aspg-20-2383-1791307620 20261006172700 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Wireless and Ad Hoc Communication IJWAC 2692-4056 2023 7 1 Anil Audumbar Pise Siatik Premier Google Cloud Platform Partner Johannesburg South Africa, University of the Witwatersrand Johannesburg-South Africa Computer Science, Head of Data Science & Machine Learning, Ad Saurabh Singh Department of AI and Big data, woosong University, Daejeon South Korea Hemachandran K. School of Business, Woxsen University, Hyderabad, India Shraddhesh Gadilkar Associate Engineer, TSYS Global Payments, Pune, India Zakka Benisemeni Esther Federal Polytechnic Bauchi, Nigeria Ganesh Shivaji Pise Pune Institute of Computer Technology Pune Jude Imuede University of Prince Edward Island With the use of deep learning algorithms, we provide in this work a novel approach, called "DeepDiffNet," to investigate the most recent advancements in the comprehension of coded diffraction patterns. Comprehensive tool DeepDiffNet decodes complicated coded diffraction patterns using deep neural networks. Encoding, decoding, and preprocessing are the three main algorithms used in the method.Preprocessing is an essential initial step in preparing coded diffraction patterns for analysis. It includes bringing intensity data into a standard range and employing a windowing tool to minimize noise and emphasize features. The Encoding Algorithm leverages a convolutional neural network (CNN) to extract valuable data from the diffraction patterns that have been analyzed. Critically significant patterns and structures are recognized by the CNN via encoding them as feature vectors, which is how it learns to evaluate input. To reconstruct the original objects or specimens from the encoded information, the Decoding Algorithm uses a recurrent neural network (RNN). The RNN models the relationships between these features and the spatial arrangements of things to reconstruct them properly. We use many measures, such as Mean Absolute Error (MAE), the Structural Similarity Index (SSI), and the Peak Signal-to-Noise Ratio (PSNR), to evaluate DeepDiffNet's performance. These measures guarantee the reliability and efficacy of our approach to pattern reconstruction. When compared to conventional approaches, DeepDiffNet is light years ahead in terms of accuracy, precision, recall, and processing efficiency when analyzing coded diffraction patterns. The method's outstanding efficacy, flexibility, and resilience make it a priceless resource for a wide range of scientific, medical, and industrial endeavors. 2023 62 71 2383 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJWAC.070106 https://americaspg.com/journal/20/article/2383 https://americaspg.com/storage/41711710857.pdf