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Öğe Tree fruit load calculation with image processing techniques(2024) Aral, Merve; Misk, Nada; Silahtaroğlu, GökhanTurkey holds a significant position in global olive production, with olives being a crucial component of its agricultural industry. The fruit load on trees directly correlates with olive tree yield, which in turn determines productivity. The Tabit Smart Agriculture R&D Center, located in the Koçarlı district of Aydın within Turkey’s Aegean region, conducted a study using the YOLOv3 Convolutional Neural Network model to estimate olive tree loads. The primary aim of this research was to offer a more precise and objective perspective on olive harvesting, moving away from subjective assumptions based on predictions. Olive trees, playing a significant role in Turkey’s agricultural output, are cultivated across various regions in the country. However, olive sales in Turkey still rely on approximations. To tackle this, image processing techniques were employed to introduce a more technological and practical approach to agricultural applications, particularly in estimating olive tree loads. Throughout the study, real-time datasets were generated by capturing images of olive trees at the Tabit Smart Agriculture R&D Center in Aydın. The focus was on accurately detecting and counting olives. After 6000 iterations, the obtained results were as follows: mAP 61%, Precision 70%, Recall 45%. The results of the study proved that the agricultural industry can actively shape the trajectory of future farming practices by adeptly embracing image processing techniques and deep learning models.Öğe Bridging the gap between technology and farming in agri-tech: a bibliometric analysis(2024) Onursal, Fatma Serab; Öz, SabriAgri-tech, or the application of technology to agriculture, has the power to transform farming methods and find solutions to the problems the industry faces. With an emphasis on comprehending the significance of numerous disciplines, including Artificial Intelligence (AI), this study presents a bibliometric analysis that attempts to analyze the trends and important disciplines engaged in bridging the gap between technology and farming in agri-tech. The analysis highlights the growing interest and research activity in agri-tech and its related fields by looking at a wide range of academic publications. The results show that AI is becoming a key discipline in agri-tech, with an increase in publications highlighting its potential to boost production, improve resource management, and support sustainable farming. The analysis emphasizes the necessity for interdisciplinary cooperation among researchers, practitioners, policymakers, and farmers in order to close the gap between technology and farming. The agriculture sector may unleash the potential of cutting-edge technologies, resulting in more effective, sustainable, and fruitful farming techniques, by utilizing AI and encouraging interdisciplinary cooperation. The conclusions drawn from this bibliometric analysis lay the groundwork for additional agri-tech research and innovation, opening the door to a revolutionary future for agriculture.Öğe A decision support system for detecting FIP disease in cats based on machine learning methods(2024) Doğuç, Özge; Bilgi, Şevval Beyhan; Çağdaş, Seval; Yılmaztürk, NevinCats are close friends who live with us in all aspects of life. Many diseases endanger the quality of life of cats that live with us. One of the most dangerous is infectious peritonitis in cats, also known as FIP; which is a coronavirus that affects a cat’s overall metabolism. There is no specific treatment for FIP and existing drugs are difficult to find and very expensive; therefore, early detection is very important. The most important thing for early detection is to know the body changes caused by the disease, i.e., symptoms, to take appropriate measures. By collecting and interpreting information such as the combination of symptoms, the age at which cats are most common, and the breeds most encountered, cat owners can take precautions even when they cannot be alert. Therefore, in this study, an early detection method for FIP disease in cats is introduced by making predictions using Naive Bayes algorithm. The dataset includes of 300 FIP symptoms used by Jones et al. [11], and from Ümraniye Vita Veterinary Clinic data were obtained from 150 cats who did not have FIP but went to the clinic for other diseases. This generated dataset is resampled using the Smote algorithm to enlarge the dataset. Then the Google Colab program is used to create a naive Bayesian model using the Python programming language. For this study a model is built using the Naive Bayes algorithm, and it is shown that the model can predict the FIP disease with 96% accuracy.Öğe Secure future healthcare applications through federated learning approaches(Springer Science and Business Media Deutschland GmbH, 2024) Tabassum, Maliha; Kuzlu, Murat; Çatak, Ferhat Özgür; Sarp, Salih; Şahinbaş, KevserThe healthcare field is so sensitive to data privacy and security due to including medical and personal information. Almost all healthcare applications are required to increase data security and privacy, which use traditional machine learning approaches relying on centralized systems, both computing resources and the entirety of the data. Federated learning, a sort of machine learning technique, has been used to exactly address this issue. The training data is disseminated across numerous devices in federated learning, and the learning process is collaborative. There are numerous privacy attacks on Deep Learning (DL) models that attackers can use to obtain sensitive information. As a result, the DL model should be safeguarded from adversarial attacks, particularly in healthcare applications that use sensitive medical data. This paper provides a comprehensive review of federated learning on future healthcare applications. It also discusses the types of federated learning along with its implementation in healthcare applications.Öğe Non-cryptographic privacy preserving machine learning methods: a review(Springer Science and Business Media Deutschland GmbH, 2024) Şahinbaş, Kevser; Çatak, Ferhat Özgür; Kuzlu, Murat; Tabassum, Maliha; Sarp, SalihIn recent years, the use of Machine Learning (ML) techniques to exploit data and produce predictive models has become widespread in decision-making and problem-solving across various fields, including healthcare, energy, retail, transportation, and many more. Generally, a well-performing ML model requires large volumes of training data. However, collecting data and using it to predict behavior poses significant challenges to the privacy of individuals and organizations, such as data breaches, loss of privacy, and corresponding financial damage. Therefore, well-designed privacy-preserving ML (PPML) methods are significantly required for many emerging applications to mitigate these problems. This paper provides a comprehensive review of non-cryptographic privacy-preserving ML along with selected methods, such as differential privacy and federated learning. This paper aims to provide a roadmap for future research directions in the PPML field.Öğe Customer segmentation in the retail sector: A data analytics approach(Institute of Electrical and Electronics Engineers Inc., 2022) Şahinbaş, Kevser; Çatak, Ferhat ÖzgürData analytics techniques are widely used in customer segmentation, which groups objects according to the similarity difference on each object and provides a high level of homogeneity in the same cluster or a high level of heterogeneity between each group. In this study, the behavior of customers in the retail sector was analyzed using customer segmentation data mining methods such as OPTICS, BIRCH, Agglomerative Clustuering, K-Means and DBSCAN algithms. The aim of the study is to investigate different data analytics algorithms using a private textile and retail company that has an agreement with e-commerce sites and marketplaces. OPTICS, BIRCH, Agglomerative Clustuering, K-Means have shown almost same clustering results, DBSCAN has outperformed with 0.206086 Silhouette value. The purpose of this paper is to provide a proof of concept of how e-commerce data analytics can be used in customer segmentation.Öğe Employee promotion prediction by using machine learning algorithms for imbalanced dataset(Institute of Electrical and Electronics Engineers Inc., 2022) Şahinbaş, KevserPromotion processes are one of the most important processes in terms of human resources. A promotion process organized fairly within the organization is a managerial tool that motivates employees and contributes to business continuity. Promotion is an important extrinsic motivation for many employees. It ensures the employee's engagement and commitment to the organization and contributes to the continuity of his current performance. It is also an important rewarding and performance control mechanism for the organization. Many factors such as seniority, performance level, competencies, age, awards, training score, organizational commitment of the personnel who will be promoted are taken into consideration. In this study, a prediction methodology will be studied based on the criteria evaluated for the employees in the promotion processes by Machine Learning algorithms such as Support Vector Machine, Artificial Neural Network, and Random Forest. Random Forest achieved the highest performance with 98% accuracy, 96% precision, 1.0% recall and 98% f1-score values with ROS approach. This study could be used by HR and manager to predict the probability of promotion so that managers can find the right parameters for someone to get promoted.Öğe Using or not using business ıntelligence and big data for strategic management: An empirical study based on interviews with executives in various sectors(Elsevier Science, 2016) Silahtaroğlu, Gökhan; Alayoğlu, NihatInformation Technology being an inevitable part of our lives changes the way of doing business every five or eight years. This known as technological cycle. The technology we use today becomes obsolete in several years’ time and managers have to adapt themselves to new systems and new management styles. When big data is so big and important, its usage for business planning and decision making is getting more crucial as well. Business Intelligence tools or Executive Information Systems are improving their ability with the help of the Big Data available. Executives may use the merits of new systems when they make their decisions easily and more accurately as subordinates do when they use computers for daily business operations. Today’s software systems can help the top management with making long term business decisions as much as they can with tactical and operational activities. However, it is difficult to say that those tools are being used by the top managements as they should be. Executives avoid using them for different purposes. They may not refuse them but might simply think it is not the right time to depend on such a system when they make business plans. In this study ten interviews have been conducted with the top executives of the firms which are doing business in various sectors. The executives talked about how or how much they use Executive Information Systems when they make decisions. The findings of the study partly cover expectations or anticipations of the authors.Öğe Analysis and prediction of e-customers' behavior by mining clickstream data(IEEE, 2015) Silahtaroğlu, Gökhan; Dönertaşlı, HaleIn a regular retail shop the behavior of customers may yield a lot to the shop assistant. However, when it comes to online shopping it is not possible to see and analyze customer behavior such as facial mimics, products they check or touch etc. In this case, clickstreams or the mouse movements of e-customers may provide some hints about their buying behavior. In this study, we have presented a model to analyze clickstreams of e-customers and extract information and make predictions about their shopping behavior on a digital market place. After collecting data from an e-commerce market in Turkey, we performed a data mining application and extracted online customers' behavior patterns about buying or not. The model we present predicts whether customers will or will not buy their items added to shopping baskets on a digital market place. For the analysis, decision tree and multi-layer neural network prediction data mining models have been used. Findings have been discussed in the conclusion.
