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Öğe ERIM: An ensemble of rare itemset mining and its application in the automotive industry(Wiley, 2022) Akdaş, Devrim Naz; Birant, Derya; Taşer, Pelin YıldırımDiscovering previously unknown anomalies that are rare and dramatically differ from the majority of the data is a critical need for the automotive industry. Rare itemset mining (RIM), one of the pattern-based methods, has been used for anomaly detection due to providing successful analysis results. However, several aspects still need to be explored, such as improving the mining process by identifying more targeted, valuable and reliable rare itemsets. Motivated by this fact, this study proposes a novel approach, named ensemble of rare itemset mining (ERIM), which investigates weak rare itemsets (WRIs) using different algorithms and aggregates these rules to obtain strong rare itemsets (SRIs). This study also combines four different RIM algorithms (Apriori Rare, Apriori Inverse, CORI and RP-Growth) as base learners for the first time. The proposed ERIM approach is a general methodology that can be applied to any field, but, in this study, it was used in the automotive industry as a case study. In the experiments, ERIM was applied to a real-world gear manufacturing dataset to discover anomalies in machine downtimes. The experimental results were evaluated in terms of the number of itemsets and the length of itemsets by giving some samples, as well. The results showed that the proposed ERIM approach gives more reliable common knowledge by jointly considering the relation between WRIs discovered by the base learners. The findings indicated that the proposed ERIM technique was successful in detecting anomalies whose support values are below 7.12. Furthermore, it is clear from the experimental results that the ERIM discovered the highest number of SRIs, 1403, each of which is a 3-itemset. Finally, the results showed that our method performed 43.37% better on average than state-of-the-art methods on the same dataset.Öğe An intelligent multi-output regression model for soil moisture prediction(Springer Science and Business Media Deutschland GmbH, 2022) Küçük, Cansel; Birant, Derya; Yıldırım Taşer, PelinSoil moisture prediction plays a vital role in developing plants, soil properties, and sustenance of agricultural systems. Considering this motivation, in this study, an intelligent Multi-output regression method was implemented on daily values of meteorological and soil data obtained from Kemalpaşa-Örnekköy station in Izmir, Turkey, at three soil depths (15, 30, and 45 cm) between the years 2017 and 2019. In this study, nine different machine learning algorithms (Linear Regression (LR), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (Lasso), Random Forest (RF), Extra Tree Regression (ETR), Adaptive Boosting (AdaBoost), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Histogram-Based Gradient Boosting (HGB)) were compared each other in terms of MAE, RMSE, and R2 metrics. The experiments indicate that the implemented Multi-output regression models show good soil moisture prediction performance. Also, the ETR algorithm provided the best prediction performance with an 0.81 R2 value among the other models. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Öğe Multi-label classification of line chart images using convolutional neural networks(Springer International Publishing Ag, 2020) Kösemen, Cem; Birant, DeryaIn this paper, we propose a new convolutional neural network (CNN) architecture to build a multi-label classifier that categorizes line chart images according to their characteristics. The class labels are organized in the form of trend property (increasing or decreasing) and functional property (linear or exponential). In the proposed method, the Canny edge detection technique is applied as a data preprocessing step to increase both the classification accuracy and training speed. In addition, two different multi-label solution approaches are compared: label powerset (LP) and binary relevance (BR) methods. The experimental studies show that the proposed LP-CNN model achieves 93.75% accuracy, while the BR-CNN model reaches 92.97% accuracy on the test set, which contains real-world line chart images. The aim of this study is to build an efficient classifier that can be used for many purposes, such as automatically captioning the chart images, providing recommendations, redesigning charts, organizing a collection of chart images and developing better search engines.Öğe Multitask-based association rule mining(2020) Taşer, Pelin Yıldırım; Birant, Kökten Ulaş; Birant, DeryaRecently, there has been a growing interest in association rule mining (ARM) in various fields. However,standard ARM algorithms fail to discover rules for multitask problems as they do not consider task-oriented investigationand, therefore, they ignore the correlation among the tasks. Considering this situation, this paper proposes a novelalgorithm, named multitask association rule miner (MTARM), that tends to jointly discover rules by considering multipletasks. This paper also introduces two novel concepts: single-task rule and multiple-task rule. In the first phase of theproposed approach, highly frequent local rules (single-task rules) are explored for each task separately and then theselocal rules are combined to produce the global result (multitask rules) using a majority voting mechanism. Experimentswere conducted on four different real-world multitask learning datasets. The experimental results indicated that theproposed MTARM approach discovers more information than that of traditional ARM algorithms by jointly consideringthe relationships among multiple tasks.Öğe A novel machine learning approach: Soil temperature ordinal classification (STOC)(Ankara Univ, Fac Agr, 2022) Kucuk, Cansel; Birant, Derya; Yıldırım Taşer, PelinSoil temperature prediction is an important task since soil temperature plays an important role in agriculture and land use. Although some progress has been made in this area, the existing methods provide a regression or nominal classification task. However, ordinal classification is yet to be explored. To bridge the gap, this paper proposes a novel approach: Soil Temperature Ordinal Classification (STOC), which considers the relationships between the class labels during soil temperature level prediction. To demonstrate the effectiveness of the proposed approach, the STOC method using five different traditional machine learning methods (Decision Tree, Naive Bayes, K-Nearest Neighbors, Support Vector Machines, and Random Forest) was applied on daily values of meteorological and soil data obtained from 16 stations in three states (Utah, Alabama, and New Mexico) of United States at five soil depths (2, 4, 8, 20, and 40 inches) between the years of 2011 and 2020. The experiments show that the proposed STOC approach is an efficient method for soil temperature level (very low, low, medium, high, and very high) prediction. The applied STOC models (STOC.DT, STOC.NB, STOC.KNN, STOC.SVM, and STOC.RF) showed average accuracy rates of 90.95%, 77.09%, 90.84%, 89.94%, and 90.91% on the experimental datasets, respectively. It was observed from the experimental results that the STOC.DT method achieved the best soil temperature level prediction among the others.Öğe Recommendation system with association rule mining and mobile augmented reality(IEEE, 2019) Birant, Kökten Ulaş; Taşer, Pelin Yıldırım; Dindar, Halil Özgen; Birant, DeryaMobile augmented reality is a popular technology field that allows simultaneous interaction between the real world and the virtual world with the help of mobile devices. The notion of object recognition and classification, which plays an important role in mobile augmented reality applications, is the process of identifying an object on the image and assigning it to a certain class label. In this study, a novel three-dimensional and real time recommendation system has been developed with association rule mining and mobile augmented reality. The proposed system consists of three stages: (1) Recognition and classification of the object on the image (2) Determination of the related rules of the object with other objects in the dataset by using the Apriori algorithm (3) Adding recommendations that generated from the rules to the image as virtual objects with mobile augmented reality. Although the developed system in this study is a general recommendation system, it was applied in the field of market basket analysis as an experimental study.
