Jaw fracture classification using meta heuristic firefly algorithm with multi-layered associative neural networks

Hashem, Mohamed; Hassanein, Azza S.

doi:10.1007/s10586-018-2668-z

Cited by 7 publications

(1 citation statement)

References 14 publications

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“…The proposed technique was later, compared with some benchmark methods such as LLS [ 145 ], SFC [ 146 ] etc and found high classification accuracy with a rate of 98.14 % for solving the classification problem of breast tumor. In the year 2018, Hashem et al [ 147 ] has proposed a novel methodology for the classification of the jaw fracture problem, with the help of FA. Considering precision, recall as well as accuracy as performance factors and for evaluating the performance, the proposed method has been applied over benchmark datasets CT [ 148 ] etc and a comparison has been made with some state-of-the-art algorithms such as GA, PSO etc.…”

Section: Applications Of Famentioning

confidence: 99%

Firefly Algorithm in Biomedical and Health Care: Advances, Issues and Challenges

Nayak

Naik

Dinesh³

et al. 2020

SN COMPUT. SCI.

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Since the past decades, most of the nature inspired optimization algorithms (NIOA) have been developed and become admired due to their effectiveness for resolving a variety of complex problems of dissimilar domain. Firefly algorithm (FA) is well-known, yet efficient nature inspired swarm intelligence (SI) based metaheuristic algorithm. Since from its initiation, FA has become well-liked between the researchers due to its competence and turn out to be an interesting technique for the practitioners as well as researchers for solving the problems of numerous fields of research such as classifications, clustering, neural networks, biomedical engineering, healthcare as well as other research domain. Moreover, there is an outstanding track record of FA in solving biomedical engineering (BME) and healthcare (HC) problems. Abundant complexities have been worked out with the assist of FA and its variants. By taking these particulars into concern, in this paper, a first ever in-depth analysis has been addressed on the variants, importance, applications as well as enhancements of FA in BME as well as HC. The major intention behind this investigative work is to motivate the researchers to improve and innovate new solutions for multifaceted problems of healthcare and biomedical engineering using FA.

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Section: Applications Of Famentioning

confidence: 99%