The size of datasets is growing exponentially as information technology advances, and it is becoming more and more crucial to provide efficient learning algorithms for neural networks to handle massive amounts of data. Due to their potential for handling huge datasets, feed-forward neural networks with random weights (FNNRWs) have drawn a lot of attention. In this paper, we introduced an efficient feed-forward neural network scheme (FNNS) for processing massive datasets with random weights. The FNNS divides large-scale data into subsets of the same size, and each subset derives the corresponding submodel. According to the activation function, the optimal range of input weights and biases is calculated. The input weight and biases are randomly generated in this range, and the iterative scheme is used to evaluate the output weight. The MNIST dataset was used as the basis for experiments. The experimental results demonstrate that the algorithm has a promising future in processing massive datasets.
Coal is an important resource that is closely related to people’s lives and plays an irreplaceable role. However, coal mine safety accidents occur from time to time in the process of working underground. Therefore, this paper proposes a coal mine environmental safety early warning model to detect abnormalities and ensure worker safety in a timely manner by assessing the underground climate environment. In this paper, support vector machine (SVM) parameters are optimized using an improved artificial hummingbird algorithm (IAHA), and its safety level is classified by combining various environmental parameters. To address the problems of insufficient global exploration capability and slow convergence of the artificial hummingbird algorithm during iterations, a strategy incorporating Tent chaos mapping and backward learning is used to initialize the population, a Levy flight strategy is introduced to improve the search capability during the guided foraging phase, and a simplex method is introduced to replace the worst value before the end of each iteration of the algorithm. The IAHA-SVM safety warning model is established using the improved algorithm to classify and predict the safety of the coal mine environment as one of four classes. Finally, the performance of the IAHA algorithm and the IAHA-SVM model are simulated separately. The simulation results show that the convergence speed and the search accuracy of the IAHA algorithm are improved and that the performance of the IAHA-SVM model is significantly improved.
As the need for medical services has grown in recent years, medical image critical point detection has emerged as a new subject of research for academics. In this paper, a search decision network method is proposed for medical image landmark detection. Unlike the conventional coarse-to-fine methods which generate bias prediction due to poor initialization, our method is to use the neural network structure search strategy to find a suitable network structure and then make reasonable decisions for robust prediction. To achieve this, we formulate medical landmark detection as a Markov decision process and design a shooting reward function to interact with the task. The task aims to maximize the discount of the received value and search for the optimal network architecture over the entire search space. Furthermore, we embed the central difference convolution, which typically extracts the data invariant feature representation, into the architectural search space. In experiments using standard accessible datasets, our approach achieves a detection accuracy of 98.59% in the 4 mm detection range. Our results demonstrate that, on standard datasets, our proposed approach consistently outperforms the majority of methods.
Coal is an important resource that is closely related to people’s lives and has an irreplaceable role. However, coal mine safety accidents occur from time to time in the process of working underground. To this end, this paper proposes a coal mine environmental safety early warning model to detect abnormalities and ensure worker safety in a timely manner by assessing the underground climate environment. In this paper, the support vector machine (SVM) parameters are optimized using an improved artificial hummingbird algorithm (IAHA), and its safety level is classified by combining various environmental parameters. To address the problems of insufficient global exploration capability and slow convergence of the artificial hummingbird algorithm during iterations, a strategy incorporating Tent chaos mapping and backward learning is used to initialize the population, a Levy flight strategy is introduced to improve the search capability during the guided foraging phase, and then the simplex method is introduced to replace the worst value before the end of each iteration of the algorithm. The IAHA-SVM safety warning model was established using the improved algorithm to classify and predict the safety of the coal mine environment into four classes. Finally, the performance of the IAHA algorithm and the IAHA-SVM model are simulated separately. The simulation results show that the convergence speed and the search accuracy of the IAHA algorithm are improved, and the performance of the IAHA-SVM model has a significant improvement.
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