Article: InCO-GBdeN: multi-modal gradient boosting enabled deep convolutional neural network for emotion detection Journal: International Journal of Biometrics (IJBM) 2026 Vol.18 No.5 pp.415 - 446 Abstract: Emotion detection finds valuable applications in healthcare, human-computer interaction, and so on. Numerous techniques and strategies have been developed recently, which impact the detection accuracy and robustness of the models. In addition, these conventional models have instability, generalisation, and interpretability issues that reduce the model's efficiency. To overcome these issues, we propose a novel gradient-boosting-enabled deep convolutional neural network with intellect clustering optimisation (InCO-GBdeN) for effective emotion detection. The GBdeN model tends to reduce the dimensional size of multimodal signals and thereby, improves interpretability as well as generalisation ability for effective detection. The development of the InCO algorithm assists the parameter tuning process with the help of gaining-sharing behaviours, which avails accurate detection with fast convergence and improves the stability for active detection. Moreover, the developed InCO-GBdeN model is simple, lightweight, and easy to analyse positive and negative emotions such as happiness, anger, and so on. The performance of the InCO-GBdeN model for emotion detection reports an accuracy of 95.80%, precision of 95.95%, and recall of 95.62% respectively, when compared to other state-of-the-art methods. Inderscience Publishers - linking academia, business and industry through research

Title: InCO-GBdeN: multi-modal gradient boosting enabled deep convolutional neural network for emotion detection

Authors: Shruti G. Taley; M.A. Pund

Addresses: Department of Computer Science and Engineering, Prof. Ram Meghe Institute of Technology and Research, Badnera, Amravati, Maharashtra, 444701, India ' Department of Computer Science and Engineering, Prof. Ram Meghe Institute of Technology and Research, Badnera, Amravati, Maharashtra, 444701, India

Abstract: Emotion detection finds valuable applications in healthcare, human-computer interaction, and so on. Numerous techniques and strategies have been developed recently, which impact the detection accuracy and robustness of the models. In addition, these conventional models have instability, generalisation, and interpretability issues that reduce the model's efficiency. To overcome these issues, we propose a novel gradient-boosting-enabled deep convolutional neural network with intellect clustering optimisation (InCO-GBdeN) for effective emotion detection. The GBdeN model tends to reduce the dimensional size of multimodal signals and thereby, improves interpretability as well as generalisation ability for effective detection. The development of the InCO algorithm assists the parameter tuning process with the help of gaining-sharing behaviours, which avails accurate detection with fast convergence and improves the stability for active detection. Moreover, the developed InCO-GBdeN model is simple, lightweight, and easy to analyse positive and negative emotions such as happiness, anger, and so on. The performance of the InCO-GBdeN model for emotion detection reports an accuracy of 95.80%, precision of 95.95%, and recall of 95.62% respectively, when compared to other state-of-the-art methods.

Keywords: emotion detection; multi-modal signals; gradient boosting; intellect clustering optimisation; deep learning; DL.

DOI: 10.1504/IJBM.2026.156245

International Journal of Biometrics, 2026 Vol.18 No.5, pp.415 - 446

Received: 12 Oct 2024
Accepted: 06 Apr 2025
Published online: 08 Sep 2026 *

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Title: InCO-GBdeN: multi-modal gradient boosting enabled deep convolutional neural network for emotion detection

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