FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4362518972> ?p ?o ?g. }

• W4362518972 abstract "Abstract The progress of Industrial Revolution 4.0 has been supported by recent advances in several domains, and one of the main contributors is the Internet of Things. Smart factories and healthcare have both benefited in terms of leveraged quality of service and productivity rate. However, there is always a trade‐off and some of the largest concerns include security, intrusion, and failure detection, due to high dependence on the Internet of Things devices. To overcome these and other challenges, artificial intelligence, especially machine learning algorithms, are employed for fault prediction, intrusion detection, computer‐aided diagnostics, and so forth. However, efficiency of machine learning models heavily depend on feature selection, predetermined values of hyper‐parameters and training to deliver a desired result. This paper proposes a swarm intelligence‐based approach to tune the machine learning models. A novel version of the firefly algorithm, that overcomes known deficiencies of original method by employing diversification‐based mechanism, has been proposed and applied to both feature selection and hyper‐parameter optimization of two machine learning models—XGBoost and extreme learning machine. The proposed approach has been tested on four real‐world Industry 4.0 data sets, namely distributed transformer monitoring, elderly fall prediction, BoT‐IoT, and UNSW‐NB 15. Achieved results have been compared to the results of eight other cutting‐edge metaheuristics, that have been implemented and tested under the same conditions. The experimental outcomes strongly indicate that the proposed approach significantly outperformed all other competitor metaheuristics in terms of convergence speed and results' quality measured with standard metrics—accuracy, precision, recall, and f1‐score." @default.
• W4362518972 created "2023-04-06" @default.
• W4362518972 creator A5003009640 @default.
• W4362518972 creator A5006488537 @default.
• W4362518972 creator A5061798785 @default.
• W4362518972 creator A5064081550 @default.
• W4362518972 creator A5068911769 @default.
• W4362518972 creator A5069806748 @default.
• W4362518972 creator A5072469505 @default.
• W4362518972 date "2023-03-30" @default.
• W4362518972 modified "2023-10-16" @default.
• W4362518972 title "Machine learning tuning by diversity oriented firefly metaheuristics for <scp>Industry 4.0</scp>" @default.
• W4362518972 cites W1496413023 @default.
• W4362518972 cites W1523741643 @default.
• W4362518972 cites W1528741131 @default.
• W4362518972 cites W1968918799 @default.
• W4362518972 cites W1993216154 @default.
• W4362518972 cites W1999077811 @default.
• W4362518972 cites W2005551422 @default.
• W4362518972 cites W2016944307 @default.
• W4362518972 cites W2029636129 @default.
• W4362518972 cites W2043845271 @default.
• W4362518972 cites W2057848726 @default.
• W4362518972 cites W2061438946 @default.
• W4362518972 cites W2061490701 @default.
• W4362518972 cites W2064663555 @default.
• W4362518972 cites W2085891931 @default.
• W4362518972 cites W2096166399 @default.
• W4362518972 cites W2100307718 @default.
• W4362518972 cites W2111072639 @default.
• W4362518972 cites W2149848331 @default.
• W4362518972 cites W2151554678 @default.
• W4362518972 cites W2153351685 @default.
• W4362518972 cites W2156773695 @default.
• W4362518972 cites W2157833270 @default.
• W4362518972 cites W2161970727 @default.
• W4362518972 cites W2167101736 @default.
• W4362518972 cites W2232317135 @default.
• W4362518972 cites W2290883490 @default.
• W4362518972 cites W2736255997 @default.
• W4362518972 cites W2743674937 @default.
• W4362518972 cites W2744687549 @default.
• W4362518972 cites W2766900003 @default.
• W4362518972 cites W2883908928 @default.
• W4362518972 cites W2895763047 @default.
• W4362518972 cites W2907334172 @default.
• W4362518972 cites W2908162093 @default.
• W4362518972 cites W2963748489 @default.
• W4362518972 cites W2981111316 @default.
• W4362518972 cites W3003976313 @default.
• W4362518972 cites W3022917919 @default.
• W4362518972 cites W3024048126 @default.
• W4362518972 cites W3033928040 @default.
• W4362518972 cites W3045832254 @default.
• W4362518972 cites W3047928395 @default.
• W4362518972 cites W3094310242 @default.
• W4362518972 cites W3098924781 @default.
• W4362518972 cites W3100105995 @default.
• W4362518972 cites W3103706901 @default.
• W4362518972 cites W3111221273 @default.
• W4362518972 cites W3114266307 @default.
• W4362518972 cites W3119051141 @default.
• W4362518972 cites W3124270557 @default.
• W4362518972 cites W3124724827 @default.
• W4362518972 cites W3129684848 @default.
• W4362518972 cites W3135319756 @default.
• W4362518972 cites W3137304821 @default.
• W4362518972 cites W3148089430 @default.
• W4362518972 cites W3158845577 @default.
• W4362518972 cites W3161555263 @default.
• W4362518972 cites W3164271353 @default.
• W4362518972 cites W3177327711 @default.
• W4362518972 cites W3180891084 @default.
• W4362518972 cites W3185430200 @default.
• W4362518972 cites W3188490036 @default.
• W4362518972 cites W3193695748 @default.
• W4362518972 cites W3194627252 @default.
• W4362518972 cites W3199075348 @default.
• W4362518972 cites W3199967663 @default.
• W4362518972 cites W341879454 @default.
• W4362518972 cites W4200142359 @default.
• W4362518972 cites W4205530294 @default.
• W4362518972 cites W4205722673 @default.
• W4362518972 cites W4205752277 @default.
• W4362518972 cites W4210306814 @default.
• W4362518972 cites W4210977106 @default.
• W4362518972 cites W4211116959 @default.
• W4362518972 cites W4211248338 @default.
• W4362518972 cites W4220912501 @default.
• W4362518972 cites W4221079929 @default.
• W4362518972 cites W4224237294 @default.
• W4362518972 cites W4224302037 @default.
• W4362518972 cites W4225165893 @default.
• W4362518972 cites W4225586937 @default.
• W4362518972 cites W4226499541 @default.
• W4362518972 cites W4281572388 @default.
• W4362518972 cites W4281756816 @default.
• W4362518972 cites W4283721511 @default.
• W4362518972 cites W4285308189 @default.
• W4362518972 doi "https://doi.org/10.1111/exsy.13293" @default.

• next