Enhancing AI Model Reliability and Responsiveness in Image Processing: A Comprehensive Evaluation of Performance Testing Methodologies

Authors

  • Amit Bhanushali Quality Assurance Manager, West Virginia University,WV, USA .
  • Kulbir Singh Health Information Manager, IL, USA
  • Abhishek Kajal Asst. Professor , Guru Jambheshwar University of Science and Technology Hisar , India

Keywords:

Image processing, Artificial Intelligence, Performance Testing, Model Reliability, Responsiveness, AI Stability, Software Quality Optimization

Abstract

Artificial Intelligence (AI) has revolutionized numerous sectors, notably image processing, playing a pivotal role in advancements from healthcare diagnostics to autonomous vehicles. This study delves into the critical aspects of reliability and responsiveness in AI-based image processing systems, underscoring the significance of comprehensive performance testing. As AI technologies become increasingly integral to complex and dynamic applications, understanding and ensuring their stability and efficient response to varying workloads is paramount. Our research focuses on evaluating and identifying effective performance testing methodologies that enhance the reliability and speed of AI in image processing. By examining AI models in diverse operational scenarios, this paper contributes to bridging the knowledge gap in how performance testing can optimize AI models for heightened reliability and responsiveness. The findings not only offer valuable insights for integrating AI technology into a range of applications but also set a foundation for guiding future research and development in the field.

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Published

07.02.2024

How to Cite

Bhanushali, A. ., Singh, K. ., & Kajal, A. . (2024). Enhancing AI Model Reliability and Responsiveness in Image Processing: A Comprehensive Evaluation of Performance Testing Methodologies. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 489–497. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4773

Issue

Vol. 12 No. 15s (2024)

Section

Research Article

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