Forthcoming 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 Articles will not be published until the final proofs are validated by their authors.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 (3 papers in press)

Regular Issues

  • Exploring Prompt Engineering: Techniques, Challenges, and Use Cases   Order a copy of this article
    by Vani Eswari K. Kadhiresan, Vijayarani S 
    Abstract: Prompt engineering is an emerging area within natural language processing (NLP) that focuses on designing and refining prompts to guide large language models (LLMs) such as GPT and BERT. As LLMs have advanced in size and capability, the role of prompt engineering has become increasingly important in shaping model behaviour and improving response quality. Well-crafted prompts enhance accuracy, reduce ambiguity, and help mitigate issues such as bias and unintended outputs. The growth of few-shot and zero-shot learning has further highlighted the value of effective prompt design, enabling models to perform complex tasks with minimal training data. This paper provides an overview of the evolution, principles, and techniques of prompt engineering, explores its practical applications across various sectors, and discusses challenges related to ambiguity, ethics, and scalability. By examining advanced approaches and future research trends, the study emphasises the significance of prompt engineering in developing reliable and responsible AI systems.
    Keywords: Artificial Intelligence; Natural language processing; Prompt engineering; Machine Learning; Healthcare; Deep Learning; Content Generation.
    DOI: 10.1504/IJSSS.2025.10076899
     
  • Machine Learning of Control Grades Classification for Land Use Regulations   Order a copy of this article
    by Ruolan Lei, Xiao Teng, Zhenjiang Shen 
    Abstract: This study proposes a machine learning approach to classify land use control regulations in China based on control grades. By systematically organising zoning access requirements from multi-level planning documents, a regulation database is constructed. Regulations are categorised into five control grades: Encouraged, Permitted, Limited, Strictly Limited, and Prohibited. Each regulation is labelled and preprocessed for use in a Naive Bayes classification model. The model automates the classification of new regulations, enabling the rapid creation of digitised, graded control lists. This approach enhances the standardisation and digitisation of land use control and supports timely policy response to evolving planning needs.
    Keywords: Graded control; zoning access; text classification; Naive Bayes; database of regulations; control lists.
    DOI: 10.1504/IJSSS.2025.10076992
     
  • An Effective Novel Neural Architecture for Synthesising Faces from given Text Description   Order a copy of this article
    by Sonal Bankar, Satish Ket 
    Abstract: In the domain of text to image synthesis, ensuring the generated images exhibit high realism, variety, and semantic correctness remains a significant challenge. This research addresses these issues by proposing a novel generative adversarial network (GAN)-based framework aimed at improving image realism and quality. Our objectives include analysing existing text to image synthesis algorithms, developing a new framework and algorithms for enhanced visual realism, and evaluating the proposed methods against current standards using the CelebA dataset. We introduce a single-stage text-to-face generation approach that mitigates feature entanglement and enhances the semantic accuracy of generated images. Additionally, a caption generation algorithm is developed to produce diverse text descriptions from binary attributes, facilitating more effective learning for the model. The expected outcome is a substantial improvement in the quality and variety of synthesised images, contributing to the broader scope of applications in text to image synthesis. This research paves the way for advanced methodologies in generating semantically accurate and visually realistic images from textual descriptions.
    Keywords: Text To Image Synthesis; Generative Adversarial Network (GAN); Feature Entanglement; Visual Realism.
    DOI: 10.1504/IJSSS.2026.10077072
     

 

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