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The development of artificial intelligence has given birth to a series of innovations in the film industry. This article systematically summarizes these innovations into three parts; movie recommendation, movie distribution and audiovisual language creation. In the first part, the paper lists a series of movie recommendation systems based on AI technology. These systems can help users quickly find the movie resources they are interested in among the massive movie resources. In the second part, the article lists a series of cases of film production using AI, including analyzing film projects, estimating film box office, analyzing film reviews, and writing abstracts. In the third part, the article lists several cases about audio-visual language using AI systemsi1/4Eincluding classifying films and the aesthetic feature, analyzing image features through AI, changing the appearance and age of actors, and creating visual characters.

The COVID-19 (2019 novel Coronavirus) is the most widespread pandemic infectious disease encountered in human history. Its economic losses and the number of countries involved rank first in the history of human viruses. Since the outbreak of the COVID-19 pandemic around the world, AI has made a great contribution to the prevention and control of the COVID-19 pandemic. In this paper, researches on the application of artificial intelligence in COVID-19 pandemic prevention and control were analyzed by informetric method. 432 papers indexed in Thomson Reuters's Web of Science were studied by the perspectives of categories of researches, high frequency keywords, authors, institutions, journals and countries, and we get conclusions as follows: The analysis of keywords cooccurence shows application of machine learning and deep learning in COVID-19 pandemic diagnosis and prediction. The journal that received the most cites was (Radiology) and the journal that published the most papers was (Journal of Medical Internet Research). USA, India and China have the largest number of published articles. USA, China and UK are most influential countries. We also analyzed the review literature on the application of AI in COVID-19 pandemic prevention and control in the Web of Science, and found that these papers specifically can be divided into the following three categories: The first is the application of AI in clinical diagnosis and treatment, the second is the application of AI in the development of anti-epidemic drugs, and the third is the role of AI in the epidemiological research of COVID-19 and the social governance of pandemic prevention and control.

Domain generation algorithms (DGA) provide methods that use specific parameters as random seeds to generate a large number of random domain names for preventing malicious domain name detection, which greatly increases the difficulty of detecting and defending botnets and malware. State-of-the-art models for detecting algorithmically generated domain names are generally based on the principle of analyzing the statistical characteristics of the domain name and building a classifier to locate the algorithmically generated ones. However, most current models have problems of requiring the manual construction of feature sets for classification, as they are sensitive to the imbalance of the sample distribution in the domain name dataset and are difficult to adapt to frequent changes of the domain-name algorithm. To address this issue, we propose a hybrid model that combines a convolutional neural network (CNN) and a bidirectional long-term memory network (BLSTM). First, to solve the problem of the number of domain names generated by DGAs being relatively small and the sample distribution being unbalanced, which consequently decreases detection accuracy, the borderline synthetic minority over-sampling technique is employed to optimize the sample balance of the domain name dataset. Second, a hybrid deep neural network that combines CNN and BLSTM is introduced to extract the semantic and context-dependency features from the domain names. The experimental results from different domain-name datasets demonstrate that the proposed model achieves significant improvement over state-of-the-art models with regard to precision and robustness.

It remains a challenge to classify different categories of fruits because of the similarities of shape, color, and texture among them. We presented a novel approach in order to classify fruits accurately and efficiently based on computer vision techniques. We obtained the coefficients using fractional Fourier transform. The entropies extracted from the coefficients were fed into the classifier as the features. A multilayer perceptron optimized by an improved hybrid genetic algorithm was used as the classifier. The experiment results on 1653 fruit images demonstrated that the proposed method achieved an overall accuracy of 89.59%, which was superior to the state-of-the art approaches. Our method is effective in identifying fruit cagegories.

Keyword: rigid image registration, mutual information, bacteria chemotaxis, bacteria multiple colony chemotaxis Abstract A novel approach for image registration based on mutual information is presented in this paper. Firstly, normalized mutual information is introduced as the fitness function to supersede the traditional mutual information. Meanwhile, the extrema in fitness function resulted from interpolation is analyzed and the solution is presented as adding a weighted parameter into the variables set. Secondly, search model is improved. The 2 nd generation wavelet is utilized to decompose the original image into multi-resolution sub-images to search optimal solution in different precision layer, which harmonizes the contradiction between search precision and convergence rate. Finally, we introduce a novel optimization means—bacterial multiple colony chemotaxis. Experiments demonstrate the legitimacy and efficiency of our betterment.

When optical coherence tomography (OCT) is used for human retina imaging, its transverse resolution is limited by the aberrations of human eyes. To overcome this disadvantage, a high resolution imaging system for living human retina, which consists of a time domain OCT system and a 37-elements adaptive optics (AO) system, has been developed. The AO closed loop rate is 20 frames per second, and the OCT has a 6.7-μm axial resolution. In this paper, this system is introduced and the high resolution imaging results for retina are presented.