Bulut, Nazmiye Ebru

Profile URL
https://hdl.handle.net/20.500.13091/11578
Name Variants
Bulut, N. Ebru Bulut, Nazmiye Ebru Bulut, Nazmiye E. Bulut, N. E.
Job Title
Email Address
neduysak@ktun.edu.tr
Main Affiliation
08. Distance Education Application and Research Centre
Status
Current Staff
Website
ORCID ID
0000-0003-1918-7373
Scopus Author ID
Turkish CoHE Profile ID
15CEF1B20096EE91
Google Scholar ID
WoS Researcher ID

Research Topics

Domains
Physical Sciences
Fields
Computer ScienceEngineering
Subfields
Computer Vision and Pattern RecognitionComputational Mechanics
Specific Research Areas
Robotic Path Planning Algorithms
3D Shape Modeling and Analysis
Augmented Reality Applications
Advanced Numerical Analysis Techniques

Sustainable Development Goals

SDG data is not available
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.

Publication Collaboration

Affiliation Name Count
Bilecik Şeyh Edebali Üniversitesi 2
Konya Technical University 1
Texas A&M University 1
1 / 1
Data obtained from OpenAlex
Scholarly Output

2

Articles

2

Views / Downloads

5/14

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

2

Scopus Citation Count

3

Patents

0

Projects

0

WoS Citations per Publication

1.00

Scopus Citations per Publication

1.50

Open Access Source

2

Supervised Theses

0

JournalCount
IEEE Access1
Journal of The Faculty of Engineering and Architecture of Gazi University1
Current Page: 1 / 1

Scopus Quartile Distribution

Competency Cloud

GCRIS Competency Cloud

Filters

2022 - 20252

Settings

Author of Publication: search.filters.isAuthorOfPublication.8649ee85-bd42-401e-9dff-b576a013a066

Scholarly Output Search Results

Now showing 1 - 2 of 2
  • Article
    Simulation of Deformation on 3d Organ Models Using Haptic Device
    (Gazi Univ, Fac Engineering Architecture, 2022-10-07) Bulut, Nazmiye Ebru; Dandil, Emre
    Nowadays, thanks to the improvements in computer graphics, remarkable improvements are achieved in 3D modelling in virtual environment in accordance with many original objects. In medical training, simulators are developed to enable physician candidates to make numerous repetitions using various scenarios and gain practical experience. In this study, a simulator software platform is developed to simulate the deformation of 3D organ models with soft tissues such as gallbladder, kidney and spleen in the human body using deformation algorithms. In the system, firstly, organs are modelled in 3D according to their anatomical structure. On the developed simulator, feedback is provided by the deformation realized on the model when the organ is touched or applied tensile force by designed haptic device. In the study, mass spring method and molecular modelling method are used to obtain deformation 3D models. In addition, collision detection method is preferred to determine which point is contacted in the mesh structure modelled with haptic device. All simulations on the simulator developed in the study are performed in real time using the functions available in the OpenGL library. In addition, simulation feedbacks for 11 novice and expert users are collected from the simulator and the operation times obtained by the users for each soft tissue are compared. As a result of the experimental studies and statistical analysis, it has been seen that the collision detection, reaction force, real-time simulation and deformation results of the software platform are at the desired level visually and in terms of speed.
  • Article
    Citation - WoS: 2
    Citation - Scopus: 3
    Cmacgsa: Improved Gravitational Search Algorithm Based on Cerebellar Model Articulation Controller for Optimization
    (IEEE-Inst Electrical Electronics Engineers inc, 2025) Bulut, Nazmiye Ebru; Dandil, Emre; Yuzgec, Ugur; Duysak, Alpaslan; Ebru Bulut, Nazmiye
    Metaheuristic algorithms have gained significant attention in recent years for addressing complex and challenging optimization problems, especially in engineering. These algorithms often take inspiration from natural phenomena, systems or biological behaviour to find optimal solutions. Recent advances in the field often involve hybrid methods that combine several algorithms to improve performance. This study introduces an improved Gravitational Search Algorithm, named CMACGSA, which incorporates the Cerebellar Model Articulation Controller (CMAC)-a neural network model-to enhance the performance of Gravitational Search Algorithm (GSA). By employing the CMAC neural network, CMACGSA dynamically learns the masses of particles/agents of GSA, enabling a learning-driven approach to mass computation. Additional enhancements include L & eacute;vy mutation, boundary control methods and an error handling mechanism, which together improve the robustness and adaptability of the algorithm. The effectiveness of CMACGSA is demonstrated through extensive testing on a set of 2D CEC 2014 benchmark functions, where it significantly outperforms the original GSA. Further evaluations on multidimensional CEC 2014 test problems, including 30-dimensional cases, reveal improved performance over widely used optimization algorithms and state-of-the-art (SOTA) algorithms. Furthermore, CMACGSA consistently achieves top-tier average performance metrics when benchmarked against four well-established GSA variants. The applicability of the algorithm is further validated by engineering design problems where it demonstrates outstanding performance, confirming its value in solving complex engineering challenges.