At present, reliable and precise ship detection in high-resolution optical remote sensing images affected by wave clutter, thin clouds, and islands under complex sea conditions is still challenging. At the same time, object detection algorithms in satellite remote sensing images are challenged by color, aspect ratio, complex background, and angle variability. Even the results obtained based on the latest convolutional neural network (CNN) method are not satisfactory. In order to obtain more accurate ship detection results, this paper proposes a remote sensing image ship object detection method based on a brainlike visual attention mechanism. We refer to the robust expression mode of the human brain, design a vector field filter with active rotation capability, and explicitly encode the direction information of the remote sensing object in the neural network. The progressive enhancement learning model guided by the visual attention mechanism is used to dynamically solve the problem, and the object can be discovered and detected through time–space information. To verify the effectiveness of the proposed method, a remote sensing ship object detection data set is established, and the proposed method is compared with other state-of-the-art methods on the established data set. Experiments show that the object detection accuracy of this method and the ability to capture image details have been improved. Compared with other models, the average intersection rate of the joint is 80.12%, which shows a clear advantage. The proposed method is fast enough to meet the needs of ship detection in remote sensing images.

Particle swarm optimization (PSO) is an evolutionary algorithm for solving global optimization problems. PSO has a fast convergence speed and does not require the optimization function to be differentiable and continuous. In recent two decades, a lot of researches have been working on improving the performance of PSO, and numerous PSO variants have been presented. According to a recent theory, no optimization algorithm can perform better than any other algorithm on all types of optimization problems. Thus, PSO with mixed strategies might be more efficient than pure strategy algorithms. A mixed strategy PSO algorithm (MSPSO) which integrates five different PSO variants was proposed. In MSPSO, an adaptive selection strategy is used to adjust the probability of selecting different variants according to the rate of the fitness value change between offspring generated by each variant and the personal best position of particles to guide the selection probabilities of variants. The rate of the fitness value change is a more effective indicator of good strategies than the number of previous successes and failures of each variant. In order to improve the exploitation ability of MSPSO, a Nelder–Mead variant method is proposed. The combination of these two methods further improves the performance of MSPSO. The proposed algorithm is tested on CEC 2014 benchmark suites with 10 and 30 variables and CEC 2010 with 1000 variables and is also conducted to solve the hydrothermal scheduling problem. Experimental results demonstrate that the solution accuracy of the proposed algorithm is overall better than that of comparative algorithms.

Multivariate time series prediction models perform the required operation on a specific window length of a given input. However, capturing complex and nonlinear interdependencies in each temporal window remains challenging. The typical attention mechanisms assign a weight for a variable at the same time or the features of each previous time step to capture spatio-temporal correlations. However, it fails to directly extract each time step’s relevant features that affect future values to learn the spatio-temporal pattern from a global perspective. To this end, a temporal window attention-based window-dependent long short-term memory network (TWA-WDLSTM) is proposed to enhance the temporal dependencies, which exploits the encoder–decoder framework. In the encoder, we design a temporal window attention mechanism to select relevant exogenous series in a temporal window. Furthermore, we introduce a window-dependent long short-term memory network (WDLSTM) to encode the input sequences in a temporal window into a feature representation and capture very long term dependencies. In the decoder, we use WDLSTM to generate the prediction values. We applied our model to four real-world datasets in comparison to a variety of state-of-the-art models. The experimental results suggest that TWA-WDLSTM can outperform comparison models. In addition, the temporal window attention mechanism has good interpretability. We can observe which variable contributes to the future value.

Click-through rate (CTR) prediction is an important issue in online advertising and recommendation systems. It is used to estimate the likelihood that a user will click on ads. The method used in traditional CTR prediction tasks is nothing more than the combination of feature engineering +LR/FM. This kind of work to improve the prediction effect through a large number of feature engineering is timeconsuming and laborious, and the construction process is not universal. Due to the sparse and highdimensional characteristics of data features, it is necessary to measure the importance of sparse features and obtain efficient feature interactions. A novel CTR model based on Mutual Information and Feature Interaction (MiFiNN) is proposed to measure feature importance and fine-grained feature interaction. To weigh the importance of sparse features, we use mutual information to find the correlation between each sparse feature and click behavior, and assign weights to features based on their contribution to the click results. The weighted features are interacted by combining the outer product with the inner product. The resulting feature interactions and the dense features set of the original input are taken as DNN inputs. We verify the proposed model on four datasets. In addition, several widely known models are introduced for comparison. The experimental results indicate the superiority of the model.