RDNN for Classification and Prediction of Rock or Mine in Underwater Acoustics
Article Sidebar
Main Article Content
Abstract
Mines in the waters are just explosives that detonate upon contact with an object. The underwater submarine must foresee if it will encounter a mine or a rock. Lacking the development of the Ranging Sound Navigation approach, which utilizes particular variables to identify whether a surface or a barrier is made of a mine or rock, finding mines or rocks would have been extremely difficult. In our study, we demonstrate a technique for predicting underwater rocks and mines using SONAR waves. At 60 different angles, SONAR pings are employed to record the various frequencies of submerged objects. To identify whether the object in the ocean is a mine or just a rock, the submarine uses SONAR signals, which transmit sound and receive switchbacks. The mine and rock categories are predicted using the prediction models. To create these prediction models, Supervised Machine Learning Classification methods were employed.
Article Details
References
Analysis of Hidden Units in a Layered Network Trained to Classify SONAR Targets” in Neural Networks, Vol. 1, pp. 75–89(1988).
Z. Yang, B. Luo, D. Liu, and Y. Li, “Adaptive synchronization of delayed memristive neural networks with unknown parameters,” IEEE Trans. Syst., Man, Cybern. Syst., vol. 50, no. 2, pp. 539–549, Feb. 2020.
L. Fei-Fei, R. Fergus, and P. Perona, “One-shot learning of object categories,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 4, pp. 594–611, Apr.
Baburao Markapudi, Kavitha Chaduvula, D.N.V.S.L.S. Indira &Meduri V. N. S. S. R. K. Sai Somayajulu, “Content-based video recommendation system (CBVRS): a novel approach to predict videos using multilayer feed forward neural network and Monte Carlo sampling method”, published in the journal of Multimedia Tools and Applications (Springer Nature), published on 11th August 2022. PP:1-27, https://doi.org/10.1007/s11042-022-13583-8 Impact factor: 2.577 (2021), Five year impact factor: 2.396 (2021) (SCIE)
J. Kim, V. D. Calhoun, E. Shim, and J.-H. Lee, “Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia,” Neuroimage, vol. 124, pp. 127–146, 2016.
D. Deng, Y. Zhou, J. Pi, and B. E. Shi, “Multimodal utterance-level affect analysis using visual, audio and text features,” 2018, arXiv: 1805.00625.
X. Zhong and Y. Gao, “Synchronization of inertial neural networks with time-varying delays via quantized sampled-data control,” IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 11, pp. 4916–4930, Nov. 2021.
L. Wang, Y. Shen, Q. Yin, and G. Zhang, “Adaptive synchronization of memristor-based neural networks with time-varying delays,” IEEE Trans. Neural Netw. Learn. Syst., vol. 26, no. 9, pp. 2033–2042, Sep. 2015.
Dr GVSNRV Prasad, "Adaptive Optimization-enabled Neural Networks to handle the imbalance churn data in churn prediction”, International Journal of Computational Intelligence and Applications, Dec-2011.
Shaik Salma Begum & Dr D. Rajya Lakshmi, “Combining optimal wavelet statistical texture and Recurrent Neural Network for Tumor detection and Classification over MRI”, Multimedia Tools and Applications, ISSN 1380-7501, January 2020, Springer.
Shaik Salma Begum & Dr D. Rajya Lakshmi, “ An Efficient Spatial Fuzzy C-Means Algorithm with Optimized Recurrent Neural Network for MRI Brain Tissue Classification”, TEST Engineering and Management, ISSN:0193-4120 Page No: 13254-13266, March- April 2020.
Shaik Salma Begum & Dr D. Rajya Lakshmi, “GLCM of Fuzzy Clustering Means for Textural Future Extraction of Brain Tumor in Probabilistic Neural Networks”, International Journal of Innovative Technology and Exploring Engineering, ISSN: 2278-3075, Volume-9, Issue-1, November 2019.