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In today's time, the research mainly focuses on designing a Chat-bot which responds to a user's query in the most efficient manner. However, state-of-the-art works on chat-bot design are unable to emotionally connect with the user and follow-up to a conversation. In this paper, a socially and emotionally active intelligent assistant, AMIBO is proposed. It detects and recognizes the face, perceives emotion via speech and vision capabilities, provides empathetic and intelligent responses. In order to enhance the experience of AMIBO, navigation and information delivery system have been integrated in the bot. The analysis of the feature vector (on 450 subjects) is done for all the initially taken 75 distances and 15 angles reduced to 26 distances and 11 angles feature vector. An encouraging accuracy of 99% was achieved on CK+ dataset and 97% on KDEF dataset. [ABSTRACT FROM AUTHOR]

The economic, social, cultural growth of most developing countries depends upon the agriculture sector. However, plant disease may lead to inferior crop yield economic growth. Various computer vision and artificial intelligence-based schemes have been presented in past for automatic plant leaf disease detection but their performance is inadequate due to underprivileged feature representation, lower-order correlation of raw features, data imbalance problem, less generalization. This paper presents automatic plant leaf disease detection using Deep Convolutional Neural Network (DCNN) to increase the feature representation and correlation and Generative Adversarial Network (GAN) for data augmentation to cope up with data imbalance problem. Extensive experimentations are performed on ten classes of tomato plant disease from Plant Village leaf disease database. Effectiveness of proposed scheme is evaluated based on accuracy, precision, recall, F1 score which has shown momentous improvement over customary methods for plant leaf disease database (Accuracy- 99.74%, precision- 0.99, recall-0.99, F1 score -0.99). [ABSTRACT FROM AUTHOR]