This study sought academic staff and students’ views of electronic exams (e-exams) system and the benefits and challenges of e-exams in general. The respondents provided useful feedback for future adoption of e-exams at an Australian university and elsewhere too. The key findings show that students and academic staff are optimistic about the future adoption of e-exams if the e-exams system is sufficiently improved. They are fully aware of the benefits the technology could offer in supporting learning and education in general and see e-exams as an innovation for learning and teaching in higher education.

Deep learning has emerged as a potential tool for crop yield prediction, allowing the model to automatically extract features and learn from the datasets. Meanwhile, smart farming technology enables the farmers to achieve maximum crop yield by extracting essential parameters of crop growth. This systematic literature review highlights the existing research gaps in a particular area of deep learning methodologies and guides us in analyzing the impact of vegetation indices and environmental factors on crop yield. To achieve the aims of this study, prior studies from 2012 to 2022 from various databases are collected and analyzed. The study focuses on the advantages of using deep learning in crop yield prediction, the suitable remote sensing technology based on the data acquisition requirements, and the various features that influence crop yield prediction. This study finds that Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) are the most widely used deep learning approaches for crop yield prediction. The commonly used remote sensing technology is satellite remote sensing technology—in particular, the use of the Moderate-Resolution Imaging Spectroradiometer (MODIS). Findings show that vegetation indices are the most used feature for crop yield prediction. However, it is also observed that the most used features in the literature do not always work for all the approaches. The main challenges of using deep learning approaches and remote sensing for crop yield prediction are how to improve the working model for better accuracy, the practical implication of the model for providing accurate information about crop yield to agriculturalists, growers, and policymakers, and the issue with the black box property.

User-generated multi-media content, such as images, text, videos, and speech, has recently become more popular on social media sites as a means for people to share their ideas and opinions. One of the most popular social media sites for providing public sentiment towards events that occurred during the COVID-19 period is Twitter. This is because Twitter posts are short and constantly being generated. This paper presents a deep learning approach for sentiment analysis of Twitter data related to COVID-19 reviews. The proposed algorithm is based on an LSTM-RNN-based network and enhanced featured weighting by attention layers. This algorithm uses an enhanced feature transformation framework via the attention mechanism. A total of four class labels (sad, joy, fear, and anger) from publicly available Twitter data posted in the Kaggle database were used in this study. Based on the use of attention layers with the existing LSTM-RNN approach, the proposed deep learning approach significantly improved the performance metrics, with an increase of 20% in accuracy and 10% to 12% in precision but only 12–13% in recall as compared with the current approaches. Out of a total of 179,108 COVID-19-related tweets, tweets with positive, neutral, and negative sentiments were found to account for 45%, 30%, and 25%, respectively. This shows that the proposed deep learning approach is efficient and practical and can be easily implemented for sentiment classification of COVID-19 reviews.

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Due to the rapid increase in the use of electrical and electronic equipment (EEE) worldwide, e-waste has become a critical environmental issue for many governments around the world. Several studies have pointed out that failure to adopt appropriate recycling practices for e-waste may cause environmental disasters and health concerns to humans due to the presence of hazardous materials. This warrants the need for a review of the existing processes of e-waste management. In view of the growing e-waste generation in the Asia Pacific region and the importance of e-waste management, this study critically reviews previous research on e-waste generation and management practices of major e-waste producing nations (Australia, China, India, Indonesia, and Malaysia) in the Asia Pacific region, provides an overview of progress made and identifies areas for improvement. To fulfil the aims of this research, previous studies from 2005 to 2020 are collected from various databases. Accordingly, this study focuses on e-waste generation and environmental management of these countries. This study found that e-waste management practices of the selected countries need to be enhanced and recommends several best practices for effectively managing e-waste.