Titlebook: ;

Calibrate

Matching-Graphs for?Building Classification Ensemblesates significant benefits, in particular a better classification performance than the individual ensemble members. However, in order to work properly, ensemble methods require a certain diversity of its members. One way to increase this diversity is to randomly select a subset of the available data

字形刻痕

poliosis

Detecting Abnormal Communication Patterns in?IoT Networks Using Graph Neural Networksited hardware capabilities of these devices, host-based countermeasures are unlikely to be deployed on them, making network traffic analysis the only reasonable way to detect malicious activities. In this paper, we face the problem of identifying abnormal communications in IoT networks using graph-b

收養(yǎng)

aerial

Glycogen

Reducing the?Computational Complexity of?the?Eccentricity Transform of?a?Treelysis of shapes. The ECC assigns to each point within a shape its geodesic distance to the furthest point, providing essential information about the shape’s geometry, connectivity, and topology. Although the ECC has proven valuable in numerous applications, its computation using traditional methods

ADORE

Graph-Based Deep Learning on?the?Swiss River Networknt quality. We use GIS data to extract the structure of each river and link this structure to 81 river water stations (that measure both water temperature and discharge). Since the water temperature of a river is strongly dependent on the air temperature, we also include 44 weather stations (which m

饑荒

Hdl348

清澈

https://doi.org/10.1007/978-3-322-90760-8techniques. The results show a clear improvement in the performance of the initial method. Furthermore, our findings rank among the best in terms of classification accuracy and computation speed compared to other graph kernels.