Performance Evaluation of Attention Based Neural Network Models through Loss Function | IJCT Volume 13 – Issue 2 | IJCT-V13I2P43

[1]. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin “Attention Is All You Need” arXiv-2017, arXiv:1706.03762. [2]. Anand Tumma, Arathi Chitla “Autonomous Driving Simulators: The advantages of CARLA” International Journal of Computer Applications (0975 – 8887) Volume 187 – No.19, June 2025. [3]. Varundev Suresh Babu and Madhur Behl. “f1tenth. dev-An Open-source ROS based F1/10 Autonomous Racing Simulator”. In: 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). IEEE. 2020, pp. 1614–1620. [4]. Mrinal R. Bachute and Javed M. Subhedar. “Autonomous Driving Archi- tectures: Insights of Machine Learning and Deep Learning Algorithms”. In: Machine Learning with Applications volume-6 (2021),p.100164.ISSN:2666-8270. DOI:https://doi.org/10.1016/j.mlwa.2021.100164. [5]. Chen Chen et al. Combination of deep neural network with attention mechanism enhances the explainability of protein contact prediction. 2020. [6]. Liangkai Liu et al. “Computing Systems for Autonomous Driving: State- of the-Art and Challenges”. In: CoRR abs/2009.14349 (2020). arXiv: 2009 .14349. URL: https://arxiv.org/abs/2009.14349. [7]. Alexander Makrigiorgos et al. “Human Visual Attention Prediction Boosts Learning & Performance of Autonomous Driving Agents”. In: CoRRabs/1909.05003(2019).arXiv:1909.05003.URL:http://arxiv.org/abs/1909.05003. [8]. Gon¸calo Neto. From Single-Agent to Multi-Agent Reinforcement Learning: Foundational Concepts and Methods Learning Theory Course. 2005. [9]. Aizaz Sharif and Dusica Marijan. Evaluating the Robustness of Deep Reinforcement Learning for Autonomous and Adversarial Policies in a Multi- agent Urban Driving Environment. 2021. arXiv: 2112.11947 [cs.AI]. [10]. Xinyi Wang and Yi Yang. “Neural Topic Model with Attention for Super- vised Learning”. In: Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics. Ed. by Silvia Chiappa and Roberto Calandra. Vol. 108. Proceedings of Machine Learning Research. PMLR, Aug. 2020, pp. 1147–1156. URL: https://proceedings.mlr.pres s/v108/wang20c.html. [11]. Mohsin Raza “Autonomous Vehicles: Levels, Technologies, Impacts and Concerns” International Journal of Applied Engineering Research ISSN 0973 4562 Volume 13, Number 16 (2018) pp. 12710-12714. [12]. Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez and Vladlen Koltun “CARLA: An Open Urban Driving Simulator” arXiv:1711.03938v1 [cs.LG] 10 Nov 2017. [13]. Sumbal Malika, Manzoor Ahmed Khana, Hesham El Sayed “CARLA: Car learning to Act-An inside out” International Workshop on Smart Communication and Autonomous Driving (SCAD 2021) November1-4, 2021, Leuven, Belgium. [14]. Symphorien Karl Yoki Donzia, Haeng-Kon Kim “A Study on Autonomous Driving Simulation Using a Deep Learning Process Model” International Journal of Software Innovation Volume 10 Issue 1. [15]. Emily Barbour, Kevin McFall “AUTONOMOUS VEHICLE SIMULATION USING OPEN-SOURCE SOFTWARE CARLA” Journal of UAB ECTC Volume 18, 2019. [16]. Li, C.; Liu, K.; Liu, S. “A Survey of Loss Functions in Deep Learning”. Mathematics 2025, 13, 2417. https://doi.org/10.3390/ math13152417. [17].Lorenzo Ciampiconi, Adam Elwood, Marco Leonardi, Ashraf Mohamed, Alessandro Rozza “A survey and taxonomy of loss functions in machine learning” arXiv-2023, arXiv:2301.05579.