Humans can exhibit diverse emotional facial expressions, including happiness, sadness, anger, disgust, surprise, and fear. Distinct and distinctive traits characterise these emotional expressions. Integrating emotion-specific features into landmark-based face recognition systems by developing features based on emotional expressions can significantly enhance face recognition capability. Such landmark-based systems are essential for face recognition tasks in resource-constrained environments. By combining these emotion-specific features with the traditional features based on neutral faces, we can develop more accurate and robust face recognition systems that can better adapt to the dynamic nature of human emotional expressions, promising a new level of efficacy in face recognition.
Credit risk prediction is essential for financial institutions. Several studies have shown that oversampling and instance selection can improve credit risk prediction. However, the combination of these methods for credit risk prediction has been less studied. This paper conducts empirical research comparing the performance of the most widely used oversampling and instance selection methods and their combined use through successful supervised classifiers for credit risk prediction. The practical significance of our research is the assessment of the different options of application of oversampling and instance selection (including the combination of both that has not been studied before) in credit risk prediction under the same framework employing the most used standard-public data sets and one new dataset that allowed us to conclude which is the best option for credit risk prediction.
We propose a lightweight facial expression recognition (FER) model that integrates local binary pattern (LBP) and grey level co-occurrence matrix (GLCM) hybrid features with a channel attention mechanism based on the ECA.Net variant to effectively balance the accuracy-efficiency trade-offs. This model leverages texture features extracted via LBP and LBP+GLCM, combined with attention mechanisms to enhance the focus on salient facial cues. Built upon the VGG16 CNN architecture within the TensorFlow framework, the model was tested on the FER2013 and RAF-DB datasets for cross-validation, achieving a recognition accuracy of 79.89% and 86.77%, respectively. To validate its practical effectiveness, a deployable QT-based user interface system was developed, enabling real-time FER from images, videos, and live camera feeds. This approach aims to meet the increasing demand for lightweight, reliable solutions, particularly in scenarios with limited computational resources, while maintaining high recognition accuracy.
Accurate emotion detection from speech signals is essential for enhancing human-computer interaction (HCI) systems. However, existing SER methods often suffer from poor feature representation and limited dataset diversity, resulting in suboptimal performance. To address these challenges, this paper proposes an advanced deep bottleneck residual convolutional neural network (DBR-CNN) integrated with the SEResNeXt-101 feature extraction framework and optimised using the coati optimisation algorithm (COA). The model is trained and evaluated on four diverse benchmark datasets: URDU, EMO-DB, EMOVO, and SAVEE. In the pre-processing phase, speech signals undergo noise reduction and normalisation to enhance data quality. The SEResNeXt-101 extractor then captures high-level features with reduced complexity, which are subsequently processed by the DBR-CNN to classify emotions with greater accuracy. The COA fine-tunes the model to improve classification efficiency. Experimental results demonstrate that the proposed model significantly outperforms state-of-the-art (SOTA) methods. The optimised DBR-CNN framework provides robust, scalable, and highly accurate emotion recognition.
