Forthcoming Articles

International Journal of Dynamical Systems and Differential Equations

International Journal of Dynamical Systems and Differential Equations (IJDSDE)

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 Articles will not be published until the final proofs are validated by their authors.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 Dynamical Systems and Differential Equations (2 papers in press)

Regular Issues

  • Related Technologies for Image Feature and Affective Computing Mapping via Psychological Cognition   Order a copy of this article
    by Jie Jiang 
    Abstract: Emotional computing has high computational costs, poor classification accuracy, lack of psychological theoretical support, and physiological signal acquisition is susceptible to noise interference. In response, this study proposes a deep learning based emotion recognition model that predicts a person's psychological state by using EEG signal data. This study preprocesses the proposed framework and extracts features from image data using discrete wavelet transform (DWT) and power spectral density (PSD). DWT extracts features as frequency bands, while PSD exports statistical features and parameters. Afterwards, this study retrieves spatial (channel) and temporal (brain peak and related latency) features from EEG data, and uses a 3D convolutional neural network for emotion classification to evaluate the proposed model. Meanwhile, this study also employs both subject dependent and subject independent procedures. The results indicate that extracting multidimensional complementary features in both frequency and spatial domains can improve recognition ability.
    Keywords: Affective computing; Psychological cognition; Emotional recognition; Convolutional Neural Network; Image Features; Deep Learning; Electroencephalogram Data.
    DOI: 10.1504/IJDSDE.2026.10076011
     
  • Existence and Trajectory Controllability of Fractional Order Evolution Systems Driven by Almost Sectorial Operators.   Order a copy of this article
    by Dibyajyoti Hazarika, Jayanta Borah 
    Abstract: Trajectory controllability represents the strongest form of controllability and has a wide range of applications. This notion has been extensively studied by many researchers for various types of evolution systems. However, to the best of our knowledge, no work in the existing literature has addressed trajectory controllability for evolution systems governed by almost sectorial operators. Motivated by this gap, the present article establishes sufficient conditions for the trajectory controllability of a class of fractional integro-differential evolution systems driven by almost sectorial operators. In our study, both systems with and without state delays are considered. The fractional derivatives involved are of order r 2 (0, 1) in the Caputo sense. Existence results are obtained using the Banach fixed point theorem, while the controllability results rely on tools such as properties of Mittag-Leffler functions, Gronwalls inequality, functions of type (M), and other key results from fractional calculus and functional analysis. To further support the theoretical findings, illustrative examples are provided.
    Keywords: Trajectory controllability; Fractional differential equation; Integro-differential system; Almost sectorial operator; State delay.
    DOI: 10.1504/IJDSDE.2026.10076528
     

 

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