| Paper | Title | Page |
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WEP044 |
Key Factors and Drivers for Utilizing Machine Learning in Experimental Data Analysis: A Case Study of Synchrotron Experimental Data | |
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| Concurrently with the application of AI and Machin learning (ML), their remarkable influences are being observed. This study reviews the use of ML in analyzing experimental data, focusing on synchrotron data. It explores key factors and drivers shaping the application of ML in this context. The research model employs a forward-looking approach, aiming to advance ML in experimental data analysis. The study addresses challenges unique to synchrotron data, such as high dimensionality, complexity, large volume, noise, and uncertainty. Advanced techniques like dimensionality reduction, pattern recognition, anomaly detection, and predictive modeling are introduced as novel approaches. Results highlight the potential of ML in improving performance and obtaining more accurate outcomes in synchrotron data analysis. In conclusion, this research offers valuable insights and proposes strategies to enhance the analysis of synchrotron experimental data using ML. Identified drivers and research trends benefit synchrotron analysis and other scientific disciplines. The discussion explores broader implications and future directions for utilizing ML in experimental data analysis. | ||
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WEP045 |
Harnessing the Power of Emerging Technologies: Data Science and Synchrotron Advancing Scientific Discoveries | |
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| This research review explores the impact of data science and synchrotron technology as emerging technologies in scientific research. The research model begins with an overview of the significance of data science and synchrotron technology in advancing scientific discoveries. The research methodology involves a comprehensive analysis of interdisciplinary applications in materials science, structural biology, and environmental science. By employing data science techniques, including machine learning and statistical modeling, researchers can effectively analyze the complex datasets generated by synchrotron facilities. The results obtained from this integration showcase accelerated scientific discoveries and the emergence of new phenomena. The research concludes with a discussion on the challenges related to data quality and accessibility to synchrotron facilities, while also highlighting future advancements and emerging trends in data science and synchrotron technology. This research review underscores the transformative impact of these emerging technologies and their potential to reshape the landscape of scientific research. | ||
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