Dağ, Hasan
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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
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
Main Affiliation
Management Information Systems
Status
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Scholarly Output
83
Articles
17
Citation Count
238
Supervised Theses
24
83 results
Scholarly Output Search Results
Now showing 1 - 10 of 83
Master Thesis The Performance Wise Comparison of the Most Widely Used Nosql Databases(Kadir Has Üniversitesi, 2015) Aladily, Ahmed; Dağ, Hasan; Dağ, HasanThis 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.Conference Object Citation - Scopus: 0Power 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.Master Thesis Audio detection using machine learning & transfer learning models(Kadir Has Üniversitesi, 2021) Acar, Mesut; Dağ, Hasan; Dağ, HasanIn this paper, using datasets ESC-50 & ESC-10 of environmental sounds, machine learning algorithms, and feature extraction methods are used to develop recognition performance. K-NN, SVM, Random Forest are used for comparing the recognition results. The different feature extraction methods in the literature are used to get more meaningful attributes from these datasets and obtain a higher accuracy rate. This approach shows that SVM algorithm has a significantly good result with accuracy scores. The best accuracy scores obtained by classic machine learning algorithms are %42,15 for ESC-50 and %77,7 for ESC-10. In addition to this, the experiments have been done with a pre-trained ResNet neural network as a backbone, which achieves successful results despite the machine learning models. In this study, a higher accuracy rate is achieved from baseline machine learning algorithms in literature and using transfer learning with pre-trained Resnet backbones to reach some state of art results. The accuracy scores are %68,95 for ESC-50 and %87,25 for ESC-10.Conference Object Citation - WoS: 0Parallel Implementation Of Iterative Rational Krylov Methods For Model Order Reduction(IEEE, 2010) Yetkin, E. Fatih; Dağ, Hasan; Dağ, Hasan; Yetkin, Emrullah FatihModel 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.Article Citation - WoS: 0Citation - Scopus: 0Mathematical Foundations and Implementation of Coniks Key Transparency(Mdpi, 2024) Dağ, Hasan; Dag, Hasan; Dimitrova, VesnaThis 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.Conference Object Citation - Scopus: 5Double Branch Outage Modeling and Its Solution Using Differential Evolution Method(2011) Ceylan, Oğuzhan; Dağ, Hasan; Ozdemir, Aydogan; Ceylan, Oğuzhan; Dağ, HasanPower system operators need to check the system security by contingency analysis which requires power flow solutions repeatedly. AC power flow is computationally slow even for a moderately sized system. Thus fast and accurate outage models and approximated solutions have been developed. This paper adopts a single branch outage model to a double branch outage one. The final constrained optimization problem resulted from modeling is then solved by using differential evolution method. Simulation results for IEEE 30 and 118 bus test systems are presented and compared to those of full AC load flow in terms of solution accuracy. © 2011 IEEE.Conference Object Citation - WoS: 0Power Consumption Estimation using In-Memory Database Computation(Ieee, 2016) Dag, Hasan; Dağ, Hasan; Alamin, MohamedIn order to efficiently predict electricity consumption, we need to improve both the speed and the reliability of computational environment. Concerning the speed, we use inmemory database, which is taught to be the best solution that allows manipulating data many times faster than the hard disk. For reliability, we use machine learning algorithms. Since the model performance and accuracy may vary depending on data each time, we test many algorithms and select the best one. In this study, we use SmartMeter Energy Consumption Data in London Households to predict electricity consumption using machine learning algorithms written in Python programming language and in-memory database computation package, Aerospike. The test results show that the best algorithm for our data set is Bagging algorithm. We also emphatically prove that R-squared may not always be a good test to choose the best algorithm.Article Citation - WoS: 20Citation - Scopus: 28An ensemble of pre-trained transformer models for imbalanced multiclass malware classification(Elsevier Advanced Technology, 2022) Dağ, Hasan; Demirkıran, Ferhat; Unal, Gur; Dag, HasanClassification of malware families is crucial for a comprehensive understanding of how they can infect devices, computers, or systems. Hence, malware identification enables security researchers and incident responders to take precautions against malware and accelerate mitigation. API call sequences made by malware are widely utilized features by machine and deep learning models for malware classification as these sequences represent the behavior of malware. However, traditional machine and deep learning models remain incapable of capturing sequence relationships among API calls. Unlike traditional machine and deep learning models, the transformer-based models process the sequences in whole and learn relationships among API calls due to multi-head attention mechanisms and positional embeddings. Our experiments demonstrate that the Transformer model with one transformer block layer surpasses the performance of the widely used base architecture, LSTM. Moreover, BERT or CANINE, the pre-trained transformer models, outperforms in classifying highly imbalanced malware families according to evaluation metrics: F1-score and AUC score. Furthermore, our proposed bagging-based random transformer forest (RTF) model, an ensemble of BERT or CANINE, reaches the state-of-the-art evaluation scores on the three out of four datasets, specifically it captures a state-of-the-art F1-score of 0.6149 on one of the commonly used benchmark dataset. (C) 2022 Elsevier Ltd. All rights reserved.Conference Object Citation - WoS: 0On the Selection of Interpolation Points for Rational Krylov Methods(Springer-Verlag Berlin, 2012) Yetkin, E. Fatih; Dağ, Hasan; Dağ, Hasan; Yetkin, Emrullah FatihWe suggest a simple and an efficient way of selecting a suitable set of interpolation points for the well-known rational Krylov based model order reduction techniques. To do this some sampling points from the frequency response of the transfer function are taken. These points correspond to the places where the sign of the numerical derivation of transfer function changes. The suggested method requires a set of linear system's solutions several times. But they can be computed concurrently by different processors in a parallel computing environment. Serial performance of the method is compared to the well-known H-2 optimal method for several benchmark examples. The method achieves acceptable accuracies (the same order of magnitude) compared to that of H-2 optimal methods and has a better performance than the common selection procedures such as linearly distributed points.Conference Object Citation - Scopus: 1Heterosim: Heterogeneous Simulation Framework(Association for Computing Machinery, 2009) Dursun, Taner; Dağ, Hasan; Dağ, HasanIn 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.
