Matches in SemOpenAlex for { <https://semopenalex.org/work/W3093946813> ?p ?o ?g. }
Showing items 1 to 98 of
98
with 100 items per page.
- W3093946813 endingPage "2050074" @default.
- W3093946813 startingPage "2050074" @default.
- W3093946813 abstract "Today, data processing has become a challenging task due to the significant increase in the amount of data collected using various sensors. To put up knowledge and forecast the data, the existing data mining techniques compute all numerical attributes in the memory simultaneously. However, the over-abundance of entire factors in the data makes accurate prediction infeasible. This paper attempts to implement a new data prediction model using an optimized machine learning algorithm. The proposed data prediction model involves four main phases: (a) data acquisition, (b) feature extraction, (c) data normalization, and (d) prediction. Initially, few data from the UCI repository like Bike Sharing Dataset, Carbon Nanotubes, Concrete Compressive Strength, Electrical Grid Stability Simulated Data, and SkillCraft-1 Master Table are collected. Further, the feature extraction process extracts the first-order statistics like mean, median, standard deviation, the maximum value of entire data, and the minimum value of entire data, and the second-order statistics like kurtosis, skewness, energy, and entropy. Next, the data or feature normalization is done to arrange the data within a certain limit. The normalized features are then subjected to a hybrid prediction system by integrating the Recurrent Neural Network (RNN) and Fuzzy Regression model. As a modification, the number of hidden neurons in the RNN and membership limits of the Fuzzy Regression model are optimized by a hybrid optimization algorithm by merging the concepts of Whale Optimization Algorithm (WOA) and Cat Swarm Optimization (CSO), which is called the Whale Updated Seek Mode-based CSO (WS-CSO) algorithm. Then, the efficiency of the optimized hybrid classifier for all-time prediction of data in different applications is confirmed based on its valuable performance and comparative analysis." @default.
- W3093946813 created "2020-10-29" @default.
- W3093946813 creator A5015590614 @default.
- W3093946813 creator A5030898251 @default.
- W3093946813 date "2020-12-16" @default.
- W3093946813 modified "2023-10-16" @default.
- W3093946813 title "Optimized recurrent neural network with fuzzy classifier for data prediction using hybrid optimization algorithm: Scope towards diverse applications" @default.
- W3093946813 cites W1190374815 @default.
- W3093946813 cites W1575501007 @default.
- W3093946813 cites W1577517492 @default.
- W3093946813 cites W1983884835 @default.
- W3093946813 cites W1990480090 @default.
- W3093946813 cites W2017108778 @default.
- W3093946813 cites W2045324102 @default.
- W3093946813 cites W2053823116 @default.
- W3093946813 cites W2061438946 @default.
- W3093946813 cites W2077913352 @default.
- W3093946813 cites W2094689349 @default.
- W3093946813 cites W2115330677 @default.
- W3093946813 cites W2127414816 @default.
- W3093946813 cites W2150455084 @default.
- W3093946813 cites W2154029067 @default.
- W3093946813 cites W2164126356 @default.
- W3093946813 cites W2170747753 @default.
- W3093946813 cites W2215065037 @default.
- W3093946813 cites W2290883490 @default.
- W3093946813 cites W2343146666 @default.
- W3093946813 cites W2460404912 @default.
- W3093946813 cites W2507548850 @default.
- W3093946813 cites W2609731728 @default.
- W3093946813 cites W2610135452 @default.
- W3093946813 cites W2791019495 @default.
- W3093946813 cites W2802223983 @default.
- W3093946813 cites W2890487029 @default.
- W3093946813 cites W2920802601 @default.
- W3093946813 cites W2945065837 @default.
- W3093946813 cites W2949767632 @default.
- W3093946813 cites W2953618482 @default.
- W3093946813 cites W2959063495 @default.
- W3093946813 cites W2969279223 @default.
- W3093946813 cites W2974181587 @default.
- W3093946813 cites W2988622501 @default.
- W3093946813 doi "https://doi.org/10.1142/s0219691320500745" @default.
- W3093946813 hasPublicationYear "2020" @default.
- W3093946813 type Work @default.
- W3093946813 sameAs 3093946813 @default.
- W3093946813 citedByCount "14" @default.
- W3093946813 countsByYear W30939468132021 @default.
- W3093946813 countsByYear W30939468132022 @default.
- W3093946813 countsByYear W30939468132023 @default.
- W3093946813 crossrefType "journal-article" @default.
- W3093946813 hasAuthorship W3093946813A5015590614 @default.
- W3093946813 hasAuthorship W3093946813A5030898251 @default.
- W3093946813 hasConcept C11413529 @default.
- W3093946813 hasConcept C119857082 @default.
- W3093946813 hasConcept C124101348 @default.
- W3093946813 hasConcept C136886441 @default.
- W3093946813 hasConcept C144024400 @default.
- W3093946813 hasConcept C153180895 @default.
- W3093946813 hasConcept C154945302 @default.
- W3093946813 hasConcept C19165224 @default.
- W3093946813 hasConcept C2780724565 @default.
- W3093946813 hasConcept C41008148 @default.
- W3093946813 hasConcept C50644808 @default.
- W3093946813 hasConcept C85617194 @default.
- W3093946813 hasConceptScore W3093946813C11413529 @default.
- W3093946813 hasConceptScore W3093946813C119857082 @default.
- W3093946813 hasConceptScore W3093946813C124101348 @default.
- W3093946813 hasConceptScore W3093946813C136886441 @default.
- W3093946813 hasConceptScore W3093946813C144024400 @default.
- W3093946813 hasConceptScore W3093946813C153180895 @default.
- W3093946813 hasConceptScore W3093946813C154945302 @default.
- W3093946813 hasConceptScore W3093946813C19165224 @default.
- W3093946813 hasConceptScore W3093946813C2780724565 @default.
- W3093946813 hasConceptScore W3093946813C41008148 @default.
- W3093946813 hasConceptScore W3093946813C50644808 @default.
- W3093946813 hasConceptScore W3093946813C85617194 @default.
- W3093946813 hasIssue "02" @default.
- W3093946813 hasLocation W30939468131 @default.
- W3093946813 hasOpenAccess W3093946813 @default.
- W3093946813 hasPrimaryLocation W30939468131 @default.
- W3093946813 hasRelatedWork W2359348847 @default.
- W3093946813 hasRelatedWork W2591697403 @default.
- W3093946813 hasRelatedWork W2904022177 @default.
- W3093946813 hasRelatedWork W2904857019 @default.
- W3093946813 hasRelatedWork W2944728705 @default.
- W3093946813 hasRelatedWork W2953716828 @default.
- W3093946813 hasRelatedWork W3011538607 @default.
- W3093946813 hasRelatedWork W4294432981 @default.
- W3093946813 hasRelatedWork W4321441197 @default.
- W3093946813 hasRelatedWork W4322731620 @default.
- W3093946813 hasVolume "19" @default.
- W3093946813 isParatext "false" @default.
- W3093946813 isRetracted "false" @default.
- W3093946813 magId "3093946813" @default.
- W3093946813 workType "article" @default.