# The Application of Artificial Intelligence in Magnetic Hyperthermia Based Research
## 4 Artificial Intelligence and Machine Learning Based Approach
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Accuracy** &
\begin{tabular}{l} **Database (Type and** \\ **Size if Available)** \\ \end{tabular} & **Input Parameters** & **Output Parameters** & **Reference** \\ \hline \hline
**Algorithm Type:** Artificial Neural Network & & & & & \\ \hline
0.93 (for [PERSON]’s modulus) & 153 datasets from & - & weight percent & - & [PERSON]’s modulus \\
0.96 (ultimate tensile strength) & the literature & - & - particle size & - & ultimate tensile \\  & & & & strength & \\ \hline
0.97 & \
\begin{tabular}{l} 3404 experimental \\ dataset \\ \end{tabular} & - & wavelength & - & particle size & \\
\begin{tabular}{l} - \\ - \\ - \\ - \\ \end{tabular} & - & peak intensity & - & reaction yield & [72] \\  & - & full width at half-maximum & - & quantum yield & \\  & - & peak area of the main peak & & \\ \hline
0.98 & \
\begin{tabular}{l} \end{tabular} &
\begin{tabular}{l} - \\ experimental data sets \\ \end{tabular} & - & extraction time & - & extraction yield & \\  & - & temperature & & of essential oils & [73] \\  & & - & modifier volume & & \\ \hline
0.99 &
\begin{tabular}{l} experimental data sets \\ \end{tabular} & - & composition & - & specific & \\ \hline
0.99 & \
\begin{tabular}{l} 420 experimental \\ data sets \\ \end{tabular} & - &
\begin{tabular}{l} particle concentration \\ alternating magnetic field \\ strength \\ - \\ - \\ - \\ - \\ \end{tabular} & - & optimal & \\  & - & temperature & & parameters & [75] \\ \hline
**Algorithm Type:** Random Forest & & & & \\ \hline
0.75 & \begin{tabular}{l} - \\ - \\ - \\ - \\ - \\ - \\ - \\ - \\ - \\ - \\ \end{tabular} & - & \
\begin{tabular}{l} nanoparticle type \\ nanoparticle core \\ surface modification \\ - \\ - \\ - \\ - \\ - \\ - \\ - \\ - \\ - \\ \end{tabular} & - &
\begin{tabular}{l} optimal \\ composition \\ \end{tabular} & [76] \\ \hline
**Algorithm Type:** multilayer perceptron & & & & \\ \hline
0.94 (compressive strength) & data collected from the & - &
\begin{tabular}{l} elastic modulus \\ fracture toughness diopside \\ hardystonite \\ - \\ - \\ \end{tabular} & - & compressive & \\  & & - \\ \end{tabular}
\end{table} 
Table 4: The comparison of the algorithms for prediction of the optimal properties of nanomaterials performance. In [74], the application of the genetic algorithm for the optimization of agar nanospheres, which were used in the manufacturing process of drug loading, was proposed. The optimization problem was formulated to minimize the size of the particle, release efficiency, and PDI, as well as to maximize the absolute value of zeta potential and loading efficiency. It turned out that a genetics algorithm could successfully predict the parameters of Bupropion hydrochloride-loaded agar nanospheres. In turn, [75] the closed-loop optimization of the release process of the Poly (lactic-co-glycolic acid) (PLGA) biodegradable particles with ANN and genetic algorithms was described. As input data, particle size, and initial burst percent at the desired levels were chosen. It was postulated that the proposed algorithm can predict drug delivery. In [76], the ANN was used to predict the optimal composition of two-dimensional graphene-Fe\({}_{3}\)O\({}_{4}\) nanohybrids, which are dedicated to magnetic hyperthermia. It enables the prediction of the composition of the optimal nanohybrid, which can be applied to magnetic hypothermia in low dosage. The optimization based on multilayer perceptron neural networks of the experimental conditions of nanoparticles was described in [77]. The influence on nanoparticle characteristics factors like environmental conditions and type of precipitating agent was investigated. In turn, the mathematical framework for the magnetic drug delivery taking into account the ferrofluid flow was shown in [17]. Another critical issue in the manufacturing of nanoparticles is the synthesis process [78; 79]. It should maintain precisely controlled characteristics.