Cognitive Analysis of Neural Networks Using Fractional Metric Dimension and Applications
Fractional versions of metric related parameters have been introduced as an equivalent to solve linear optimization problems which have applications in various fields like computer science and chemistry. The understanding and analysis of various parameters in the context of networks involved in tran...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10445233/ |
| _version_ | 1797243383201136640 |
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| author | Faiza Jamil Agha Kashif Omar Alharbi Sohail Zafar Amer Aljaedi Yazeed Mohammad Qasaymeh |
| author_facet | Faiza Jamil Agha Kashif Omar Alharbi Sohail Zafar Amer Aljaedi Yazeed Mohammad Qasaymeh |
| author_sort | Faiza Jamil |
| collection | DOAJ |
| description | Fractional versions of metric related parameters have been introduced as an equivalent to solve linear optimization problems which have applications in various fields like computer science and chemistry. The understanding and analysis of various parameters in the context of networks involved in transmitting the data is referred as the cognitive analysis. These parameters analyze the abstract structures of networks which widens the scope of application in the areas such as networking and linear optimization. In particular, the metric related parameters are used in navigation of robots to their destinations by minimum utilization time and nodes. The application of neural networks can be seen in diverse areas including data flow optimization, healthcare, cognitive psychology, geographical routing, supply chain optimization problems, wireless communication networks and Internet of Things (IoT). In the current work, the cognitive analysis of certain artificial neural networks have been conducted using fractional metric dimension. The fractional metric dimension of probabilistic neural networks <inline-formula> <tex-math notation="LaTeX">$P_{n,k}^{m}$ </tex-math></inline-formula> and convolutional neural networks <inline-formula> <tex-math notation="LaTeX">$C_{n,k}^{m}$ </tex-math></inline-formula> have been computed. Further, an application is discussed in the context of IoT where sensor networks are deployed for the optimal installation of base stations in a fire and smoke monitoring sensor system in a 3- storey hospital building with each floor considered as layer of the artificial neural network. |
| first_indexed | 2024-04-24T18:54:14Z |
| format | Article |
| id | doaj.art-ae57d87fef714e438bacb4e7ac1dce60 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:54:14Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ae57d87fef714e438bacb4e7ac1dce602024-03-26T17:45:19ZengIEEEIEEE Access2169-35362024-01-0112375133752010.1109/ACCESS.2024.337047210445233Cognitive Analysis of Neural Networks Using Fractional Metric Dimension and ApplicationsFaiza Jamil0Agha Kashif1https://orcid.org/0000-0002-1097-3450Omar Alharbi2https://orcid.org/0000-0003-4583-1863Sohail Zafar3https://orcid.org/0000-0002-8177-7799Amer Aljaedi4https://orcid.org/0000-0003-4099-5025Yazeed Mohammad Qasaymeh5https://orcid.org/0000-0002-4096-4707Department of Mathematics, University of Management and Technology, Johar Town, Lahore, PakistanDepartment of Mathematics, University of Management and Technology, Johar Town, Lahore, PakistanDepartment of Electrical Engineering, College of Engineering, Majmaah University, Al-Majma’ah, Saudi ArabiaDepartment of Mathematics, University of Management and Technology, Johar Town, Lahore, PakistanCollege of Computing and Information Technology, University of Tabuk, Tabuk, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, Majmaah University, Al-Majma’ah, Saudi ArabiaFractional versions of metric related parameters have been introduced as an equivalent to solve linear optimization problems which have applications in various fields like computer science and chemistry. The understanding and analysis of various parameters in the context of networks involved in transmitting the data is referred as the cognitive analysis. These parameters analyze the abstract structures of networks which widens the scope of application in the areas such as networking and linear optimization. In particular, the metric related parameters are used in navigation of robots to their destinations by minimum utilization time and nodes. The application of neural networks can be seen in diverse areas including data flow optimization, healthcare, cognitive psychology, geographical routing, supply chain optimization problems, wireless communication networks and Internet of Things (IoT). In the current work, the cognitive analysis of certain artificial neural networks have been conducted using fractional metric dimension. The fractional metric dimension of probabilistic neural networks <inline-formula> <tex-math notation="LaTeX">$P_{n,k}^{m}$ </tex-math></inline-formula> and convolutional neural networks <inline-formula> <tex-math notation="LaTeX">$C_{n,k}^{m}$ </tex-math></inline-formula> have been computed. Further, an application is discussed in the context of IoT where sensor networks are deployed for the optimal installation of base stations in a fire and smoke monitoring sensor system in a 3- storey hospital building with each floor considered as layer of the artificial neural network.https://ieeexplore.ieee.org/document/10445233/Neural networksfractional metric dimensionmetric dimensionwireless communication networksIoTsensor networking |
| spellingShingle | Faiza Jamil Agha Kashif Omar Alharbi Sohail Zafar Amer Aljaedi Yazeed Mohammad Qasaymeh Cognitive Analysis of Neural Networks Using Fractional Metric Dimension and Applications IEEE Access Neural networks fractional metric dimension metric dimension wireless communication networks IoT sensor networking |
| title | Cognitive Analysis of Neural Networks Using Fractional Metric Dimension and Applications |
| title_full | Cognitive Analysis of Neural Networks Using Fractional Metric Dimension and Applications |
| title_fullStr | Cognitive Analysis of Neural Networks Using Fractional Metric Dimension and Applications |
| title_full_unstemmed | Cognitive Analysis of Neural Networks Using Fractional Metric Dimension and Applications |
| title_short | Cognitive Analysis of Neural Networks Using Fractional Metric Dimension and Applications |
| title_sort | cognitive analysis of neural networks using fractional metric dimension and applications |
| topic | Neural networks fractional metric dimension metric dimension wireless communication networks IoT sensor networking |
| url | https://ieeexplore.ieee.org/document/10445233/ |
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