Missing Data Estimation in Temporal Multilayer Position-Aware Graph Neural Network (TMP-GNN)

Context-aware Modeling for Spatio-temporal Data Transmitted from a Wireless Body Sensor Network

Context-aware systems must be interoperable and work across different platforms at any time and in any place. Context data collected from wireless body area networks (WBAN) may be heterogeneous and imperfect, which makes their design and implementation difficult. In this research, we introduce a model which takes the dynamic nature of a context-aware system into consideration. This model is con...

Neural Network Application to Ship Position Estimation

The real time estimation of ship motion is considered in this paper. The problem is viewed as an estimatiod prediction problem for partially unknown systems. A neural estimator based on a dynamic recurrent neural network is considered. The model that describes the ship motion dynamics is presented, and the neural algorithm is tested and evaluated via extensive simulations. The results show that...

Temporal Modeling and Missing Data Estimation for MODIS Vegetation data

LIQUEFACTION POTENTIAL ASSESSMENT USING MULTILAYER ARTIFICIAL NEURAL NETWORK

In this study, a low-cost, rapid and qualitative evaluation procedure is presented using dynamic pattern recognition analysis to assess liquefaction potential which is useful in the planning, zoning, general hazard assessment, and delineation of areas, Dynamic pattern recognition using neural networks is generally considered to be an effective tool for assessing of hazard potential on the b...

Performance evaluation of different estimation methods for missing rainfall data

There are numerous methods to estimate missing values of which some are used depending on the data type and regional climatic characteristics. In this research, part of the monthly precipitation data in Sarab synoptic station, east Azerbaijan province, Iran was randomly considered missing values. In order to study the effectiveness of various methods to estimate missing data, by seven classic s...