In order to access the performance of our model detection algorithm, we generated random networks with prescribed modular structure and then used our method to identify the predefined modules.
In this paper, with the aid of more realistic LTE and WiMAX simulation chains and different channel models, several MIMO detection algorithms are applied to LTE and WiMAX systems and with their performance quantitatively evaluated.
For the epidemiological study of the syndromic time series, we will develop models and anomaly detection algorithms on the number of cases of each syndrome per time unit from the historical database [ 62].
Consequently, if a patient presented several infectious diseases corresponding to different syndrome groups, the patient's data were used in the different models for building detection algorithms of each syndrome group.
Based on this model, an anomaly detection algorithm can detect out of profile behaviour, i.e., outliers.
Moreover, the more advanced motif detection algorithms explicitly model the phylogenetic relatedness between the orthologous input sequences and thus should be well adapted towards using orthologous information.
In future work we will add negation detection algorithms and model the context in which the keyword occurs, as well as expanding the indicators which are searched for in free text.
A low sensing power consumption of 56 mW was observed and the performance of non-linear model based gas detection algorithm was analysed using Relative Prediction Error (RPE), Root Mean Square Error for Cross Validation (RMSECV) and percentage recovery (% recovery).
The system model uses our detection algorithm, in Additional file 1, Algorithm 1, to find out the potential factors that optimize the system performance.
Recently, studies have shown that incorporating the use of a hidden Markov model in a detection algorithm can improve results [ 15].
