Abstract
Missing data is characterized by the presence of absent values in data (i.e., missing values) and it is currently categorized into three different mechanisms: Missing Completely at Random, Missing At Random, and Missing Not At Random. When performing missing data experiments and evaluating techniques to handle absent values, these mechanisms are often artificially generated (a process referred to as data amputation) to assess the robustness and behavior of the used methods. Due to the lack of a standard benchmark for data amputation, different implementations of the mechanisms are used in related research (some are often not disclaimed), preventing the reproducibility of results and leading to an unfair or inaccurate comparison between existing and new methods. Moreover, for users outside the field, experimenting with missing data or simulating the appearance of missing values in real-world domains is unfeasible, impairing stress testing in machine learning systems. This work introduces mdatagen, an open source Python library for the generation of missing data mechanisms across 20 distinct scenarios, following different univariate and multivariate implementations of the established missing mechanisms. The package therefore fosters reproducible results across missing data experiments and enables the simulation of artificial missing data under flexible configurations, making it very versatile to mimic several real-world applications involving missing data. The source code and detailed documentation for mdatagen are available at https://github.com/ArthurMangussi/pymdatagen.

Abstract
Image inpainting is a crucial technique in computer vision, particularly for reconstructing corrupted images. In medical imaging, it addresses issues from instrumental errors, artifacts, or human factors. The development of deep learning techniques has revolutionized image inpainting, allowing for the generation of high-level semantic information to ensure structural and textural consistency in restored images. This paper presents a comprehensive review of 53 studies on deep image inpainting in medical imaging, analyzing its evolution, impact, and limitations. The findings highlight the significance of deep image inpainting in artifact removal and enhancing the performance of multi-task approaches by localizing and inpainting regions of interest. Furthermore, the study identifies magnetic resonance imaging and computed tomography as the predominant modalities and highlights generative adversarial networks and U-Net as preferred architectures. Future research directions include the development of blind inpainting techniques, the exploration of techniques suitable for 3D/4D images, multiple artifacts, and multi-task applications, and the improvement of architectures.

Abstract
Missing data is a common challenge in real-world datasets and can arise for various reasons. This has led to the classification of missing data mechanisms as missing completely at random, missing at random, or missing not at random. Currently, the literature offers various algorithms for imputing missing data, each with advantages tailored to specific mechanisms and levels of missingness. This paper introduces a novel approach to missing data imputation using the well-established label propagation algorithm, named Label Propagation for Missing Data Imputation (LPMD). The method combines, weighs, and propagates known feature values to impute missing data. Experiments on benchmark datasets highlight its effectiveness across various missing data scenarios, demonstrating more stable results compared to baseline methods under different missingness mechanisms and levels. The algorithms were evaluated based on processing time, imputation quality (measured by mean absolute error), and impact on classification performance. A variant of the algorithm (LPMD2) generally achieved the fastest processing time compared to other five imputation algorithms from the literature, with speed-ups ranging from 0.7 to 23 times. The results of LPMD were also stable regarding the mean absolute error of the imputed values compared to their original counterparts, for different missing data mechanisms and rates of missing values. In real applications, missingness can behave according to different and unknown mechanisms, so an imputation algorithm that behaves stably for different mechanisms is advantageous. The results regarding ML models produced using the imputed datasets were also comparable to the baselines. © 2013 IEEE.

Abstract

Abstract
Capsule endoscopy has emerged as a non-invasive alternative to traditional gastrointestinal inspection procedures, such as endoscopy and colonoscopy. Removing sedation risks, it is a patient-friendly and hospital-free procedure, which allows small bowel assessment, region not easily accessible by traditional methods. Recently, deep learning techniques have been employed to analyse capsule endoscopy images, with a focus on lesion classification and/or capsule location along the gastrointestinal tract. This research work presents a novel approach for testing the generalization capacity of deep learning techniques in the lesion location identification process using capsule endoscopy images. To achieve that, AlexNet, InceptionV3 and ResNet-152 architectures were trained exclusively in normal frames and later tested in lesion frames. Frames were sourced from KID and Kvasir-Capsule open-source datasets. Both RGB and grayscale representations were evaluated, and experiments with complete images and patches were made. Results show that the generalization capacity on lesion location of models is not so strong as their capacity for normal frame location, with colon being the most difficult organ to identify. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Technological developments are presenting new ways for companies to organize their businesses and offer new products, services, and experiences to their customers. The Metaverse allows the participation and interaction of individuals in immersive experiences that merge virtual and real worlds. The adoption of metaverse platforms by companies worldwide is growing steadily, with the potential to change business in various industries, including tourism. However, the literature on the Metaverse applied to tourism is very scarce. This chapter addresses this gap by exploring a case study of the implementation of a Metaverse strategy by a Portuguese wine brand, Sandeman, as part of their wine tourism experience offerings. The case study is built on secondary data, observation, and interviews with tourists. © 2025, IGI Global Scientific Publishing. All rights reserved.