Abstract:The aim of this study was the prediction model of retention indices of
compounds from the aboveground parts of Achillea clypeolata Sibth. & Sm.
essential oil, obtained by hydrodistillation and analysed by GC-MS. The
quantitative structure-retention relationship analysis was applied in order
to anticipate the retention time of the obtained compounds. The selection of
the seven molecular descriptors was done by a genetic algorithm. The chosen
descriptors were uncorrelated and were used to c… Show more
“…The application of Artificial Neural Network (ANN) in aromatherapy using EOs has been well established (Acimovic et al, 2021). According to Niazian et al (2021), ANN performs better than MLR with an RMSE of 0.262 and an R2 of 0.748.…”
The COVID-19 pandemic has emphasized the significance of utilizing essential oils (EO) as one of the holistic ways of supporting and enhancing health. As a consequence of growing knowledge of connected health concerns, people all over the world are looking for natural ways to avoid different ailments. It has been proven that excellent health and psychological awareness increase the human body's immune response, therefore boosting disease resistance. Essential oils are derived in a number of ways from valued plants containing active chemicals with medicinal qualities. In Malaysia, many have used EO in their daily lives. This paper identifies the hierarchy of importance among factors which contribute towards the usage frequency of essential oils in Malaysia using an artificial neural network. Two-layer neural network (NN) models have been applied, which are multilayer perceptron (MLP) and radial basis function (RBF). Based on the analysis done, RBF-NN performed the best with SSE=4.436 and RE=0.548. It can be concluded that, based on sensitivity analysis, the top five factors toward usage frequency are consumption, age, external use, clinic visit, and occasion, with normalized importance of 100%, 90.8%, 89.3%, 68.2%, and 42.2% respectively.
“…The application of Artificial Neural Network (ANN) in aromatherapy using EOs has been well established (Acimovic et al, 2021). According to Niazian et al (2021), ANN performs better than MLR with an RMSE of 0.262 and an R2 of 0.748.…”
The COVID-19 pandemic has emphasized the significance of utilizing essential oils (EO) as one of the holistic ways of supporting and enhancing health. As a consequence of growing knowledge of connected health concerns, people all over the world are looking for natural ways to avoid different ailments. It has been proven that excellent health and psychological awareness increase the human body's immune response, therefore boosting disease resistance. Essential oils are derived in a number of ways from valued plants containing active chemicals with medicinal qualities. In Malaysia, many have used EO in their daily lives. This paper identifies the hierarchy of importance among factors which contribute towards the usage frequency of essential oils in Malaysia using an artificial neural network. Two-layer neural network (NN) models have been applied, which are multilayer perceptron (MLP) and radial basis function (RBF). Based on the analysis done, RBF-NN performed the best with SSE=4.436 and RE=0.548. It can be concluded that, based on sensitivity analysis, the top five factors toward usage frequency are consumption, age, external use, clinic visit, and occasion, with normalized importance of 100%, 90.8%, 89.3%, 68.2%, and 42.2% respectively.
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