Volume 7 , Issue 1 , PP: 62-71, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Anil Audumbar Pise 1 * , Saurabh Singh 2 , Hemachandran K. 3 , Shraddhesh Gadilkar 4 , Zakka Benisemeni Esther 5 , Ganesh Shivaji Pise 6 , Jude Imuede 7
Doi: https://doi.org/10.54216/IJWAC.070106
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.
DeepDiffNet , coded diffraction patterns , deep learning, Preprocessing Algorithm , Encoding Algorithm , Decoding Algorithm , Data Preparation , Feature Extraction , Pattern Reconstruction.
[1] A. Thakkar and R. Lohiya, “A review on machine learning and deep learning perspectives of IDS for IoT: recent updates, security issues, and challenges,” Archives of Computational Methods in Engineering, vol. 28, no. 4, pp. 3211–3243, 2021.
[2] D. Miorandi, S. Sicari, F. De Pellegrini, and I. Chlamtac, “Internet of Things: vision, applications and research challenges,” Ad Hoc Networks, vol. 10, no. 7, pp. 1497–1516, 2012.
[3] Ngarianto H, Purwanto ES, Andrean H (2022) Cultivation of Flowerhorn Species in Search of Superior Quality Seeds using IoT and Open CV. Int J Emerg Technol Adv Eng 12(12):75–83
[4] A. Arshad, V. Tiwari, M. Lovanshi, and R. Shrivastava, "Role identification from human activity videos using recurrent neural networks," in 2022 IEEE International Women in Engineering (WIE) Conf. on Electrical and Computer Engineering (WIECON-ECE), 2022, pp. 356-361, IEEE.
[5] T. Mohapatra, S. S. Mishra, M. Bathre, and S. S. Sahoo, "Taguchi and ANN-based optimization method for predicting maximum performance and minimum emission of a VCR diesel engine powered by diesel, biodiesel, and producer gas," World J. Eng., vol. ahead-of-print, no. ahead-of-print, 2023.
[6] A. Verma, V. Tiwari, M. Lovanshi, and R. Shrivastava, "A Human Body Part Semantic Segmentation Enabled Parsing for Human Pose Estimation," in Proc. of the 2023 5th International Conf. on Image, Video and Signal Processing, 2023, pp. 43-50.
[7] K. K. Hasan, M. A. Hairuddin, R. F. Mustapa, S. A. Nordin, and N. D. K. Ashar, "Machine Learning Approach of Optimal Frequency Tuning for Capacitive Wireless Power Transfer System," Int. J. Emerg. Technol. Adv. Eng., vol. 12, no. 11, pp. 65-71, 2022.
[8] M. Bathre and P. K. Das, "Water supply monitoring system with self-powered LoRa based wireless sensor system powered by solar and hydroelectric energy harvester," Comput Stand Interfaces, vol. 82, 103630, 2022.
[9] D. T. Mane, S. Sangve, G. Upadhye, S. Kandhare, S. Mohole, S. Sonar, and S. Tupare, "Detection of Anomaly using Machine Learning: A Comprehensive Survey," Int. J. Emerg. Technol. Adv. Eng., vol. 12, no. 11, pp. 134-152, 2022.
[10] Yap R, Rosario ED, Munchua RMF (2022) An FPGA Library Based Design of Variable CNN Weight Compression using Resizable K-Means Clustering. Int J Emerg Technol Adv Eng 12(12):84–93
[11] V. Tiwari and B. Tiwari, "A Data Driven Multi-Layer Framework of Pervasive Information Computing System for eHealthcare," International Journal of E-Health and Medical Communications (IJEHMC), vol. 10, no. 4, pp. 66-85, 2019.
[12] R. K. Bhujade and S. Asthana, "An Extensive Comparative Analysis on Various Efficient Techniques for Image Super-Resolution," Int. J. Emerg. Technol. Adv. Eng., vol. 12, no. 11, pp. 153-158, 2022.
[13] M. Bathre and P. K. Das, "Smart dual battery management system for expanding lifespan of wireless sensor node," Int J Commun Syst, vol. 36, no. 3, e5389, 2023.
[14] Z. K. Maseer, R. Yusof, N. Bahaman, S. A. Mostafa, and C. F. M. Foozy, “Benchmarking of machine learning for anomaly-based intrusion detection systems in the CICIDS2017 dataset,” IEEE Access, vol. 9, pp. 22351–22370, 2021.
[15] Akashdeep, I. Manzoor, and N. Kumar, “A feature reduced intrusion detection system using ANN classifier,” Expert Systems with Applications, vol. 88, pp. 249–257, 2017.
[16] V. Mohanakurup et al., "Breast Cancer Detection on Histopathological Images Using a Composite Dilated Backbone Network," Computational Intelligence and Neuroscience, vol. 2022, Article ID 8517706, pp. 1–10, 2022. [Online]. Available: https://doi.org/10.1155/2022/8517706
[17] Y. Rbah, M. Mahfoudi, Y. Balboul et al., “Machine learning and deep learning methods for intrusion detection systems in IoMT: a survey,” in 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), pp. 1–9, Meknes, Morocco, March 2022.
[18] K. Albulayhi, A. A. Smadi, F. T. Sheldon, and R. K. Abercrombie, “IoT intrusion detection taxonomy, reference architecture, and analyses,” Sensors, vol. 21, no. 19, p. 6432, 2021.
[19] A. Das and S. G. Balakrishnan, “A comparative analysis of deep learning approaches in intrusion detection system,” in 2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), pp. 555–562, Bangalore, India, August 2021.
[20] C. Sekhar, K. Pavani, and M. S. Rao, "Comparative analysis on intrusion detection system through ML and DL techniques: survey," in 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA), pp. 1–5, Nagpur, India, November 2021.
[21] O. M. Surakhi, A. M. Garcia, M. Jamoos, and M. Y. Alkhanafseh, "A comprehensive survey for machine learning and deep learning applications for detecting intrusion detection," in 2021 22nd International Arab Conference on Information Technology (ACIT), pp. 1–13, Muscat, Oman, December 2021.
[22] D. N. P. Suthishni and K. S. S. Kumar, "A review on machine learning based security approaches in intrusion detection system," in 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 341–348, New Delhi, India, March 2022.
[23] V. Roy and S. Shukla, "Effective EEG Motion Artifacts Elimination Based on Comparative Interpolation Analysis," Wireless Pers Commun, vol. 97, pp. 6441–6451, 2017. https://doi.org/10.1007/s11277-017-4846-3.
[24] P.K. Shukla, V. Roy, P.K. Shukla, A.K. Chaturvedi, A.K. Saxena, M. Maheshwari, P.R. Pal, "An Advanced EEG Motion Artifacts Eradication Algorithm," The Computer Journal, pp. bxab170, 2021. https://doi.org/10.1093/comjnl/bxab170.
[25] G. Meena and R. R. Choudhary, "A review paper on IDS classification using KDD 99 and NSL KDD dataset in WEKA," in 2017 International Conference on Computer, Communications and Electronics (Comptelix), pp. 553–558, Jaipur, India, July 2017.
