Barbara Pes

Assessing cheese quality and ripeness is a crucial challenge in the dairy industry, with significant implications for product quality, consumer satisfaction, and economic impact. Traditional evaluation methods relying on visual inspection and human expertise are susceptible to errors and time constraints. This study proposes an innovative approach leveraging machine learning and computer vision techniques for automated cheese ripeness detection to address these limitations. The key contributions of this work include the release of the first comprehensive public dataset of cheese wheel images depicting various products at different ripening stages comprising more than 775 images, CR-IDB, an extensive comparative analysis of the performance of machine learning classifiers trained with features extracted from convolutional neural networks and handcrafted descriptors, along with the evaluation of different feature selection techniques, and finally, a proposal of a novel AI-based framework built upon a Random Forest classifier for cheese ripeness detection, called CRDet. The novelty of CRDet lies in its enforceability across multiple types and dairy industries, which has not been previously addressed in the literature. Unlike earlier methodologies that focused on specific cheese types or relied on subjective visual inspections, this study introduces a comprehensive, noninvasive, and automated approach that demonstrates superior classification performance in differentiating ripeness phases. Thus, it overcomes the limitations of traditional methods and enhances the reliability of cheese ripening assessments. With performance in terms of F1 above 90%, the proposed approach reduces reliance on human expertise, ensuring efficient and reliable evaluation methods for the diverse cheese production landscape. The findings provide valuable insights into the potential of feature selection methods for advancing cheese quality analysis, with implications for the broader dairy industry.

The production of cheese, a beloved culinary delight worldwide, faces challenges in maintaining consistent product quality and operational efficiency. One crucial stage in this process is determining the precise cutting time during curd formation, which significantly impacts the quality of the cheese. Misjudging this timing can lead to the production of inferior products, harming a company’s reputation and revenue. Conventional methods often fall short of accurately assessing variations in coagulation conditions due to the inherent potential for human error. To address this issue, we propose an anomaly-detection-based approach. In this approach, we treat the class representing curd formation as the anomaly to be identified. Our proposed solution involves utilizing a one-class, fully convolutional data description network, which we compared against several state-of-the-art methods to detect deviations from the standard coagulation patterns. Encouragingly, our results show F1 scores of up to 0.92, indicating the effectiveness of our approach.

The production of cheese, a beloved culinary delight worldwide, faces challenges in maintaining consistent product quality and operational efficiency. One crucial stage in this process is determining the precise cutting time during curd formation, which significantly impacts the quality of the cheese. Misjudging this timing can lead to the production of inferior products, harming a company's reputation and revenue. Conventional methods often fall short of accurately assessing variations in coagulation conditions due to the inherent potential for human error. To address this issue, we propose an anomaly detection-based approach. In this approach, we treat the class representing curd formation as the anomaly to be identified. Our proposed solution involves utilizing a one-class, fully convolutional data description network, which we compared against several state-of-the-art methods to detect deviations from the standard coagulation patterns. Encouragingly, our results show F1 scores of up to 0.92, indicating the effectiveness of our approach.

When devising recommendation services, it is important to account for the interests of all content providers, encompassing not only newcomers but also minority demographic groups. In various instances, certain provider groups find themselves underrepresented in the item catalog, a situation that can influence recommendation results. Hence, platform owners often seek to regulate the exposure of these provider groups in the recommended lists. In this paper, we propose a novel cost-sensitive approach designed to guarantee these target exposure levels in pairwise recommendation models. This approach quantifies, and consequently mitigate, the discrepancies between the volume of recommendations allocated to groups and their contribution in the item catalog, under the principle of equity. Our results show that this approach, while aligning groups exposure with their assigned levels, does not compromise to the original recommendation utility. Source code and pre-processed data can be retrieved at https://github.com/alessandraperniciano/meta-learning-strategy-fair-provider-exposure.

With the increasing growth of data dimensionality, feature selection has become a crucial step in a variety of machine learning and data mining applications. In fact, it allows identifying the most important attributes of the task at hand, improving the efficiency, interpretability, and final performance of the induced models. In recent literature, several studies have examined the strengths and weaknesses of the available feature selection methods from different points of view. Still, little work has been performed to investigate how sensitive they are to the presence of noisy instances in the input data. This is the specific field in which our work wants to make a contribution. Indeed, since noise is arguably inevitable in several application scenarios, it would be important to understand the extent to which the different selection heuristics can be affected by noise, in particular class noise (which is more harmful in supervised learning tasks). Such an evaluation may be especially important in the context of class-imbalanced problems, where any perturbation in the set of training records can strongly affect the final selection outcome. In this regard, we provide here a two-fold contribution by presenting (i) a general methodology to evaluate feature selection robustness on class noisy data and (ii) an experimental study that involves different selection methods, both univariate and multivariate. The experiments have been conducted on eight high-dimensional datasets chosen to be representative of different real-world domains, with interesting insights into the intrinsic degree of robustness of the considered selection approaches.

COVID-19 is an infectious disease that was declared a pandemic by the World Health Organization (WHO) in early March 2020. Since its early development, it has challenged health systems around the world. Although more than 12 billion vaccines have been administered, at the time of writing, it has more than 623 million confirmed cases and more than 6 million deaths reported to the WHO. These numbers continue to grow, soliciting further research efforts to reduce the impacts of such a pandemic. In particular, artificial intelligence techniques have shown great potential in supporting the early diagnosis, detection, and monitoring of COVID-19 infections from disparate data sources. In this work, we aim to make a contribution to this field by analyzing a high-dimensional dataset containing blood sample data from over forty thousand individuals recognized as infected or not with COVID-19. Encompassing a wide range of methods, including traditional machine learning algorithms, dimensionality reduction techniques, and deep learning strategies, our analysis investigates the performance of different classification models, showing that accurate detection of blood infections can be obtained. In particular, an F-score of 84% was achieved by the artificial neural network model we designed for this task, with a rate of 87% correct predictions on the positive class. Furthermore, our study shows that the dimensionality of the original data, i.e. the number of features involved, can be significantly reduced to gain efficiency without compromising the final prediction performance. These results pave the way for further research in this field, confirming that artificial intelligence techniques may play an important role in supporting medical decision-making.