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Titlebook: Intelligent Information and Database Systems; 13th Asian Conferenc Ngoc Thanh Nguyen,Suphamit Chittayasothorn,Bogdan Conference proceeding

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51#
發(fā)表于 2025-3-30 11:48:57 | 只看該作者
52#
發(fā)表于 2025-3-30 13:40:19 | 只看該作者
Complexes of Low Dimensional Linear Classifiers with , Marginsrs, inter alia, in in the case of genetic data. Various types of classifiers are designed on the basis of such data sets. Small number of multivariate feature vectors are almost always linearly separable. For this reason, the linear classifiers play a fundamental role in the case of small samples of
53#
發(fā)表于 2025-3-30 17:30:52 | 只看該作者
Automatic Identification of Bird Species from Audioomatic bird classification using machine learning techniques is an important trend in the scientific community. Analyzing bird behavior and population trends helps detect other organisms in the environment and is an important problem in ecology. Bird populations react quickly to environmental change
54#
發(fā)表于 2025-3-30 20:46:23 | 只看該作者
55#
發(fā)表于 2025-3-31 01:38:01 | 只看該作者
UVDS: A New Dataset for Traffic Forecasting with Spatial-Temporal Correlationid growth of computer vision for intelligent transportation systems, using detection systems for estimating traffic flow become an emergent issue. In this study, we first discuss the main differences between UVDS and existing datasets in terms of spatial-temporal dependencies for accurate traffic pr
56#
發(fā)表于 2025-3-31 05:22:44 | 只看該作者
A Parallelized Frequent Temporal Pattern Mining Algorithm on a Time Series Databasepplication domains such as finance, medicine, geology, meteorology, and telecommunication. Among the time series mining tasks, frequent temporal pattern discovery is an interesting task because this task brings us a deep insight view of relationships between many objects and events through time. How
57#
發(fā)表于 2025-3-31 12:11:55 | 只看該作者
An Efficient Approach for Mining High-Utility Itemsets from Multiple Abstraction Levels is a useful tool for retail stores to analyze customer behaviors. However, it ignores the categorization of items. To solve this issue, the ML-HUI Miner algorithm was presented. It combines item taxonomy with the HUIM task and is able to discover insightful itemsets, which are not found in traditio
58#
發(fā)表于 2025-3-31 17:10:01 | 只看該作者
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