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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Mitoulis, Stergios-Aristoteles
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Computer Science & IT Electrical & Electronics Engineering Engineering

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An approach toward improvement of ensemble method’s accuracy for biomedical data classification Izonin, Ivan; Muzyka, Roman; Tkachenko, Roman; Gregus, Michal; Kustra, Natalya; Mitoulis, Stergios-Aristoteles
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5949-5960

Abstract

Amidst rapid technological and healthcare advancements, biomedical data classification using machine learning (ML) is pivotal for revolutionizing medical diagnosis, treatment, and research by organizing vast healthcare-related data. Despite efforts to apply single ML models on clean datasets, satisfactory classification accuracy can still be elusive. In such cases, ML-based ensembles offer a promising solution. This paper explores cascaded ensembles as highly accurate methods. Existing cascade classifiers often partition large datasets into equal unique parts, limiting accuracy due to insufficient amount of useful information processed by weak classifiers of all levels of the cascade ensemble. To address this, we propose an improved cascaded ensemble scheme using a different data sampling approach. Our method forms larger subsamples at each cascade level, enhancing accuracy, and generalization properties during biomedical data analysis. Experimental comparisons demonstrate substantial increases in classification accuracy and generalization properties of the improved cascade ensemble.
Parallel rapidly exploring random tree method for unmanned aerial vehicles autopilot development using graphics processing unit processing Mochurad, Lesia; Davidekova, Monika; Mitoulis, Stergios-Aristoteles
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp712-723

Abstract

Autonomous air movement systems hold great potential for transforming various industries, making their development essential. Autopilot design involves advanced technologies like artificial intelligence, machine learning, and big data. This paper focuses on developing a parallel rapidly-exploring random tree (RRT) algorithm using compute unified device architecture (CUDA) technology for efficient processing on graphics processing units (GPUs). The study evaluates the algorithm's performance in automated trajectory planning for unmanned aerial vehicles (UAVs). Numerical experiments show that the parallel algorithm outperforms the sequential central processing unit (CPU)-based version, especially as task complexity and state space dimensions increase. In scenarios with numerous obstacles, the parallel algorithm maintains stable performance, making it well-suited for various applications. Comparisons with CPU-based methods highlight the advantages of GPU use, particularly in terms of speed and efficiency. Additionally, the performance of two GPU models, NVIDIA RTX 2070 and T4 is compared, with the T4 demonstrating superior performance for similar tasks. Future research should explore integrating multiple algorithms for a more comprehensive UAV autopilot system. The proposed approach stands out for its stability and practical applicability in real-world autopilot implementations.
Co-Authors Davidekova, Monika Gregus, Michal Izonin, Ivan Kustra, Natalya Mochurad, Lesia Muzyka, Roman Tkachenko, Roman