Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Control Systems and Optimization Letters
Asaad, Ameer Yalmaz
Unknown Affiliation
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
A Particle Swarm Optimization-Enhanced Support Vector Regression Model for Accurate Prediction of Concrete Compressive Strength Using Slump Test Data Al-Rammahi, Hussein; Asaad, Ameer Yalmaz
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.224

Abstract

This paper proposes a hybrid machine learning model combining Radial Basis Function (RBF) kernel-based Support Vector Regression (SVR) with Particle Swarm Optimization (PSO) to predict the compressive strength of concrete using slump test data. Conventional methods rely on labor- and resource-intensive destructive testing, posing challenges for large-scale projects. To address this, SVR models the nonlinear slump-strength relationship, while PSO (swarm size=50, 100 iterations) automates hyperparameter tuning. The SVR-PSO model is benchmarked against Decision Trees, Neural Networks, K-Nearest Neighbors (KNN), and Naïve Bayes, evaluated using R², MAE, MAPE, and RMSE. Results show SVR-PSO achieves  and the lowest error rates, reducing prediction costs by up to 40% compared to traditional testing. Limitations include the model’s validation on a specific concrete mix dataset; generalizability to broader formulations requires further study. For reproducibility, code and data will be made publicly available. This work demonstrates how PSO-optimized SVR enables faster, cost-effective strength estimation, supporting data-driven decisions in civil engineering.
Enhanced Compressive Strength Prediction of Slump Concrete Using a Hybrid Support Vector Regression-Genetic Algorithm Model Al-Rammahi, Hussein; Asaad, Ameer Yalmaz
Control Systems and Optimization Letters Vol 2, No 3 (2024)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v2i3.223

Abstract

This paper proposes a hybrid predictive model that integrates Support Vector Regression (SVR) with a Genetic Algorithm (GA) to estimate the compressive strength of concrete using slump test data, thereby offering an alternative to conventional, resource-intensive laboratory testing. The employed dataset encapsulates the nonlinear relationship between concrete slump and compressive strength. Given the sensitivity of SVR to hyperparameter selection, its standalone application yielded suboptimal predictive performance. To mitigate this, GA was utilized for hyperparameter optimization, selected for its effectiveness in global search and handling complex parameter spaces compared to traditional optimization techniques. The SVR-GA model was systematically evaluated against established machine learning algorithms, including Decision Tree, Neural Network, Naïve Bayes, and K-Nearest Neighbors, chosen based on their prevalence and diverse methodological characteristics. Performance evaluation incorporated robust validation methods to prevent overfitting and ensure generalizability. The results indicate that the proposed model delivers rapid and accurate predictions, suitable for practical, on-site application, with the potential to significantly reduce time and costs associated with traditional testing. Limitations related to dataset specificity and model generalizability are acknowledged. Future research directions include extending the framework to additional concrete properties and the development of real-time predictive systems. A schematic representation of the SVR-GA integration is included.
A Proposal for Designing a Novel Blockchain-Based Data Provisioning Mechanism for IoT Integration in Smart Applications Al-Rammahi, Hussein; Asaad, Ameer Yalmaz
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.222

Abstract

The Internet of Things (IoT) presents unique communication and security challenges due to the diverse nature and sheer volume of connected devices. Traditional communication solutions are often insufficient to meet these demands. This paper addresses the critical need for robust security in IoT by proposing a novel, blockchain-based mechanism for secure data provision in smart applications. While security is frequently overlooked in IoT in favor of application and hardware development, distributed ledger technology (blockchain) offers a promising solution. Beyond its role in cryptocurrencies, blockchain offers inherent capabilities for device identity, secure data transfer, and immutable data storage, all within a decentralized and transparent system that provides auditable cryptographic proofs. This paper aims to thoroughly analyze blockchain technology and evaluate prominent frameworks such as Ethereum and Bitcoin. We will examine the distinct features and target use cases of these frameworks to determine the most suitable blockchain architecture for the IoT ecosystem. This paper provides a high-level comparison of the evaluated architectures, alongside sample use cases and ongoing research, to aid developers and managers in selecting an appropriate framework based on their specific application requirements. The core contribution is the design and initial validation of our novel mechanism, which enhances data integrity and trust within IoT environments.
Co-Authors Al-Rammahi, Hussein