People use various communication methods to interact with each other, such as speech and body movements. However, communication methods in babies differ according to their developmental periods. Babies may not be able to express themselves verbally, but they do have their own communication methods. Naturally, it becomes important for parents to understand these signs/poses. Human pose estimation is extensively employed in different applications such as video surveillance, sports analysis and medical support/aid. The goal of this research is to assist new parents by addressing pose-based real-time body movements of babies (such as arching back, head banging, kicking legs, rubbing eyes, stretching, sucking fingers) and making sense of their activities. This is the first study to estimate poses on babies. In this paper, a baby pose dataset is created from 156 video clips through online video sharing platforms. Key-points are obtained from three different pose estimators -OpenPose, AlphaPose, and KAPAO. Different LSTM models are used to recognize the babies' activities and different performance metrics are used to compare these models. The best model has 99% accuracy and 0.0712 loss ratio. Also, babies are tracked in real-time via DeepSORT algorithm. Experimental results show that the proposed system is very promising and filling a gap in making sense of baby poses and monitoring them.

Topic mining and sentiment polarity analysis together can adequately represent the topics and attitudes of users. The goal of this article is to use Reddit’s location-based subreddits to look at country-level differences in attitudes towards COVID-19 vaccine passports. We used sentiment analysis and latent topic modelling on textual data obtained from 18 Reddit communities concentrating on COVID-19 vaccine passports from 1 January 2021 to 28 February 2022 to study COVID-19 vaccine passports–related discussion on Reddit. To discover changes in sentiment and latent topics, 11,168 comments were aggregated and examined by month. The number of comments on postings from country-specific subreddits was positively proportional to the number of new COVID-19 cases reported each day. The more subjective expressions and positive/negative interpretations occurred after July 2021. Communities indicated more positive sentiment than negative sentiment towards vaccine passports–related topics, according to polarity analysis. Topic modelling found that community members were concerned about a variety of concerns related to their socioeconomic status. Throughout the topic modelling, keywords suggesting people’s privacy concerns and acceptance of various COVID-19 control methods were found. The use of public opinion and topic modelling to analyse vaccine passports could help with important global health informatics concerns associated with their socioeconomic status.

Summary Digital document collections have been created with the transfer of a large number of documents to digital media. These digital archives have provided many benefits to users. As the diversity and size of digital image collections have grown exponentially, it has become increasingly important and difficult to obtain the desired image from them. The images on the document might contain critical information about the subject of it. In this study, an architecture is developed that can work on large‐scale data by creating regular expressions together with full‐text search approaches. The performance of the system has been tested on different academic documents and Elasticsearch and Apache Solr insert times are compared. Compared to Elasticsearch, Apache Solr achieved faster and more successful results.