  According to the latest trend of artificial intelligence, AI-systems needs to
clarify regarding general,specific decisions,services provided by it. Only
consumer is satisfied, with explanation , for example, why any classification
result is the outcome of any given time. This actually motivates us using
explainable or human understandable AI for a behavioral mining scenario, where
users engagement on digital platform is determined from context, such as
emotion, activity, weather, etc. However, the output of AI-system is not always
systematically correct, and often systematically correct, but apparently
not-perfect and thereby creating confusions, such as, why the decision is
given? What is the reason underneath? In this context, we first formulate the
behavioral mining problem in deep convolutional neural network architecture.
Eventually, we apply a recursive neural network due to the presence of
time-series data from users physiological and environmental sensor-readings.
Once the model is developed, explanations are presented with the advent of XAI
models in front of users. This critical step involves extensive trial with
users preference on explanations over conventional AI, judgement of credibility
of explanation.
