# Prediction of Emotional Empathy in Intelligent Agents to Facilitate Precise Social Interaction
## 5 Discussion  
This paper explored EE paradigms to understand better how we can create more life-like social AI in time-constrained, task-oriented environments. EE paradigms offer an ideal setting to examine the interplay of the interaction context with agent behavior during human-agent or agent-agent interaction, since both are interdependent, particularly when the human and agent must collaborate to achieve some goal under some specific circumstances [44]. To this end, we focused on exploring methods for developing modifiable components of an EE and SI to enable the simultaneous manipulation of both the agent and the environment. We also evaluated how a data-driven and model-driven approach could be used to develop various components of EE, utilizing interaction data. Results showed success as well as areas of improvement for different components [45].  
Emotional intelligence is an essential component of human intellect and one of the most crucial factors for social success. However, endowing machines with this level of intelligence for affective human-machine interaction is not straightforward [46]. Complicating matters is that humans use multiple MECs concurrently to evaluate affective states, as emotion influences nearly all modes of audio-visual, physiological, and contextual stimuli. Compared to conventional unimodal techniques, multimodal emotion detection presents several unique challenges, particularly in the fusion architecture of multimodal input. Studies reveal that visual expressions of emotion are more universal than prosody across cultures [47]. The multimodal interpretation of cross-cultural emotions could thus be more effective than verbal interpretation alone. Literature indicates that the application space for MECs is broad and diverse.  
There are 18 classes for the EB that certainly depend upon the multimodal cues consisting of vocal, facial, and other sensory inputs. The classification of EB and prediction based on slight changes in multimodal cues is challenging and requires robust techniques sensitive to the minor changes in the multimodal cues due to the dynamic social environment. In this study, we used an ODCNN, and more efficiency is achieved as it reduces the parameters by using the benefit of feature locality. The locality-preserving feature analysis of ODCNN makes it more robust in classification problems based on historical data. This feature predicts the diverse configurations related to EE in a dynamic environment. The experimental results and comparison of different classifiers show that ODCNN performed better EB classification and prediction based on performance measures, such as accuracy, area Under the curve, precision, recall, and F1-score, in a socially dynamic environment by representing the relative EB based on the multimodal inputs. ODCNNs are the extended form of conventional DL-based algorithms and outperform classical ML approaches. Given below, Table 16 presents a comparison of the current study technique with previous similar studies' designs.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithms** & **Accuracy** \\ \hline Hilbert-Huang Transform and Fusion Based [48] & 44\% \\ Hilbert-Huang Transform and Fission Based [48] & 52\% \\ Kim’s Approach [48] & 61\% \\ K-Nearest Neighbors [48] & 64\% \\ Sequential Floating Forward Search [48] & 92\% \\ AFFDEX [49] & 95\% \\ Model-Agnostic Meta-Learning [50] & 81.69\% \\ Gaussian Mixture Model and K-Nearest Neighbor [51] & 83\% \\ Decision Trees [52] & 80\% \\ Logistic Regression [53] & 90.57\% \\ Gaussian Process Regression [54] & 95.23\% \\ Cubic Support Vector Machine [55] & 98.01\% \\ Proposed study (ODCNN) & 98.98\% \\ \hline \hline \end{tabular}
\end{table}
Table 16: Comparison with previous research.