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Naive Bayes (NB) is an extremely simple and remarkably effective approach to classification learning, but its conditional independence assumption rarely holds true in real-world applications. Attribute weighting is known as a flexible model via assigning each attribute a different weight discriminatively to improve NB. Attribute weighting approaches can fall into two broad categories: filters and wrappers. Wrappers receive a bigger boost in terms of classification accuracy compared with filters, but the time complexity of wrappers is much higher than filters. In order to improve the time complexity of a wrapper, a filter can be used to optimize the initial weight of all attributes as a preprocessing step. So a hybrid attribute weighting approach is proposed in this paper, and the improved model is called correlation-based weight adjusted naive Bayes (CWANB). In CWANB, the correlation-based attribute weighting filter is used to initialize the attribute weights, and then each weight is optimized by the attribute weight adjustment wrapper where the objective function is designed based on dynamic adjustment of attribute weights. Extensive experimental results show that CWANB outperforms NB and some other existing state-of-the-art attribute weighting approaches in terms of the classification accuracy. Meanwhile, compared with the existing wrapper, the CWANB approach reduces the time complexity dramatically.

This paper uses big data to establish a cloud logistics management platform, and builds a large-scale cloud computing logistics platform for unified processing of massive bill of lading data according to distribution requirements, and collects the logistics scheduling center of each enterprise to formulate an optimal scheduling scheme. Based on this, the paper firstly designs the cloud logistics platform scheduling system according to the scheduling requirements, which includes detailed design of multiple modules such as data layer, storage layer and scheduling algorithm layer. Secondly, the paper uses the combination of timeline and dynamic information to record the information generated during the network distribution process, construct a vehicle scheduling mathematical model with constraint definition and objective function, and use improved genetic algorithm combined with MapReduce programming model to realize logistics vehicles. Intelligent scheduling. Finally, the paper verifies the excellent performance of the logistics vehicle intelligent dispatching system.

The corresponding concepts and calculation methods are very in line with the requirements of information platform resource scheduling. Based on this, this paper discusses the related concepts of information grid in detail, which includes the key elements of grid computing. At the same time, it analyses and studies the key technology of information grid, web services, and puts forward the framework of information grid for resource scheduling of information platform. In order to better realise the storage and management of information resources in the information platform, this paper innovatively proposes a meta database data storage and management algorithm for the integration of grid computing and cloud computing in the information grid environment, and designs the corresponding data management and storage framework. Furthermore, we compare it with the cloud platform resource scheduling platform in order to reflect the advantages of our proposed method.

The rapid and accurate assessment of building damage states using only post-event remote sensing data is critical when performing loss estimation in earthquake emergency response. Damaged roof detection is one of the most efficient methods of assessing building damage. In particular, airborne LiDAR is often used to detect roofs damaged by earthquakes, especially for certain damage types, due to its ability to rapidly acquire accurate 3D information on individual roofs. Earthquake-induced roof damages are categorized into surface damages and structural damages based on the geometry features of the debris and the roof structure. However, recent studies have mainly focused on surface damage; little research has been conducted on structural damage. This paper presents an original 3D shape descriptor of individual roofs for detecting roofs with surface damage and roofs exhibiting structural damage by identifying spatial patterns of compact and regular contours for intact roofs, as well as jagged and irregular contours for damaged roofs. The 3D shape descriptor is extracted from building contours derived from airborne LiDAR point clouds. First, contour clusters are extracted from contours that are generated from a dense DSM of individual buildings derived from point clouds. Second, the shape chaos indexes of contour clusters are computed as the information entropy through a contour shape similarity measurement between two contours in a contour cluster. Finally, the 3D shape descriptor is calculated as the weighted sum of the shape chaos index of each contour cluster corresponding to an individual roof. Damaged roofs are detected solely using the 3D shape descriptor with the maximum entropy threshold. Experiments using post-event airborne LiDAR point clouds of the 2010 Haiti earthquake suggest that the proposed damaged roof detection technique using the proposed 3D shape descriptor can detect both roofs exhibiting surface damage and roofs exhibiting structural damage with a high accuracy.