VMCNN: Integrating Vedic Mathematics for Enhanced Convolutional Neural Network Performance in Image Classification Tasks

In the rapidly advancing field of deep learning, Convolutional Neural Networks (CNNs) have emerged as a cornerstone for various image classification tasks. However, the existing CNN architectures and optimization methods often grapple with trade-offs between computational efficiency, accuracy, and generalizability. This work addresses these limitations by integrating principles of Vedic Mathematics, an ancient Indian mathematical system known for its efficiency and simplicity, into CNN optimization. Traditional optimization techniques often fall short in balancing the computational load with the accuracy of the model, particularly in diverse and complex datasets. To overcome these challenges, we propose a novel model that incorporates specific Vedic Mathematical Sutras, namely Urdhva-Tiryakbhyam, Anurupyena, Nikhilam Navatashcaramam Dashatah, Shunyam Saamyasamuccaye, Paravartya Yojayet, Sankalana-vyavakalanabhyam, Ekadhikina Purvena, and Gunitasamuchyah, for optimizing internal operations of CNNs. These Sutras were meticulously selected for their potential to simplify computational processes, enhance parallel processing capabilities, and optimize training algorithms. The application of these sutras has led to a remarkable improvement in the performance of CNNs across various datasets, including ImageNet, CIFAR, ChestXRay8, and Architectural Heritage Datasets. The optimized CNN models demonstrated a significant enhancement in classification accuracy (9.5% increase), precision (8.3% increase), recall (8.5% increase), area under the curve (AUC) (4.9% increase), MAE (5.5% improvement), and reduction in delay (6.5% decrease) compared to existing methods. The integration of Vedic Mathematics into CNN optimization not only paves the way for more efficient and accurate image classification models but also opens new avenues for interdisciplinary research, blending ancient mathematical wisdom with contemporary artificial intelligence techniques. This advancement has profound implications for various applications, including medical imaging, autonomous vehicles, and heritage conservation, thereby contributing significantly to the field of AI and computational efficiency.