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All Journal The Indonesian Journal of Computer Science
Hawar Bahzad Ahmad
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Teaching-Learning-Based Optimization Algorithm for Pressure Vessel Design Problem Hawar Bahzad Ahmad; Danial William Odeesho; Reving Masoud Abdulhakeem; Merdin Shamal Salih; Zebari, Dilovan
The Indonesian Journal of Computer Science Vol. 14 No. 5 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i5.5004

Abstract

This paper presents the utilization of the Teaching-Learning-Based Optimization (TLBO) algorithm to tackle the intricate problem of pressure vessel design. Design optimization for pressure vessels holds a critical role in various engineering domains, demanding effective techniques for achieving designs that are both optimal and safe. The TLBO algorithm, drawing inspiration from the dynamics of teaching and learning, offers a unique approach by amalgamating exploration and exploitation strategies. In this research, we investigate the incorporation of TLBO within the realm of pressure vessel design, with the objective of improving design efficiency while strictly adhering to demanding safety and performance benchmarks. Through a comprehensive assessment, we analyze the performance of TLBO in generating optimal designs and draw comparisons with established optimization methods. Our findings underscore the proficiency of TLBO in effectively converging towards competitive solutions, thus highlighting its potential to bring about a paradigm shift in the domain of pressure vessel design optimization. This paper underscores the importance of the Teaching-Learning-Based Optimization algorithm as a transformative instrument, providing invaluable insights for researchers, practitioners, and experts involved in fields such as structural engineering, optimization, and related disciplines.
Hybrid Transfer Learning Model for Facial Attractiveness Prediction Hawar Bahzad Ahmad; Abdulazeez , Adnan Mohsin
The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i6.5018

Abstract

Prediction of facial attractiveness greatly depends on the subjective terminology applied according to the diverse cultural, social and psychological considerations. This task is important for applications in many fields, such as aesthetics, entertainment, wardrobe recommendations, etc., and requires accurate and robust models. Current methods predominantly adopt a single model, which is unable to learn the diverse attributes that can influence the quality of facial beauty. In order to overcome these challenges, this study proposes a hybrid transfer learning framework for feature extraction and prediction that combines ResNet50 and InceptionV3. In this methodology, Multi-task Cascaded Convolutional Networks (MTCNN) is used for accurate face detection and preprocessing, then features extraction is done using pretrained ResNet50 and InceptionV3 architectures. The features extracted are then normalized and fused together and passed through a dense classification layer with application of dropouts and regularization in order to make the model robust. The CelebA dataset was used to train the model, utilizing class weights to account for imbalanced data and callbacks to optimize performance. Test accuracy and F1 Score of the proposed model is found to be 83.58% and 0.8384 respectively, which shows good generalization on unseen data. The validation frames the performance of the hybrid framework which leverages the complementary strengths of multiple CNNs, and thus provides robust performance.
Co-Authors Abdulazeez , Adnan Mohsin Danial William Odeesho Merdin Shamal Salih Reving Masoud Abdulhakeem Zebari, Dilovan