Forthcoming and Online First Articles

International Journal of Web and Grid Services

International Journal of Web and Grid Services (IJWGS)

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 Articles will not be published until the final proofs are validated by their authors.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

Regular Issues

  • Improving Accessibility of IT Devices for Individuals with Disabilities by Examining Their Characteristics and Voice of Customers   Order a copy of this article
    by Junghoon Park 
    Abstract: Accessibility is crucial for individuals with disabilities to participate fully in society. While the USA has implemented systems to ensure accessibility, many countries have more relaxed approaches. Advancements in IT technology have highlighted the importance of IT accessibility, as the expanding range of tasks achievable with these devices may present new challenges for disabled individuals. This paper collects voice of the customer (VoC) data from physically, hearing, or visually impaired people, focusing on the upper limb disabled who are often overlooked and propose guidelines to improve IT device accessibility, including smartphones, tablets, TVs, PCs, and eyemouse. In addition, this paper detailly analyses the characteristics of equipment and devices for three types of physical impairments, investigate VoC, and propose solutions to enhance future IT device accessibility.
    Keywords: Accessibility; Physically Impaired People; eyemouse; Digital Hands; Disability.
    DOI: 10.1504/IJWGS.2025.10067590
     
  • Intelligent Cognitive Internet of Things -based Spectrum Sensing Algorithm for Future Communication   Order a copy of this article
    by Haewon Byeon, Mahmood Alsaadi, Aadam Quraishi, Ismail Keshta, Mukesh Soni, Pankaj Kumar, Mohit Bhadla, Muhammad Attique Khan, Robertas Damasevicius 
    Abstract: The emergence of fifth-generation (5G) mobile communication technologies has propelled the advancement of the Internet of Things (IoT). Nevertheless, the intricate nature of the IoT mobile communication environment and the fluctuating characteristics of the signal’s present substantial obstacles to current spectrum detection techniques for future communication. Hence, an artificial intelligent spectrum sensing technique is introduced, which integrates artificial intelligent, IoT and denoising autoencoder (DAE) with an enhanced long short-term memory (LSTM) neural network. The DAE utilizes encoding and decoding to retrieve the fundamental structural characteristics of mobile signals, while the enhanced LSTM spectrum sensing classifier model incorporates previous moment information features to classify the time-series signal sequences. This method has demonstrated a 45% improvement in perception performance compared to SVM, RNN, LeNet5, LVQ, and Elman algorithm.
    Keywords: Future Communication; Internet of Things; Spectrum Sensing; Artificial Intelligent; LSTM; DAE.
    DOI: 10.1504/IJWGS.2025.10068453
     
  • Bi-Phase LSTM: A LSTM-Based Autoencoder Architecture for Dynamic Social Network Prediction   Order a copy of this article
    by Hui Lin, Yi-Cheng Chen 
    Abstract: In recent years, social networks have grown in popularity, with most people actively engaging on these platforms. These networks hold valuable insights into users’ values and interests, allowing us to analyse relationships between connected individuals and even predict potential friendships. However, social networks are dynamic, and their structure evolves over time. To account for this, we employed a dual approach using a bi-phase LSTM autoencoder and a bi-phase LSTM predictor. These tools capture the changing characteristics of social networks and predict future graph structures. We rigorously tested our model on three datasets and compared its performance with other models. The bi-phase LSTM consistently delivered strong results across all datasets. Additionally, the model’s hyperparameters were fine-tuned to improve predictive accuracy, demonstrating its reliability in forecasting the evolution of social network structures.
    Keywords: feature extraction; autoencoder; decoder; long short-term memory; dynamic social network.
    DOI: 10.1504/IJWGS.2025.10068502
     

 

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