Forthcoming and Online First Articles

International Journal of Intelligent Information and Database Systems

International Journal of Intelligent Information and Database Systems (IJIIDS)

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International Journal of Intelligent Information and Database Systems (4 papers in press)

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  • A fire detection and localisation method based on keyframes and superpixels for large-space buildings   Order a copy of this article
    by Qiansheng Fang, Zhuang Peng, Pu Yan, Jing Huang 
    Abstract: Relevant fire detection and localisation methods suffer from the problems of slow detection speeds, low detection accuracies and low localisation precisions when applied to large-space buildings. To address these problems, a fire detection and localisation method based on keyframes and superpixels is proposed herein. Initially, we improve the SuperPoint method to extract video keyframes; then, we detect and locate fires only on these extracted video keyframes, thus extensively improving the detection speed. Then, we perform fire detection and localisation via superpixel and multi-feature fusion methods applied to the extracted video keyframes, and the results are more accurate than those obtained with single features, thus realising precise localisation. The experimental results obtained using a publicly available fire dataset reveal that our method realises good keyframe extraction, fire detection and fire localisation performances and is thus suitable for detecting and localising fires under large-building surveillance.
    Keywords: fire detection; SuperPoint; keyframes; superpixels; multi-feature; fire localisation.
    DOI: 10.1504/IJIIDS.2022.10048691
  • Implementation of a framework for graph-based keyword search over relational data   Order a copy of this article
    by Vittoria Cozza 
    Abstract: The challenge of easily interconnecting and exploiting the increasing amount of data from structured data sources is still a primary concern for researchers from the industry and the academy. Keyword search over structured data systems (KSSs) has attracted much interest as it provides a simple interface to query structured data. At the best of our knowledge, KSSs evolved neither into a standard model nor into a commercial product. The implementation of every new system published so far was isolated from the previous systems, even when it advanced the state of the art for a single aspect. Also, the source code of these systems is not shared and the experimental results are not easily replicable. The present study aims at filling this gap, by the design and the shared implementation of a unified framework for graph-based KSSs.
    Keywords: relational data; keyword search; information system; knowledge management systems.
    DOI: 10.1504/IJIIDS.2022.10050259
  • Improved Hamming-space-based similarity search algorithm   Order a copy of this article
    by Vikram Singh, Chandradeep Kumar 
    Abstract: In the modern context, the similarity is driven by the quality-features of the data objects and steered by content preserving stimuli, as retrieval of relevant 'nearest neighbourhood' objects and the way similar objects are pursued. Current similarity searches in Hamming-space-based strategies finds all the data objects within a threshold Hamming-distance for a user query. Though, the numbers of computations for Hamming-distance and candidate generation are the key concerns from the several years. The Hamming-space paradigm extends the range of alternatives for an optimised search experience. A novel 'counting-based' similarity search strategy is proposed, with an aprior and improved Hamming-space estimation, e.g., optimised candidate generation and verification functions. The strategy adapts towards the lesser set of user query dimensions and subsequently constraints the Hamming-space computations with each data objects, driven by generated statistics. The extensive evaluation asserts that the proposed counting-based approach can be combined with any pigeonhole principle-based similarity search to further improve its performance.
    Keywords: Hamming-space; information retrieval; similarity search.
    DOI: 10.1504/IJIIDS.2022.10050492
  • Intelligent computational techniques of machine learning models for demand analysis and prediction   Order a copy of this article
    by G. Naveen Sundar, K. Anushka Xavier, D. Narmadha, K. Martin Sagayam, A. Amir Anton Jone, Marc Pomplun, Hien Dang 
    Abstract: In the proposed model, a novel approach is introduced to discover an optimal machine learning model for food demand prediction. To create an exemplary model, we used twelve different machine learning models to analyse and interpret the historical data. Feature engineering techniques have been deployed to yield better performance. All methods were evaluated using RMSE evaluation metrics to determine the optimal model. Our methodology is one of its kind to reduce the error rate to a marginal level. The novelty of our research is that the root mean square error (RMSE) value for the demand prediction was reduced to 2.61e-16 using linear regression, thus achieving a better performance. The random forest, decision tree, and extreme gradient boosting regression also performed well, producing an RMSE value of 1.42e-9, 1.93e-15, and 4.87e-18 respectively. The predictive power of the system was 100% for R-squared metrics.
    Keywords: demand prediction; machine learning; linear regression; feature extraction.
    DOI: 10.1504/IJIIDS.2022.10051510