İşletmeler için Personel Yemek Talep Miktarının Yapay Sinir Ağları Kullanılarak Tahmin Edilmesi

Calp, M. Hanefi

doi:10.2339/politeknik.444380

Cited by 15 publications

(11 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…ANN has a structure that is different from traditional calculation techniques, adapts to the environment in which it is located and can decide in cases of uncertainty. ANN is used effectively in many different fields such as estimation, optical character handling, fingerprint recognition, pattern recognition, robot technology, job scheduling, quality control, power systems, system modelling, finance applications, image processing, industrial applications and defense applications [25][26][27][28][29]. A simple neural network is a structure consisting of a single neuron, very similar to biological neurons with dendrites and axons, with a single output node that connects to the input nodes and each input node.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Estimation of burned areas in forest fires using artificial neural networks

Calp

Köse

2020

ing. Solidar

Self Cite

View full text Add to dashboard Cite

Introduction: This article is the product of the research “Developing an Artificial Neural Network Based Model for Estimating Burned Areas in Forest Fires”, developed at Karadeniz Technical University in the year 2020. Problem: Forest Fires are an issue that greatly affect human life and the ecological order, leaving long-term issues. It should be estimated because it is not known when, where and how much the fire will be in the area. Objective: The objective of the research is to use artificial neural networks to estimate the burned areas in forest fires. Methodology: A feed-forward backpropagation neural network model was used for estimating the burned areas. Results: We performed a performance evaluation over the proposed model by considering Regression values, Mean Absolute Percentage Error (MAPE) and Mean Square Error (MSE). The results show that the model is efficient in terms of its estimation of burnt areas. Conclusions: The proposed artificial neural network model has low error rate and high estimation accuracy. It is more effective than traditional methods for estimating burned areas in forests. Originality: To the best of our knowledge, this is the first time that this real, unique data has been used for building and testing the model’s estimations and the improvements that have been made in producing results faster and more accurately than with traditional methods. Limitations: Since there are regional differences over different forest areas, effective criteria need to be analysed regarding the target regions.

show abstract

Section: Artificial Neural Networkmentioning

confidence: 99%

Estimation of burned areas in forest fires using artificial neural networks

Calp

Köse

2020

ing. Solidar

Self Cite

View full text Add to dashboard Cite

show abstract

“…Bu yöntemde ortalama kare hatanın değeri sıfıra ne kadar yakın olursa modelin performansı o kadar yüksektir (Yavuz ve Deveci, 2012). Ortalama kare hata değerini hesaplamada eşitlik (6)'da verilen matematiksel ifade kullanılmaktadır (Calp, 2019). Eşitlikte gerçek değerleri, ̂ tahmin değerlerini ve son olarak ise tahmin sayısını ifade etmektedir.…”

Section: Schwarz Bilgi Kriteri (Schwarz Information Criteria)unclassified

Arima Yapay Zeka Yöntemi̇ Kullanilarak Isparta İli̇ndeki̇ Örnek Bi̇r Kavşak İçi̇n Araç Sayisi Ve Araç Geçi̇ş Süreleri̇ni̇n Tespi̇ti̇

Kadiroğullari

Aksoy

Sayin

et al. 2020

Mühendislik Bilimleri Ve Tasarım Dergisi

View full text Add to dashboard Cite

Image Processing, Deep Learning, ARIMA Today, with the rapid advancement of technology, artificial intelligence has become an indispensable part of ourlives. Artificial intelligence can be described as a method of predicting computer or computer-controlled activities like human or intelligent creatures. Artificial intelligence are frequently used in many application areas such as health, education, security and robotics. One of the important uses of artificial intelligence is traffic signaling systems used for the controlled and safe passage of vehicles. Traffic signaling generally works on fixed time basis at intersections with heavy traffic, regardless of the traffic density. Thus, fixed time traffic signaling systems are not preferred much today. The waiting time in smart-traffic signaling systems depends on the number of vehicles and the transit time of the vehicles. The study, video footage of the vehicles at the Otogar intersection, which is one of the intersections with heavy traffic, was taken from the Directorate of Transport and Traffic Services in Isparta. Image processing and ARIMA deep-learning method were applied on the captured images. With the ARIMA deep-learning method, the number of time-dependent vehicles and the R 2 of vehicle transit times were evaluated according to the performance evaluation criteria and accuracy rate of 82% and 89% was obtained.

show abstract

“…The unknown system is identified with Hammerstein, Wiener and proposed hybrid models. Model successes for testing process were compared according to MSE [43], correlation and run time values, and the results are presented in Table 1. According to these comparisons proposed hybrid model identifies the unknown system with the lowest error.…”

Section: Example-imentioning

confidence: 99%

A New Hybrid Model Based On Neuro Fuzzy Network Soft Switching Mechanism For System Identification

Mete¹,

Zorlu²,

Özer³

2019

Bilişim Teknolojileri Dergisi

View full text Add to dashboard Cite

This paper aims to improve a new hybrid model for system identification area. The proposed hybrid model consists of an adaptive Hammerstein model, an adaptive Wiener model, and a Neuro-Fuzzy (NF) network based softswitching mechanism (SSM). SSM structure in hybrid model increases the success of block model by selecting the best results of Hammerstein and Wiener model outputs. In literature, there are various studies about NF based on Hammerstein or Wiener model types applied to system identification. In the proposed model, Hammerstein and Wiener models with NF network are used together different from the literature. In simulation studies, five different type of systems are identified with different models (Hammerstein, Wiener and the proposed hybrid model) optimized by Recursive Least Square (RLS). Then the performances of these models are compared. Simulation results reveal the effectiveness and robustness of the proposed identification model. Özet-Bu çalışmanın amacı sistem kimliklendirme alanında yeni bir karma model geliştirmektir. Önerilen karma model uyarlanabilen bir Hammerstein model, bir Wiener model ve esnek anahtarlama mekanizmasına dayanan bulanık sinir ağını içermektedir. Karma modeldeki esnek anahtarlama mekanizması Hammerstein ve Wiener model çıkışlarının en iyi sonuçlarını seçerek blok model başarısını arttırmaktadır. Literatürde, sistem kimliklendirmede uygulanan bulanık sinir ağı temelli Hammerstein ya da Wiener modellerle ilgili birçok çalışma vardır. Önerilen modelde, bulanık sinir ağıyla birlikte Hammerstein ve Wiener modelleri literatürden farklı olarak bir arada kullanılmıştır. Simülasyon çalışmalarında, farklı tipteki beş sistem tekrarlayan en küçük kare ile optimize edilmiş olan farklı modeller (Hammerstein, Wiener ve önerilen model) ile kimliklendirilmiştir. Daha sonra bu modellerin performansları karşılaştırılmıştır. Simülasyon çalışmaları önerilen modelin etkinliğini ve sağlamlığını ortaya koymaktadır. Anahtar Kelimeler-bulanık sinir ağı, karma zeki sistemler, optimizasyon, esnek anahtarlama, sistem kimliklendirme

show abstract

İşletmeler için Personel Yemek Talep Miktarının Yapay Sinir Ağları Kullanılarak Tahmin Edilmesi

Cited by 15 publications

References 20 publications

Estimation of burned areas in forest fires using artificial neural networks

Estimation of burned areas in forest fires using artificial neural networks

Arima Yapay Zeka Yöntemi̇ Kullanilarak Isparta İli̇ndeki̇ Örnek Bi̇r Kavşak İçi̇n Araç Sayisi Ve Araç Geçi̇ş Süreleri̇ni̇n Tespi̇ti̇

A New Hybrid Model Based On Neuro Fuzzy Network Soft Switching Mechanism For System Identification

Contact Info

Product

Resources

About