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Background Heart sound measurement is crucial for analyzing and diagnosing patients with heart diseases. This study employed phonocardiogram signals as the input signal for heart disease analysis due to the accessibility of the respective method. This study referenced preprocessing techniques proposed by other researchers for the conversion of phonocardiogram signals into characteristic images composed using frequency subband. Image recognition was then conducted through the use of convolutional neural networks (CNNs), in order to classify the predicted of phonocardiogram signals as normal or abnormal. However, CNN requires the tuning of multiple hyperparameters, which entails an optimization problem for the hyperparameters in the model. To maximize CNN robustness, the uniform experiment design method and a science-based methodical experiment design were used to optimize CNN hyperparameters in this study. Results An artificial intelligence prediction model was constructed using CNN, and the uniform experiment design method was proposed to acquire hyperparameters for optimal CNN robustness. The results indicate Filters ($${X}_{1}$$ X 1 ), Stride ($${X}_{3}$$ X 3 ), Activation functions ($${X}_{6}$$ X 6 ), and Dropout ($${X}_{7}$$ X 7 ) to be significant factors considerably influencing the ability of CNN to distinguish among heart sound states. Finally, the confirmation experiment was conducted, and the hyperparameter combination for optimal model robustness was Filters ($${X}_{1}$$ X 1 ) = 32, Kernel Size ($${X}_{2})$$ X 2 ) = 3 × 3, Stride ($${X}_{3}$$ X 3 ) = (1,1), Padding ($${X}_{4})$$ X 4 ) as same, Optimizer ($${X}_{5})$$ X 5 ) as the stochastic gradient descent, Activation functions ($${X}_{6}$$ X 6 ) as relu, and Dropout ($${X}_{7}$$ X 7 ) = 0.544. With this combination of parameters, the model had an average prediction accuracy rate of 0.787 and standard deviation of 0. Conclusion In this study, phonocardiogram signals were used for the early prediction of heart diseases. The science-based and methodical uniform experiment design was used for the optimization of CNN hyperparameters to construct a CNN with optimal robustness. The results revealed that the constructed model exhibited robustness and an acceptable accuracy rate. Other literature has failed to address hyperparameter optimization problems in CNN; a method is subsequently proposed for robust CNN optimization, thereby solving this problem.

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Background Researchers have attempted to apply deep learning methods of artificial intelligence for rapidly and accurately detecting acute lymphoblastic leukemia (ALL) in microscopic images. Results A Resnet101-9 ensemble model was developed for classifying ALL in microscopic images. The proposed Resnet101-9 ensemble model combined the use of the nine trained Resnet-101 models with a majority voting strategy. Each trained Resnet-101 model integrated the well-known pre-trained Resnet-101 model and its algorithm hyperparameters by using transfer learning method to classify ALL in microscopic images. The best combination of algorithm hyperparameters for the pre-trained Resnet-101 model was determined by Taguchi experimental method. The microscopic images used for training of the pre-trained Resnet-101 model and for performance tests of the trained Resnet-101 model were obtained from the C-NMC dataset. In experimental tests of performance, the Resnet101-9 ensemble model achieved an accuracy of 85.11% and an F1-score of 88.94 in classifying ALL in microscopic images. The accuracy of the Resnet101-9 ensemble model was superior to that of the nine trained Resnet-101 individual models. All other performance measures (i.e., precision, recall, and specificity) for the Resnet101-9 ensemble model exceeded those for the nine trained Resnet-101 individual models. Conclusion Compared to the nine trained Resnet-101 individual models, the Resnet101-9 ensemble model had superior accuracy in classifying ALL in microscopic images obtained from the C-NMC dataset.

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This study proposes a method of designing quadratic optimal fuzzy parallel-distributed-compensation controllers for a class of time-varying Takagi–Sugeno fuzzy model–based time-delay control systems used to solve the finite-horizon optimal control problem. The proposed method fuses the orthogonal function approach and the improved hybrid Taguchi-genetic algorithm. The Taguchi-genetic algorithm only requires algebraic computation to perform the algorithm used to solve time-varying Takagi–Sugeno fuzzy model–based time-delay feedback dynamic equations. The fuzzy parallel-distributed-compensation controller design problem is simplified by using the Taguchi-genetic algorithm to transform the static parameter optimization problem into an algebraic equation. The static optimization problem can then be solved easily by using the improved hybrid Taguchi-genetic algorithm to find the quadratic optimal parallel-distributed-compensation controllers of the time-varying Takagi–Sugeno fuzzy model–based time-delay control systems. The applicability of the proposed integrative method is demonstrated in a real-world design problem.

This paper presents a multiobjective evolutionary approach that can solve integrated airline scheduling and rescheduling problems under conditions of disruption. The integrated problem simultaneously considers both aircraft routing and crew pairing to meet several objectives under real-world constraints and disturbance events. Because of their high complexity, we formulated integrated problems as combinational optimization problems and used the NSGA-II variant method combined with a repair strategy as the solver. To verify and validate the proposed approach, real-world flight data were used to build study cases. In the experiment, we first studied the convergence of the algorithm by using the repair method. We then reviewed real-world plans and evaluated the improvement obtained using the proposed integrated approach. Finally, a disruption was simulated to study rescheduling capability. Experimental results showed that the proposed approach yields better schedules than real-world expert-made plans and that Pareto solutions after the disruption can, under safety and legal constraints, be successfully explored in rescheduling problems. INDEX TERMS Airline rescheduling, aircraft routing, crew pairing, integrated airline scheduling, multiobjective optimization.