Multiple birth support vector machine based on dynamic quantum particle swarm optimization algorithm

Enhanced depression detection from speech using Quantum Whale Optimization Algorithm for feature selection

There is an urgent need to detect depression using a non-intrusive approach that is reliable and accurate. In this paper, a simple and efficient unimodal depression detection approach based on speech is proposed, which is non-invasive, cost-effective and computationally inexpensive. A set of spectral, temporal and spectro-temporal features is derived from the speech signal of healthy and depressed subjects. To select a minimal subset of the relevant and non-redundant speech features to detect depression, a two-phase approach based on the nature-inspired wrapper-based feature selection Quantum-based Whale Optimization Algorithm (QWOA) is proposed. Experiments are performed on the publicly available Distress Analysis Interview Corpus Wizard-of-Oz (DAICWOZ) dataset and compared with three established univariate filtering techniques for feature selection and four well-known evolutionary algorithms. The proposed model outperforms all the univariate filter feature selection techniques and the evolutionary algorithms. It has low computational complexity in comparison to traditional wrapper-based evolutionary methods. The performance of the proposed approach is superior in comparison to existing unimodal and multimodal automated depression detection models. The combination of spectral, temporal and spectro-temporal speech features gave the best result with the LDA classifier. The performance achieved with the proposed approach, in terms of F1-score for the depressed class and the non-depressed class and error is 0.846, 0.932 and 0.094 respectively. Statistical tests demonstrate that the acoustic features selected using the proposed approach are non-redundant and discriminatory. Statistical tests also establish that the performance of the proposed approach is significantly better than that of the traditional wrapper-based evolutionary methods.

Risk Assessment of Deep Coal and Gas Outbursts Based on IQPSO-SVM

Coal and gas outbursts seriously threaten the mining safety of deep coal mines. The evaluation of the risk grade of these events can effectively prevent the occurrence of safety accidents in deep coal mines. Characterized as a high-dimensional, nonlinear, and small-sample problem, a risk evaluation method for deep coal and gas outbursts based on an improved quantum particle swarm optimization support vector machine (IQPSO-SVM) was constructed by leveraging the unique advantages of a support vector machine (SVM) in solving small-sample, high-dimension, and nonlinear problems. Improved quantum particle swarm optimization (IQPSO) is used to optimize the penalty and kernel function parameters of SVM, which can solve the optimal local risk and premature convergence problems of particle swarm optimization (PSO) and quantum particle swarm optimization (QPSO) in the training process. The proposed algorithm can also balance the relationship between the global search and local search in the algorithm design to improve the parallelism, stability, robustness, global optimum, and model generalization ability of data fitting. The experimental results prove that, compared with the test results of the standard SVM, particle swarm optimization support vector machine (PSO-SVM), and quantum particle swarm optimization support vector machine (QPSO-SVM) models, IQPSO-SVM significantly improves the risk assessment accuracy of coal and gas outbursts in deep coal mines. Therefore, this study provides a new idea for the prevention of deep coal and gas outburst accidents based on risk prediction and also provides an essential reference for the scientific evaluation of other high-dimensional and nonlinear problems in other fields. This study can also provide a theoretical basis for preventing coal and gas outburst accidents in deep coal mines and help coal mining enterprises improve their safety management ability.