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Bankruptcy prediction has been extensively investigated by data mining techniques since it is a critical issue in the accounting and finance field. In this paper, a new hybrid algorithm combining switching particle swarm optimization (SPSO) and support vector machine (SVM) is proposed to solve the bankruptcy prediction problem. In particular, a recently developed SPSO algorithm is exploited to search the optimal parameter values of radial basis function (RBF) kernel of the SVM. The new algorithm can largely improve the explanatory power and the stability of the SVM. The proposed algorithm is successfully applied in the bankruptcy prediction problem, where experiment data sets are originally from the UCI Machine Learning Repository. The simulation results show the superiority of proposed algorithm over the traditional SVM-based methods combined with genetic algorithm (GA) or the particle swarm optimization (PSO) algorithm alone.

To realize the highly precise and real-time monitoring of seeding performance in suction-type corn planter, and intelligent detection technology was presented. In this monitoring system, firstly, the sensor was designed based on the photoelectric technology. Meanwhile, in order to reduce the influence of dust in the field on the photoelectric sensor, the installation position of the sensor was changed to the space under the seed plate instead of the traditional position, that is, the middle of the seed tube. Secondly, the scattering angle of the highlighting light-emitting diodes was considered to calculate the spacing of transmitters to realize non-blind area detection. Last but not least, the peak-detection algorithm was utilized to increase the detection accuracy. Therefore, after a lot of the indoor and field experiments, the analysis shows that the detection accuracy of seeding quantity can reach 98.45%, alarm delay time under abnormal circumstances is not more than 2 s. Obviously, this system can meet the requirements of seeding completely and improve its reliability greatly.

Aiming at the problems of fuzzy detection characteristics, high false positive rate and low accuracy of traditional network intrusion detection technology, an improved intelligent intrusion detection method of industrial Internet of Things based on deep learning is proposed. Firstly, the data set is preprocessed and transformed into 122 dimensional intrusion data set after one-hot coding; Secondly, aiming at the problem that convolution network cannot deal with data with long-distance attributes, Bidirectional long short-term memory (BiLSTM) is used to mine the relationship between data features; At the same time, the Batch Normalization mechanism is introduced to speed up the training of deep neural network. After the activation function performs nonlinear transformation on the input data of the previous layer, it is normalized to ensure the trainability of the network. The experimental results on NSL-KDD data set show that the accuracy of the proposed CNN-BiLSTM model is 96.3%, the detection rate is 97.1%, and the performance is the best.

Purpose This paper aims to design a multi-layer convolutional neural network (CNN) to solve biomimetic robot path planning problem. Design/methodology/approach At first, the convolution kernel with different scales can be obtained by using the sparse auto encoder training algorithm; the parameter of the hidden layer is a series of convolutional kernel, and the authors use these kernels to extract first-layer features. Then, the authors get the second-layer features through the max-pooling operators, which improve the invariance of the features. Finally, the authors use fully connected layers of neural networks to accomplish the path planning task. Findings The NAO biomimetic robot respond quickly and correctly to the dynamic environment. The simulation experiments show that the deep neural network outperforms in dynamic and static environment than the conventional method. Originality/value A new method of deep learning based biomimetic robot path planning is proposed. The authors designed a multi-layer CNN which includes max-pooling layer and convolutional kernel. Then, the first and second layers features can be extracted by these kernels. Finally, the authors use the sparse auto encoder training algorithm to train the CNN so as to accomplish the path planning task of NAO robot.