Abstract
In this study, we evaluated the use of small pre-trained 3D Convolutional Neural Networks (CNN) on land use and land cover (LULC) slide-window-based classification. We pre-trained the small models in a dataset with origin in the Eurosat dataset and evaluated the benefits of the transfer-learning plus fine-tuning for four different regions using Sentinel-2 L1C imagery (bands of 10 and 20m of spatial resolution), comparing the results to pre-trained models and trained from scratch. The models achieved an F1 Score of between 0.69-0.80 without significative change when pre-training the model. However, for small datasets, pre-training the model improved the classification by up to 3%.

Abstract
Gastric Cancer (GC) and Colorectal Cancer (CRC) are some of the most common cancers in the world. The most common diagnostic methods are upper endoscopy and biopsy. Possible expert distractions can lead to late diagnosis. GC is a less studied malignancy than CRC, leading to scarce public data that difficult the use of AI detection methods, unlike CRC where public data are available. Considering that CRC endoscopic images present some similarities with GC, a CRC Transfer Learning approach could be used to improve AI GC detectors. This paper evaluates a novel Transfer Learning approach for real-time GC detection, using a YOLOv4 model pre-trained on CRC detection. The results achieved are promising since GC detection improved relatively to the traditional Transfer Learning strategy.

Abstract
Object detection (OD) coupled with active learning (AL) has emerged as a powerful synergy in the field of computer vision, harnessing the capabilities of machine learning (ML) to automatically identify and perform image-based objects localisation while actively engaging human expertise to iteratively enhance model performance and foster machine-based knowledge expansion. Their prior success, demonstrated in a wide range of fields (e.g., industry and medicine), motivated this work, in which a comprehensive and systematic review of OD and AL techniques was carried out, considering reputed technical/scientific publication databases-such as ScienceDirect, IEEE, PubMed, and arXiv-and a temporal range between 2010 and December 2022. The primary inclusion criterion for papers in this review was the application of AL techniques for OD tasks, regardless of the field of application. A total of 852 articles were analysed, and 60 articles were included after full screening. Among the remaining ones, relevant topics such as AL sampling strategies used for OD tasks and groups categorisation can be found, along with details regarding the deep neural network architectures employed, application domains, and approaches used to blend learning techniques with those sampling strategies. Furthermore, an analysis of the geographical distribution of OD researchers across the globe and their affiliated organisations was conducted, providing a comprehensive overview of the research landscape in this field. Finally, promising research opportunities to enhance the AL process were identified, including the development of novel sampling strategies and their integration with different learning techniques.

Abstract
A major public transport company, working in a urban environment, in Portugal, has adapted a telemetric system for automatic vehicle location, AVL, in order to optimise vehicle use, timetabling and scheduling. The system is able to provide real time location and load of the buses, to exchange voice messages with the driver and to propose preventive maintenance actions on the vehicles. The control centre exchanges messages with all the buses. These messages have two different components: the real-time control component, that is thrown away after use, and the off-line component that has its data stored sequentially on disk, far later analysis. Our main concern is to extract relevant information from this bulk of data to provide the bus management expert with a more accurate knowledge of the bus fleet performance, namely the bus operation efficiency, to improve vehicle use and scheduling. Several factors may corrupt the data that is stored on disk and make it impossible to automatically extract useful information. A pre-processing stage is needed to classify data as consistent or inconsistent. A strategy to implement this preprocess stage has been proposed in a previous paper [Cunha 1997], The idea is based on a virtual bus model. The virtual bus travels on a bus route and recreates the real bus service. It compares messages which have been received with those that could be expected in the model. The model is extended in this paper, in order to analyse inconsistent data and take automatic correction actions. In less common situations, control is passed to a human operator for him/her to make an appropriate correction.

Abstract
Rapid increase of elder population and the appearance of more diseases needs the creation of new medical devices, as minimal invasive as possible. Nowadays, the endoscopic capsule allows good image and much less stress and pain to the patient than traditional endoscopic catheters. The endoscopy to become as developed as today had many improvements. We present on this paper a brief survey of the historical background of equipment developments, some of the most commonly used endoscopic procedures, their drawbacks and virtues. © 2011 IEEE.

Abstract
A smart house is usually described as an interactive house where information and communications technology is used to support the daily tasks of its inhabitants. One of the important components of a family house context is the food storeroom or pantry. In a fully smart house, the pantry should also be supported by information technologies, as it happens with the others components of the house. By other means, in a fully smart house, the pantry should be smart too. In this paper we present an UML specification for a smart pantry management system. We also discuss all the process that was followed to identify the system requirements, to build the systems models and to create the system prototype. As final remarks we present some guidelines for the future development of a complete smart pantry system. © 2008 IADIS.