Human Deep Neural Networks with Artificial Intelligence and Mathematical Formulas
Harsha Magapu1, Magapu Radha Krishna Sai2, Bhimaraju Goteti3
1Harsha Magapu, Department of Computer Science, ICICI LOMBARD GIC, Mumbai (Maharashtra), India.
2Magapu Radha Krishna Sai, Department of Computer Science, Blue younder, Hyderabad (Telangana), India.
3Bhimaraju Goteti, Department Computer Science, Colryt, Hyderabad (Telangana), India.
Manuscript received on 14 January 2024 | Revised Manuscript received on 12 March 2024 | Manuscript Accepted on 15 March 2024 | Manuscript published on 30 March 2024 | PP: 1-2 | Volume-12 Issue-4, March 2024 | Retrieval Number: 100.1/ijese.C980313030224 | DOI: 10.35940/ijese.C9803.12040324
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Human deep neural networks (HDNNs) are a type of artificial neural network that is inspired by the structure and function of the human brain. HDNNs are composed of multiple interconnected layers of neurons, which are able to learn complex patterns from data. HDNNs have been shown to be very effective at solving a wide range of problems, including image recognition, natural language processing, and machine translation. HDNNs are often used in conjunction with artificial intelligence (AI) to create intelligent systems that can mimic human cognitive abilities. For example, HDNNs have been used to develop AI systems that can understand and respond to human language, and that can learn from their experiences and improve their performance over time. Human deep neural networks (HDNNs) are a type of artificial neural network that is inspired by the structure and function of the human brain. HDNNs are composed of multiple interconnected layers of neurons, which are able to learn complex patterns from data. HDNNs have been shown to be very effective at solving a wide range of problems, including image recognition, natural language processing, and machine translation. HDNNs are often used in conjunction with artificial intelligence (AI) to create intelligent systems that can mimic human cognitive abilities. For example, HDNNs have been used to develop AI systems that can understand and respond to human language, and that can learn from their experiences and improve their performance over time.
Keywords: HDNNs, AI, Deep Learning
Scope of the Article: Deep Learning