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Fingerprints are widely used as effective personal authentication systems, because they constitute unique, robust, and risk-free evidence. Fingerprinting techniques refer to biometric procedures used for identifying individuals based on their physical characteristics. A fingerprint image contains ridges and valleys forming a directionally-oriented pattern. The robustness of the fingerprint authentication technique determines the quality of the fingerprint image. This study proposed an intelligent 12-layered Convolutional Neural Network (CNN) model using Deep learning (DL) for gender determination based on fingerprints. Further, the study compared the performance of this model to existing state-of-the-art methods. The primary goal of this study was to reduce the number of comparisons within a large database obtained from automatic fingerprint recognition systems. The classification process was found to be swifter and more accurate when analysis of the DL algorithm was performed. With reference to the criteria of precision, recall, and accuracy evaluation during classification, this proposed 12-layered CNN model outperformed the Residual Neural Network with 50 Layers (ResNet-50) and Dense Convolutional Network with 201 Layers (DenseNet-201) models. The accuracies obtained were 97.0%, 95.8%, 98.0%, and 96.8% for female-left, female-right, male-left, and male-right classes respectively, while achieving an overall accuracy of 94.0%. [ABSTRACT FROM AUTHOR]

Based on big data, this paper studies the influence of new type of filling pneumonia on the development of sports industry. When selecting the typical economic indicators that reflect the development trend of sports industry, it is found that the data is huge according to the big industrial data, but the information that can be reflected is poor and complex. Therefore, it is necessary to process these big economic data in order to obtain the impact of new coronary pneumonia on the development of sports industry. This paper studies the feature selection algorithm of big data samples, so as to select typical economic indicators from many economic indicators of sports industry to reflect the development trend of sports industry. A deep learning algorithm based on feature selection of big data is proposed. Firstly, a feature selection framework for big data is constructed, and then data fusion and deep learning are carried out. Experiments show that the algorithm can solve the contradiction between large data and poor information. This method has a certain forward-looking, and has a certain reference value for the information discrimination of the development trend of sports industry. [ABSTRACT FROM AUTHOR]

By controlling the transaction background and data of supply chain enterprises, supply chain finance can reduce the degree of information asymmetry in the process of enterprise financing and provide more financing mode options for enterprises. In this paper, the author analyzes the statistical optimization of supply chain financial credit based on deep learning and fuzzy algorithm. We use particle swarm optimization to train BP neural network and improve the previous algorithm. By changing the speed of the particle search in the weight space, that is, updating the weight of the net-work, the mean square error of the network output is gradually reduced. Simulation results show that the model is helpful to analyze the correlation between supply chain finance and economy, compared with the traditional BP neural network, the original data of BP neural network based on particle swarm optimization is better fitted, so it can be used to predict supply chain financial credit level. [ABSTRACT FROM AUTHOR]

In order to determine the explosion value in the confined space, this time the simulation model of the deep learning algorithm is used to study it. The research status of deep learning algorithm is first expounded, and the numerical record of gas explosion in confined space is constructed according to computer technology. In order to ensure the optimization of numerical processing, the deep learning algorithm is used to process the simulation data to ensure the accuracy of the explosion value. In order to further test the numerical accuracy of the numerical model of gas limited space explosion, the comparison of different values in the constrained space is carried out, and the efficiency and accuracy of the deep learning algorithm are tested. The test results show the application of deep learning algorithm. The accuracy of the explosion value is further guaranteed and needs further application. [ABSTRACT FROM AUTHOR]