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This study deals with the problem of the recognition of dermatological and exanthematic diseases through the use of deep learning techniques in order to diagnose malignant diseases at an early stage and in general to bring the pathology identified by the models to the attention of the person. A fundamental part of the research was the study of the methodologies present in the state of the art and for this reason in this paper we report the studies considered as most relevant. In this paper, two different types of models are reported, the Convolutional Network Disease (CND) model and the CND-InceptionV3 model using the transfer learning technique. The use of these two models made it possible to carry out an experimental phase in which the performance that can be achieved using the ISIC-archive dataset was analysed. Subsequently, the description of the work carried out for the improvement of the dataset through the association of syntactic-semantic information is reported. Finally, in the last section, conclusions are drawn on the values obtained and future developments that can be made to improve the performance of the models reported are reported.

This study stems from the observation of the growing demand, interest and incredible potential offered by Artificial Intelligence and Machine Learning techniques in the fields of content website or ecommerce. In particular, we will study the Content Management System (CMS), a system capable of creating and maintaining websites, forums and applications for customers. These systems have an intrinsic predisposition to provide services based on Machine Learning (ML), thus being able to add to the solutions offered by the most innovative ML systems. These include Sentiment Analysis, the Recommender System and the more innovative Chatbot . Integrating them into a website or web platform very often leads to significantly improved results for the user experience offered to the user. This study aims to analyse the new artificial intelligence techniques and how to implement them in a company. Reporting also a case of Study based on a real agency

The application of CCS in the iron and steel industry faces particular challenges for achieving European CO2 transportation and storage in meeting CO2 stream impurity limit specifications due to the unique and diverse composition of the steelworks off-gases targeted for CO2 capture and the separation efficiency of proposed CO2 capture solutions. This paper reviews the range and levels of compounds that could form potential CO2 impurities in steelworks off-gases and provides estimates of the quality of CO2 products obtained in primary CO2 capture steps from Blast Furnace Gas (BFG) using different technologies of Pressure-Swing Adsorption (PSA) and amine scrubbing. Published CO2 specifications from European transportation and storage operators are reviewed and compared. Additional suitable purification steps that are needed in order to reduce the levels of impurities from primary CO2 product streams in order to achieve European CO2 impurity limit specifications are identified, characterised and the associated cost implications discussed.

Since the publication of the original article, the authors have noticed that in "Acknowledgements" section the following funding Statement was erroneously missing: "CS was recipient of a MIUR fellowship for the PhD program in Molecular, Cellular and Environmental Biology at the Department of Science, University Roma Tre (Supervisor Prof. Sandra Moreno)". This has now been corrected in both the PDF and HTML versions of the paper. The authors apologize for any inconvenience this may have caused.

Control charts are one of the principal tools to monitor dynamic processes with the aim of rapid identification of changes in the behaviour of these processes. Such changes are usually associated with a move from an in-control condition to an out-of-control condition. The paper briefly reviews the historical origins and includes examples of recent developments, focussing on their use in fields different from the industrial applications in which they were initially derived and often employed. It also focusses on cases which depart from the commonly used Gaussian assumption and then considers potential effects of the big data revolution on future uses. A bibliometric analysis is also presented to identify distinct groups of research themes, including emerging and underdeveloped areas, which are hence potential topics for future research.

Consumer contracts often contain unfair clauses, in apparent violation of the rel- evant legislation. In this paper we present a new methodology for evaluating such clauses in online Terms of Services. We expand a set of tagged documents (terms of service), with a structured corpus where unfair clauses are liked to a knowledge base of rationales for unfairness, and experiment with machine learning methods on this expanded training set. Our experimental study is based on deep neural net- works that aim to combine learning and reasoning tasks, one major example being Memory Networks. Preliminary results show that this approach may not only pro- vide reasons and explanations to the user, but also enhance the automated detection of unfair clauses.