Social media occupies an important place in people’s daily lives where users share various contents and topics such as thoughts, experiences, events and feelings. The massive use of social media has led to the generation of huge volumes of data. These data constitute a treasure trove, allowing the extraction of high volumes of relevant information particularly by involving deep learning techniques. Based on this context, various research studies have been carried out with the aim of studying the detection of mental disorders, notably depression and anxiety, through the analysis of data extracted from the Twitter platform. However, although these studies were able to achieve very satisfactory results, they nevertheless relied mainly on binary classification models by treating each mental disorder separately. Indeed, it would be better if we managed to develop systems capable of dealing with several mental disorders at the same time. To address this point, we propose a well-defined methodology involving the use of deep learning to develop effective multi-class models for detecting both depression and anxiety disorders through the analysis of tweets. The idea consists in testing a large number of deep learning models ranging from simple to hybrid variants to examine their strengths and weaknesses. Moreover, we involve the grid search technique to help find suitable values for the learning rate hyper-parameter due to its importance in training models. Our work is validated through several experiments and comparisons by considering various datasets and other binary classification models. The aim is to show the effectiveness of both the assumptions used to collect the data and the use of multi-class models rather than binary class models. Overall, the results obtained are satisfactory and very competitive compared to related works.

The Internet of Things refers to a network of interconnected devices, objects, and systems, that can interact with one another without human intervention. The adoption of IoT technology has expanded rapidly, significantly impacting various fields, including smart healthcare, intelligent transportation, agriculture, and smart homes. This paper focuses on smart street lighting, which represents the core piece of the smart city and the key public service for citizens’ safety. Nevertheless, it poses substantial challenges related to energy consumption, especially during energy crises. This work aims to provide an advanced solution that enables intelligent control of street lighting, enhances human safety, reduces CO₂ emissions and light pollution, and optimizes energy consumption, as well as facilitates maintenance of the lighting network. The solution is twofold: First, it introduces IoT-based smart street lighting referential models; second, it presents a framework for controlling smart street lighting based on the referential models. The proposal uses an IoT-based fuzzy multi-agent systems approach to address the challenges of smart street lighting. The approach leverages the strengths and properties of fuzzy logic and multi-agent systems to address the system requirements. This is illustrated through a testbed case study conducted on a concrete IoT prototype.

Artificial Intelligence (AI) is increasingly recognized for its potential in improving the detection, classification, prediction, and segmentation of colon cancer. Yet, the reliability of these applications depends on the quality and completeness of the underlying studies. This systematic review evaluates the current state of AI applications in colon cancer research, focusing on their impact on diagnostic accuracy, treatment planning, and patient outcomes. A comprehensive search was conducted in PubMed, Scopus, and Web of Science for articles published between 2020 and 2024. The quality of the included studies was assessed using standardized criteria. A meta-analysis was performed where applicable, and a subgroup analysis was conducted based on the type of AI technology (e.g., deep learning, machine learning) and its application (detection, classification, etc.). Additionally, we recorded whether each study incorporated Explainable AI (XAI) techniques or Generative AI (e.g., GANs) as part of its methodology. In 80 articles, AI models showed significant improvements in diagnostic accuracy, particularly in polyp detection during colonoscopies and histopathological analysis. Deep learning approaches often outperformed traditional methods. However, clinical integration remains challenging due to data and validation gaps. AI holds great promise in colon cancer diagnosis and treatment. Future work should focus on integrating AI tools into clinical workflows through explainable models and standardized validation.

Colon cancer diagnosis from histopathology is challenging due to limited annotated data and the lack of interpretability in deep models. We present a data-efficient framework combining few-shot learning and explainable AI for accurate and transparent diagnosis. A Prototypical Network with a ConvNeXt-Tiny backbone was trained on small colon-tissue image sets. Explanations from Grad-CAM and LIME were validated by a pathologist, and generalization was tested on an external dataset. The model achieved 98.5 % accuracy in-domain and 90 % on the EBHI dataset, showing strong generalization. This few-shot and explainable model performs well with minimal data and generates clinically interpretable visual outputs, supporting its potential for reliable colon cancer diagnosis.

Currently, advanced technological hardware can offer mobile devices which fits in the hand with a capacity to consult documents at anytime and anywhere. Multiple user context constraints as well as mobile device capabilities may involve the adaptation of multimedia content. In this article, the authors propose a new graph-based method for adapting multimedia documents in complex situations. Each contextual situation could correspond to a physical handicap and therefore triggers an adaptation action using ontological reasoning. Consequently, when several contextual situations are identified, this leads to multiple disabilities and may give rise to inconsistency between triggered actions. Their method allows modeling relations between adaptation-actions to select the compatible triggerable ones. In order to evaluate the feasibility and the performance of their proposal, an experimental study has been made on some real scenarios. When tested and compared with some existing approaches, their proposal showed improvements according to various criteria.