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This study aims to reduce data bias during human activity and increase the accuracy of activity recognition.

A convolutional neural network and a bidirectional long short-term memory model are used to automatically capture feature information of time series from raw sensor data and use a self-attention mechanism to learn select potential relationships of essential time points. The proposed model has been evaluated on six publicly available data sets and verified that the performance is significantly improved by combining the self-attentive mechanism with deep convolutional networks and recursive layers.

The proposed method significantly improves accuracy over the state-of-the-art method between different data sets, demonstrating the superiority of the proposed method in intelligent sensor systems.

Using deep learning frameworks, especially activity recognition using self-attention mechanisms, greatly improves recognition accuracy.

Recent attention to the developments focusing on the educational services has been noteworthy, with the educational environment specifically the smart campus emerging both as a domain and as an opportunity. As a domain worthy of exploration, a number of research efforts are being conceptualized around smart campus initiatives. The existing bouquet of research publications on smart campus provides a testimony of the enthusiasm and also exposes the heterogeneous attempts the domain has witnessed to date. The available evidence is still inadequate to provide clarity on the thrust areas of research around smart campus.

Given the understanding, this study intends to decode the domain to get an early impression of the focus of the research concentration around smart campus. Thereby the study resorts to an automated text-mining approach using Python on contents shortlisted systematically, and published between the period 2010 and May 2022, from select databases.

Based on the analysis it was possible to identify eight themes (i.e. smart campus characteristics, smart campus stakeholders, smart campus frameworks, smart campus technologies, smart campus infrastructure, smart campus evaluation, smart learning environment and smart campus applications) characterizing research efforts within the smart campus literature.

The themes around the smart campus showcase the thrust areas receiving attention. These characterize extant research endeavours in the smart campus domain and can offer useful pointers to researchers going forward. This awareness can also be beneficial to institutional leadership and technology providers intending to implement smart campus initiatives, contributing to the development of the educational environment.