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Cited by 185 publications
(60 citation statements)
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References 36 publications
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“…We can see that the model with feature selection performs better than the model without feature selection. This finding is in accordance with previous works who stated that feature selection not only can facilitate the model's forecasting performance, but also can reduce the storage requirement (Peng & Fan, 2017;Zhang, Song, & Gong, 2017;Costea, Ferrara, & Şerban, 2017). Among all of the experiments, the model with FRST reaches the best forecasting quality.…”
Section: The Practical Results and Statistical Examinationsupporting
confidence: 91%
“…We can see that the model with feature selection performs better than the model without feature selection. This finding is in accordance with previous works who stated that feature selection not only can facilitate the model's forecasting performance, but also can reduce the storage requirement (Peng & Fan, 2017;Zhang, Song, & Gong, 2017;Costea, Ferrara, & Şerban, 2017). Among all of the experiments, the model with FRST reaches the best forecasting quality.…”
Section: The Practical Results and Statistical Examinationsupporting
confidence: 91%
“…In such areas (e.g., western China), UAVs can collect many high-resolution outcrop images, which could be analyzed using the proposed method to assist in both mapping and geological interpretation while improving efficiency and reducing costs. In order to improve the efficiency of labeling, the feature extraction algorithm [35] will be studied to automatically extract the advantageous factors in the image. We also plan to apply other deep learning models, such as the state-of-art Mask RCNN [36], to identify many types of rock in the same image.…”
Section: Resultsmentioning
confidence: 99%
“…In addition to the MBO algorithm studied in this paper, many other intelligent algorithms [65] have been proposed, such as elephant herding optimization (EHO) [33,57], simulated annealing (SA) [27], evolutionary strategy (ES) [2], particle swarm optimization (PSO) [26,44], moth search (MS) algorithm [47], bat algorithm (BA) [35,68], differential evolution (DE) [43,53], biogeography-based optimization (BBO) [42,52], krill herd (KH) [16,51,54], cuckoo search (CS) [5,69], arti cial bee colony (ABC) [25,64], genetic algorithm (GA) [22], reworks algorithm (FWA) [46], earthworm optimization algorithm (EWA) [61], and harmony search (HS) [18,63]. These algorithms are used widely in various engineering applications [73,74].…”
Section: Mbo Algorithmmentioning
confidence: 99%