FSOA‐DNFNet: Incremental indexing and image classification using hybrid optimization‐based deep neuro Fuzzy network
The rapid evolution and tremendous growth of internet has provided massive growth of unstructured data that leads to a complexity while retrieving dynamic data effectively. The rapid growth in data volume has imposed many challenging constraints, such as necessity to retrieve data completely even if...
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| Veröffentlicht in: | Concurrency and computation Jg. 34; H. 19 |
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| Sprache: | Englisch |
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Hoboken, USA
John Wiley & Sons, Inc
30.08.2022
Wiley Subscription Services, Inc |
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| Abstract | The rapid evolution and tremendous growth of internet has provided massive growth of unstructured data that leads to a complexity while retrieving dynamic data effectively. The rapid growth in data volume has imposed many challenging constraints, such as necessity to retrieve data completely even if newly arrived samples are occurred and storing of huge volume of data. This has paved a way for concentrating more on incremental learning that functions on information streams. To speed up retrieval, clustering methods and indexes are utilized and periodic updating of clusters is very substantial because of dynamic nature of databases. Moreover, the standard of clustering techniques purely based on data representation techniques, in which traditional methods faced problems like dimensionality explosion and sparsity. To address such limitations, an effectual strategy is developed for incremental indexing and image classification using proposed Feedback Social Optimization Algorithm (FSOA). The image classification is effectively carried out using Deep neuro fuzzy optimizer and it is trained by employing the proposed FSOA and newly FSOA is derived by the integration of Feedback Artificial Tree (FAT) Algorithm and Social Optimization Algorithm (SOA). Moreover, the proposed FSOA has achieved the maximum clustering accuracy of 93.382, the maximum testing accuracy of 94.4, the maximum sensitivity of 91.892, and the maximum specificity of 96.058. |
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| AbstractList | The rapid evolution and tremendous growth of internet has provided massive growth of unstructured data that leads to a complexity while retrieving dynamic data effectively. The rapid growth in data volume has imposed many challenging constraints, such as necessity to retrieve data completely even if newly arrived samples are occurred and storing of huge volume of data. This has paved a way for concentrating more on incremental learning that functions on information streams. To speed up retrieval, clustering methods and indexes are utilized and periodic updating of clusters is very substantial because of dynamic nature of databases. Moreover, the standard of clustering techniques purely based on data representation techniques, in which traditional methods faced problems like dimensionality explosion and sparsity. To address such limitations, an effectual strategy is developed for incremental indexing and image classification using proposed Feedback Social Optimization Algorithm (FSOA). The image classification is effectively carried out using Deep neuro fuzzy optimizer and it is trained by employing the proposed FSOA and newly FSOA is derived by the integration of Feedback Artificial Tree (FAT) Algorithm and Social Optimization Algorithm (SOA). Moreover, the proposed FSOA has achieved the maximum clustering accuracy of 93.382, the maximum testing accuracy of 94.4, the maximum sensitivity of 91.892, and the maximum specificity of 96.058. |
| Author | Srikrishna, Atluri Rao, Kancherla Gangadhara Nadendla, Hanumantha Rao |
| Author_xml | – sequence: 1 givenname: Hanumantha Rao orcidid: 0000-0002-6617-1086 surname: Nadendla fullname: Nadendla, Hanumantha Rao email: hanu.nadendla@gmail.com organization: RVR & JC College of Engineering – sequence: 2 givenname: Atluri surname: Srikrishna fullname: Srikrishna, Atluri organization: RVR & JC College of Engineering – sequence: 3 givenname: Kancherla Gangadhara surname: Rao fullname: Rao, Kancherla Gangadhara organization: Acharya Nagarjuna University |
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| Cites_doi | 10.1007/s10044-019-00831-1 10.1145/1348246.1348248 10.1007/978-1-4471-4929-3_10 10.1016/j.patcog.2019.06.001 10.1002/2050-7038.12593 10.1002/spe.2851 10.1109/IWCMC.2019.8766673 10.1016/j.ins.2020.08.052 10.1016/j.asoc.2018.07.016 10.1109/CSCI.2014.60 10.1016/j.neucom.2018.12.080 10.1016/j.aej.2015.08.009 10.1016/j.eswa.2019.01.017 10.1109/LGRS.2018.2854847 10.3233/JIFS-179579 10.1016/j.eswa.2007.08.049 10.1109/TPAMI.2007.28 10.1007/s00500-020-04758-2 10.1016/j.dsp.2020.102898 10.1080/13682199.2017.1378285 10.1145/1815330.1815377 10.1016/j.future.2020.08.031 10.1016/j.patcog.2018.03.034 |
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| SubjectTerms | Algorithms Clustering deep neuro fuzzy network Feedback Feedback Artificial Tree Algorithm Image classification incremental indexing Indexing Optimization Optimization algorithms Social Optimization Algorithm Unstructured data |
| Title | FSOA‐DNFNet: Incremental indexing and image classification using hybrid optimization‐based deep neuro Fuzzy network |
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