Forthcoming and Online First Articles

International Journal of Society Systems Science

International Journal of Society Systems Science (IJSSS)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Society Systems Science (2 papers in press)

Regular Issues

  • Predicting Student Employment before Graduation: A Comparison of Machine Learning Models   Order a copy of this article
    by Linsey Hugo, William A. Young II, Marco Habermann, Ashley Metcalf 
    Abstract: As universities are increasingly held accountable for students’ career outcomes and competition for jobs increases, institutions need to understand which students are more likely to be employed upon graduation and why. This study aims to determine to what extent undergraduate student academic and experience employability signals including major, GPA, co-curricular activities, and internships can predict if a student secures full-time employment before graduation. Therefore, this study uses and compares the effectiveness of commonly recognised and advanced machine learning models, including logistic regression, discriminant analysis, decision trees, and neural networks. Results demonstrate that employment before graduation can be predicted with 74% accuracy with a neural network as the most accurate predictive model compared to the other approaches. Moreover, a sensitivity analysis identified co-curricular activities and majors as statistically significant variables predicting employment upon graduation.
    Keywords: student employment; higher education; machine learning; logistic regression; discriminant analysis; decision trees; neural networks.
    DOI: 10.1504/IJSSS.2024.10066558
     
  • Image Identification using Convolutional Neural Networks   Order a copy of this article
    by Hameem Ahsan, Sohana Jahan, Md. Anwarul Islam Bhuiyan 
    Abstract: Image recognition is an element of computer vision that includes techniques for detecting, processing, and classifying images. Due to its frequent use in artificial intelligence, the demand for better image recognition methods is increasing exponentially. Key spatial characteristics in image data can be identified through convolutional neural networks (CNN), a subset of deep learning. In this article, CNN-based frameworks for the MNIST, CIFAR-10, and cats vs. dogs datasets have been developed. A supervised learning technique is used to train the proposed models. The proposed dataset-specific method of using CNN to solve categorisation issues has produced remarkable results. Numerical experiments suggest that the efficacy of the proposed models is comparable to that of state-of-the-art CNN frameworks.
    Keywords: image identification; CNN; regularisation; dropout; max pooling; loss function.
    DOI: 10.1504/IJSSS.2024.10067773
     

 

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