S-MAS proposes a distributed hierarchical multi-agent architecture that implements a classification mechanism in two phases.
We have developed and implemented a classification scheme representing 24 coastal (subtidal, intertidal, mangrove, supratidal and terrestrial) cover types relevant to the ecology of estuarine animals, nekton and shorebirds.
Lastly, we implemented a classification process using the selected features to confirm whether they can be used to classify the two types.
The primary reason to implement a classification process for non-specific LBP is to facilitate the 'clustering' of signs and symptoms in order to help better direct treatment and improve efficiency of care.
In the results, we are most interested in autocoefficients to represent time series sequential random effects in the model and we implemented a classification between TDE (temporally differential expression over time) and EE (equally expression over time) set of genes.
To confirm whether or not the properties can be used as criteria for distinguishing LoF and GoF mutations, we implemented a classification technique using the support vector machine, random forest, and linear logistic regression methods with 50 data sets which contain equal numbers of LoF and GoF mutations to avoid bias.
We then switch our study to a more realistic network setting and augment our CFFR technique by adopting an entirely distributed architecture and by implementing a location classification algorithm based on logistic regression.
For this study, we address some of the gaps in one of our earlier papers by adopting an entirely distributed architecture and by implementing a location classification algorithm based on logistic regression.
In general, the robustness to malfunctions was increased by implementing an automatic classification of anomalies in different levels relative to the seriousness of the error.
