# The Application of Artificial Intelligence in Magnetic Hyperthermia Based Research
## 4 Artificial Intelligence and Machine Learning Based Approach
Since the synthesis of nanoparticles is a long-term and cost-consuming process due to the involvement of multiple chemical substances, the AI-based algorithm provides the opportunity to develop efficient experimental protocols. The following research describes the application of AI to the synthesis of semiconductor, metal, carbon-based and polymeric nanoparticles [80]. In [72], based on ultraviolet-visible (UV-vis) and PL spectrum data, the prediction of the optimal parameters of the synthesis of combinatorial CdSe nanoparticles was proposed. Thus, the heuristic and Bayesian optimization can be applied to the evaluation of the synthesis of the nanoparticles. Such an example is far from the magnetic hyperthermia application, while AI support can improve the experimental work also in the magnetic nanoparticles and magnetic hyperthermia field. In [81] the genetics algorithm particle swarm optimization (PSO) was used to predict the magnetic field generation. In [81], GA was used to optimize the Specific Absorption Rate in the case of hyperthermia treatment of the human head. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Accuracy** &
\begin{tabular}{l} **Database (Type and** \\ **Size if Available)** \\ \end{tabular} & \multicolumn{2}{l}{**Input Parameters**} & \multicolumn{2}{l}{**Output Parameters**} & \multicolumn{1}{l}{**Reference**} \\ \hline \hline \multicolumn{2}{l}{**Algorithm Type: Neural Network**} & & & & & \\ \hline \multirow{6}{*}{0.90} & & - & temperature & & & \\  & & - & vertex field & & & \\  & 3963 records of & - & nanoparticles diameter & - & coercive field & \\  & simulated records & - & magnetic anisotropy & - & magnetic remanence & \\  & & - & saturation magnetization & - & hysteresis loop area & \\  & & - & the identity of & & & \\  & & - & nanoparticles & & & \\ \hline \hline \multicolumn{2}{l}{**Algorithm Type: Random Forrest**} & & & & & \\ \hline \multirow{6}{*}{0.90} & & - & temperature & & & \\  & & - & vertex field & & & \\ \cline{1-1}  & & - & nanoparticles diameter & - & coercive field & \\ \cline{1-1}  & 3963 records of & - & magnetic anisotropy & - & magnetic remanence & \\ \cline{1-1}  & simulated records & - & saturation magnetization & - & hysteresis loop area & \\ \cline{1-1}  & & - & the identity of & & & \\ \cline{1-1}  & & - & nanoparticles & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The comparison of the algorithm’s performance takes into account the prediction of power losses of magnetic particles. Recently, attempts were made to apply AI-based algorithms in the research of hydrogels. In reference [82], the Artificial Neural Network and Least Square Support Vector Machine were used to evaluate the swelling degree in the hydrogel, namely poly(NIPAAm-co-AAc) IPN. It turned out that Artificial Intelligence-based algorithms can, successfully and with high accuracy, predict the influence of pH and temperature on hydrogel deswelling behaviors. At the same time, the ANN model has higher computational efficiencies than the LS-SVM approach while maintaining this similar accuracy. Thus, in [83], ANN was used to evaluate the deswelling and heating behavior of the field-sensitive hydrogels, like poly(NIPAAm-co-VSA)/Fe\({}_{3}\)O\({}_{4}\) IPN. The comparison of the algorithms for predicting deswelling behaviors is made in Table 6. It turned out that ANN achieved the highest efficiency in predicting deswelling degrees. Magnetic nanoparticles are also used to remove various types of substances. The efficiency of the approach is strictly connected with the percent of compounds adsorbed onto modified magnetic nanoparticles [83; 84; 85]. Thus, AI-based algorithms can be applied to predict removal efficiency. In [85] the application of the Artificial Neural Network and adaptive neuro-fuzzy inference system for the prediction of the chromium removal efficiency was shown. In Table 7, the comparison of algorithms for the evaluation of the removal efficiency is presented.