## 4 Evaluation
### Discussion  
MIRA was evaluated by 33 participants belonging to different domains, age groups, genders, and diverse nationalities. The participants were given 60 minutes to complete a list of tasks during the interaction with MIRA. Among the 33 participants, 27 completed their tasks at an average time of 40 minutes because their interaction was smooth with little or no misinterpretation. However, 6 participants took an average of 55 minutes due to several misinterpretations such as 'thirsty' as 'thirty', 'tired' as 'tire', 'driving' as 'diving', and 'tear' as 'tire'. Based on these interactions, MIRA gets an overall accuracy of 89% because it used the deep learning predictive model, which learns from the data incrementally and manages complex dialogues efficiently. We considered the macro-average instead of micro-average for calculating precision (90%), sensitivity (89.8%), specificity (94.9%), and F-measure (89.8%) of the complete system. Please note that the macro-average gives equal weight to each class label, while the micro-average results are biased towards the larger class label. Therefore, we showed impressive results for MIRA in terms of efficiency and effectiveness. Moreover, the PARADISE framework was used to evaluate the task completion of MIRA, where the actual agreement \((P(A)=0.898)\) is better than agreement-by-chance \((P(E)=0.334)\). Because the stock phrases were designed from real conversations that facilitated MIRA for a better understanding of natural language input. The Cohen's Kappa value \((k=0.848)\) was interpreted as 'Almost Perfect' because MIRA generated the response in a real and natural way using a female voice that keeps the user motivated to continue the interaction. MIRA also keeps a record of the conversational dialogue corpus along with final recommendation about the appropriate medical specialist that supports personalized interactions with the established user. For the prototype version of MIRA, we considered authentication instead of confidentiality, integrity, and availability. A strong authentication mechanism minimizes the risk of exploiting security vulnerabilities but will affect the performance and efficiency of the system. Therefore, we used the lightweight version of our designed voice-based authentication protocol, which identifies the user based on the extracted MFCC value of natural language utterances; this method was evaluated for masquerading attack. The results showed that MIRA successfully identified the user in real time based on their voice samples and strongly resisted a masquerading attack. We used UEQ for evaluating the user experience because it provides ease of data analysis and calculates the necessary statistics accordingly. Due to reliability, different organizations used UEQ for evaluating their products and consider it to be a good measure. According to UEQ, MIRA was evaluated in terms of attractiveness, pragmatic quality, and hedonic quality, where the value of pragmatic is smaller than the other two qualities (attractiveness and hedonic). This is due to the low value of secure and predictable items under the category of pragmatic quality because some  
Figure 11: MIRA user experience questionnaire scores on six dimensions scales along with benchmark data. participants considered secure in terms of security, but it evaluates the user's feelings regarding the interaction control. Moreover, MIRA uses synonyms of specific words for generating a relevant response, which may be unpredictable for a conversational scenario in some situations. Suppose in one interaction MIRA asked a user, 'How about your empty-belled?' instead of 'Do you feel extremely hungry?'. The value of pragmatic quality is affected by these two factors. However, the overall results of UEQ present positive feedback, and users were satisfied with MIRA's interactive communication. After the completion of tasks, the participants were awarded a shopping coupon worth 30,000 KRW as an incentive.