Abstract
Mammography imaging is the gold standard for breast cancer detection and involves capturing two projections: mediolateral oblique and craniocaudal projections. The implementation of an approach that allows the acquisition of only one projection and reconstructs the other could mitigate patient burden, minimize radiation exposure, and reduce costs. Image-to-image translation has showcased the ability to generate realistic synthetic images in different medical imaging modalities which make these techniques a great candidate for the novel application in mammography. This study aims to compare five image-to-image translation approaches to assess the feasibility of reconstructing a mammography projection from its counterpart. The results indicate that ResViT shows the best overall performance in translating between both projections. © 2024 ESANN. All Rights Reserved.

Abstract
Progressively more advanced and complex models are proposed to address problems related to computer vision, forecasting, Internet of Things, Big Data and so on. However, these disciplines require preprocessing steps to obtain meaningful results. One of the most common problems addressed in this stage is the presence of missing values. Understanding the reason why missingness occurs helps to select data imputation methods that are more adequate to complete these missing values. Missing at Random synthetic generation presents challenges such as achieving extreme missingness rates and preserving the consistency of the mechanism. To address these shortcomings, three new methods that generate synthetic missingness under the Missing at Random mechanism are proposed in this work and compared to a baseline model. This comparison considers a benchmark covering 33 data sets and five missingness rates $(10\%, 20\%, 40\%, 60\%, 80\%)$ . Seven data imputation methods are compared to evaluate the proposals, ranging from traditional methods to deep learning methods. The results demonstrate that the proposals are aligned with the baseline method in terms of the performance and ranking of data imputation methods. Thus, three new feasible and consistent alternatives for synthetic missingness generation under Missing at Random are presented.

Abstract
Mammography imaging remains the gold standard for breast cancer detection and diagnosis, but challenges in image quality can lead to misdiagnosis, increased radiation exposure, and higher healthcare costs. This comprehensive review evaluates traditional and machine learning-based techniques for improving mammography image quality, aiming to benefit clinicians and enhance diagnostic accuracy. Our literature search, spanning 2015 - 2024, identified 115 articles focusing on contrast enhancement and noise reduction methods, including histogram equalization, filtering, unsharp masking, fuzzy logic, transform-based techniques, and advanced machine learning approaches. Machine learning, particularly architectures integrating denoising autoencoders with convolutional neural networks, emerged as highly effective in enhancing image quality without compromising detail. The discussion highlights the success of these techniques in improving mammography images' visual quality. However, challenges such as high noise ratios, inconsistent evaluation metrics, and limited open-source datasets persist. Addressing these issues offers opportunities for future research to further advance mammography image enhancement methodologies.

Abstract
Medical imaging classification improves patient prognoses by providing information on disease assessment, staging, and treatment response. The high demand for medical imaging acquisition requires the development of effective classification methodologies, occupying deep learning technologies, the pool position for this task. However, the major drawback of such techniques relies on their black-box nature which has delayed their use in real-world scenarios. Interpretability methodologies have emerged as a solution for this problem due to their capacity to translate black-box models into clinical understandable information. The most promising interpretability methodologies are concept-based techniques that can understand the predictions of a deep neural network through user-specified concepts. Concept activation regions and concept activation vectors are concept-based implementations that provide global explanations for the prediction of neural networks. The explanations provided allow the identification of the relationships that the network learned and can be used to identify possible errors during training. In this work, concept activation vectors and concept activation regions are used to identify flaws in neural network training and how this weakness can be mitigated in a human-in-the-loop process automatically improving the performance and trustworthiness of the classifier. To reach such a goal, three phases have been defined: training baseline classifiers, applying the concept-based interpretability, and implementing a human-in-the-loop approach to improve classifier performance. Four medical imaging datasets of different modalities are included in this study to prove the generality of the proposed method. The results identified concepts in each dataset that presented flaws in the classifier training and consequently, the human-in-the-loop approach validated by a team of 2 clinicians team achieved a statistically significant improvement.

Abstract
The main aim of this article is to investigate the effect of perceived greenwashing on consumers' purchasing behavior of eco-friendly products. Twelve research hypotheses were defined based on contributions from the literature. To test these hypotheses, a quantitative methodology was employed, collecting data through an online survey (N = 270) and using SmartPLS for analysis. The results confirm that perceived both perceived greenwashing and perceived risk have a negative influence on consumer attitudes. While their direct effects on purchase intention were found to be insignificant, both perceived greenwashing and perceived risk had a significant negative indirect effect on purchase intention through attitude. Additionally, it was confirmed that purchase behavior is positively affected by attitude and by willingness to pay more. These results contribute to addressing the limited knowledge regarding the impact of consumers' perceived greenwashing on their behavior, especially concerning different product types. Furthermore, they provide valuable insights for managers, highlighting the importance of mitigating greenwashing and risk perceptions associated with eco-friendly products due to their indirect negative impacts on purchase intention and behavior.

Abstract
The telecommunications industry is highly competitive, as operators engage in fierce attacks, especially in bundled services, to acquire new customers originating high churn rate. The objective of this paper is to gain a comprehensive understanding of the factors influencing the switching of operators for bundled services among telecom operators. The paper includes a quantitative study with 3,004 customers utilizing bundled services from a Portuguese telecom operator. Employing covariance-based structural equation modeling and logit regression, the research shows that internet service, television service, and the service provided by the contact center exert the greatest impact on loyalty to the operator. In contrast, landline service has an insignificant effect, while loyalty has a negative influence on customer churn. This study offers telecommunications managers insights for identifying the main factors to retain customers and curbing customer defection. Additionally, it provides a framework for assessing customer experience within bundled telecom services, which is useful for researchers, managers and marketing practitioners alike.