## 3 Methods
### The NeuroPredict Platform  
#### 3.1.1 Overview  
The project \"Advanced Artificial Intelligence Techniques in Science and Applied Domains\" [65] is currently in its second year of development, with an overall schedule of four years. The NeuroPredict platform is one of the project's most important biomedical applications. This platform is committed to the development of predictive models that are fundamental for the prevention, early detection, and long-term monitoring of people suffering from neurodegenerative diseases. NeuroPredict platform draws on advanced algorithms based on AI to enhance delivery of healthcare by precisely forecasting disease evolution, optimizing therapeutical procedures, and improving patient outcomes through proactive personalized care approaches. The platform, that encompasses effective AI algo \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Previous** & **Dataset Used** & \begin{tabular}{c} **Best Average** \\ **Accuracy [\%]** \\ \end{tabular} & \
\begin{tabular}{c} **Classification Algorithm** \\ **Basis** \\ \end{tabular} & **Features** & **Year** \\ \hline \multirow{4}{*}{[57]} & EMO-DB1 & 95.71 & \multirow{4}{*}{1D CNN} & MFCCs, chromagram, \\  & EMO-DB2 & 86.1 & & spectrograms, Tonnetz \\ \cline{2-5}  & RAVDESS & 71.61 & & representation, spectral \\ \cline{2-5}  & IEMOCAP & 64.3 & & contrast features \\ \hline \multirow{4}{*}{[56]} & EMO-DB & 90.50 & \multirow{4}{*}{1D Deep CNN for feature extraction + CFS + SVM/MLP} & \multirow{4}{*}{2020} \\ \cline{2-5}  & SAVEE & 66.90 & & \\ \cline{2-5}  & IEMOCAP & 76.60 & & \\ \cline{2-5}  & RAVDESS & 73.50 & & \\ \hline \multirow{4}{*}{[58]} & **TESS** & **97.1** & \multirow{4}{*}{1D CNN} & \multirow{4}{*}{40 MFCCs} & \multirow{4}{*}{2022} \\ \cline{2-5}  & RAVDESS & 72.2 & & \\ \hline \multirow{4}{*}{[61]} & RAVDESS & 82.31 & \multirow{4}{*}{
\begin{tabular}{c} Parallelized CNNs + transformer (CTENet) \\ \end{tabular} } & \multirow{4}{*}{MFCCs-based features} & \multirow{4}{*}{2023} \\ \cline{2-5}  & IEMOCAP & 79.42 & & \\ \hline \multirow{4}{*}{[62]} & **SAVEE** & **74.00** & \multirow{4}{*}{LSTM-Based Network with Attention Mechanism} & \multirow{4}{*}{MFCCs} & \multirow{4}{*}{2023} \\ \cline{2-5}  & RAVDESS & 77.00 & & \\ \cline{2-5}  & **Crema-D** & **82.96** & & \\ \hline \multirow{4}{*}{[63]} & **Ravdess** & **84.1** & \multirow{4}{*}{Masked autoencoder} & Spectrograms derived features using vector quantization & \multirow{4}{*}{2023} \\ \cline{2-5}  & IEMOCAP & 66.4 & & \\ \cline{2-5}  & EMODB & 90.2 & & \\ \hline \multirow{4}{*}{[64]} & IEMOCAP & 73.26 & \multirow{4}{*}{Linear classifier} & MFCCs, transformer-based features, spectrograms & \multirow{4}{*}{2024} \\ \cline{1-1} \cline{5-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative presentation of other works for SER. rithms with comprehensive physiological information, is targeted to deliver accurate and timely insights that have the potential to impact neurodegenerative disease management and comprehension. It intends to support customized healthcare delivery by focusing on both the physical and emotional aspects of patient care. AD poses an array of challenges, mostly associated with cognitive decline and memory loss. Patients frequently report difficulty with everyday tasks, behavioral changes, and, finally, diminished autonomy. The NeuroPredict platform addresses these issues by incorporating cognitive evaluations and digital biomarkers gathered from the platform's user-friendly interfaces to evaluate cognitive performance over time. The platform, which uses AI algorithms to analyze data on memory capacity, executive functioning, and behavioral patterns, allows for early identification of cognitive decline and prediction of disease development. This preventative approach promotes timely interventions and customized care plans that maintain cognitive function and improve quality of life, which could slow disease progression while also preserving cognitive function and maximizing treatment options based on individual patient profiles.