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

Main Affiliation

Management Information Systems

Sustainable Development Goals Report Points

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Scholarly Output

85

Articles

17

Citation Count

238

Supervised Theses

26 Scholarly Output Search Results

Now showing 1 - 10 of 85

Citation - Scopus: 0
Power consumption estimation using in-memory database computation
(Institute of Electrical and Electronics Engineers Inc., 2016) Dag,H.; Dağ, Hasan; Alamin,M.; Management Information Systems
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.
The Performance Wise Comparison of the Most Widely Used Nosql Databases
(Kadir Has Üniversitesi, 2015) Aladily, Ahmed; Dağ, Hasan; Dağ, Hasan; Management Information Systems
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.
Citation - WoS: 0
On the Selection of Interpolation Points for Rational Krylov Methods
(Springer-Verlag Berlin, 2012) Yetkin, E. Fatih; Dağ, Hasan; Dağ, Hasan; Yetkin, Emrullah Fatih; Business Administration; Management Information Systems
We 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.
Citation - Scopus: 5
Double Branch Outage Modeling and Its Solution Using Differential Evolution Method
(2011) Ceylan, Oğuzhan; Dağ, Hasan; Ozdemir, Aydogan; Ceylan, Oğuzhan; Dağ, Hasan; Management Information Systems
Power 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.
Citation - WoS: 0
Power Consumption Estimation using In-Memory Database Computation
(Ieee, 2016) Dag, Hasan; Dağ, Hasan; Alamin, Mohamed; Management Information Systems
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 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.
Citation - WoS: 25
Citation - Scopus: 41
An ensemble of pre-trained transformer models for imbalanced multiclass malware classification
(Elsevier Advanced Technology, 2022) Dağ, Hasan; Demirkıran, Ferhat; Unal, Gur; Dag, Hasan; Management Information Systems
Classification 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.
Citation - WoS: 4
Citation - Scopus: 5
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; Management Information Systems
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.
Web Page Redesign With the User Experience Component of Usability: the Tksd Case Study
(Kadir Has Üniversitesi, 2020) Bağan, Burcu; Dağ, Hasan; Dağ, Hasan; Management Information Systems
User experience design changes the perspective of human-computer interaction in product design with the concept of usability. The availability of the product makes the company stand out in the market in its field and increases the satisfaction rate of the user. Nowadays, companies use their web sites as one of the tools of promotion themselves in the market. In this context, the applicability of the concept of usability in web site designs has started to gain importance. The company focuses on the user in the design of the web page, collects data about the target audience through usability testing and makes product design. This thesis was designed to measure the usability of a web page by using usability testing, one of the user experience research methods. During the test planning process, "Handbook of Usability Testing (Second Edition): How to Plan, Design and Conduct Effective Tests (2 ed)" was chosen as a guide. The test was carried out with two different focus groups with a total of 30 participants using the unmoderated-remote test method. In the unmoderated-remote test design, Google sheet forms open source automation software testing was used. Paper-based test was used to measure the demographic profile of the participants before the usability test. According to the results of the test, web page usability can be improved in terms of loading speed, visual and social media. According to the findings, solution suggestions were made and a sample wireframe design was made. Keywords: Human–Computer Interaction, User Experience, System Usability, Usability Testing, Web Page Redesign, Web Page Maintenance, Software Testing, TKSD
Citation - Scopus: 2
Post Outage Bus Voltage Calculations for Double Branch Outages
(IEEE, 2012) Ceylan, Oğuzhan; Dağ, Hasan; Ozdemir, Aydogan; Ceylan, Oğuzhan; Dağ, Hasan; Management Information Systems
Secure operation of electrical power systems is vital hence fast and accurate post-outage state calculations are important for contingency analysis. Contingency analysis includes simulations of both the single and double branch outages. This paper presents constrained optimization problem of a recently developed double branch outage model. Harmony search algorithm is used as an optimization tool. IEEE 30 Bus Test system simulation results are given and compared with those of the AC load flow in terms of computational accuracy. Speed test results of IEEE 14 30 57 118 and 300 Bus Test Systems are illustrated and compared with those of the AC load flow calculations. © 2012 IEEE.
Citation - Scopus: 7
Investigation of Cyber Situation Awareness Via Siem Tools: a Constructive Review
(Institute of Electrical and Electronics Engineers Inc., 2021) Ünal, U.; Dağ, Hasan; Kahya, C.N.; Kurtlutepe, Y.; Dağ, H.; Management Information Systems
Awareness, in the sense of security, builds the backbone of operations understanding the current and future cyber activities. Situation awareness has become the focal point of securing systems due to dynamic nature of cyber domain. Technological advancements cause the volatility to transform into upcoming challenges. Understanding those is the key to keep cyber Situation Awareness (SA) progression. Earlier studies define required steps to administer cyber SA. These steps (perceive, comprehend, project, and resolve) are also adapted to cyber domain. Rapid technological changes redefine the content of those and thus, it creates demands improving automated tools, which play as systematic factor in nurturing SA. As a system factor, SIEM tools can be basis for comprehending cyber domain. In this work, we investigate recent studies contributed mainly to SIEM (Security Information and Event Management) tool’s enhancement to evaluate current state and help predict upcoming challenges for maintaining awareness. We use various criteria in our investigation such as; architecture improvement, affected SIEM process, utilized CTI (Cyber Threat Intelligence) artefact, implementation area, and type of produced result. In doing so, we aim to impart upward trends on CSA (Cyber Situation Awareness) to academia and industry professionals. © 2021 IEEE