همایش بین المللی هوش مصنوعی

صفحه اصلی / اولین همایش بین المللی هوش مصنوعی

Optimization of Neural Data Processing with Distributed Algorithms: An Analysis of the Application of Distributed Algorithms in Neural Image and Signal Processing for Feature Extraction Speed and Accuracy Enhancement

نویسندگان :

Arian Baymani¹ Maryam Naderi Soorki²

1- دانشگاه شهید چمران اهواز 2- دانشگاه شهید چمران اهواز

کلمات کلیدی :

optimization of neural data،neural imaging،efficient algorithms

چکیده :

This paper presents a detailed investigation into the optimization of neural data processing through the implementation of distributed algorithms. As the volume and complexity of data generated by advanced neural imaging modalities such as functional Magnetic Resonance Imaging (fMRI) and Electroencephalography (EEG) continue to escalate, traditional single-node processing techniques prove inadequate for real-time analysis and interpretation. This study systematically explores the transformative impact of distributed algorithms on enhancing the speed and accuracy of feature extraction from neural data streams. We develop robust mathematical models to characterize the performance of distributed methodologies, alongside comprehensive simulations that evaluate their efficiency in comparison to conventional approaches. Our findings reveal substantial improvements in computational efficiency, with execution times reduced by up to 70%, while accuracy in feature extraction exhibits enhancements of approximately 15%. These results highlight the potential of distributed paradigms to manage large-scale neural datasets effectively. Furthermore, we discuss the inherent challenges associated with distributed processing, including communication overhead and algorithm scalability, providing insights into future directions for research in this domain. This work lays the groundwork for the integration of distributed algorithms in practical neural data applications, with implications for advancements in brain-computer interfaces, neurological diagnostics, and cognitive neuroscience research.