Detecting corn tassels using computer vision and support vector machines

Bankruptcy visualization and prediction using neural networks: A study of U.S. commercial banks

“…Recently some new models have emerged, such as the random forest (Adusumilli, Bhatt, Wang, Bhattacharya & Devabhaktuni, 2013;Booth, Gerding & McGroarty, 2014;Calderoni, Ferrara, Franco & Maio, 2015) and the support vector machines (Czarnecki & Tabor, 2014;Harris, 2015;Horta & Camanho, 2013;Kurtulmuş & Kavdir, 2014). Random forest (RF) is a general data mining tool proposed by Breiman (2001), in which a set of decision trees is generated on bootstrap samples of the data and then combined by majority voting.…”

“…In bankruptcy analyses, it is a common practice to use one-to-one match of failure and non-failure cases (Davies & Bouldin, 1979;Kurtulmuş & Kavdir, 2014;Wu & Liu, 2007). Consequently, our training sample is made up of 386 failed and 386 non-failed banks.…”

Economic Sustainability in Franchising: A Model to Predict Franchisor Success or Failure

“…These approaches overcame some of the statistical restrictions that are associated with logistic regression. More recently, scholars have applied other machine learning methods such as random forest [72][73][74], support vector machines [75][76][77], and combinations of these approaches, obtaining increasingly accurate results. The purpose of these hybrid models is to obtain the advantages of individual models without their weaknesses [78,79].…”

Optical Methods and Techniques for Meat Quality Inspection

“…Machine vision has proven to be effective for quality detection of agricultural products (Dowlati et al, 2012;ElMasry et al, 2012c;Khazaei et al, 2013;Kurtulmus and Kavdir, 2014;Wang et al, 2011). Machine vision methods have been widely used for surface quality detection of different meats and meat products.…”