Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385776257> ?p ?o ?g. }
- W4385776257 endingPage "110735" @default.
- W4385776257 startingPage "110735" @default.
- W4385776257 abstract "This study suggests the use of unsupervised and supervised machine learning algorithms to categorize companies according to their innovation capabilities. Companies are categorized into three groups: good, satisfactory, and unsatisfactory, in order to create a thorough and reliable assessment procedure. In this study, unsupervised and supervised machine learning methods are used to solve an innovation capability evaluation problem. Data is provided via a survey which is performed in manufacturing industry in Turkiye Firstly, dimensions of innovation capability were determined Principal Component Analysis (PCA). Then data labels were determined by k-means clustering algorithm which is an unsupervised learning technique. A model is first trained using data provided via questionnaire survey, and it is then tested using fresh, unused data. The model is trained using classification algorithms including KNN, GaussianNB, RandomForest, Gradient Boosting, AdaBoost, DesisionTree, XGBOOST and LightGBMC, MLPC, and SVMC and its performance is evaluated against test data. Each classification techniques are evaluated using the performance metrics. With the highest accuracy rate of 93% and lowest MAE, MSE and RMSE values, The LightGBMC and SVMC methods were found the most efficient supervised learning method for innovation capability evaluation." @default.
- W4385776257 created "2023-08-12" @default.
- W4385776257 creator A5012337022 @default.
- W4385776257 creator A5032146051 @default.
- W4385776257 creator A5063032086 @default.
- W4385776257 creator A5074105857 @default.
- W4385776257 date "2023-11-01" @default.
- W4385776257 modified "2023-10-06" @default.
- W4385776257 title "Assessing innovation capabilities of manufacturing companies by combination of unsupervised and supervised machine learning approaches" @default.
- W4385776257 cites W1485606771 @default.
- W4385776257 cites W1506838574 @default.
- W4385776257 cites W1807984730 @default.
- W4385776257 cites W1939701833 @default.
- W4385776257 cites W1973207366 @default.
- W4385776257 cites W1975367507 @default.
- W4385776257 cites W1995354923 @default.
- W4385776257 cites W1996873527 @default.
- W4385776257 cites W1997376466 @default.
- W4385776257 cites W2001093799 @default.
- W4385776257 cites W2004195467 @default.
- W4385776257 cites W2005844706 @default.
- W4385776257 cites W2013716681 @default.
- W4385776257 cites W2014640268 @default.
- W4385776257 cites W2023134523 @default.
- W4385776257 cites W2033044641 @default.
- W4385776257 cites W2036156224 @default.
- W4385776257 cites W2045874197 @default.
- W4385776257 cites W2049499495 @default.
- W4385776257 cites W2052129015 @default.
- W4385776257 cites W2052452825 @default.
- W4385776257 cites W2060629694 @default.
- W4385776257 cites W2061852632 @default.
- W4385776257 cites W2065922020 @default.
- W4385776257 cites W2070519578 @default.
- W4385776257 cites W2071649941 @default.
- W4385776257 cites W2077039562 @default.
- W4385776257 cites W2084725963 @default.
- W4385776257 cites W2095149004 @default.
- W4385776257 cites W2101348440 @default.
- W4385776257 cites W2109868564 @default.
- W4385776257 cites W2121953399 @default.
- W4385776257 cites W2133927398 @default.
- W4385776257 cites W2148179342 @default.
- W4385776257 cites W2159178015 @default.
- W4385776257 cites W2160703039 @default.
- W4385776257 cites W2171920216 @default.
- W4385776257 cites W2173578770 @default.
- W4385776257 cites W2299632847 @default.
- W4385776257 cites W2319848999 @default.
- W4385776257 cites W2517850251 @default.
- W4385776257 cites W2529587104 @default.
- W4385776257 cites W2551160892 @default.
- W4385776257 cites W2585654202 @default.
- W4385776257 cites W2610886376 @default.
- W4385776257 cites W2790150901 @default.
- W4385776257 cites W2799636392 @default.
- W4385776257 cites W2802503214 @default.
- W4385776257 cites W2806587241 @default.
- W4385776257 cites W2899086901 @default.
- W4385776257 cites W2911964244 @default.
- W4385776257 cites W2912118475 @default.
- W4385776257 cites W2945637340 @default.
- W4385776257 cites W2981392244 @default.
- W4385776257 cites W3026222245 @default.
- W4385776257 cites W3034622080 @default.
- W4385776257 cites W3038189590 @default.
- W4385776257 cites W3041693093 @default.
- W4385776257 cites W3049761991 @default.
- W4385776257 cites W3093821457 @default.
- W4385776257 cites W3108896238 @default.
- W4385776257 cites W3119871675 @default.
- W4385776257 cites W3134600226 @default.
- W4385776257 cites W3139243332 @default.
- W4385776257 cites W3159933633 @default.
- W4385776257 cites W3191826301 @default.
- W4385776257 cites W3192526842 @default.
- W4385776257 cites W3201163790 @default.
- W4385776257 cites W3205435604 @default.
- W4385776257 cites W4230922133 @default.
- W4385776257 cites W4249777288 @default.
- W4385776257 cites W4293859728 @default.
- W4385776257 cites W4295549167 @default.
- W4385776257 cites W4304842675 @default.
- W4385776257 cites W4311750857 @default.
- W4385776257 cites W4315433554 @default.
- W4385776257 doi "https://doi.org/10.1016/j.asoc.2023.110735" @default.
- W4385776257 hasPublicationYear "2023" @default.
- W4385776257 type Work @default.
- W4385776257 citedByCount "1" @default.
- W4385776257 countsByYear W43857762572023 @default.
- W4385776257 crossrefType "journal-article" @default.
- W4385776257 hasAuthorship W4385776257A5012337022 @default.
- W4385776257 hasAuthorship W4385776257A5032146051 @default.
- W4385776257 hasAuthorship W4385776257A5063032086 @default.
- W4385776257 hasAuthorship W4385776257A5074105857 @default.
- W4385776257 hasConcept C119857082 @default.
- W4385776257 hasConcept C12267149 @default.
- W4385776257 hasConcept C124101348 @default.