Dağ, Hasan
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HASAN DAĞ
Hasan DAĞ
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Hasan Dağ
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D.,Hasan
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| Journal | Count |
|---|---|
| 2010 International Conference on Power System Technology | 2 |
| TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES | 2 |
| UBMK 2023 - Proceedings: 8th International Conference on Computer Science and Engineering -- 8th International Conference on Computer Science and Engineering, UBMK 2023 -- 13 September 2023 through 15 September 2023 -- Burdur -- 193873 | 2 |
| Computers & Security | 2 |
| 2009 Fifth International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control | 1 |
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Master Thesis Bulut Ortamlarında Güvenli Uygulama Dağıtımının Sağlanması(2025) Bostancı, Hakan; Dağ, HasanBulut bilişim, ölçeklenebilirlik, maliyet avantajı ve esneklik gibi faydaları nedeniyle modern BT altyapılarının ayrılmaz bir parçası haline gelmiştir. Organizasyonlar, operasyonel verimliliklerini artırmak, uzaktan çalışma süreçlerini desteklemek ve daha esnek iş modelleri oluşturmak için giderek daha fazla bulut çözümlerine yönelmektedir. Ancak, bu artan bağımlılık, güvenli uygulama teslimi açısından önemli güvenlik risklerini de beraberinde getirmektedir. Bulut bilişime olan adaptasyonun ve bağımlılığın artmasıyla birlikte, güvenli uygulama teslimini sağlamak kuruluşlar için kritik bir zorluk haline gelmiştir. Bulut ortamlarının dinamik yapısı ve gelişen siber tehditler, hassas verilerin korunması ve sistem bütünlüğünün sağlanması için güçlü güvenlik önlemlerini zorunlu kılmaktadır. Bu tez, bulut tabanlı uygulama tesliminde karşılaşılan temel güvenlik sorunlarını inceleyerek riskleri azaltmaya yönelik kapsamlı bir güvenlik yaklaşımı sunmaktadır.Çalışmada, uygulama güvenliği, API güvenliği, ağ güvenliği, veri şifreleme, kimlik ve erişim yönetimi ve gerçek zamanlı tehdit algılama mekanizmalarını entegre eden çok katmanlı bir güvenlik yaklaşımı benimsenmiştir. Bu yaklaşımda önerilen iyileştirmeler uygulamalı olarak gösterilmiştir. Microsoft Azure üzerinde dağıtılan bulut tabanlı bir e-ticaret uygulaması, güvenlik kontrollerinin uygulanması ve değerlendirilmesi için bir test ortamı olarak kullanılmıştır. Sistem direncini değerlendirmek için penetrasyon testleri, güvenlik açığı değerlendirmeleri ve DDoS ve saldırı simülasyonları gibi güvenlik test metodolojileri uygulanmıştır.Bulgular, geleneksel bulut dağıtımlarında önemli güvenlik açıklarının bulunduğunu ve proaktif güvenlik stratejilerinin gerekliliğini ortaya koymaktadır. Uygulama mimarisini güvenli hale getirme, ağ güvenliği, web uygulama güvenlik duvarı, ddos koruması, kimlik doğrulandırma ve yetkilendirme yönetimi gibi gelişmiş güvenlik önlemlerinin uygulanmasının ardından sistemin siber tehditlere karşı daha güçlü bir koruma sağladığı gözlemlenmiştir. Elde edilen sonuçlar, kuruluşların bulut tabanlı uygulamalarını etkili bir şekilde güvence altına almalarına yönelik uygulanabilir bilgiler sunarak bulut güvenliği alanına katkıda bulunmaktadır.Conference Object Towards Better Energy Efficiency Through Coil-Based Electricity Consumption Forecasting in Steel Manufacturing(IEEE, 2024) Koca, Asli; Erdem, Zeki; Dag, HasanForecasting electricity consumption with the possibly-highest accuracy is crucial for cost optimization, operational efficiency, competitiveness, contract negotiation, and achieving the global goals of sustainable development in steel manufacturing. This study focuses on identifying the most appropriate prediction algorithm for coil-based electricity consumption and the most effective implementation purposes in a steel company. Random Forest, Gradient-Boosted Trees, and Deep Neural Networks are preferred because they are suitable for the given problem and widely used for forecasting. The performance of the prediction models is evaluated based on the root mean squared error (RMSE) and the coefficient of determination (R-squared). Experiments show that the Random Forest model outperforms the Gradient-Boosted Trees and Deep Neural Network models. The results will provide benefits for many different purposes. Firstly, during contract negotiations, it will enable us to gain a competitive advantage when purchasing electricity in the day-ahead market. Secondly, in the production scheduling phase, the ones with the highest electricity consumption will be produced during the hours when there is the least demand at the most affordable prices. Finally, when prioritizing sales orders, the use of the existing capacity for orders with lower energy intensity or a higher profit margin will be ensured.Article Citation - WoS: 2Citation - Scopus: 2A New Preconditioner Design Based on Spectral Division for Power Flow Analysis(Praise Worthy Prize Srl, 2011) Dağ, Hasan; Yetkin, E. Fatih; Manguoglu, MuratSolution of large sparse linear systems is the most lime consuming part in many power system simulations. Direct solvers based on LU factorization although robust are known to have limited satiability on parallel platforms. Thus. Krylov subspace based iterative methods (i.e. Conjugate Gradient method Generalized Minimal Residuals (GMRES) method) can be used as alternatives. To achieve competitive performance and robustness however the Krylov subspace methods need a suitable preconditioner. In this work we propose a new preconditioner iterative methods which can be used in Newton-Raphson process of power flow analysis. The suggested preconditioner employs the basic spectral divide and conquer methods and invariant subspaces for clustering the eigenvalues of the Jacobian matrix appearing in Newton-Raphson steps of power flow simulation. To obtain the preconditioner we use Matrix Sign Function (MSF) and to obtain the MSF itself we use Sparse Approximate Inverse (SPAI) algorithm with Newton iteration. We compare the convergence characteristics of our preconditioner against the well-known black-box preconditioners such as incomplete-LU and SPAI. Copyright (C) 2011 Praise Worthy Prize S.r.l. - All rights reserved.Master Thesis The Social Return of Investment Metrics: Successful Social Media Measurement(Kadir Has Üniversitesi, 2017) Cengiz, Yasemin; Da?, HasanThe world economy has been formed based on needs, innovations, wars, and trends until today. Primary sector became stepping stone, industrial development forwarded economy to 2000's and now, today, internet is shaping it with accordance to trends. If someone is aiming for an investment, clicking for stock market or F/X rate is too simple, if someone is looking for renting a house, it takes a few minutes to conclude, if someone is surfing for online shopping, there are many options to the point there it became too easy to buy. Therefore, companies and firms have started to put an emphasis to e-commerce to edge ahead from the competitors. Nowadays, almost all brands/companies are presenting online sales services to consumers, and one of the keys for surviving in this huge market is managing social media networks and guiding consumers with these tools; Facebook, Twitter, Instagram, YouTube etc. Today, social media is becoming more than a place of entertainment and communication. Popular social media sites such as Facebook, Twitter and Instagram, which house millions of people from all corners of the world, have also become the focus of advertisers. Social media is now an essential part of our lives. Even when an employer recruits someone, he can first review the person's social media account and make various assessments. The employer can use the social media as a tool to earn money as the person chooses according to the social media account. Monetary counterparts have begun advertising through social circles, and the return of these ads to investors has been constantly researched and examined. Although advertisers and investors are removed from manipulated data from time to time, the results of the advertising campaign in social media can now be analyzed more healthily in various ways and the social media campaign ROI calculation process gives more accurate results. The results from the calculations to give you the return that you spend on advertising are called "ROI". The ROI, which is the abbreviation of Return of Investment, is a concept that tells what an invested investment is as a return to investor. With an ROI, you can see how much profit you make or how much you suffer from an investment you make. While the posters you put on a street can only be seen by people passing through that street, ads given to social media sites with a preference for successful targeting can be seen by all active users interested in that space. Social media ads offer more advantages in both measurement and return than classic ads. Social media ads offer more advantages in both measurement and return than classic ads. According to classical advertising concept; suppose you advertise on a billboard on the street, in a newspaper or on a television channel. It will take quite a long time to measure the return of this ad and you will have to spend as much money as you learn how to recycle these ads, and you will continue to invest in the same ad until you know you have failed because at first glance it is difficult to see that an advertising project failed. On the other hand, you can measure what an ad you give on social media brings back to you from the moment you advertise it. The return on each digital advertising campaign or ad investment you've made is crucial to your campaign metrics and future brand goals. Being able to measure your return on investment will help you in the same way that you will be new at the same time whether or not your campaign is efficient and where you should optimize your advertising campaigns. It is easy to measure in a certain way the budget of your budgets, such as mobile video ads, natural advertising, programmatic advertising, etc., as you try to make a new marketing move for your brand and go through digital channels. It may also be true on paper, but this excess base will be far less reflective of the rate of return on investment. You'll also need more granular metrics to capture the big picture, such as viewership, engagement, brand awareness, brand image, website visits, fill in lead forms, and downloads. Return on investment (ROI) is one of the most important factors for digital marketing as well as for traditional marketing because it tells us that our campaigns are not making money for us. If you do not earn it, it is a very critical operation to identify the causes and revise it to make it better. Of course, in order to be able to do all of these things, we need to efficiently measure the return on investment in our digital marketing campaigns. My research is, "The social return on investment metrics: successful social media measurement", and the time interval that I am observing resources and references is between 2010 to 2017.Master Thesis Bellek Tabanli Verı Platformların Karşılaştırması(Kadir Has Üniversitesi, 2016) Akbari, Amirmahdi; Dağ, HasanBellek tabanli verı platformların karşılaştırmasıDoctoral Thesis Random Capsule Network (capsnet) Forest Model for Imbalanced Malware Type Classification Task(Kadir Has Üniversitesi, 2021) Çayır, Aykut; Dağ, HasanBehavior of malware varies depending the malware types, which affect the strategies of the system protection software. Many malware classification models, empowered by machine and/or deep learning, achieve superior accuracy for predicting malware types. Machine learning-based models need to do heavy feature engineering work, which affects the performance of the models greatly. On the other hand, deep learning-based models require less effort in feature engineering when compared to that of the machine learning-based models. However, traditional deep learning architectures' components, such as max and average pooling, cause architecture to be more complex and the models to be more sensitive to data. The capsule network architectures, on the other hand, reduce the aforementioned complexities by eliminating the pooling components. Additionally, capsule network architectures based models are less sensitive to data, unlike the classical convolutional neural network architectures. This thesis proposes an ensemble capsule network model based on the bootstrap aggregating technique. The proposed method is tested on two widely used, highly imbalanced datasets (Malimg and BIG2015), for which the-state-of-the-art results are well-known and can be used for comparison purposes. The proposed model achieves the highest F-Score, which is 0.9820, for the BIG2015 dataset and F-Score, which is 0.9661, for the Malimg dataset. Our model also reaches the-state-of-the-art, using 99.7% lower the number of trainable parameters than the best model in the literature.Master Thesis Network Science : a Case of Bollywood(Kadir Has Üniversitesi, 2018) Yılmaz, Bahar; Da?, HasanBollywood is a regional movie industry that has made a name for itself globally. Over the years stars of Bollywood have amassed critical acclaim for their success. But what is Bollywood actually like as an industry? Are megastars like Shah Rukh Khan in fact as influential as they seem to be? The purpose of this study is to investigate Bollywood the movie industry in Mumbai india from a network perspective. Taking advantage of graph theory data science and network science a list of connections between actors is constructed out of iMDb’s film data. The list has been subjected to processing and graph visualization algorithms to construct a network graph. in this paper the network of Bollywood is treated as a case study. The question of how actors of international status are situated within the network is addressed. Following the discussion on the network and its resulting implications potential future work on the matter is considered.Conference Object A Comprehensive Review of Open Source Intelligence in Intelligent Transportation Systems(Ieee Computer Soc, 2024) Ucar, Bilal Emir; Ecevit, Mert Ilhan; Dag, Hasan; Creutzburg, ReinerThis paper offers an insightful review of Open Source Intelligence (OSINT) within Intelligent Transportation Systems (ITS), emphasizing its heightened importance amidst the digital and connected evolution of the transportation sector. It highlights the integration of technologies like IoT and SCADA systems, which, while beneficial, introduce new cyber vulnerabilities. Focusing on the utilization of OSINT for surveillance, threat detection, and risk assessment, the study evaluates key tools such as Shodan and Aircrack-ng, addressing their roles in enhancing transportation system security. The paper also tackles challenges in OSINT application, from data reliability to ethical and legal considerations, stressing the need for a balance between technological advancement and privacy protection. Through realworld case studies, the paper illustrates OSINT's practical applications in scenarios like maritime security and military surveillance. Conclusively, it underscores the necessity for continuous dialogue among experts to navigate the complexities of OSINT in transportation, particularly as technology evolves and data volumes increase.Book Part Citation - Scopus: 12Comparison of Post Outage Bus Voltage Magnitudes Estimated by Harmony Search and Differential Evolution Methods(2009) Ceylan, Oğuzhan; Özdemir, Aydoğan; Dağ, HasanContingency studies are indispensable tools of both the power system planning and operational studies. Real time implementation of operational problems makes necessary the use of high speed computational methods while requiring reasonable accuracies. On the other hand, accuracy of the results and the speed of calculation depend on branch outage modeling as well as solution algorithm used. This paper presents a comparison of post outage bus voltage magnitudes calculated by two meta-heuristic approaches; namely differential evolution (DE) and harmony search (HS) methods. The methods are tested on IEEE 14, IEEE 30, IEEE 57, and IEEE 118 bus test systems and the results are compared both in terms of accuracy and calculation speed.Conference Object Citation - WoS: 3Citation - Scopus: 12Improving Item-Based Recommendation Accuracy with User's Preferences on Apache Mahout(IEEE, 2016) Jabakji, Ammar; Dağ, HasanRecommendation systems play a critical role in the Information Science application domain especially in e-commerce ecosystems. In almost all recommender systems statistical methods and machine learning techniques are used to recommend items to the users. Although the user-based collaborative filtering approaches have been applied successfully in many different domains some serious challenges remain especially in regards to large e-commerce sites for recommender systems need to manage millions of users and millions of catalog products. In particular the need to scan a vast number of potential neighbors makes it very hard to compute predictions. Many researchers have been trying to come up with solutions like using neighborhood-based collaborative filtering algorithms model-based collaborative filtering algorithms and text mining algorithms. Others have proposed new methods or have built various architectures/frameworks. In this paper we proposed a new data model based on users'preferences to improve item-based recommendation accuracy by using the Apache Mahout library. We also present details of the implementation of this model on a dataset taken from Amazon. Our experimental results indicate that the proposed model can achieve appreciable improvements in terms of recommendation quality.
