Forthcoming Articles

International Journal of Computational Intelligence Studies

International Journal of Computational Intelligence Studies (IJCIStudies)

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 Articles will not be published until the final proofs are validated by their authors.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 Computational Intelligence Studies (2 papers in press)

Special Issue on: OA Trustworthy and Hybrid Computational Intelligence for Edge-to-Society AI Systems

  •   Free full-text access Open AccessHybrid intelligent multimodal music emotion recognition via deep time-frequency and semantic fusion
    ( Free Full-text Access ) CC-BY-NC-ND
    by Jiang Qi, Fangfang Li 
    Abstract: Music emotion recognition (MER) is crucial for understanding human affective states, yet existing methods often suffer from limited accuracy and efficiency. This study proposes a hybrid intelligent multimodal MER model integrating deep time-frequency features and semantic information. Audio features are extracted using MFCC, WaveNet, bidirectional long short-term memory (BiLSTM), and an attention mechanism, while lyric semantics are captured through a RoBERTa-BiGRU framework. A hybrid fusion strategy combines multimodal features, and a particle swarm optimisation-based support vector machine (PSO-SVM) performs classification. Experimental results on EmoMusic and Emo163 datasets demonstrate superior performance. On the EmoMusic dataset, the model achieved an accuracy of 90.45%. On the Emo163 dataset, it achieved an accuracy of 92.34% and an F1 score of 91.63%, both of which outperformed the baseline model. The model also shows improved computational efficiency, with reduced parameters and faster convergence. By integrating neural and semantic intelligence, the proposed approach enhances robustness and generalisation, providing an effective solution for multimodal emotion recognition in intelligent computing and human-computer interaction applications.
    Keywords: music emotion recognition; MER; bidirectional long short-term memory; BiLSTM; Mel frequency cepstral coefficients; MFCC; WaveNet; multimodal information.
    DOI: 10.1504/IJCISTUDIES.2026.10081082
     

Regular Issues

  • Beyond sequential prediction: learning financial market dynamics in volatile and non-stationary environments through sentiment-conditioned generative modelling   Order a copy of this article
    by Alexis Lazanas, Spyridon Karpouzis 
    Abstract: The problem of time-series forecasting in non-stationary and complex environments is a challenging task in machine learning, especially with heterogeneous numerical and textual data present. Traditional statistical models like autoregressive integrated moving average (ARIMA) are based on the assumptions of linearity and stationarity, whereas recurrent neural networks like long short-term memory (LSTM) models do not necessarily represent distributional properties in highly volatile settings. This paper proposes a hybrid model that combines generative adversarial networks (GANs) with natural language processing (NLP)-based sentiment analysis to enable sentiment-conditioned time-series prediction. The model integrates adversarial learning on numerical sequences with contextual sentiment representations derived from unstructured text, enabling them to be jointly modelled to capture temporal dynamics and exogenous information. These results demonstrate the promise of hybrid generative and language-aware methods to enhance prediction robustness in non-stationary environments.
    Keywords: computational intelligence; generative models; sentiment analysis; financial time-series forecasting; volatile and non-stationary environments.
    DOI: 10.1504/IJCISTUDIES.2026.10079804
     

 

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