Zincir, İbrahim

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Profile URL
https://hdl.handle.net/20.500.14365/7605
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
0000-0002-4910-7437
Scopus Author ID
55575855800
Turkish CoHE Profile ID
59FB866E58B3767E
Google Scholar ID
WoS Researcher ID
GKW-1357-2022
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Ibrahim_Zincir.jpg (9.92 KB)

Sustainable Development Goals

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DECENT WORK AND ECONOMIC GROWTH
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9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
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10

REDUCED INEQUALITIES
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PARTNERSHIPS FOR THE GOALS
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12

RESPONSIBLE CONSUMPTION AND PRODUCTION
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7

AFFORDABLE AND CLEAN ENERGY
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CLIMATE ACTION
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GOOD HEALTH AND WELL-BEING
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WoS Citation Count

2

Scopus Citation Count

3

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1

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0.67

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  • Thumbnail Image
    Master Thesis
    Su Akışı ve Su Kalitesi İndeksi (WQI) Tahmini için Uzun Kısa Süreli Bellek (lSTM) Derin Öğrenme Algoritmasının Uygulanması
    (2025) Tüler, Ali; Zincir, İbrahim
    Dünyanın 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.
  • Thumbnail Image
    Article
    Citation - WoS: 2
    Citation - Scopus: 2
    Architectural 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.
  • Thumbnail Image
    Article
    Citation - Scopus: 1
    Can We Detect Malicious Behaviours in Encrypted Dns Tunnels Using Network Flow Entropy?
    (River Publishers, 2022) Khodjaeva Y.; Zincir-Heywood N.; Zincir I.
    This 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.