Perceptron in Supervised, Semi-Supervised, Unsupervised Learning and Artificial Neural Network
Keywords:
Deep-learning, Neural networks, Artificial neurons, Supervised learning, Machine learning, Regression
Abstract
The proficient realization of a distinct neuron is needed for on a broad scale, software defined recognition of an artificial neural network (ANN). The majority of reconfigurable computing systems equipped with FPGAs are suitable for NN hardware execution. Understanding ANNs on an FPGA It's difficult to work with a huge number of neurons. Since relatively high statistic (HOS) maintain spectral analysis, this study uses one-dimensional slices from the higher-order spectral domain of normal and ischemic subjects. A feedforward multilayer neural network (NN) with error back propagation is used in this learning algorithm (BP). Different NN structures are evaluated using two data sets derived from polyspectrum slices and polycoherence. This paper compares and contrasts reviews of numerous research papers on neural networks, with an emphasis on the FPGA-based activity of multiple activation function and mesoporous with or without linearity properties. It is intended to change signed decimal facts using a reserve substitution execution technique. For the proposed work, a thorough analysis of numerous research papers was conducted. To find a template for the diagnosis, the suggested paper uses a Multi-Layer Perceptron with a back-proliferation learning technique. A brief introduction to artificial neural networks, as well as applications, is given in this paper.
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124. S. Santhoshkumar and E. Murugan, “Rationally designed SERS AgNPs/GO/g-CN nanohybrids to detect methyleneblue and Hg2+ ions in aqueous solution,” Applied Surface Science, vol. 553, p. 149544, 2021.
125. E. Murugan, S. Santhoshkumar, S. Govindaraju and M. Palanichamy, “Silver nanoparticles decorated g-C3N4: An efficient SERS substrate formonitoring catalytic reduction and selective Hg2+ ions detection,” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol. 246, 119036, 2021.
126. E. Murugan, S. Santhosh Kumar, K. M. Reshna and S. Govindaraju, “Highly sensitive, stable g-CN decorated with AgNPs for SERS sensing of toluidine blue and catalytic reduction of crystal violet,” Journal of materials science, vol. 54, no.7, p. 5294, 2019.
127. E. Murugan, J. N. Jebaranjitham and A. Usha, “Synthesis of polymer-supported dendritic palladium nanoparticle catalysts forSuzuki coupling reaction,” Applied Nanoscience, vol. 2, no.3, p. 211, 2012.
128. E. Murugan, S. Arumugam and P. Panneerselvam, “New nanohybrids from poly (propylene imine) dendrimer stabilized silvernanoparticles on multiwalled carbon nanotubes for effective catalytic andantimicrobial applications,” International Journal of Polymeric Materials and Polymeric Biomaterials, vol. 65 no. 3, p. 111, 2016.
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Published
2022-05-12
How to Cite
Steffi. R, D.K. Sharma, S. Suman Rajest, R. Regin, J. Obaid, A., & G. Jerusha Angelene Christabel. (2022). Perceptron in Supervised, Semi-Supervised, Unsupervised Learning and Artificial Neural Network. Central Asian Journal of Theoretical and Applied Science, 3(5), 176-199. https://doi.org/10.17605/OSF.IO/KJ68Q
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Copyright (c) 2022 Ahmed J. Obaid; Steffi. R, D.K. Sharma, S. Suman Rajest, R. Regin, G. Jerusha Angelene Christabel
This work is licensed under a Creative Commons Attribution 4.0 International License.
