Technology Enabled Learning is a cognitive, constructive, systematic, collaborative learning procedure, which transforms teaching-learning pedagogy where role of emotion is very often neglected. Emotion plays significant role in the cognitive process of human being, so the transformation is incomplete without capturing the learner's emotional state. This paper, proposes a new Affective Computing Module in E-Learning system that focuses on capturing of emotional aspects, managing learning activities, timing and their reflection on the overall learning process. Human Computer Interaction aims at recognizing the learner's emotional state in order to provide a horizontal interface between human and computer. We have adopted bio-physical (Heart Rate, Skin Conductance and Blood Volume Pressure) and facial expression methods, as these can be made both in-obtrusive and robust against number of environmental conditions, to extract affective state of the learner. This study explores how emotion evolves during the learning process and how emotion feedback is used to improve learning experiences. We propose a learner emotion detection and automated lesson selection model, where two emotion attributes have been fused and achieved an efficient model for Affective E-Learning System, which obtained the sound classification rate. The system can select the learning pedagogy of the learner from detected emotion through neuro-fuzzy logic. Our research shows reasonable results in comparison with the existing systems. The result proves that the recognizer system is efficient.