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Jonhariono Sihotang
Universitas Putra Abadi Langkat, Indonesia
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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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
Men's facial foam selection decision support system based on skin type Jonhariono Sihotang; Roma Sinta Simbolon; Amran Manalu
Vertex Vol. 11 No. 2 (2022): June: Engineering
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/vxydbw52

Abstract

This research presents the development of a Decision Support System (DSS) aimed at assisting men in selecting facial foam products based on their skin types. The growing interest in skincare and grooming among men has led to an abundance of facial care products in the market, making it challenging for consumers to choose the most suitable option for their individual needs. The WDSS addresses this predicament by intelligently analyzing user input, classifying skin types, and generating personalized product recommendations. The conceptual framework of the WDSS combines content-based filtering and collaborative filtering techniques to ensure accuracy and relevance in recommending facial foam products. The Decision Support System offers a valuable tool for men seeking the most suitable facial foam products based on their individual skin types. The system's ability to provide personalized recommendations contributes to improved self-confidence and promotes proactive self-care practices among users. Continuous efforts in refining algorithms and updating the product database are essential to ensure the DSS's accuracy and relevancy as the skincare industry continues to evolve. The research seeks to empower men in their skincare journey, fostering a positive impact on their overall well-being and self-image.
Co-Authors Amran Manalu Patrisius Michaud Felix Marsoit Roma Sinta Simbolon