Abstract: There are many factors that need to be considered when planning a city’s green economy, so it is difficult to simulate the planning effect through manual models. In order to improve the effect of urban green economic planning, this paper improves the traditional algorithm and combines the principle of machine learning algorithm to build a model that can be used in urban green economic planning. Moreover, this paper considers the measurement of green economic efficiency from the perspective of input, expected output and undesired output. In addition, this paper compares and analyzes the green efficiency calculated by the SE-SBM model,…including horizontal comparison analysis and vertical comparison analysis, and conducts model simulation analysis in combination with data simulation research. Finally, this paper sets the simulation area, combines the data to perform model performance analysis, summarizes the data with statistical analysis methods, and draws charts. The research results show that the model constructed in this paper has a certain effect and can be applied to the design stage of urban green planning.
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Keywords: Genetic algorithm, improved algorithm, machine learning, green economy
Abstract: The eco-economic activity modeling is an effective method to analyze the eco-economic system. From the existing models, it can be seen that the disadvantages of eco-economic activity modeling are that the model evaluation accuracy is not high, and the system stability is poor. In order to improve the evaluation effect of the ecological economic activity, based on the machine learning algorithm, this study establishes a PNN evaluation model based on the probabilistic neural network classification principle. Moreover, in this study, a certain number of learning samples are generated by random interpolation of evaluation index standards, and then Matlab software is…used to simulate the training and test of the model, and the feasibility and effectiveness of the model are verified by statistical indicators. In addition, this study combines the actual case to analyze the performance of the model and analyze the test results by statistical analysis methods. The research results show that the model proposed in this study has certain effects and high stability.
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Abstract: Query processing over uncertain data is very important in many applications due to the existence of uncertainty in real-world data. In this paper, we propose a novel and important query for uncertain data, namely probabilistic top-(k , l ) range (PTR) query, which retrieves l uncertain tuples that are expected to meet score range constraint [s 1 , s 2 ] and have the maximum top-k probabilities but no less than a given probability threshold q . In order to accelerate the PTR query, we present some effective pruning techniques to reduce the search space of PTR query, which are…integrated seamlessly into an efficient PTR query procedure. Extensive experiments over both real-world and synthetic datasets verify the efficiency and effectiveness of our proposed approaches.
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Keywords: Data management, probabilistic top-k query, query processing, range query, uncertain data
Abstract: Since 1990 s, the Internet as the representative’s information technology fast development, has provided richer innovation way and the innovation space for enterprises compared to formerly any time all. Many new pattern enterprises arise at the historic moment based on the Internet technology. They create and provide the value with the traditional enterprise completely different way to the customer, and obtained huge success that the traditional enterprise to be unable. Under such background, business model–this beginning be seen in the computer specialized glossary, starts to arouse the social widespread interest, and becomes one of the most popular glossaries in academic circles…in the worldwide scale in very short time. Now, the business model and the business model innovation in the global scope, brings to the unprecedented attention and the widespread application. Even so, some very important concepts and fundamental theories about the business model fundamental research, such as the concept of business model, the factors that influence the innovation of business model and so on, do not form the uniform conclusion which accepts generally in the theorists. All of that on objective, has formed certain adverse effect to the business model innovation practice, therefore, the fundamental research related to business model and the innovation of business model urgently awaits to strengthen. In this paper, we investigate the multiple attribute decision making with hesitant fuzzy information. Motivated by the induced Choquet ordered averaging operator (I-COA) operator and geometric mean, we develop the induced hesitant fuzzy Hamacher correlated geometric (IHFHCG) operator and then utilize IHFHCG operator to develop the model for multiple attribute decision making with hesitant fuzzy information. Finally, an illustrative example for evaluating the innovation ability of traditional enterprise’s business model for internet transition is given to verify the developed approach.
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Keywords: Multiple attribute decision making, hesitant fuzzy information, induced hesitant fuzzy Hamacher correlated geometric (IHFHCG) operator, induced Choquet ordered averaging operator (I-COA) operator, innovation ability of traditional enterprise’s business model
Abstract: The estimation of gangue content is the main basis for intelligent top coal caving mining by computer vision, and the automatic segmentation of gangue is crucial to computer vision analysis. However, it is still a great challenge due to the degradation of images and the limitation of computing resources. In this paper, a hybrid connected attentional lightweight network (HALNet) with high speed, few parameters and high accuracy is proposed for gangue intelligent segmentation on the conveyor in the top-coal caving face. Firstly, we propose a deep separable dilation convolution block (DSDC) combining deep separable convolution and dilation convolution, which can…provide a larger receptive field to learn more information and reduce the size and computational cost of the model. Secondly, a bridging residual learning framework is designed as the basic unit of encoder and decoder to minimize the loss of semantic information in the process of feature extraction. An attention fusion block (AFB) with skip pathway is introduced to capture more representative and distinctive features through the fusion of high-level and low-level features. Finally, the proposed network is trained through the expanded dataset, and the gangue image segmentation results are obtained by pixel-by-pixel classification method. The experimental results show that the proposed HALNet reduces about 57 percentage parameters compared with U-Net, and achieves state-of-the art performance on dataset.
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