Environmental and Geographical (EG) Image Classification Using FLIM and CNN Algorithms

P Ajay; B Nagaraj; Ruihang Huang; M S Pradeep Raj; P Ananthi

doi:10.1155/2022/4989248

Environmental and Geographical (EG) Image Classification Using FLIM and CNN Algorithms

Contrast Media Mol Imaging. 2022 Aug 17:2022:4989248. doi: 10.1155/2022/4989248. eCollection 2022.

Authors

P Ajay¹, B Nagaraj², Ruihang Huang³, M S Pradeep Raj², P Ananthi⁴

Affiliations

¹ Faculty of Information and Communication Engineering, Anna University, Chennai, India.
² Department of Electronics and Communication Engineering, Rathinam Technical Campus, Coimbatore, India.
³ Donghua University, Shanghai, China.
⁴ Department of Artificial Intelligence and Data Science, Rathinam Technical Campus, Coimbatore, India.

Abstract

Intelligent machines have grown in importance in recent years in object recognition in terms of their ability to envision, comprehend, and reach decisions. There are a lot of complicated algorithms that accomplish AI utilities. In addition to their use in the medical industry, these methods of object recognition have a wide range of other fields, most notably industries, in which they can be applied. In contrast to the proposed calculation, the proposed calculation is less complex and more accurate under certain SNR conditions. In the deep nervous tissue fine-tuning discriminator, phantom highlights and binding highlights are separated as sources; modified direct components are used as neuronal activation abilities; and cross entropy is used as unfortunate abilities. Optimized recognition of profound nervous tissue builds profound and periodic nervous tissue for regulatory confirmation of the corresponding signal.

Publication types

Retracted Publication

MeSH terms

Algorithms*
Neural Networks, Computer*