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.

Abstract
In global commerce, marked by the relentless advance of digital technology, businesses find themselves constantly challenged to devise innovative and disruptive marketing strategies. Adapting to these changes is no longer a choice but a necessity. To thrive, companies must remain vigilant, updating their resources and adopting emerging trends with unwavering agility. Contemporary Trends in Innovative Marketing Strategies explores the demands and dynamics of modern marketing. This book is tailored to meet the needs of students, educators, and managers seeking a profound understanding of today's marketing trends. Firstly, the book delves deep into the current trends steering marketing innovation. It dissects the latest developments that are reshaping the marketing landscape, identifies pivotal trends, and elucidates their ramifications for businesses. Secondly, the book embarks on a journey to explore innovative marketing strategies engineered to confront contemporary business challenges and seize emerging opportunities. It unlocks novel approaches that adeptly cater to the market, providing insights into strategic frameworks, methodologies, and practices. Lastly, the book illustrates these concepts with real-world case studies, offering proof of innovative marketing's successful applications across diverse business sectors. These cases serve to inspire and demonstrate how innovative marketing strategies can be put into action, resulting in tangible outcomes. This book is designed for a diverse audience, including academics and students keen on exploring the latest trends in innovative marketing, educators searching for compelling case studies to enhance their teaching materials, and practitioners eager to bridge the gap between research and practical application in innovative marketing. Its contents span a wide array of topics at the forefront of marketing innovation. Key themes encompass digital marketing strategies, sustainability-driven marketing, the marriage of data analytics and marketing intelligence, and the impact of emerging technologies on marketing strategies. Additionally, it delves into the challenges and best practices of driving marketing innovation within organizations, touching on subjects such as entrepreneurship, supply chain management, and internal marketing. © 2024 by IGI Global. All rights reserved.

Abstract
This study aims to test the forecasting accuracy of recently implemented econometric tools as compared to the forecasting accuracy of widely used traditional models when predicting cash demand at ATMs. It also aims to verify whether the pandemic-driven change in market conditions impacted the predictive power of the tested models. Our conclusions were derived based on a data set that consisted of daily withdrawals from 61 ATMs of one of the largest European ATM networks operating in Krakow, Poland, and covered the period between January 2017 and April 2021. The results proved that the recently implemented methods of forecasting ATM withdrawals were more accurate as compared to the traditional ones, with XGBoost providing the best forecasts in the majority of the tested cases. Moreover, it was found that the pandemic-driven change in market conditions affected the predictive power of the models. Both of these results seem particularly useful for improving the efficiency of ATM networks.