Resolve the regression analysis challenge by iterating the price function, and obtain good results, a without the need of complicating the model. Additionally, it improves the interpretability of explanatory variables. We apply the fractional differential to gradient descent, and examine the efficiency of fractional-order gradient descent with that of integer-order gradient descent. It was discovered that the fractional-order includes a more rapidly convergence rate, larger fitting accuracy and lower prediction error than the integer-order. This offers an alternative process for fitting and forecasting GDP and has a particular reference worth.Axioms 2021, 10,9 ofAuthor Contributions: J.W. supervised and led the preparing and execution of this analysis, proposed the research concept of combining fractional calculus with gradient descent, formed the overall analysis objective, and reviewed, evaluated and revised the manuscript. As outlined by this study target, X.W. collected data of financial indicators and applied statistics to create a model and used Python software program to write codes to analyze data and optimize the model, and lastly wrote the very first draft. M.F. reviewed, evaluated and revised the manuscript. All authors have study and agreed for the published version from the manuscript. Funding: This function is partially supported by Coaching Object of Higher Level and Innovative Talents of Guizhou Province ((2016)4006), Important Analysis Project of Revolutionary Group in Guizhou Education Department ([2018]012), the Slovak Study and Improvement Agency beneath the contract No. APVV-18-0308 and by the Slovak Grant Agency VEGA No. 1/0358/20 and No. 2/0127/20. Institutional Overview Board L-Norvaline custom synthesis Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: https://data.worldbank.org.cn/. Acknowledgments: The authors are grateful to the referees for their careful reading of the manuscript and beneficial comments. The authors thank the assistance from the editor too. Conflicts of Interest: The authors declare no conflict of interest.
massive data and cognitive computingArticleEffects of Neuro-Cognitive Load on Learning Transfer Utilizing a Virtual Reality-Based Driving SystemUsman Alhaji Abdurrahman 1, , Shih-Ching Yeh 2 , Yunying Wong 3 and Liang WeiSchool of Information Science and Technology, Fudan University, Shanghai 200433, China; [email protected] Department of Laptop Science and Data Engineering, National Central University, Taoyuan City 32001, Taiwan; [email protected] School of Psychology, Fudan University, Shanghai 200433, China; [email protected] Correspondence: [email protected] or [email protected]: Abdurrahman, U.A.; Yeh, S.-C.; Wong, Y.; Wei, L. Effects of Neuro-Cognitive Load on Learning Transfer Making use of a Virtual Reality-Based Driving System. Big Data Cogn. Comput. 2021, five, 54. https://doi.org/ 10.3390/bdcc5040054 Academic Editors: Achim Ebert, Peter Dannenmann and Gerrit van der Veer Received: 13 August 2021 Accepted: 7 October 2021 Published: 13 OctoberAbstract: Understanding the ways distinctive people today perceive and apply acquired information, 3-Methylbenzaldehyde Protocol specially when driving, is definitely an crucial location of study. This study introduced a novel virtual reality (VR)-based driving method to identify the effects of neuro-cognitive load on studying transfer. Within the experiment, simple and difficult routes were introduced towards the participants, and also the VR technique is capable of recording eye-gaze, pupil dilation, heart rate, too as driving functionality information. So.