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12th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2021 ; : 515-521, 2021.
Article in English | Scopus | ID: covidwho-1722946


Domestic violence is a prevalent crime in our society, more so with the introduction of COVID19 restrictions. For the victim, it can be a traumatic experience, so much as to not report the crime. Consequently, the 'Signal for Help' hand gestures were recently introduced as a discrete method to enable the victim to confidently express their need for help. This research investigates the classification of these hand gestures using a deep learning approach, which has not previously been implemented in this context. A deep learning approach is chosen due to the favourable results obtained in different contexts on hand gesture classification. Due to the unavailability of a dataset containing images of these hand gestures, a 'Signal for Help' dataset containing 112 images is generated as part of this study. These images are pre-processed to be of size 50x50 dimensions. Furthermore, a synthetic version of this dataset is also generated from the pre-processed images containing 2,352 images. The aims of this research are to show that using a synthetic 'Signal for Help' dataset improves model performance, and using deep learning is effective in 'Signal for Help' hand gesture classification. The results in this research show that using a synthetic 'Signal for Help' dataset improves model performance and is effective for 'Signal for Help' hand gesture classification. © 2021 IEEE.

2021 ASEE Virtual Annual Conference, ASEE 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1696409
Water International ; 45(5):416-422, 2021.
Article in English | GIM | ID: covidwho-1532260


Household water insecurity may exacerbate the COVID-19 pandemic and exact an even greater toll on people, especially in Africa, Asia and Latin America, simply because too many people do not have access to safe and secure water services, including water supply and sanitation, at home. Recent studies have shown that as many as a quarter of households in the Global South may be unable to practise necessary hand hygiene. Megacities may be at particular risk of being unable to manage the COVID-19 pandemic due to sheer population density as well as a lack of reliable clean water and sanitation. Problems of water insecurity are not restricted to the Global South but extend into higher-income countries as well. The steady decline in provision of public sanitation around the world, even in wealthy countries, makes adequate hygiene an even more intractable problem.

2021 International Conference on Smart Applications, Communications and Networking, SmartNets 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1483770


The ongoing COVID-19 pandemic has changed people's lives in ways that many would not have predicted. In the days, weeks and months since mandatory lockdowns and restrictions came into effect worldwide, people have had to adjust their daily lives in an effort to slow and restrict the spread of the virus - like regularly sanitising their hands, maintaining social distancing in crowded places, and wearing facemasks. The latter is contentious for some but has been a necessary deterrent in slowing the spread of this virus. There is potential for utilising technology as a supplementary deterrent and monitoring tool to help detect non-compliance of mask wearing. This research investigates the efficacy of AI for such purposes, exploring the applicability of a Convolutional Neural Network (CNN), for predicting if a person in a real time video feed is wearing a facemask. A dataset of over 10, 000 images was created to effectively evaluate this research. The CNN developed was tested against the validation dataset to evaluate its performance, the model demonstrated 98.47% accuracy on a varied and balanced dataset. © 2021 IEEE.