A5101704086- Manufacturing Process and Optimization
- Product Development and Customization
- Technology Assessment and Management
- Industrial Vision Systems and Defect Detection
- Building Energy and Comfort Optimization
- Design Education and Practice
- Textile materials and evaluations
- 3D Surveying and Cultural Heritage
- Structural Integrity and Reliability Analysis
- Digital Imaging for Blood Diseases
- Advanced Numerical Analysis Techniques
- Advanced Neural Network Applications
- Cellular and Composite Structures
- Probabilistic and Robust Engineering Design
- Quality Function Deployment in Product Design
- 3D Shape Modeling and Analysis
- Innovation Diffusion and Forecasting
- BIM and Construction Integration
- Brain Tumor Detection and Classification
Aja University of Medical Sciences
2024
Chalmers University of Technology
2023-2024
Jönköping University
2020-2023
Iran University of Science and Technology
2018-2019
Abstract Despite the recognized importance of datasets in data-driven design approaches, their extensive study remains limited. We review current landscape and highlight ongoing need for larger more comprehensive datasets. Three categories challenges dataset development are identified. Analyses show critical gaps process where future studies can be directed. Synthetic end-to-end suggested as two less explored avenues. The recent application Generative Pretrained Transformers (GPT) shows...
Today digital qualification tools are part of many design processes that make them dependent on long and expensive simulations, leading to limited ability in exploring alternatives. Conventional surrogate modelling techniques depend the parametric models come short addressing radical changes. Existing data-driven lack dealing with geometrical complexities. Thus, address resulting development lead time problem product enable parameter-independent modelling, this paper proposes a method use...
Many high-level technical products are associated with changing requirements, drastic design changes, lack of information, and uncertainties in input variables which makes their process iterative simulation-driven. Regression models have been proven to be useful tools during design, altering the resource-intensive finite element simulation models. However, building regression from computer-aided (CAD) parameters is challenges such as dealing too many low or coupled impact on studied outputs...
<title>Abstract</title> Background and Objectives: Recent advancements in artificial intelligence (AI), particularly through convolutional neural networks (CNNs), have led to significant progress tumor detection classification systems. This study aims train evaluate the performance of four distinct CNN models for 4-way glioma, meningioma, pituitary tumors, non-tumor magnetic resonance images (MRI). Methods Data augmentation techniques were applied MRI slices from Figshare, Br35H, Brain Tumor...