Browsing by Author "Hindistan, Yavuz Selim"

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Citation Count: 3
Alternative Credit Scoring and Classification Employing Machine Learning Techniques on a Big Data Platform
(Institute of Electrical and Electronics Engineers Inc., 2019) Hindistan, Yavuz Selim; Kiyakoğlu, Burhan Yasin; Rezaeinazhad, Arash Mohammadian; Korkmaz, Halil Ergun; Dağ, Hasan
With the bloom of financial technology and innovations aiming to deliver a high standard of financial services, banks and credit service companies, along with other financial institutions, use the most recent technologies available in a variety of ways from addressing the information asymmetry, matching the needs of borrowers and lenders, to facilitating transactions using payment services. In the long list of FinTechs, one of the most attractive platforms is the Peer-to-Peer (P2P) lending which aims to bring the investors and borrowers hand in hand, leaving out the traditional intermediaries like banks. The main purpose of a financial institution as an intermediary is of controlling risk and P2P lending platforms innovate and use new ways of risk assessment. In the era of Big Data, using a diverse source of information from spending behaviors of customers, social media behavior, and geographic information along with traditional methods for credit scoring prove to have new insights for the proper and more accurate credit scoring. In this study, we investigate the machine learning techniques on big data platforms, analyzing the credit scoring methods. It has been concluded that on a HDFS (Hadoop Distributed File System) environment, Logistic Regression performs better than Decision Tree and Random Forest for credit scoring and classification considering performance metrics such as accuracy, precision and recall, and the overall run time of algorithms. Logistic Regression also performs better in time in a single node HDFS configuration compared to a non-HDFS configuration.
Citation Count: 3
A Hybrid Approach With Gan and Dp for Privacy Preservation of Iiot Data
(IEEE-Inst Electrical Electronics Engineers Inc, 2023) Hindistan, Yavuz Selim; Yetkin, E. Fatih
There are emerging trends to use the Industrial Internet of Things (IIoT) in manufacturing and related industries. Machine Learning (ML) techniques are widely used to interpret the collected IoT data for improving the company's operational excellence and predictive maintenance. In general, ML applications require high computational resource allocation and expertise. Manufacturing companies usually transfer their IIoT data to an ML-enabled third party or a cloud system. ML applications need decrypted data to perform ML tasks efficiently. Therefore, the third parties may have unacceptable access rights during the data processing to the content of IIoT data that contains a portrait of the production process. IIoT data may include hidden sensitive features, creating information leakage for the companies. All these concerns prevent companies from sharing their IIoT data with third parties. This paper proposes a novel method based on the hybrid usage of Generative Adversarial Networks (GAN) and Differential Privacy (DP) to preserve sensitive data in IIoT operations. We aim to sustain IIoT data privacy with minimal accuracy loss without adding high additional computational costs to the overall data processing scheme. We demonstrate the efficiency of our approach with publicly available data sets and a realistic IIoT data set collected from a confectionery production process. We employed well-known privacy six assessment metrics from the literature and measured the efficiency of the proposed technique. We showed, with the help of experiments, that the proposed method preserves the privacy of the data while keeping the Linear Regression (LR) algorithms stable in terms of the R-Squared accuracy metric. The model also ensures privacy protection for hidden sensitive data. In this way, the method prevents the production of hidden sensitive data from the sub-feature sets.
Makine Öğrenmesinde Endüstriyel Veri Mahremiyetinin Üretken Düşman Ağları ve Diferansiyel Gizlilik Kullanarak Korunması
(2023) Hindistan, Yavuz Selim; Yetkin, Emrullah Fatih
Yapay Zeka (AI) ve Makine Öğreniminin (ML) hızla yaygınlaşması, mahremiyetin korunmasına ilişkin endişeleri artırdı. Bu teknolojiler, endüstriyel IoT, sosyal medya ve çevrimiçi platformlar gibi kaynaklardan kişisel ve hassas bilgiler içeren ve gizlilik riskleri getiren kapsamlı veri kümelerine dayanır. Güçlü gizlilik koruma önlemlerinin alınması, AI ve ML uygulama risklerini azaltmak için çok önemlidir. Bu tez, AI ve ML sistemlerinde gizliliğin korunmasını incelemektedir. Araştırmamız, ML doğruluğunu korurken bir gizlilik koruma yöntemi geliştirmek için herkese açık veri kümelerinden yararlandı. Gizliliği artırmak için, yaklaşımımızı Diferansiyel Gizlilik (DP) ve Üretken Düşman Ağları (GAN) ile güçlendirdik. Etkinliğini altı gizlilik ölçüsü kullanarak değerlendirdik. Yaklaşımımız, ML performansından ödün vermeden gizliliği koruyarak fizibilite ve etkinlik göstermektedir. Ayıklanan özellik alt kümeleri, ML modelleriyle hassas verileri açığa çıkarabildiğinden, gizli hassas bilgilerin ortaya çıkarılması vurgulanmıştır. Yöntemin mahremiyet endişelerini ele almadaki etkinliğini deneysel bir çalışmada gösteriyoruz. Bulgular, AI ve ML sistemlerinde gizliliğin anlaşılmasına katkıda bulunur. Araştırma, bilgileri korumak için içgörüler ve yaklaşımlar sunarak güvenilir ML sonuçları sağlar. Bu çalışma, gizlilik bilgisini ilerleterek, gizliliğin korunmasında AI ve ML teknolojilerinin sorumlu gelişimini destekler.