Browsing by Author "Hatira, N."
Now showing 1 - 2 of 2
- Results Per Page
- Sort Options
Conference Object Citation Count: 0A Data Science Perspective on Global Trends in Energy Production(Institute of Electrical and Electronics Engineers Inc., 2024) Hatira, N.; Alsan, H.F.; Arsan, T.As global demand for energy continues to rise, understanding the trends and dynamics of energy generation is crucial to ensure a sustainable and efficient energy future. This study employs data science techniques to analyze global energy production data from 48 countries spanning 2010 to 2023. Initially, we use clustering methods to categorize countries based on their energy production profiles into three distinct groups: high, medium, and low production. This clustering provides insights into the diverse energy strategies and capacities across different regions. Subsequently, we apply and compare two classification models, specifically Random Forest and Gradient Boosting, to predict the dominant energy source for each cluster. Furthermore, we perform a comparative analysis of two forecasting models, SARIMA and Prophet, to predict future renewable energy production for countries with high production profiles, such as the USA and China. The forecasting results show the efficacy of these models in capturing seasonal trends and providing accurate predictions. © 2024 IEEE.Conference Object Citation Count: 0Enhancing Eye-Hand Coordination in Volleyball Players: a Comparative Analysis of Vr, Ar, and 2d Display Technologies and Task Instructions(Institute of Electrical and Electronics Engineers Inc., 2024) Hatira, N.; Aliza, A.; Batmaz, A.U.; Sarac, M.Previous studies analyzed user motor performance with Virtual Reality (VR) and Augmented Reality (AR) Eye-Hand Coordination Training Systems (EHCTSs) while asking participants to follow specific task instructions. Although these studies suggested VR & AR EHCTSs as potential training systems for sports players, they recruited participants for their user studies among general population. In this paper, we examined the training performance of 16 professional volleyball players over 8 days using EHCTSs with three display technologies (VR, AR, and 2D touchscreen) and with four distinct task instructions (prioritizing speed, error rate, accuracy, or none). Our results indicate that volleyball players performed best with 2D touchscreen in terms of time, error rate, accuracy, precision, and throughput. Moreover, their performance was superior when using VR over AR. They also successfully followed the task instructions given to them and consistently improved their throughput performance. These findings underscore the potential of EHCTS in volleyball training and highlight the need for further research to optimize VR & AR user experience and performance. © 2024 IEEE.