Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940
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Conference Object Analyzing Customer Churn: a Comparative Study of Machine Learning Models on Pay-Tv Subscribers in Turkey(IEEE, 2023) Obalı, Emir; Çakar, Tuna; Karani Yılmaz, Veysel; Kara, Erkan; Meşe, Yasemin Kürtcü; Çakar, Tuna; Yıldız, Ayşenur; Hataş, Tuğce Aydın; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityUnderstanding the reasons for customer churn provides added value in terms of retaining existing customers, as customer attrition leads to revenue loss for companies and incurs marketing costs for acquiring new customers. In this study, the 6-month historical data of a Pay-TV company operating in Turkey was used, and due to the imbalanced nature of the dataset on a label basis, the oversampling method was applied. During the model development phase, various artificial learning algorithms (Random Forest, Logistic Regression, KNearest Neighbors, Decision Tree, AdaBoost, XGBoost, Extra Tree Classifier) were utilized, and their performances were compared. Based on the evaluation of success criteria for each model, it was observed that the tree-based Random Forest, Extra Tree Classifier and XGBoost achieved the highest performance for this dataset.Conference Object Optimizing Collective Building Management Through a Machine Learning-Based Decision Support System(IEEE, 2023) Güvençli, Mert; Çakar, Tuna; Doğan, Erkan; Çakar, Tuna; Özyürüyen, Burcu; Kiran, Halil; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThis study presents the design, implementation, and evaluation of a Decision Support System (DSS) developed for Collective Building Management. Given the potential advantages of machine learning techniques in this domain, the research explores how these techniques can be used to improve collective building management. The dataset consists of 824,932 records and 15 attributes, after preprocessing the data to fill in missing values with the median. The random forest algorithm was chosen for model training and achieved a performance rate of 71.2%. This model can be used to optimize decision processes in collective building management. The proposed prototype is notable for its ability to automatically generate operational plans. In conclusion, machine learning-based DSSs are effective tools for collective building management.Conference Object Fault Detection Model Using Measurement Data in Fiber Optic Internet Lines(IEEE, 2023) Çakar, Tuna; Çakar, Tuna; Battal, Eray; Özkan, Gözde; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityIn this study, a model has been developed to predict potential faults in advance based on performance metrics of various fiber-optic internet lines, as well as alarm (fault data) and performance measurement values from the 5 hours prior to the occurrence of the alarm. Performance metrics that vary over time have been analyzed in a time-series format based on alarm numbers, and anomaly detection methods have been used to label the data for any potential patterns that may occur in the performance metrics specific to the alarm. The labeled data was then fed into a classification model to create a model that enables to detect possible patterns in the relevant performance values for the specific fault type. The best performing model was Random Forest Classifier with accuracy and F1 scores of 0.89 and 0.84 respectively.Conference Object Citation - Scopus: 3Grafraud: Fraud Detection Using Graph Databases and Neural Networks(IEEE, 2023) Raina, Ajeet Singh; Çakar, Tuna; Ertuğrul, Seyit; Arslan, Şuayip; Sayar, Alperen; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThe issue of fraud has become a significant concern for many companies, particularly in the finance sector, but the traditional methods of detecting fraud are no longer adequate. Innovative technologies are necessary to identify complex fraudulent activities, and RedisGraph, a high-performance graph database, may offer a solution. With the assistance of neural networks, RedisGraph can accurately and efficiently detect fraudulent transactions in vast and intricate environments. Companies typically use a combination of Python and Oracle Databases to design fraud detection systems. which provide robust data management and real time AI processing capabilities. These technologies allow to create fraud detection systems that can determine fraudulent activities in real-time. But according to advancements of fraud methods only using of these systems not efficient nowadays. This article presents a proof of concept based on an essential use case of RedisGraph-powered neural networks in detecting financial fraud. It demonstrates the value of carefully employing Python and Oracle Database to construct and deploy real-time systems that can efficiently detect fraudulent activities.Conference Object Citation - Scopus: 1Analytical Approaches in Customer Relationship Management(IEEE, 2023) Akata, Mustafa Aşkım; Çakar, Tuna; Kaya, Büşra; Kızılay, Ayşe; Çakar, Tuna; Şahin, Zeynep; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThis study examines the impact of analytical customer relationship management (aCRM) strategies, specifically the segmentation approach using RFM analysis and artificial learning methods, on customer satisfaction, revenue performance, and loyalty in businesses. The research adopts an approach that integrates data from both online and offline channels onto a single platform, providing a holistic view of customer behaviors. Combining the segmentation obtained through RFM analysis and artificial learning methods with timely campaigns has enhanced shopping opportunities for customers and increased customer satisfaction and loyalty. The use of aCRM as a strategic marketing and sales tool has enabled businesses to manage customer relationships more effectively. This paper contributes to the literature in this field by presenting in detail the advantages offered by aCRM, its application methods, and the results obtained.Conference Object Citation - Scopus: 1Enhancing Quality Control in Plastic Injection Production: Deep Learning-Based Detection and Classification of Defects(IEEE, 2023) Mutlu, İsmail; Çakar, Tuna; Çakar, Tuna; Yıldız, Ahmet; Sayar, Alperen; Şimsek, Kamil; Tunalı, Mustafa Mert; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThis study investigates the applicability of diverse deep learning techniques in detecting and classifying defects within plastic injection manufacturing processes. The findings derived from the models yield several feasible solutions that hold potential practical implications. Notably, the implementation of the Xception model as a classification framework presents a potential domain for enhancing quality control procedures. The developed models, trained on the prepared data sets, provide compelling evidence for the potential utilization of artificial intelligence technologies in the manufacturing industry. Consequently, this study represents a noteworthy contribution to the limited yet auspicious academic research in the field.Conference Object Spine Posture Detection for Office Workers With Hybrid Machine Learning(IEEE, 2023) Öke, Deniz; Çakar, Tuna; Yıldız, Ahmet; Mise, Pelin; Terzibaşıoğlu, Aynur Metin; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityThis study aims to detect bad spine posture using an al-ternative approach that doesn't rely on deep learning or excessive energy. The goal is to improve accuracy and effectiveness without disrupting workflow. A custom dataset was created, numerical inferences were made from posture values, and a hybrid approach using Light Gradient Boosting achieved a 96 % success rate.Conference Object Development of a Knowledge-Based Multimodal Deep Learning System for Automatic Breast Lesion Segmentation and Diagnosis in Mg/Dmr Images(IEEE, 2023) Orhan, Gözde; Çakar, Tuna; Sürmeli, Hulusi Emre; Çakar, Tuna; Araz, Nusret; Bayram, Bülent; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityDeep learning networks (DLNs) rely on labeled training datasets as their fundamental building blocks. While various databases exist worldwide, there is currently no domestic solution available in our country. This project aims to create a domestic database by automatically segmenting breast lesions in MG/DMR images based on their types and developing a knowledge-based multimodal DL-based integrated computer-aided diagnosis system to analyze the images, thereby providing the system with continuous learning capability. Different brands of devices exist for MG/DMR, necessitating the multimodal operation of image processing/artificial intelligence algorithms. To achieve this goal, the network was trained first, and then prelearned data were transferred to enable the training of data from different networks once accurate results are obtained. The developed system has the potential to enable the automatic detection of breast lesions, ensuring fast and high diagnostic accuracy. Additionally, it might also facilitate the retrospective analysis of patients' periodic check-up results.Article Minimum Repair Bandwidth Ldpc Codes for Distributed Storage Systems(IEEE, 2023) Pourmandi, Massoud; Arslan, Şefik Şuayb; Arslan , Şuayb Şefik; Haytaoğlu, Elif; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityIn distributed storage systems (DSS), an optimal code design must meet the requirements of efficient local data regeneration in addition to reliable data retention. Recently, lowdensity parity-check (LDPC) codes have been proposed as a promising candidate that can secure high data rates as well as low repair bandwidth while maintaining low complexity in data reconstruction. The main objective of this study is to optimize the repair bandwidth characteristics of LDPC code families for a DSS application while meeting the data reliability requirements. First, a data access scenario in which nodes contact other available nodes randomly to download data is examined. Later, a minimum-bandwidth protocol is considered in which nodes make their selections based on the degree numbers of check nodes. Through formulating optimization problems for both protocols, a fundamental trade-off between the decoding threshold and the repair bandwidth is established for a given code rate. Finally, conclusions are confirmed by numerical results showing that irregular constructions have a large potential for establishing optimized LDPC code families for DSS applications.Conference Object Citation - Scopus: 3Segmentation for Factoring Customers: Using Unsupervised Machine Learning Algorithms(IEEE, 2023) Yalçuva, Berat; Akçay, Ahmet; Çakar, Tuna; Çakar, Tuna; Sayar, Alperen; Ayyıldız, Nur Seher; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityNowadays the fact that technology facilitates data collection is an important opportunity, as well as making the management of all this data difficult and makes no sense unless it is well processed. This stored data is extremely important, and companies use data provided by their customers. Catching the needs of the customer profiles of the changing world is now a necessity and takes the first place for companies. With the increase in the amount of stored data over time, it has become difficult to establish a relationship between the data and to separate them from each other. At this point, machine learning methods have become more involved in our lives. In this study, what segmentation is and its change over the years are mentioned. It has been mentioned which machine learning techniques will be useful in data selection. Then, possible machine learning methods are shown in real life segmentation problem by using the domestic factoring company’s customer check data. Since this study aims to group unlabeled data, unsupervised learning techniques are emphasized. Among these methods, Hierarchical Clustering, DBSCAN, Gaussian Mixture Modeling methods, Fuzzy c- Means were used as well as the most popular K-Means algorithm. When the clustering results were examined, the optimal number of clusters was calculated very high with GMM, DBSCAN could not assign clusters, and Hierarchical clustering could not produce expected results. It was observed that the best results were obtained with the K-Means and Fuzzy c - Means algorithms.