, the study develops predictive models capable of accurately estimating future attendance patterns. The best-performing model, utilizing Bayesian regularization, 0.91444 for the training set, 0.25119 for the test set, and 0.91342 overall. These findings underscore the transformative potential of predictive analytics in science center management. Leveraging machine learning techniques, the study provides valuable insights into visitor preferences and behavior. This knowledge can empower science centers to make data-driven decisions, optimize resource allocation, and adapt their offerings to meet the evolving needs of their target audience. Ultimately, the study highlights how predictive analytics can enhance the long-term sustainability and global competitiveness of science center operations.

 Accurately predicting visitor attendance has become increasingly vital for science centers to optimize operations, improve visitor experiences, and stay competitive in attracting and engaging global audiences. As the demand for advanced predictive analytics grows, this study explores the use of artificial neural networks ANNs to forecast visitor numbers at science centers. In order to achieve this objective, data pertaining to the number of visitors to the Konya Science Centre was utilized. By analyzing a dataset of ten input factors, such as weather conditions and past visitor behavior, the study develops predictive models capable of accurately estimating future attendance patterns. The best-performing model, utilizing Bayesian regularization, 0.91444 for the training set, 0.25119 for the test set, and 0.91342 overall. These findings underscore the transformative potential of predictive analytics in science center management. Leveraging machine learning techniques, the study provides valuable insights into visitor preferences and behavior. This knowledge can empower science centers to make data-driven decisions, optimize resource allocation, and adapt their offerings to meet the evolving needs of their target audience. Ultimately, the study highlights how predictive analytics can enhance the long-term sustainability and global competitiveness of science center operations.

Makale detayları

Predicting the Number of Visitors with Artificial Neural Networks to Support Strategic Decision-Making for Science Centers

Yazarlar:

Ali Çetinkaya | Havva Kırgız | Ferzan Kara

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Artificial Intelligence (Other)

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.13 Issue 3, pp.836-843

Belge Tipi:

Makale

Yayın Tarihi:

9/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001327666

pISSN:

2147-3129

Science center

Prediction

Neural networks

Visitor

Bayesian

Machine learning

Sayı

Makale detayları

Predicting the Number of Visitors with Artificial Neural Networks to Support Strategic Decision-Making for Science Centers

Yazarlar: Ali Çetinkaya | Havva Kırgız | Ferzan Kara

Yayıncı: Bitlis Eren University

Ana Başlıklar: Artificial Intelligence (Other)

Dergi: Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.13 Issue 3, pp.836-843

Belge Tipi: Makale

Yayın Tarihi: 9/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001327666

pISSN: 2147-3129

Science center

Prediction

Neural networks

Visitor

Bayesian

Machine learning

Sayı

Yazarlar:

Ali Çetinkaya | Havva Kırgız | Ferzan Kara

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Artificial Intelligence (Other)

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.13 Issue 3, pp.836-843

Belge Tipi:

Makale

Yayın Tarihi:

9/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001327666

pISSN:

2147-3129