Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal International Journal of Enterprise Modelling Vertex
Patrisius Michaud Felix Marsoit
Ideas for Future Research and Technology, Indonesia
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Quantum-inspired fuzzy genetic programming for enhanced rule generation in complex data analysis Patrisius Michaud Felix Marsoit
International Journal of Enterprise Modelling Vol. 15 No. 3 (2021): Sep: Enterprise Modelling: Quantum computing
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (452.685 KB) | DOI: 10.35335/emod.v15i3.51

Abstract

Rule generation in complex data analysis tasks poses challenges in terms of accuracy and interpretability. This research proposes a novel approach called Quantum-Inspired Fuzzy Genetic Programming (QIFGP) that integrates concepts from fuzzy logic, genetic programming, and quantum-inspired computing to address these challenges. The QIFGP model enhances the exploration of the solution space, increases the diversity of generated rules, and improves the accuracy and interpretability of the generated rules. The model is applied to a credit risk assessment problem, and the results are compared with traditional fuzzy logic-based approaches and genetic programming without quantum-inspired features. The experimental results demonstrate that the QIFGP model outperforms the baseline methods in terms of accuracy, achieving an accuracy of 87.5%. The generated rules exhibit a high level of interpretability, providing linguistic labels that capture meaningful relationships between the input features and risk classes. The incorporation of quantum-inspired features enables efficient exploration of the solution space while maintaining computational efficiency. The generalizability and robustness of the QIFGP model are demonstrated through consistent performance across multiple experiments and datasets. The QIFGP model offers a promising approach for enhanced rule generation in complex data analysis tasks, with potential applications in various domains where accurate and interpretable rule generation is crucial.
Advancements in predictive modeling of nuclear magnetic resonance parameters: integrating quantum mechanics, machine learning, and quantum computing Jonhariono Sihotang; Patrisius Michaud Felix Marsoit
Vertex Vol. 12 No. 1 (2022): December: Nuclear
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/qc6shb61

Abstract

This research explores the integration of quantum mechanics, machine learning, and quantum computing to advance predictive modeling of nuclear magnetic resonance (NMR) parameters. The aim is to develop a hybrid quantum-enhanced machine learning model that combines the accuracy of quantum calculations with the efficiency of machine learning techniques for predicting NMR chemical shifts. The conceptual framework involves quantum mechanical calculations for accurate reference NMR parameters, supervised machine learning models trained on diverse molecular datasets, and hybrid quantum-classical algorithms to leverage quantum computing resources. A simplified numerical example demonstrates the potential of the proposed model for predicting NMR chemical shifts for small molecular systems. The results showcase the model's ability to capture underlying relationships between molecular features and NMR observables, indicating promise for larger and more complex systems. This interdisciplinary approach opens new avenues for advancing NMR spectroscopy and understanding molecular structures, dynamics, and interactions in various scientific domains. The research also discusses challenges and opportunities in integrating quantum mechanics, machine learning, and quantum computing, emphasizing the importance of diverse datasets and quantum algorithm selection. The proposed model holds significant implications for transforming NMR parameter predictions and contributing to chemistry, biochemistry, and materials science research
Co-Authors Jonhariono Sihotang