Addressing the pressing issue of food waste is vital for environmental sustainability and resource conservation. While computer vision has been widely used in food waste reduction research, existing food image datasets are typically aggregated into broad categories (e.g., fruits, meat, dairy, etc.) rather than the fine-grained singular food items required for this research. The aim of this study is to develop a model capable of identifying individual food items to be integrated into a mobile application that allows users to photograph their food items, identify them, and offer suggestions for recipes. This research bridges the gap in available datasets and contributes to a more fine-grained approach to utilising existing technology for food waste reduction, emphasising both environmental and research significance. This study evaluates various (n = 7) convolutional neural network architectures for multi-class food image classification, emphasising the nuanced impact of parameter tuning to identify the most effective configurations. The experiments were conducted with a custom dataset comprising 41,949 food images categorised into 20 food item classes. Performance evaluation was based on accuracy and loss. DenseNet architecture emerged as the top-performing out of the seven examined, establishing a baseline performance (training accuracy = 0.74, training loss = 1.25, validation accuracy = 0.68, and validation loss = 2.89) on a predetermined set of parameters, including the RMSProp optimiser, ReLU activation function, ‘0.5’ dropout rate, and a $160\times 160$ image size. Subsequent parameter tuning involved a comprehensive exploration, considering six optimisers, four image sizes, two dropout rates, and five activation functions. The results show the superior generalisation capabilities of the optimised DenseNet, showcasing performance improvements over the established baseline across key metrics. Specifically, the optimised model demonstrated a training accuracy of 0.99, a training loss of 0.01, a validation accuracy of 0.79, and a validation loss of 0.92, highlighting its improved performance compared to the baseline configuration. The optimal DenseNet has been integrated into a mobile application called FridgeSnap, designed to recognise food items and suggest possible recipes to users, thus contributing to the broader mission of minimising food waste.

Industry 4.0 has become a crucial part in the majority of processes, components, and related modelling, as well as predictive tools that allow a more efficient, automated and sustainable approach to industry. The availability of large quantities of data, and the advances in IoT, AI, and data-driven frameworks, have led to an enhanced data gathering, assessment, and extraction of actionable information, resulting in a better decision-making process. Product picking and its subsequent packing is an important area, and has drawn increasing attention for the research community. However, depending of the context, some of the related approaches tend to be either highly mathematical, or applied to a specific context. This article aims to provide a survey on the main methods, techniques, and frameworks relevant to product packing and to highlight the main properties and features that should be further investigated to ensure a more efficient and optimised approach.

With the rapid development of online technologies and the widespread usage of mobile phones among children, it is crucial to protect their online safety. Some studies reported that online abuse and incidents negatively affect children's mental health and development. In this paper, we examine how Android applications follow the rules related to children's data protection in the EU General Data Protection Regulation (GDPR) and the UK and EU children's online safeguarding guidelines. Our findings show that the number of non-compliant apps is still significant. Even the apps designed for children do not always comply with legislation or guidance. This lack of compliance could contribute to creating a path to causing physical or mental harm to children. We then discuss the relevance of automating the compliance verification and online safety risk assessment, including open questions, challenges, possible approaches, and directions.