**4. Conclusion**

Surface plots were generated to describe the relationship between two operating variables and predicted responses.

Methanol and ethanol were confirmed to be superior and were chosen as the extraction solvents of first choice for the PCO and capsaicin under studied process condition. Regarding capsanthin, it is apparent that *n*-hexane offers optimal values with the highest desirability.

Process conditions, i.e. optimal extraction time and temperature with the highest desirability of analytes content of interest, were developed and verified.


**Abstract**

The high number of cases reported of antibiotic resistance use and mortality due to gram-negative sepsis, triggered the development of natural agents to be used in the prevention and treatment of sepsis. Studies continue to be developed on the use of tropical fruits such as coconut fruit and kaffir lime fruit which contain high antioxidants and many potential compounds. Recent experimental data has proven that the high antioxidant activity found in the coconut fruit mixture, namely processed fruit flesh and coconut water and added kaffir lime juice, can be used as an immunonutrition agent that can improve body physiology and can increase the survival rate of test animals from endotoxemia lipopolysaccharide induced by Eschercia coli intraperitoneally. This chapter provides an overview of the potential of natural products that can be used as immunonutrition preparations. Finally, this provides information showing the importance of the intake of immunonutrition in conditions of sepsis infection.

**Keywords:** sepsis, natural product, immunonutrition, coconut fruit, kaffir lime


*5.2. Methodology*

We present hereafter the methodology, which consists of four parts: (1) hyperparameter tuning; (2) feature selection; (3) impact of feature selection; and (4) relabeling.

#### 5.2.1. Hyperparameter Tuning

This section describes how to set the training hyperparameters values of our proposed model BrainShield based on the neural networks to detect malware.

As initial settings, the dropout rate (i.e., 0.3), the learning rate (i.e., 0.002) and the activation function (i.e., Relu) are by default proposed by Keras [34]. Moreover, the number of 50 iterations and the number of 1119 neurons as input are chosen as those large enough to have viable results and to complete hundreds of training in a suitable time. The final values of hyperparameters are illustrated in Table 2.

**Table 2.** Final values of hyperparameters.

In order to obtain the best value for the epoch number (i.e., number of iterations), we consider 7 values for the epoch number (i.e., 50, 100, 150, 200, 250, 300, 400), and we compare the different results of training using the evaluation metrics (i.e., the accuracy, the recall, the precision, the AUC, and the F1 score). The results are obtained from statistical averages over 10 training sessions and are illustrated in Figure 4. Such results show that the evaluation metrics are being improved for up to 250 iterations. Then, for higher values

of epoch number, no improvement in the evaluation metrics is observed (e.g., 70 training sessions with a total duration of 157 min were performed).

Hereafter, we justify the choice of (1) the dropout rate; (2) the learning rate; (3) the number of neurons, and (4) the activation functions.


**Appendix A**

This appendix presents relevant sections of the online softGIS survey used for data collection.

**Table A1.** Relevant sections of the softGIS survey.

**Table A1.** *Cont.*

**Table A1.** *Cont.*

**Table A1.** *Cont.*