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By introducing Stacking algorithm to solve the underfitting problem caused by insufficient data in traditional machine learning, this paper provides a new solution to the cold start problem of entrepreneurial borrowing risk control.

The authors introduce semi-supervised learning and integrated learning into the field of migration learning, and innovatively propose the Stacking model migration learning, which can independently train models on entrepreneurial borrowing credit data, and then use the migration strategy itself as the learning object, and use the Stacking algorithm to combine the prediction results of the source domain model and the target domain model.

The effectiveness of the two migration learning models is evaluated with real data from an entrepreneurial borrowing. The algorithmic performance of the Stacking-based model migration learning is further improved compared to the benchmark model without migration learning techniques, with the model area under curve value rising to 0.8. Comparing the two migration learning models reveals that the model-based migration learning approach performs better. The reason for this is that the sample-based migration learning approach only eliminates the noisy samples that are relatively less similar to the entrepreneurial borrowing data. However, the calculation of similarity and the weighing of similarity are subjective, and there is no unified judgment standard and operation method, so there is no guarantee that the retained traditional credit samples have the same sample distribution and feature structure as the entrepreneurial borrowing data.

From a practical standpoint, on the one hand, it provides a new solution to the cold start problem of entrepreneurial borrowing risk control. The small number of labeled high-quality samples cannot support the learning and deployment of big data risk control models, which is the cold start problem of the entrepreneurial borrowing risk control system. By extending the training sample set with auxiliary domain data through suitable migration learning methods, the prediction performance of the model can be improved to a certain extent and more generalized laws can be learned.

This paper introduces the thought method of migration learning to the entrepreneurial borrowing scenario, provides a new solution to the cold start problem of the entrepreneurial borrowing risk control system and verifies the feasibility and effectiveness of the migration learning method applied in the risk control field through empirical data.

With the advent of the web computing era, the transmission mode of the Internet of Everything has caused an explosion in data volume, which has brought severe challenges to traditional routing protocols. The limitations of the existing routing protocols under the condition of rapid data growth are elaborated, and the routing problem is remodeled as a Markov decision process. this paper aims to solve the problem of high blocking probability due to the increase in data volume by combining deep reinforcement learning. Finally, the correctness of the proposed algorithm in this paper is verified by simulation.

The limitations of the existing routing protocols under the condition of rapid data growth are elaborated and the routing problem is remodeled as a Markov decision process. Based on this, a deep reinforcement learning method is used to select the next-hop router for each data transmission task, thereby minimizing the length of the data transmission path while avoiding data congestion.

Simulation results show that the proposed method can significantly reduce the probability of data congestion and increase network throughput.

This paper proposes an intelligent routing algorithm for the network congestion caused by the explosive growth of data volume in the future of the big data era. With the help of deep reinforcement learning, it is possible to dynamically select the transmission jump router according to the current network state, thereby reducing the probability of congestion and improving network throughput.

This study aims to focus on the analysis of the internal mechanism of farmers’ ecological cognition and the behaviour of Grain for Green Project (GGP), and the further relationship between ecological cognition and ecological aspiration, proposing climate change strategies and management from the perspective of farmers.

Theory of planned behaviour and social exchange theory were used to construct a theoretical framework and an ecological cognition under the influence of external factors, the aspiration and the behaviour of GGP, using ecological fragile areas in Bazhou and Changji, Xinjiang of 618 peasant households’ survey data. The structural equation model and Heckman two-step model were applied to analyse the relationship between ecological cognition and ecological aspiration of farmers, the impact of peasant households’ ecological cognition and aspiration to the behaviour of GGP and the influence factors of GGP behaviour.

This research’s results show that the three characterizations of ecological cognitive variables, attitude towards the behaviour (AB), subjective norms (SN) and perceived behaviour control (PBC), have significant positive impact on farmers’ GGP ecological aspiration. The comprehensive impact path coefficients of ecological cognition are PBC (0.498) > SN (0.223) > AB (0.177). Also, income change is a moderating variable, which has a significant moderating effect on the influence of AB and SN on ecological aspiration. Further, farmers’ ecological cognition has an influence on the behaviour of GGP, and the change of farmers’ income has a significant positive effect on farmers’ choice of returning farmland to forests.

The ecological protection policy suggestions and countermeasures can be drawn from the research conclusions, adapted to China’s ecologically fragile regions and even similar regions in the world to response the climate change.

Combining the theory of planning behaviour and social exchange, this paper empirically analyses the path of farmers’ ecological cognition and ecological aspiration, as well as the influencing factors.