Human Deep Neural Networks with Artificial Intelligence and Mathematical Formulas
Article Sidebar
Main Article Content
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.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
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