Browsing by Author "Cakmakci, Yusuf"

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Changes in the Current Patterns of Beef Consumption and Consumer Behavior Trends-Cross Study Brazil-Spain
(Mdpi, 2023) Magalhaes, Danielle Rodrigues; Cakmakci, Cihan; Campo, Maria del Mar; Cakmakci, Yusuf; Makishi, Fausto; Silva, Vivian Lara dos Santos; Trindade, Marco Antonio
This cross-cultural study aimed to determine the main factors behind potential changes in eating habits by analyzing changes in the patterns of beef consumption currently observed in Brazil, Spain, and Turkey. To achieve this aim, 412 regular beef consumers from Brazil, 407 from Spain, and 424 from Turkey answered a self-administered questionnaire. The study surveyed the effects of economic factors, switching from beef to other sources of protein, aspects of credence, health-related concerns, the influence of lifestyle on beef consumption patterns, and purchasing decision factors. The most important factors that changed consumer behavior and resulted in a decrease in consumption, mostly among Brazilian and Turkish consumers, were the economics and accessibility of the products. Beef was replaced by other alternative sources of protein that were likewise derived from animals. The consumers whose purchasing intentions were most significantly influenced by credence factors (e.g., indiscriminate use of agricultural products, substandard animal welfare requirements, among others) were Brazilian and Turkish and, to a lesser degree, Spanish consumers. Lifestyle factors (e.g., consumption of out-of-home meals, available time to cook, among others) were demonstrated to alter consumption patterns and therefore must be carefully considered by the industry, taking into account cultural differences and consumer needs. The population under investigation considered that eating beef had no impact on their health.
Determination of Consumer Perceptions of Eco-Friendly Food Products Using Unsupervised Machine Learning
(Univ Namik Kemal, 2024) Cakmakci, Yusuf; Hurma, Harun; Cakmakci, Cihan
This study aims to comparatively determine the consumer perception of food products marketed under ecologically friendly concepts (organic food, good agriculture, and natural production) and food sold directly by farmers, conventional food, and farmer cooperative branded food. For this purpose, a face-to-face survey was conducted with 171 identified consumers. R program was used to perform all of the analyses. Machine learning methods such as Logistic Regression (LR), Correspondence Analysis (CA), and Principal Component Analysis (PCA) are used for determining consumer perception from obtained data. Descriptive statistics results showed that 51.5 percent of those polled were male and 48.5 percent were female. It found that the mean age of the consumers was joined to the survey was 36.4. According to the LR findings, consumer socioeconomic characteristics have a considerable impact on the purchase of various foods (such as organic labeled foods, good agricultural practices foods, producer cooperative branded foods, etc.). It has been discovered as the result of the PCA, people perceived organic branded food and good agricultural practices foods, which are healthier, more flavorful, and more trustworthy than other food. however, it has been discovered that they believe the costs of these types of food are expensive and that they are difficult to obtain. On the other hand, they perceive the pricing of farmer cooperative branded foods and food sold directly by the farmer to be lower. Furthermore, it was observed in CA findings that there was a correlation between these various food groups and purchase locations. While products sold directly by farmers are mostly purchased from public markets, they prefer grocery stores and supermarkets when purchasing foods with good agricultural practices and natural labeled (from the markets). When seen from this perspective, it is possible to conclude that ecologically friendly food and other food products are regarded differently by customers based on product characteristics. The use of marketing techniques that create a positive perspective in terms of affordability and accessibility and the development of policies and production techniques that boost consumers' current perceptions of these items are considered will promote the consumption of these products.
Discovering the Hidden Personality of Lambs: Harnessing the Power of Deep Convolutional Neural Networks (Dcnns) To Predict Temperament From Facial Images
(Elsevier, 2023) Cakmakci, Cihan; Magalhaes, Danielle Rodrigues; Pacor, Vitor Ramos; Silva de Almeida, Douglas Henrique; Cakmakci, Yusuf; Dalga, Selma; Titto, Cristiane Goncalves
The objective of this study was to define a more practical and reliable alternative to manual temperament classification methods that rely on the behavioral responses of animals individually subjected to various tests. Specifically, this study evaluated the correlation between facial image information and temperament based on deep convolutional neural networks (DCNNs) to predict the temperament of lambs based on their facial images. In the first phase, the lambs were categorized as to their temperament based on data acquired from a behavioral test to establish a ground truth for the temperament of the lambs. This enabled us to train (70%), validate (20%), and test (10%) deep-learning models in the second phase based on facial images and the corresponding temperament labels derived from the behavioral test. The performance of a custom deep convolutional neural network (C-DCNN) was compared to that of pre-trained VGG19 and Xception models for image classification. The Xception model achieved a training accuracy of 81%, which indicated that it learned well the underlying patterns in the data; however, lower validation (0.75) and test (0.58) accuracies indicate that it overfit the training data and did not generalize well to new samples. The VGG19 model, produced lower training (0.59), validation (0.46), and test (0.34) accuracies, which indicated that it did not learn the underlying patterns in the data as well as the Xception model. Furthermore, its precision (0.47), recall (0.42), and F1 score (0.41) indicated that the model performed poorly in identifying the classes correctly. The C-DCNN produced a moderate accuracy of 60%, which indicated that the model was able to predict the temperament traits of lambs with an accuracy of 60%, which was better than random guessing (33% accuracy), and demonstrated the potential of this approach in assessing temperament. The C-DCNN precision (0.69), recall (0.61) and F1 score (0.63) indicated that it had a moderate ability to correctly identify positive cases; however, the small size of the original dataset remains a limitation of the study because it might have caused the suboptimal performance of the models. To validate this approach, further research is needed based on a larger and more diverse dataset. We will continue to investigate the potential of deep learning and computer vision to predict animal personality traits from facial images based on large, diverse datasets, which might lead to more efficient and objective methods for assessing animal temperament and improving animal welfare.