Human Deep Neural Networks with Artificial Intelligence and Mathematical Formulas
Creators
- 1. Department of Computer Science, ICICI LOMBARD GIC, Mumbai (Maharashtra), India.
Contributors
Contact person:
Researchers:
- 1. Department of Computer Science, ICICI LOMBARD GIC, Mumbai (Maharashtra), India.
- 2. Department of Computer Science, Blue younder, Hyderabad (Telangana), India
- 3. Department Computer Science, Colryt, Hyderabad (Telangana), India.
Description
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 intelligentsystemsthat 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 intelligentsystemsthat 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.
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C980313030224.pdf
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Additional details
Identifiers
- DOI
- 10.35940/ijese.C9803.12040324
- EISSN
- 2319-6378
Dates
- Accepted
-
2024-03-15Manuscript received on 14 January 2024 | Revised Manuscript received on 12 March 2024| Manuscript Accepted on 15 March 2024 | Manuscript published on 30 March 2024.
References
- A CNN-MLP Deep Model for Sensor-based Human Activity Recognition** by Agti Nadia; Sabri Lyazid; Kazar Okba; Chibani Abdelghani (2023) Research work
- DeepIQ: A Human-Inspired AI System for Solving IQ Test Problems ** by Jacek Mandizuk; Adam Zychowski (2019) Research work
- A Survey on Deep Learning for Human Activity Recognition** by Fuqiang Gu; Mu-Huan Chung; Mark Chignell; Shahrokh Valaee (2021) Research work
- Narayanan, L. G. T., & Padhy, D. S. C. (2023). Artificial Intelligence for Predictive Maintenance of Armoured Fighting Vehicles Engine. In Indian Journal of Artificial Intelligence and Neural Networking (Vol. 3, Issue 5, pp. 1–12). https://doi.org/10.54105/ijainn.e1071.083523
- Pandey, R., Verma, Dr. H. K., Parakh, Dr. A., & Khare, Dr. C. J. (2022). Artificial Intelligence Based Optimal Placement of PMU. In International Journal of Emerging Science and Engineering (Vol. 10, Issue 11, pp. 1–6). https://doi.org/10.35940/ijese.i2541.10101122
- Radhamani, V., & Dalin, G. (2019). Significance of Artificial Intelligence and Machine Learning Techniques in Smart Cloud Computing: A Review. In International Journal of Soft Computing and Engineering (Vol. 9, Issue 3, pp. 1–7). https://doi.org/10.35940/ijsce.c3265.099319
- N.S, N., & A, S. (2020). Malware Detection using Deep Learning Methods. In International Journal of Innovative Science and Modern Engineering (Vol. 6, Issue 6, pp. 6–9). https://doi.org/10.35940/ijisme.f1218.046620
- Maniraj, S. P., G, S., Sravani, P., & Reddy, Y. (2019). Object Boundary Detection using Neural Network in Deep Learning. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 1, pp. 4453–4457). https://doi.org/10.35940/ijeat.a1608.109119