In the era of digitized images, the goal is to extract information from them and create new knowledge thanks to Computer Vision techniques, Machine Learning and Deep Learning. This enables the use of images for early diagnosis and subsequent treatment of a wide range of diseases. In the dermatological field, deep neural networks are used to distinguish between melanoma and non-melanoma images. In this paper, we have underlined two essential points of melanoma detection research. The first aspect considered is how even a simple modification of the parameters in the dataset determines a change of the accuracy of classifiers. In this case, we investigated the Transfer Learning issues. Following the results of this first analysis, we suggest that continuous training-test iterations are needed to provide robust prediction models. The second point is the need to have a more flexible system architecture that can handle changes in the training datasets. In this context, we proposed the development and implementation of a hybrid architecture based on Cloud, Fog and Edge Computing to provide a Melanoma Detection service based on clinical and dermoscopic images. At the same time, this architecture must deal with the amount of data to be analyzed by reducing the running time of the continuous retrain. This fact has been highlighted with experiments carried out on a single machine and different distribution systems, showing how a distributed approach guarantees output achievement in a much more sufficient time.

Precision Agriculture (PA), also known as Smart Farming, has emerged as an innovative solution to address contemporary challenges in agricultural sustainability. A particular sector within PA, Precision Viticulture (PV), is specifically tailored for vineyards. The advent of the Internet of Things (IoT) has facilitated the acquisition of higher-resolution meteorological and soil data obtained through in situ sensing. The integration of Machine Learning (ML) with IoT-enabled farm machinery stands at the forefront of the forthcoming agricultural revolution. This data allows ML-based forecasting as an alternative to conventional approaches, providing agronomists with predictive tools essential for improved land productivity and crop quality. This study conducts a thorough examination of vineyards with a specific focus on three key aspects of PV: mitigating frost damage, analyzing soil moisture levels, and addressing grapevine diseases. In this context, several ML-based models are proposed in a real-world scenario involving a vineyard located in southern Italy. The test results affirm the feasibility and efficacy of the ML models, demonstrating their potential to revolutionize vineyard management and contribute to sustainable agricultural practices.

Nowadays, new laws and regulations, such as the European General Data Protection Regulation (GDPR), require companies to define privacy policies complying with the preferences of their users. The regulation prescribes expensive penalties for those companies causing the disclosure of sensitive data of their users, even if this occurs accidentally. Thus, it is necessary to devise methods supporting companies in the identification of privacy threats during advanced data manipulation activities. To this end, in this paper, we propose a methodology exploiting relaxed functional dependencies (RFDs) to automatically identify data that could imply the values of sensitive ones, which permits to increase the confidentiality of a dataset while reducing the number of values to be obscured. An experimental evaluation demonstrates the effectiveness of the proposed methodology in increasing compliance to the GDPR data privacy, while reducing the set of values to be partially masked, hence enhancing data usage.