Results 131 to 140 of about 2,761 (175)

An in-depth exploration of machine learning methods for mental health state detection: a systematic review and analysis. [PDF]

Front Digit Health
Hasan MJ, Shifat SH, Matubber J, Hossain R, Rahman MA, Haque BMT, Hossen MJ.   +6 more
europepmc   +1 more source

Automatic crack detection in civil infrastructure based on a hybrid fine tuned MnasNet and adaptive patching. [PDF]

Sci Rep
Li K, He F, Zhao X.
europepmc   +1 more source

Effect of particle irregularity and particle size distribution on the morphology of packed beds of biochar particles. [PDF]

Sci Rep
Monfared ZG, Hellström JGI, Umeki K.
europepmc   +1 more source

An interpretable IGWO-MKRVM model for predicting excavation damaged zone thickness of drift. [PDF]

Sci Rep
Yang R, Zhang G, Ye Y, Wan M.
europepmc   +1 more source

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An improved Dragonfly Algorithm for feature selection

Knowledge-Based Systems, 2020
Abstract Dragonfly Algorithm (DA) is a recent swarm-based optimization method that imitates the hunting and migration mechanisms of idealized dragonflies. Recently, a binary DA (BDA) has been proposed. During the algorithm iterative process, the BDA updates its five main coefficients using random values.
Majdi Mafarja, Abdelaziz Hammouri, Mohammed A Awadallah   +2 more
exaly   +2 more sources

Dragonfly algorithm: a comprehensive review and applications

Neural Computing and Applications, 2020
Dragonfly algorithm (DA) is a novel swarm intelligence meta-heuristic optimization algorithm inspired by the dynamic and static swarming behaviors of artificial dragonflies in nature. It has proved its effectiveness and superiority compared to several well-known meta-heuristics available in the literature.
Yassine Meraihi, Amar Ramdane-Cherif, Dalila Acheli, Mohammed Mahseur   +3 more
openaire   +3 more sources

Connectivity and constructive algorithms of disjoint paths in dragonfly networks

Theoretical Computer Science, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Suying Wu, Jianxi Fan, Baolei Cheng, Jia Yu 0003, Yan Wang 0078   +4 more
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Chaotic dragonfly algorithm: an improved metaheuristic algorithm for feature selection

Applied Intelligence, 2018
Selecting the most discriminative features is a challenging problem in many applications. Bio-inspired optimization algorithms have been widely applied to solve many optimization problems including the feature selection problem. In this paper, the most discriminating features were selected by a new Chaotic Dragonfly Algorithm (CDA) where chaotic maps ...
Gehad Ismail Sayed, Aboul Ella Hassanien, A Tharwat   +2 more
exaly   +2 more sources

Wind driven dragonfly algorithm for global optimization

Concurrency and Computation: Practice and Experience, 2020
SummaryDragonfly algorithm (DA) is a new swarm intelligence optimization algorithm based on the static and dynamic swarm behavior of dragonflies. The algorithm has the characteristics of simple structure, strong search ability, easy implementation, and strong robustness.
Lianlian Zhong, Yongquan Zhou, Qifang Luo, Keyu Zhong   +3 more
openaire   +1 more source

Dragonfly Algorithm (DA)

2017
The dragonfly algorithm (DA) is a new metaheuristic optimization algorithm, which is based on simulating the swarming behavior of dragonfly individuals. This algorithm was developed by Mirjalili (2016) and the preliminary studies illustrated its potential in solving numerous benchmark optimization problems and complex computational fluid dynamics (CFD)
Babak Zolghadr-Asli, Omid Bozorg-Haddad, Xuefeng Chu   +2 more
openaire   +3 more sources