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Journals / Sakarya University Journal of Computer and Information Sciences / 2022 / Volume: 5 Issue: 3

Software Development for the Use of Generalized Parabolic Blending in Data Prediction Processes

Hakan Üstünel(KIRKLARELI UNIVERSITY)0000-0001-9903-593X
Pages
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Publication Date
2022-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

At the end of the research, it was determined that the results obtained from the GPB were 0.1728 percent more accurate than the data obtained from the PB for the default values.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Parabolic blending (PB) is one of the important topics in applied mathematics and computer graphics.
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Key Findings & Data:Overhauser (OVR) elements is a special case in GPB (r=0.5, s=0.5).
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Conclusion & Impact:At the end of the research, it was determined that the results obtained from the GPB were 0.1728 percent more accurate than the data obtained from the PB for the default values.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Generalized parabolic blending · computer graphics · visualization

Abstract

Parabolic blending (PB) is one of the important topics in applied mathematics and computer graphics. The use of generalized parabolic blending (GPB) for different scenarios adds flexibility to the polynomial. Overhauser (OVR) elements is a special case in GPB (r=0.5, s=0.5). GPB can also be used in estimation. In this study, data obtained from thickness distribution of a 3mm thick high impact polystyrene product after thermoforming using a mold was used for data estimation. For this purpose, software has been developed. The software development steps and formula usages are explained. Using the developed software, polynomials for GPB and default PB (OVR) were created. The data set was compared with the y values produced by the polynomials for certain x values. At the end of the research, it was determined that the results obtained from the GPB were 0.1728 percent more accurate than the data obtained from the PB for the default values.

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Cite This Article

Üstünel, H. (2022). Software Development for the Use of Generalized Parabolic Blending in Data Prediction Processes. Sakarya University Journal of Computer and Information Sciences, Volume: 5 Issue: 3. https://doi.org/10.35377/saucis...1122506

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