In order to further curb the damage to the ecological environment from the perspective of legal synergistic supervision, a synergistic analysis method between the ecological protection environmental law and the ecological Civil Code is proposed. Coordinated supervision, with the new Civil Code as the research background, from the perspective of interpretation, explores the solution to the problem of “ecological environmental damage” in the newly promulgated Civil Code for behavior that damages the ecological environment. The research results believe that, from the perspective of rights, environmental rights should be regarded as the concentrated expression of rights in the sense of private law in the ecological environment law. Article 1234 of the Tort Liability Section stipulates that “the state-specified agency or the law-specified organization” as the representative of environmental public interests proposes damage. The request resolves the legitimacy of relevant agencies and organizations as civil subjects to represent environmental public interests. Finally, it clearly stipulates the responsibility for ecological restoration, expands the way of undertaking tort liability caused by environmental damage, and solves the problem that it was limited to “restoration” in the past and could not be actually performed.

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Synergistic Effect Analysis of Ecological Protection and Environmental Law and Ecological Civil Code

Network attack traffic detection plays a crucial role in protecting network operations and services. To accurately detect malicious traffic on the internet, this paper designs a hybrid algorithm UMAP-RF for both binary and multiclassification network attack detection tasks. First, the network traffic data are dimensioned down with UMAP algorithm. The random forest algorithm is improved based on parameter optimization, and the improved random forest algorithm is used to classify the network traffic data, distinguishing normal data from abnormal data and classifying nine different types of network attacks from the abnormal data. Experimental results on the UNSW-NB15 dataset, which are significant improvements compared to traditional machine-learning methods, show that the UMAP-RF hybrid model can perform network attack traffic detection effectively, with accuracy and recall rates of 92.6% and 91%, respectively.

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Research on Network Attack Traffic Detection HybridAlgorithm Based on UMAP-RF

In order to quickly and accurately collect the massive commodities and corresponding transaction data of large-scale e-commerce platforms, and improve the ability of data analysis and mining, this paper proposes a platform commodity information collection system based on splash technology. The system prerenders the javascript code in the product page, combined with the Scrapy crawler framework, to realize a system that quickly and effectively collects product data from different platforms, and uses “mobile phone” as the retrieval keyword to verify the designed system, respectively. The experimental results show that the system can effectively collect up to 60,000 comments and 6,000 system requests. Conclusion. The platform commodity information collection system based on splash technology has certain application value and promotion for the commodity data collection of different platforms of e-commerce.

Commodity Information Collection and System Construction of e-Commerce Platform Based on Network Data

In order to solve the problem of low accuracy of high-rise building cost evaluation, the author proposes an intelligent evaluation method of engineering cost feasibility model based on the Internet of Things. According to the overall investment composition of the construction project, this paper classifies the cost index, determines the overall cost correction coefficient, and uses the gray correlation analysis method to build the evaluation index system; according to the structure of BP neural network, the network error is calculated, and the error signals of output layer and hidden layer are defined by gradient descent method to obtain the network weight adjustment formula; finally, this paper uses the adaptive learning rate adjustment formula to set the network parameters, introduces the key parameters in the construction project to the input layer, and establishes the final cost evaluation model. Experimental results show that the error rate of this system is controlled within 10%, which meets the requirements of investment estimation. Conclusion. The proposed method can accurately and quickly evaluate the best solution for the cost of high-rise buildings with less information and has strong nonlinear information processing capabilities.

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Intelligent Evaluation Method of Engineering Cost Feasibility Model Based on Internet of Things

In order to solve the difficult problem of deep learning-based robot vision tracking algorithm research and implementation, a deep learning-based target tracking algorithm and a classical tracking algorithm were proposed. It mainly uses the combination of traditional TLD algorithm and GOTURN algorithm to benefit from a large number of offline training data and updates the learner online, so that the whole system has better performance in real-time and accuracy. The results show that the performance of the TLD algorithm is poor regardless of the accuracy curve or the accuracy curve, and the performance of GOTURN-LD is significantly improved when the illumination changes. In the face of occlusion problem, the TLD algorithm shows strong robustness. Although GOTURN-LD is not very stable, its performance is better than GOTURN on the whole.

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Research and Implementation of Robot Vision Scanning Tracking Algorithm Based on Deep Learning

Rapid Internet use growth and applications of diverse military have managed researchers to develop smart systems to help applications and users achieve the facilities through the provision of required service quality in networks. Any smart technologies offer protection in interactions in dispersed locations such as, e-commerce, mobile networking, telecommunications and management of network. Furthermore, this article proposed on intelligent feature selection methods and intrusion detection (ISTID) organization in webs based on neuron-genetic algorithms, intelligent software agents, genetic algorithms, particulate swarm intelligence and neural networks, rough-set. These techniques were useful to identify and prevent network intrusion to provide Internet safety and improve service value and accuracy, performance and efficiency. Furthermore, new algorithms of intelligent rules-based attributes collection algorithm for efficient function and rules-based improved vector support computer, were proposed in this article, along with a survey into the current smart techniques for intrusion detection systems.

https://www.mdpi.com/1999-4893/14/8/224/pdf

Ajay Nair

Papers

Intelligent Network Intrusion Prevention Feature Collection and Classification Algorithms