Search Results - (( develop smes data algorithm ) OR ( java implication based algorithm ))

  • Showing 1 - 9 results of 9
Refine Results
  1. 1
  2. 2

    Strategic capabilities, innovation strategy and the performance of food and beverage small and medium enterprises by Salisu, Yakubu

    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. …”
    Get full text
    Get full text
    Get full text
    Thesis
  3. 3

    Effect of business social responsibility (BSR) on performance of SMEs in Nigeria by Gorondutse, Abdulahi Hassan

    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. …”
    Get full text
    Get full text
    Get full text
    Thesis
  4. 4
  5. 5

    Developing an app for streamlined inventory tracking with barcode scanning and load planning optimization by Teng, Yan Xin

    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. …”
    Get full text
    Get full text
    Final Year Project / Dissertation / Thesis
  6. 6

    Hybrid neural network in medicolegal degree of injury determination based on Visum et Repertum by Wardhana, Mohammad Hadyan

    Published 2023
    “…Pre-processing phase overcomes the issue of incomplete data by performing data cleansing and data normalization. …”
    Get full text
    Get full text
    Get full text
    Thesis
  7. 7
  8. 8

    An improved diabetes risk prediction framework : An Indonesian case study by Sutanto, Daniel Hartono

    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. …”
    Get full text
    Get full text
    Get full text
    Thesis
  9. 9