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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.

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.

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.

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.

Aiming at the problems of over reliance on labor and low generalization of traditional emotion analysis methods based on dictionary and machine learning, an emotion analysis model of microblog comment text based on deep learning is proposed. Firstly, text is obtained through microblog crawler program. After data preprocessing, including data cleaning, Chinese word segmentation, removal of stop words, and so on, the Skip-gram model is used for word vector training on a large-scale unmarked corpus, and then the trained word vector is used as the text input of CNN-BiLSTM model, which combines Bidirectional Long-Short Term Memory (BiLSTM) neural network and Convolution Neural Network (CNN). Considering the historical context information and the subsequent context information, BiLSTM can better use the temporal relationship of text to learn sentence semantics. CNN can extract hidden features from the text and combine them. Finally, after Adamax optimization training, the emotion type of microblog comment text is output. The proposed model combines the learning advantages of BiLSTM and CNN. The overall accuracy of text emotion analysis has been greatly improved, with an accuracy of 0.94 and an improvement of 8.51% compared with the single CNN model.

Because the initial speed of the seeds leaving the seed disk is too high, they collide and bounce off the inner wall of the seed guide tube, resulting in poor sowing quality when corn is sown at high speeds above 12 km/h. This study clarifies the primary factors affecting the stability of seed receiving and the accuracy of the seed entering the seed cavity, establishes the dynamic model of seed clamping, transportation, and releasing, and investigates the belt-type high-speed corn seed guiding device with the seed receiving system as the research object. It also proposes an improved method of adding herringbone lines on the finger surface to address this issue. Using EDEM software, a virtual experiment of seed-receiving performance was conducted, and the change trend of stress on seeds with and without a herringbone pattern and different wheel center distance as well as the change trend of the speed of seeds with various feeder wheel speeds and finger length, were both examined. The outcomes of the simulation demonstrate that the herringbone-lined feeder wheel could increase the stress on seeds. The average value of the stress on the seeds is the highest at the wheels’ center distance of 37 mm. The stability and speed fluctuation of seeds introduced into the seed cavity were better when the feeder wheel speed was 560 r/min. The speed of fluctuation and stability of the seeds introduced into the seed cavity were better when the finger length was 12 mm. The high-speed camera test on the test bench was used to verify the seed guiding process in accordance with the simulation results, and the outcomes were largely consistent. The study’s findings can serve as a theoretical foundation for a belt-type high-speed corn seed guiding device optimization test.

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