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DNA microarray prostate cancer diagnosis systems are widely used, and hybrid feature selection methods are applied to select optimal features to address the high dimensionality of the dataset. This work proposes a new hybrid feature selection method, namely the relief-F (RF)-genetic algorithm (GA) with support vector machine (SVM) classification method. The aim is to evaluate the performance of the proposed method in terms of accuracy, computation time, and the number of selected features. The method is implemented using Python in PyCharm and is evaluated on a DNA microarray prostate cancer. The outcome of this work is a performance comparison table for the proposed methods on the dataset. The performance of GA, particle swarm optimization (PSO), and whale optimization algorithm (WOA) is compared in terms of accuracy, computation time, and the number of selected features. Results show that GA has the highest average accuracy (91.17%) compared to PSO (90.52%) and WOA (85.74%). GA outperforms PSO and WOA due to its superior convergence properties and better alignment with complex problems. [ABSTRACT FROM AUTHOR]

Previous works have considered the use of set invariance theory for fault detection and isolation in nonlinear Lure systems. This paper extends those results and proposes a new actuator fault-tolerant control approach. The fault-tolerant control scheme is designed based on linear parameter-varying (LPV) models of Lure systems. The actuator fault situation is diagnosed by an invariant set-based fault detection and isolation algorithm. Faults are compensated by adapting the controller gain based on estimates of the fault magnitude. Conditions for correct fault detection and isolation, and closed-loop stability are derived. The proposed fault-tolerant control scheme is compared with a linearised model approach and the performance of both, LPV-embedding and linearised, approaches are analysed for scalar and second-order systems. An example of a chaotic Chua circuit is also provided to illustrate the proposed fault-tolerant control scheme in higher-order systems. [ABSTRACT FROM AUTHOR]