Published Paper Details:

MANET has shown to be very successful in remote sectors, such as emergency services, disaster management, military applications, and the automation sector.Our main goal is to evaluate the network's performance using both live video feeds and static graphics. We start this assessment with transfer learning using image datasets on the networks.The next action is to assess the prediction accuracy for the same object in still images and live video streams. A range of accuracy percentages are identified, documented, and displayed in subsequent sections.Verifying if prediction accuracy varies for each CNN that is chosen is a crucial performance evaluation criterion.As an alternative to the existing distance finding approaches and semantic-based methods, the implementation of the Quadratic Assignment Problem (QAP) optimization strategy provides a novel approach to enhance document analysis and categorization.By extracting textual content from image data, the algorithm's accuracy is significantly increased.When processing a variety of photos, including ones with a wide range of features and complexity, the GSA technique proved to be robust.Moreover, contrasting the GSA's performance with other state-of-the-art optimization techniques would enable us to better understand its effectiveness in image classification.