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Human–computer interaction (HCI) and related technologies focus on the implementation of interactive computational systems. The studies in HCI emphasize on system use, creation of new techniques that support user activities, access to information, and ensures seamless communication. The use of artificial intelligence and deep learning-based models has been extensive across various domains yielding state-of-the-art results. In the present study, a crow search-based convolution neural networks model has been implemented in gesture recognition pertaining to the HCI domain. The hand gesture dataset used in the study is a publicly available one, downloaded from Kaggle. In this work, a one-hot encoding technique is used to convert the categorical data values to binary form. This is followed by the implementation of a crow search algorithm (CSA) for selecting optimal hyper-parameters for training of dataset using the convolution neural networks. The irrelevant parameters are eliminated from consideration, which contributes towards enhancement of accuracy in classifying the hand gestures. The model generates 100 percent training and testing accuracy that justifies the superiority of the model against traditional state-of-the-art models.

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The advancements in Information and Communication Technologies (ICT) made the concept of Smart Cities into reality. In a smart city several Internet of Things (IoT) sensors are deployed across several locations to collect the data about traffic, drainage, mobility of citizens etc. and the insights gained from these data are used to manage resources, assets etc. effectively. Deep Learning has been used extensively on the data generated by IoT sensors in a smart city by several researchers. In this article an attempt is made to survey several state-of-the art on usage of Deep Learning on Smart City data. Several future research directions are suggested at the end of the article.

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