An improved neurogenetic model for recognition of 3D kinetic data of human extracted from the Vicon Robot system
These days, it is crucial to discern between different types of human behavior, and artificial intelligence techniques play a big part in that. The characteristics of the feedforward artificial neural network (FANN) algorithm and the genetic algorithm have been combined to create an important work...
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University of Baghdad, College of Science for Women
2023-12-01
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| Series: | مجلة بغداد للعلوم |
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| Online Access: | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9087 |
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| author | Ivan V. Stepanyan Safa A. Hameed |
| author_facet | Ivan V. Stepanyan Safa A. Hameed |
| author_sort | Ivan V. Stepanyan |
| collection | DOAJ |
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These days, it is crucial to discern between different types of human behavior, and artificial intelligence techniques play a big part in that. The characteristics of the feedforward artificial neural network (FANN) algorithm and the genetic algorithm have been combined to create an important working mechanism that aids in this field. The proposed system can be used for essential tasks in life, such as analysis, automation, control, recognition, and other tasks. Crossover and mutation are the two primary mechanisms used by the genetic algorithm in the proposed system to replace the back propagation process in ANN. While the feedforward artificial neural network technique is focused on input processing, this should be based on the process of breaking the feedforward artificial neural network algorithm. Additionally, the result is computed from each ANN during the breaking up process, which is based on the breaking up of the artificial neural network algorithm into multiple ANNs based on the number of ANN layers, and therefore, each layer in the original artificial neural network algorithm is assessed. The best layers are chosen for the crossover phase after the breakage process, while the other layers go through the mutation process. The output of this generation is then determined by combining the artificial neural networks into a single ANN; the outcome is then checked to see if the process needs to create a new generation. The system performed well and produced accurate findings when it was used with data taken from the Vicon Robot system, which was primarily designed to record human behaviors based on three coordinates and classify them as either normal or aggressive.
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| format | Article |
| id | doaj-art-4af1011cbb2d45938ff04467c25b53f2 |
| institution | DOAJ |
| issn | 2078-8665 2411-7986 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | University of Baghdad, College of Science for Women |
| record_format | Article |
| series | مجلة بغداد للعلوم |
| spelling | doaj-art-4af1011cbb2d45938ff04467c25b53f22025-08-20T03:14:43ZengUniversity of Baghdad, College of Science for Womenمجلة بغداد للعلوم2078-86652411-79862023-12-01206(Suppl.)10.21123/bsj.2023.9087An improved neurogenetic model for recognition of 3D kinetic data of human extracted from the Vicon Robot systemIvan V. Stepanyan0Safa A. Hameed1Mechanical Engineering Research Institute of the Russian Academy of Sciences (IMASH RAN), Moscow, the Russian Federation & Department of Mechanics and Control Processes, Academy of Engineering, Рeoples’ Friendship University of Russia (RUDN University), Moscow, Russian FederationDepartment of Mechanics and Control Processes, Academy of Engineering, Рeoples’ Friendship University of Russia (RUDN University), Moscow, Russian Federation These days, it is crucial to discern between different types of human behavior, and artificial intelligence techniques play a big part in that. The characteristics of the feedforward artificial neural network (FANN) algorithm and the genetic algorithm have been combined to create an important working mechanism that aids in this field. The proposed system can be used for essential tasks in life, such as analysis, automation, control, recognition, and other tasks. Crossover and mutation are the two primary mechanisms used by the genetic algorithm in the proposed system to replace the back propagation process in ANN. While the feedforward artificial neural network technique is focused on input processing, this should be based on the process of breaking the feedforward artificial neural network algorithm. Additionally, the result is computed from each ANN during the breaking up process, which is based on the breaking up of the artificial neural network algorithm into multiple ANNs based on the number of ANN layers, and therefore, each layer in the original artificial neural network algorithm is assessed. The best layers are chosen for the crossover phase after the breakage process, while the other layers go through the mutation process. The output of this generation is then determined by combining the artificial neural networks into a single ANN; the outcome is then checked to see if the process needs to create a new generation. The system performed well and produced accurate findings when it was used with data taken from the Vicon Robot system, which was primarily designed to record human behaviors based on three coordinates and classify them as either normal or aggressive. https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9087Breaking-up process, Combining process, Crossover, Feedforward ANN, Mutation, Neuro-Genetic model, Optimization, Recognition, Vicon Robot, 3D data |
| spellingShingle | Ivan V. Stepanyan Safa A. Hameed An improved neurogenetic model for recognition of 3D kinetic data of human extracted from the Vicon Robot system مجلة بغداد للعلوم Breaking-up process, Combining process, Crossover, Feedforward ANN, Mutation, Neuro-Genetic model, Optimization, Recognition, Vicon Robot, 3D data |
| title | An improved neurogenetic model for recognition of 3D kinetic data of human extracted from the Vicon Robot system |
| title_full | An improved neurogenetic model for recognition of 3D kinetic data of human extracted from the Vicon Robot system |
| title_fullStr | An improved neurogenetic model for recognition of 3D kinetic data of human extracted from the Vicon Robot system |
| title_full_unstemmed | An improved neurogenetic model for recognition of 3D kinetic data of human extracted from the Vicon Robot system |
| title_short | An improved neurogenetic model for recognition of 3D kinetic data of human extracted from the Vicon Robot system |
| title_sort | improved neurogenetic model for recognition of 3d kinetic data of human extracted from the vicon robot system |
| topic | Breaking-up process, Combining process, Crossover, Feedforward ANN, Mutation, Neuro-Genetic model, Optimization, Recognition, Vicon Robot, 3D data |
| url | https://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/9087 |
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