Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940
Browse
3 results
Search Results
Conference Object Model for Estimating the Probability of a Customer To Have a Transaction(IEEE, 2022) Sayar Alperen; Çakar, Tuna; Ertugrul Seyit; Bozkan Tunahan; Sayar, Alperen; Cakar, Tuna; Ertugrul, Seyit; Bozkan, Tunaban; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityIn this study, it is aimed to estimate the probability of a customer who comes to the institution for the first time to make a transaction in the next 3 months, using data-driven machine learning models, in order to provide financing to the seller company by assigning the receivables arising from the sale of goods and services in a company actively operating in the factoring sector. Accordingly, it was aimed to directly contribute to the transaction volume on a business basis by acting and taking action with more effective, efficient and correct approaches by finding high-potential and low-potential customers. In this context, provided by KKB (Credit Registration Bureau); The data set to he used in machine learning models was created with feature engineering and exploratory data analysis, using the Risk, Mersis, GIB information of the prospective customers and the historical information of the customers, check issuers, customer representatives and branches kept in the database. Since the leads coming to the institution are in two different types of organizations (Individual and Legal), two different forecasting models were applied. Multiple classification models were tried, and the highest F1-Score of 86% for private companies was obtained with the Random Forest model, and the highest F1- Score for commercial companies was obtained with the Random Forest model with 82%. © 2022 IEEE.Conference Object Citation - WoS: 1Citation - Scopus: 1Hata Düzeltme Çıktı Kodları: Genel Bakış, Zorluklar ve Gelecek Yönelimler(IEEE, 2019) Arslan, Şuayb Şefik; Arslan, Şefik Şuayb; Güney, Osman B.; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityÇok sınıflı sınıflandırma problemini çözmenin en etkili yollarından biri, bir grup akıllıca tasarlanmıs ikili sınıflandırıcı kullanarak, sınıflandırıcı sonuçlarını belli bir kritere göre bir araya getirmektir. Hata Düzeltme Çıktı Kodları (HDÇK) birden fazla ikili sınıflandırma yoluyla is bölümü saglayan basarılı tekniklerden biridir. Bu çalışmamızın amacı modern HDÇK tiplerine kısa bir giris yapmak, ikili sınıflandırma sonuçlarını birlestiren çesitli kod çözme yöntemleri ve zorlukları, avantajları ve dezavantajlarını ortaya koyan karsılastırmalı bir çalısma sunmaktır. Ayrıca HDÇK tekniğinin birkaç önemli uygulaması, MNIST veri seti üzerindeki performansı ve gelecekteki egilimlerin bazıları sunulmaktadır.Conference Object Citation - Scopus: 2A Visualization Platfom for Disk Failure Analysis(IEEE, 2018) Arslan, Şuayb Şefik; Arslan, Şefik Şuayb; Yiğit, İbrahim Onuralp; Zeydan, Engin; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF UniversityIt has become a norm rather than an exception to observe multiple disks malfunctioning or whole disk failures in places like big data centers where thousands of drives operate simultaneously. Data that resides on these devices is typically protected by replication or erasure coding for long-term durable storage. However, to be able to optimize data protection methods, real life disk failure trends need to be modeled. Modelling helps us build insights while in the design phase and properly optimize protection methods for a given application. In this study, we developed a visualization platform in light of disk failure data provided by BackBlaze, and extracted useful statistical information such as failure rate and model-based time to failure distributions. Finally, simple modeling is performed for disk failure predictions to alarm and take necessary system-wide precautions.