Hasan Dağ

Dağ, Hasan

Name Variants

D., Hasan
Dağ, HASAN
DAĞ, HASAN
Hasan, Dag
HASAN DAĞ
Hasan DAĞ
DAĞ, Hasan
Daǧ H.
Hasan Dağ
Dağ, H.
Dağ,H.
D.,Hasan
Dağ, Hasan
Dag H.
Dag,H.
Dağ H.
Dag,Hasan
Dag, Hasan
H. Dağ
Da?, Hasan

Job Title

Prof. Dr.

Email Address

hasan.dag@khas.edu.tr

ORCID ID

0000-0001-6252-1870

Scopus Author ID

6507328166

Full item page

Scholarly Output

83

Articles

17

Citation Count

238

Supervised Theses

24 Scholarly Output Search Results

Now showing 1 - 10 of 83

The Performance Wise Comparison of the Most Widely Used Nosql Databases
(Kadir Has Üniversitesi, 2015) Aladily, Ahmed; Dağ, Hasan; Dağ, Hasan
This work deals with the comparison of the most widely used noSQL databases. Chapter one deals in great details with the SQL databases and the noSQL databases including characteristics and the four types of noSQL databases, the second Chapter deals with the characteristics of the SQL and noSQL databases and the main differences between SQL databases and the noSQL databases. The third chapter deals with the architecture of the Couchdb, Mongodb, Cassandra, and Hbase. Chapter four deals with installation of the Couchdb, Mongodb, Cassandra and Hbase and Chapter five deals with analysis of the four noSQL databases and it also includes the performance wise comparison.
Power consumption estimation using in-memory database computation
(Institute of Electrical and Electronics Engineers Inc., 2016) Dag,H.; Dağ, Hasan; Alamin,M.
In order to efficiently predict electricity consumption, we need to improve both the speed and the reliability of computational environment. Concerning the speed, we use in-memory database, which is taught to be the best solution that allows manipulating data many times faster than the hard disk. © 2016 IEEE.
Heterosim: Heterogeneous Simulation Framework
(Association for Computing Machinery, 2009) Dursun, Taner; Dağ, Hasan; Dağ, Hasan
In order to arrange heterogeneous simulation executions it would be useful to have simulation tools that enable easy and fast creation of simulation sessions employing real-time software components beside simulation codes. Although there have been considerable amount of research activities in simulation community the current simulation tools are not sufficiently capable to support such a cooperation between components working in real-time and simulation-time. In this paper we propose a new approach for constructing hybrid simulations that leverages usage of simulation systems. We introduce HeteroSim a Java-based simulation system that can execute discrete event simulations by employing both simulation and real world software entities. This model is applicable to many software related areas such as scenario-based software testing and development of simulators. As a case study we are able to rely on this model in order to build simulations combining both the simulated elements of a High Performance Computing (HPC) system and already implemented elements of our Policy Based Management (PBM) framework so-called POLICE [1][2]. In this manner it may be possible to study the efficiency of POLICE on management of HPC systems.
Branch Outage Simulation Based Contingency Screening by Gravitational Search Algorithm
(Praise Worthy Prize Srl, 2012) Ceylan, Oğuzhan; Ceylan, Oğuzhan; Özdemir, Aydoğan; Dağ, Hasan; Dağ, Hasan; Özdemir, Serpil
Power systems contingency analysis is an important issue for electric power system operators. This paper performs branch outage simulation based contingency screening using a bounded network approach. Local constrained optimization problem representing the branch outage phenomena is solved by the gravitational search algorithm. The proposed method is applied to IEEE 14 30 57 and 118 Bus Test systems and its performance from the point of capturing violations is evaluated. In addition false alarms and the computational accuracy of the proposed method are also analyzed by using scattering diagrams. Finally the proposed gravitational search based contingency screening is compared with full AC load flow solutions from the point of computational speed. Copyright (C) 2012 Praise Worthy Prize S.r.l. - All rights reserved.
Applying Machine Learning Algorithms in Sales Prediction
(Kadir Has Üniversitesi, 2019) Sekban, Judi; Dağ, Hasan; Dağ, Hasan
Makine öğrenimi bir çok endüstride üzerinde yoğun çalışmalar yapılan bir konu olmuştur, ve neyse ki şirketler kendi problemlerini çözebilecek çeşitli machine learning yaklaşımları hakkında günden güne daha fazla bilgi sahibi oluyorlar. Fakat, farklı makine öğreniminin modellerinden en iyi şekilde sonuç almak ve verimli sonuçlara ulaşabilmek için, modellerin uygulanış biçimlerini ve verinin doğasını iyi anlamak gerekir. Bu tez, belli bir tahmin görevi için, uygulanan farklı makine öğreniminin algoritmalarını ne kadar iyi sonuç verdiklerini araştırır. Bu amaçla tez, 4 faklı algoritma, bir istifleme topluluğu tekniği ve modeli geliştirmek için belirli bir özelllik seçme yaklaşımı sunar ve uygular. Farklı konfigürasyonlar uygulayarak sonuçlar birbiriyle test edilir. Bütün bu işlemler, gerekli veri önislemeleri ve özellik mühendisliği adımları tamamlandıktan sonra yapılır.
Alternative Credit Scoring and Classification Employing Machine Learning Techniques on a Big Data Platform
(Institute of Electrical and Electronics Engineers Inc., 2019) Dağ, Hasan; 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.
Predicting Electricity Consumption Using Machine Learning Models With R and Python
(Kadir Has Üniversitesi, 2016) El Oraıby, Maryam; Dağ, Hasan; Dağ, Hasan
Electricity load forecasting has become an important field of interest in the last years. Antic- ipating the energy usage is vital to manage resources and avoid risk. Using machine learning techniques it is possible to predict the electricity consumption in the future with high accuracy. This study proposes a machine learning model for electricity usage prediction based on size and time. For that aim multiple predictive models are built and evaluated using two powerful open source tools for machine learning R and Python. The data set used for modeling is publicly accessible and contains real electrical data usage of industrial and commercial buildings from EnerNOC. This type of analysis falls within the electricity demand management.
Parallel Implementation Of Iterative Rational Krylov Methods For Model Order Reduction
(IEEE, 2010) Yetkin, E. Fatih; Dağ, Hasan; Dağ, Hasan; Yetkin, Emrullah Fatih
Model order reduction (MOR) techniques are getting more important in large scale computational tasks like large scale electronic circuit simulations. In this paper we present some experimental work on multiprocessor systems for rational Krylov methods. These methods require huge memory and computational power especially in large scale simulations. Therefore these methods are fairly suitable for parallel computing.
Mathematical Foundations and Implementation of Coniks Key Transparency
(Mdpi, 2024) Dağ, Hasan; Dag, Hasan; Dimitrova, Vesna
This research paper explores the CONIKS key management system's security and efficiency, a system designed to ensure transparency and privacy in cryptographic operations. We conducted a comprehensive analysis of the underlying mathematical principles, focusing on cryptographic hash functions and digital signature schemes, and their implementation in the CONIKS model. Through the use of Merkle trees, we verified the integrity of the system, while zero-knowledge proofs were utilized to ensure the confidentiality of key bindings. We conducted experimental evaluations to measure the performance of cryptographic operations like key generation, signing, and verification with varying key sizes and compared the results against theoretical expectations. Our findings demonstrate that the system performs as predicted by cryptographic theory, with only minor deviations in computational time complexities. The analysis also reveals significant trade-offs between security and efficiency, particularly when larger key sizes are used. These results confirm that the CONIKS system offers a robust framework for secure and efficient key management, highlighting its potential for real-world applications in secure communication systems.
Post-outage state estimations for outage management
(IFAC Secretariat, 2011) Ceylan, Oğuzhan; Dağ, Hasan; Ozdemir, Aydogan; Ceylan, Oğuzhan; Dağ, Hasan
Real time outage information is required to the utility operators for outage management process. In addition to some basic information regarding the outage post-outage system status will help to improve the response to outages and management of system reliability. This paper presents particle swarm optimization based reactive power estimations for branch outages. Post outage voltage magnitudes and reactive power flows results for IEEE 14 and IEEE 30 bus systems are given. Simulation results show that post outage voltage magnitudes and reactive power flows can be computed with a reasonable accuracy. © 2011 IFAC.