Abstract The rapidly increasing photovoltaic (PV) technology is one of the key renewable energies expected to mitigate the impact of climate change and the energy crisis, which has been widely installed in the past few years. However, the variability of PV power generation creates different negative impacts on the electric grid systems, and a resilient and predictable PV power generation is crucial to stabilize and secure grid operation and promote large-scale PV power integration. This article proposed machine learning-based short-term PV power generation forecasting techniques by using XGBoost, SARIMA, and long short-term memory network (LSTM) algorithms. The experimental results demonstrated that the proposed resilient LSTM solution can accurately predict (around 90% <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msup> <m:mrow> <m:mi>R</m:mi> </m:mrow> <m:mrow> <m:mn>2</m:mn> </m:mrow> </m:msup> </m:math> {R}^{2} and 0.028 root mean squared error) PV power generation with minimum input data.

Abstract To solve the insufficient educational resources in offline classroom English teaching, the research focuses on online teaching and designs an online grammar correction algorithm to help students realize automatic online error correction. The algorithm is based on the Transformer model algorithm based on multi-head attention mechanism (MHAM), and integrates word order information into the encoding process. Three pseudo-parallel corpora are used to expand the number of training data. Finally, Adam is used to optimize the model parameters to improve the model performance. The accuracy, recall, and F 0.5 of the algorithm formed after two one-way optimization are the highest values in the same type of optimization model. The SP + Pre human + TF copy algorithm formed after double optimization has the best comprehensive performance. The accuracy rate reached 68.53 %, and the F 0.5 value reached 58.26 %, both of which were the highest values in the comparison model. Moreover, this method can also provide certain technical support for the establishment of multilingual interaction platforms and high-quality natural language generation.

Abstract In order to improve the analysis effect of the international competitiveness of China’s new energy vehicle industry, this paper combines the improved AHP and grey relational analysis method to analyze the international competitiveness of China’s new energy vehicle industry. This paper uses the quantitative information analysis algorithm of new energy vehicle competitiveness to process the signal data of China’s new energy vehicle industry’s international competitiveness. Moreover, this paper collects data through various channels, and applies the improved AHP and grey relational analysis to the analysis of the international competitiveness of China’s new energy vehicle industry. In addition, this paper proposes an improved SP &amp; SPWVD combined time-frequency distribution algorithm. It combines the long-window short-time Fourier transform (STFT) with better frequency resolution and the short-window STFT with better time resolution to obtain a combined window spectrogram with better time-frequency aggregation. The research results show that when the improved AHP and grey relational analysis method are used in the analysis of the international competitiveness of China’s new energy vehicle industry, the analysis effect of the international competitiveness of China’s new energy vehicle industry can be effectively improved.

Abstract The use of decision trees and artificial neural networks (ANNs) in health-care research is widespread, as they enable health-care providers with the tools they need to make better medical decisions with their patients. ANNs specifically are extremely helpful in predictive research as they can provide investigators with knowledge about future trends and patterns. However, a major downside to ANNs is their lack of interpretability. Understandability of the model is important as it ensures the outcomes are true to the dataset’s original labels and are not impacted by algorithmic bias. In comparison, decision trees map out their entire process before providing the results, which leads to a higher level of trust in the model and the conclusions it supplies the investigators with. This is essential as many historical datasets lack equal and fair representation of all races and sexes, which might directly correlate to a lesser treatment given to females and individuals in minority groups. Here, we review existing work around the differences and connections between ANNs and decision trees with implications for research in health care.

Abstract To improve the effectiveness of the construction of teacher morality in colleges and universities, this article employs the performance evaluation model of education informatization to construct an evaluation system for the construction of teacher morality in colleges and universities. The evaluation of the construction effect of teacher morality is carried out through intelligent data analysis, and the significant error is used as the parameter of the teacher’s morality to make estimations. Moreover, a binary variable is introduced to judge whether the data have a significant error. Mixed integer linear and nonlinear programming methods are employed to detect significant errors. In addition, the idea of synchronizing teachers’ moral data is added. The effectiveness of the proposed method is verified by a simulation study using MATLAB 2017. The results show that it is necessary to establish and improve the talent flow mechanism that conforms to the requirements to promote the rational flow of teachers.

Abstract For low-illumination video sequences, some existing enhancement algorithms have some problems, such as image over-enhancement, color distortion, and inadequate detail processing. Based on luminance detection, we add logarithmic tone mapping to optimize the existing algorithms. The color space of low-illumination video image is converted from the red, green, blue mode to the hue-saturation-intensity mode, and then, logarithmic tone enhancement is applied to the image. Algorithm in this study has an obvious effect on image luminance enhancement and details processing, which makes the low-illumination video show a clear image with more natural visual effect, thus improving the quality of low-illumination video. This algorithm can avoid the problems of overexposure, color distortion, and blurring of detail processing under low illumination. The infrared spectrum of the object can be captured by infrared detection equipment, and the purpose of image enhancement can be achieved by applying the infrared spectrum of the object.

Abstract With the rapid development of social informatization, the Internet has become an important channel for global information dissemination. The wireless network space and the real space are intertwined and have a significant impact on the political, economic, and cultural aspects of international society. For example, it can effectively solve the problem of information exchange between cities, regions, and countries. Wireless network crime and wireless network space security issues involve the development of information technology in various countries and, more importantly, involve all aspects of national security. In the age of science and technology, the current situation of cybercrime can be expressed by data as follows: the online crime rate is high. According to relevant statistics, nearly 5 million Internet users are involved in various cases every year due to the use of the Internet. This article starts with the digital forensics of wireless network crime and wireless network security management systems and discusses the composition, characteristics, existing problems, and future development directions of the current wireless network security management system. By comparing the wireless network security laws of China and the United States, using the latent Dirichlet allocation (LDA)-Gibbs model and the k-means algorithm to analyze the data, this article provides guidance for future research on wireless network space governance. The combination of digital forensics analysis based on cybercrime and space governance can improve the level of public security work. At present, the world has made some progress in cyberspace, but it still faces severe challenges. In the face of new situations and new measures, it is necessary to strengthen legislation, improve relevant systems, and strengthen supervision capacity. At the same time, it is necessary to establish perfect technical means and safeguards to ensure that this goal can be achieved.

Abstract Rainfall prediction is a challenging task and has extreme significance in weather forecasting. Accurate rainfall prediction can play a great role in agricultural, aviation, natural phenomenon, flood, construction, transport, etc. Weather or climate is assumed to be one of the most complex systems. Again, chaos, also called as “butterfly effect,” limits our ability to make weather predictable. So, it is not easy to predict rainfall by conventional machine learning approaches. However, several kinds of research have been proposed to predict rainfall by using different computational methods. To accomplish chaotic rainfall prediction system for Bangladesh, in this study, historical data set-driven long short term memory (LSTM) networks method has been used, which overcomes the complexities and chaos-related problems faced by other approaches. The proposed method has three principal phases: (i) The most useful 10 features are chosen from 20 data attributes. (ii) After that, a two-layer LSTM model is designed. (iii) Both conventional machine learning approaches and recent works are compared with the LSTM model. This approach has gained 97.14% accuracy in predicting rainfall (in millimeters), which outperforms the state-of-the-art solutions. Also, this work is a pioneer work to the rainfall prediction system for Bangladesh.

Abstract Through the analysis of the current research situation at home and abroad, this article finds that there is a lack of evaluation standards and methods in the virtual simulation experiment of e-commerce logistics smart warehousing. Therefore, it seriously affects the standardization and rationality of the experiment. To solve the problems in the evaluation of the current virtual simulation experiment, this article proposes a virtual simulation experiment evaluation model of e-commerce logistics smart warehousing based on multidimensional weighting. This article firstly sorts out the basic process of e-commerce logistics smart warehousing experiment activities and establishes the evaluation object. Then, based on the duality degree of the output results of the experimental steps, it proposes a method that conforms to the corresponding operation steps. Thus, a three-dimensional evaluation model of the completion degree of the operation steps, the reasonable degree of the operation steps, and the completion time of the operation steps are constructed. An automatic scoring evaluation model is proposed based on the combination of three-dimensional weighted evaluation of experimental steps. Finally, the feasibility and convenience of the evaluation model are verified through the experiment analysis.

Abstract In this study, a student-based placement model using the A* algorithm is proposed and applied to solve the problem of placing the courses in exam sessions. The application area of the model is midterm and final exams, conducted by the Open Education Faculty. The reason for choosing open education exams for the practice is that the exams are applied across the country and more than 100,000 students participate. The main problem is to obtain a suitable distribution that can satisfy many constraints simultaneously. In the current system, the lessons in the sessions were placed once using the curriculum knowledge. This placement plan is applied in all exams. When the placement is done according to the curriculum information, the courses in the sessions cannot be placed effectively and efficiently due to a large number of common courses and the large number of students taking the exam. This makes the booklets more expensive and the organization more prone to errors. Both the opening of new programs and the increase in the number of students regularly lead to the necessity of placing the classes in sessions dynamically each semester. In addition, to prevent conflicts with the calendars of other central exams, it is necessary to conduct all exams in three sessions. A better solution was obtained by using a different model than the currently used model in the study. With this solution, distribution of the courses of successful students with few courses to all sessions is provided, and difficult courses of unsuccessful students who have a large number of courses were gathered in the same session. This study can support future studies on two issues: the first issue is the approach of using the course that will be taken by most students instead of the courses taught in most departments in the selection of the course to be placed in the booklet. The second issue is to try to find the most suitable solution by performing performance tests on many algorithms whose performance has been determined by many academic studies.