Abstract The fight against crime is often an arduous task overall when huge amounts of data have to be inspected, as is currently the case when it comes for example in the detection of criminal activity on the dark web. This work presents and describes an artificial intelligence (AI) based system that combines various tools to assist police or law enforcement agencies during their investigations, or at least mitigate the hard process of data collection, processing and analysis. The system is an early warning/early action system for crime investigation that supports law enforcement with different processes to collect and process data as well as having knowledge extraction tools. It helps to extract information during the investigation of a criminal case or even to detect possible criminal hotspots that may lead to further investigation or analysis of a criminal case Abu Al‐Haija et al. (2022, Electronics, 11, 556). The functionality of the proposed system is illustrated through several examples using data collected from the dark web, which includes advertisements offering firearms‐related products.

Supporting users in their food choices for mindful eating has become one of the spotlights for investigating modern food systems. However, building integral food platforms for this purpose is challenging due to dealing with heterogeneous data sources of different scopes, such as recipe data, food data, and user and dietary specificities. This research paper presents a versatile multi-platform architecture based on micro-services for dealing with different food-related tasks. The contributions of this research are manifold: (1) Firstly, we propose an architecture that enables us to handle various food-related tasks while managing various food data sources, providing data standardisation, scalability, and security benefits; (2) We introduce a novel recipe adaptation algorithm based on intelligent search in external resources and intelligent adaptation of the recipe preparation; (3) We include AI2Cuisine, a mobile application for recipe adaptation to meet different requirements like preferences, health and sustainable goals; (4) Finally, we perform an analysis and discussion in term of the necessity and impact of such sort of applications on the population. To demonstrate the feasibility of our proposal, we have conducted an experimental evaluation, and the results have been validated for various end-users with different expertise. • We present a multi-platform and microservices-based architecture for healthy nutrition food systems. • We introduce a novel and intelligent recipe adaptation algorithm. • We re-train the RoBERTa model to the food-domain. • We present a mobile application (AI2Cuisine) for adapting recipes considering dietary preferences and allergies. • Our study showcases the necessity and potential impact of such applications.

This paper presents AdaptaFood, a system to adapt recipes to specific dietary constraints. This is a common societal issue due to various dietary needs arising from medical conditions, allergies, or nutritional preferences. AdaptaFood provides recipe adaptations from two inputs: a recipe image (a fine-tuned image-captioning model allows us to extract the ingredients) or a recipe object (we extract the ingredients from the recipe features). For the adaptation, we propose to use an attention-based language sentence model based on BERT to learn the semantics of the ingredients and, therefore, discover the hidden relations among them. Specifically, we use them to perform two tasks: (1) align the food items from several sources to expand recipe information; (2) use the semantic features embedded in the representation vector to detect potential food substitutes for the ingredients. The results show that the model successfully learns domain-specific knowledge after re-training it to the food computing domain. Combining this acquired knowledge with the adopted strategy for sentence representation and food replacement enables the generation of high-quality recipe versions and dealing with the heterogeneity of different-origin food data.

The detection of topics from large textual data volumes is currently a research area, which has many applications in the development of computational systems. A proposed solution for the detection of topics in data mining is the application of clustering methods. This paper presents the application of a new ontology-based methodology for the automatic topic detection without any previous information based on the use of hierarchical clustering algorithms and a multilingual knowledge base. The approach also includes lexical resources that allow us to enrich the semantics of the analyzed texts. The novelty of this approach consists of the dimensionality reduction of the terms present in the texts by using ontology and the introduction of a method for the creation of a term weight matrix for use in clustering algorithms. With this approach, it is possible to improve automatic topic detection in documents. The proposed methodology was assessed with four datasets (two of them in English and two in Spanish).