Search Results - (( develop smes data algorithm ) OR ( java implication based algorithm ))
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Aligning Malaysian SMEs with the megatrends: the roles of HPWPs and employee creativity in enhancing Malaysian SME performance
Published 2018“…The partial least squares algorithm and the bootstrapping technique were used for data analysis. …”
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Article -
2
Strategic capabilities, innovation strategy and the performance of food and beverage small and medium enterprises
Published 2019“…Based on the model developed, a questionnaire was constructed and personally administered at random to collect the data from 229 respondents in the study area. …”
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Thesis -
3
Effect of business social responsibility (BSR) on performance of SMEs in Nigeria
Published 2014“…A conceptual framework was developed based on extant literatures and the develop model is based on these BSR constructs Data was collected through hand delivery method by sending questionnaires to 800 SMEs managers/owners. …”
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Thesis -
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Developing an app for streamlined inventory tracking with barcode scanning and load planning optimization
Published 2025“…The application was implemented using React Native for mobile development and Firebase Firestore as the backend database to enable real-time data synchronization, while a binary tree bin packing algorithm was applied to generate efficient cargo loading arrangements. …”
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Final Year Project / Dissertation / Thesis -
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Hybrid neural network in medicolegal degree of injury determination based on Visum et Repertum
Published 2023“…Pre-processing phase overcomes the issue of incomplete data by performing data cleansing and data normalization. …”
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Thesis -
7
Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method
Published 2024thesis::doctoral thesis -
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An improved diabetes risk prediction framework : An Indonesian case study
Published 2018“…Pre-processing resolves the issue of missing data and hence normalizes the data.Outlier treatment employs k-mean clustering to validate the class.Suitable components were selected through comparison of classifier algorithms and feature selection.Attribute weighting based feature selection was selected for assigning weightage.Weighted risk factor was used on training dataset in order to improve accuracy and computation time of the prediction. …”
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Thesis -
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