Zincir, İbrahim
Loading...
Profile URL
Name Variants
Zincir, Ibrahim
Job Title
Email Address
ibrahim.zincir@ieu.edu.tr
Main Affiliation
05.04. Software Engineering
Status
Current Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
1NO POVERTY
0
Research Products
2ZERO HUNGER
1
Research Products
3GOOD HEALTH AND WELL-BEING
0
Research Products
4QUALITY EDUCATION
0
Research Products
5GENDER EQUALITY
0
Research Products
6CLEAN WATER AND SANITATION
1
Research Products
7AFFORDABLE AND CLEAN ENERGY
0
Research Products
8DECENT WORK AND ECONOMIC GROWTH
0
Research Products
9INDUSTRY, INNOVATION AND INFRASTRUCTURE
3
Research Products
10REDUCED INEQUALITIES
0
Research Products
11SUSTAINABLE CITIES AND COMMUNITIES
1
Research Products
12RESPONSIBLE CONSUMPTION AND PRODUCTION
1
Research Products
13CLIMATE ACTION
1
Research Products
14LIFE BELOW WATER
1
Research Products
15LIFE ON LAND
0
Research Products
16PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
17PARTNERSHIPS FOR THE GOALS
0
Research Products
Documents
16
Citations
120
h-index
4
Documents
14
Citations
74
Scholarly Output
7
Articles
2
Views / Downloads
2/5
Supervised MSc Theses
1
Supervised PhD Theses
0
WoS Citation Count
2
Scopus Citation Count
7
Patents
0
Projects
0
WoS Citations per Publication
0.29
Scopus Citations per Publication
1.00
Open Access Source
2
Supervised Theses
1
| Journal | Count |
|---|---|
| 7th International Symposium on Formal Methods in Architecture-FMA -- DEC 03-06, 2024 -- Porto, PORTUGAL | 1 |
| Advances in Intelligent Systems and Computing | 1 |
| Building Research and Information | 1 |
| GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion | 1 |
| Journal of Cyber Security and Mobility | 1 |
Current Page: 1 / 2
Scopus Quartile Distribution
Competency Cloud
7 results
Scholarly Output Search Results
Now showing 1 - 7 of 7
Conference Object Citation - Scopus: 3Exploring an Artificial Arms Race for Malware Detection(Association for Computing Machinery, Inc, 2020) Wilkins Z.; Zincir I.; Zincir-Heywood N.; Wilkins, Zachary; Zincir-Heywood, Nur; Zincir, IbrahimThe Android platform commands a dramatic majority of the mobile market, and this popularity makes it an appealing target for malicious actors. Android malware is especially dangerous because of the versatility in distribution and acquisition of software on the platform. In this paper, we continue to investigate evolutionary Android malware detection systems, implementing new features in an artificial arms race, and comparing different systems' performances on three new datasets. Our evaluations show that the artificial arms race based system achieves the overall best performance on these very challenging datasets. © 2020 ACM.Conference Object Automated Two-Story Housing Floor Plan Generation Using Generative Adversarial Networks(Springer International Publishing AG, 2025) Yildiz, Berfin; Cagda, Gillen; Zincir, IbrahimAutomating the generation of two-story housing floor plans has emerged as a significant area of focus in architectural design research, driven by the need to enhance efficiency, creativity, and functionality in the design process. This study introduces a GAN-based framework for the automated generation of two-story housing layouts, incorporating architectural constraints such as functional zoning, multi-level connectivity, open-plan configurations, and visual relationships. By leveraging advanced deep learning techniques, the proposed framework achieves a balance between design creativity and practical functionality, addressing the unique challenges posed by multi-level spatial arrangements. The results demonstrate the model's ability to generate diverse and coherent floor plans that effectively meet the complexities of two-story layouts. This research underscores the transformative potential of deep learning models in architectural design, while acknowledging existing limitations in managing multi-level spatial relationships and user interaction. With continued advancements, AI has the potential to play a pivotal role in supporting architects-optimizing workflows, enabling creative exploration, and fostering user-centered, innovative designs. Ultimately, this work sets the stage for further progress in automated multi-story housing design, paving the way for a more collaborative and technology-driven architectural future.Article Citation - WoS: 2Citation - Scopus: 2Architectural Space Classification Considering Topological and 3d Visual Spatial Relations Using Machine Learning Techniques(Routledge, 2023) Yıldız, B.; Çağdaş, G.; Zincir, I.The paper presents a novel method for classifying architectural spaces in terms of topological and visual relationships required by the functions of the spaces (where spaces such as bedrooms and bathrooms have less visual and physical relationships due to the privacy, while common spaces such as living rooms have higher visual relationship and physical accessibility) through machine learning (ML). The proposed model was applied to single and two-storey residential plans from the leading architects of the 20th century Among the five different ML models whose performances were evaluated comparatively, the best results were obtained with Cascade Forward Neural Networks (CFNN), and the average model success was calculated as 93%. The features affecting the classification models were examined based on SHAP values and revealed that width, control, 3D visibility and 3D natural daylight luminance were among the most influential. The results of five different ML models indicated that the use of topological and 3D visual relationship features in the automated classification of architectural space function can report very high levels of classification accuracy. The findings show that the classification model can be an important part of developing more efficient and adaptive floor plan design, building management and effective reuse strategies. © 2023 Informa UK Limited, trading as Taylor & Francis Group.Conference Object An AI-Supported Hospital Laundry Processing System (AIHLPS)(Institute of Electrical and Electronics Engineers Inc., 2025) Kurtel, Kaan; Celikkan, Ufuk; Zincir, IbrahimConference Object Citation - Scopus: 1Binary Text Representation for Feature Selection(Springer Science and Business Media Deutschland GmbH, 2021) Lang N.; Zincir I.; Zincir-Heywood N.; Lang, Nguyen; Zincir-Heywood, Nur; Zincir, IbrahimIn many real-world applications, a high number of words could result in noisy and redundant information, which could degrade the general performance of text classification tasks. Feature selection techniques with the purpose of eliminating uninformative words have been actively studied. In several information-theoretic approaches, such features are conventionally obtained by maximizing relevance to the class while the redundancy among the features used is minimized. This is an NP-hard problem and still remains to be a challenge. In this work, we propose an alternative feature selection strategy on binary representation data, with the purpose of providing a theoretical lower bound for finding a near optimal solution based on the Maximum Relevance-Minimum Redundancy criterion. In doing so, the proposed strategy can achieve a theoretical approximation ratio of 12 by a naive greedy search. The proposed strategy is validated by empirical experiments on five publicly available datasets, namely, Cora, Citeseer, WebKB, SMS Spam and Spambase. Their effectiveness is shown for binary text classification tasks when compared with well-known filter feature selection methods and mutual information-based methods. © 2021, Springer Nature Switzerland AG.Master Thesis Su Akışı ve Su Kalitesi İndeksi (WQI) Tahmini için Uzun Kısa Süreli Bellek (lSTM) Derin Öğrenme Algoritmasının Uygulanması(2025) Tüler, Ali; Zincir, İbrahimDünyanın oluşumundan bu yana su kaynakları, doğal yaşam ve insan varlığı için hayati bir öneme sahiptir. Tüm canlılar bu kaynaklara bağımlı olarak yaşamlarını sürdürmektedir. Ancak günümüzde su kaynakları, küresel ısınma, doğal afetler ve insan kaynaklı etkenler nedeniyle ciddi tehditlerle karşı karşıyadır. Hızlı sanayileşme, artan fabrika sayısı ve bilinçsiz su kullanımı, mevcut suyun miktarını ve kalitesini önemli ölçüde olumsuz etkilemektedir. Su kaynakları yalnızca içme amacıyla değil; tarım, balıkçılık ve rekreasyonel faaliyetler gibi birçok alanda da kritik öneme sahiptir. Bu nedenle, söz konusu kaynakların doğru ve sürdürülebilir kullanımı için küresel ölçekte, özel modeller ve çerçevelerle desteklenen çalışmalar yürütülmektedir. Debi ölçümü; biyolojik ve meteorolojik süreçlerin anlaşılması, endüstriyel deneylerin yürütülmesi ve hidrolik modellemelerin gerçekleştirilmesinde önemli bir rol oynamaktadır. Ayrıca, yıllar içinde su akışındaki değişimlerin izlenmesi, kayıp ya da artışların erken tespitiyle uzun vadeli sürdürülebilirlik adına önlem alınmasını mümkün kılmaktadır. Bu tezde, debi tahmini ve Su Kalitesi İndeksi (WQI) öngörüsü için LSTM tabanlı Derin Öğrenme yaklaşımı sunulmuştur. Ayrıca, bölgesel su kalitesi değerlendirmesi için özgün bir model mimarisi önerilmiştir. Bu yöntem, hem yüksek doğrulukta debi tahmini sağlamakta hem de yenilikçi ve yerelleştirilmiş bir su kalitesi değerlendirme yaklaşımı sunmaktadır. Her iki görevde elde edilen güçlü sonuçlar, karmaşık zaman serisi desenlerini yakalama konusunda LSTM'nin başarısını ve özelleştirilmiş modelimizin sürdürülebilir su yönetimi için etkili çözümler sunduğunu ortaya koymaktadır.Article Citation - Scopus: 1Can We Detect Malicious Behaviours in Encrypted Dns Tunnels Using Network Flow Entropy?(River Publishers, 2022) Khodjaeva Y.; Zincir-Heywood N.; Zincir I.; Khodjaeva, Yulduz; Zincir-Heywood, Nur; Zincir, IbrahimThis paper explores the concept of entropy of a flow to augment flow statistical features for encrypted DNS tunnelling detection, specifically DNS over HTTPS traffic. To achieve this, the use of flow exporters, namely Argus, DoHlyzer and Tranalyzer2 are studied. Statistical flow features automatically generated by the aforementioned tools are then augmented with the flow entropy. In this work, flow entropy is calculated using three different techniques: (i) entropy over all packets of a flow, (ii) entropy over the first 96 bytes of a flow, and (iii) entropy over the first n-packets of a flow. These features are provided as input to ML classifiers to detect malicious behaviours over four publicly available datasets. This model is optimized using TPOT-AutoML system, where the Random Forest classifier provided the best performance achieving an average F-measure of 98% over all testing datasets employed. © 2022 River Publishers.
