Statistical Data Analysis and Modeling

An Introduction to Uncertainty in the Development of Computational Models of Biological Processes

“…They are often used to process data to a more useful format and are particularly helpful in identifying biomarkers in large datasets. Shah et al [11] discuss a particular type of empirical models, namely the eigenvalue-based approaches. These approaches, including singular value decomposition, principle component analysis, and partial least squares regression, can identify important characteristics of big datasets through decomposition and dimensionality reduction.…”

“…The structure of this chapter (and the whole book) follows that of the model development life cycle, starting with model establishment over parameter optimisation and model adaptation and ending with model prediction. [6]; C3, F2 [8]; C4, F1 [9]; C6, F4 [11]; C9, F3 [7]; C10, F5 [14]; C11, F5 [15]; C12, F1 [17]; C13, F16 [16]; C16, F3 [20]; C16, F3 [20]; C17, F3 [21].…”

An Algorithm for the Spatiotemporal Dynamic of Insect Pest in Complex Agricultural System

“…The structure and social organization of insect colonies have been explored in many way as a complex system [9][10][11]. However, it is in their role as herbivores that conflicts arise with agricultural production due to direct consumption of cultivated crops and indirect damage by plant virus transmission or spoilage of potential yield [23].. To avoid or reduce damage on crops, farmers apply chemical pesticides against pests. Despite its ability to quickly suppress pest, the use of chemical pesticide presents too many disadvantages as it pollutes air water and soil; it contributes to the development of resistance among the target pest [24] people applying these pesticides are sometime exposed to dangerous concentration of chemical which affect their health [25].…”