Predizione del rischio di malattie lavoro-correlate attraverso analisi di clustering e ottimizzazione genetica

Il lavoro di questa tesi è stato condotto attraverso le tecniche di intelligenza computazionale per uno studio sulla predizione dei rischi per la salute nei posti di lavoro. Il dataset disponibile è stato popolato da parte delle Aziende Sanitarie Locali (ASL) nell’ambito di un programma per la realizzazione del Sistema Nazionale di Sorveglianza per le malattie professionali e gli infortuni mortali. Lo scopo principale di questo lavoro è la progettazione di un’applicazione software capace di evidenziare situazioni di maggior criticità per la manifestazione di Malattie Professionali che possa essere usata agevolmente da parte dei medici del lavoro come strumento di supporto nella loro attività di prevenzione e sorveglianza della salute dei lavoratori. Gli algoritmi proposti, utilizzano tecniche di clustering e l’ottimizzazione genetica per determinare in maniera automatica sia i pesi delle caratteristiche prese in considerazione nel calcolo della distanza interindividuale che il numero di cluster per la sintesi del classificatore finale. In particolare, si propone un nuovo approccio che consiste nel definire il classificatore generale come un insieme di classificatori specifici per ciascuna classe di patologia, ciascuno addestrato a riconoscere le condizioni di rischio che caratterizzano una singola patologia. I primi risultati sono incoraggianti e suggeriscono interessanti temi di ricerca per un ulteriore sviluppo del sistema.The study of this research deals with the health risk prediction problem in workplaces through computational intelligence techniques. The available dataset has been collected from the Italian Local Health Authority (ASL) as part of the Surveillance National System. The main aim of this work is the design of a software application that can be used by occupational physicians in monitoring workers, performing a risk assessment of contracting some particular occupational diseases. The proposed algorithms, based on clustering techniques, includes a genetic optimization in order to automatically determine the weights of the adopted distance measure between patterns and the number of clusters for the final classifier’s synthesis. In particular, we propose a novel approach, consisting in defining the overall classifier as an ensemble of class-specific ones, each trained to recognize patterns of risk conditions characterizing a single pathology. First results are encouraging and suggest interesting research tasks for further system development

Bayesian Size-and-Shape regression modelling

Building on Dryden et al. (2021), this note presents the Bayesian estimation of a regression model for size-and-shape response variables with Gaussian landmarks. Our proposal fits into the framework of Bayesian latent variable models and allows a highly flexible modelling framework

A family of consistent normally distributed tests for Poissonity

A consistent test based on the probability generating function is proposed for assessing Poissonity against a wide class of count distributions, which includes some of the most frequently adopted alternatives to the Poisson distribution. The statistic, in addition to have an intuitive and simple form, is asymptotically normally distributed, allowing a straightforward implementation of the test. The finite sample properties of the test are investigated by means of an extensive simulation study. The test shows a satisfactory behaviour compared to other tests with known limit distribution

Goodness-of-fit test for count distributions with finite second moment

A goodness-of-fit test for one-parameter count distributions with finite second moment is proposed. The test statistic is derived from the $L_1$-distance of a function of the probability generating function of the model under the null hypothesis and that of the random variable actually generating data, when the latter belongs to a suitable wide class of alternatives. The test statistic has a rather simple form and it is asymptotically normally distributed under the null hypothesis, allowing a straightforward implementation of the test. Moreover, the test is consistent for alternative distributions belonging to the class, but also for all the alternative distributions whose probability of zero is different from that under the null hypothesis. Thus, the use of the test is proposed and investigated also for alternatives not in the class. The finite-sample properties of the test are assessed by means of an extensive simulation study

Formalizing Multimedia Recommendation through Multimodal Deep Learning

Recommender systems (RSs) offer personalized navigation experiences on online platforms, but recommendation remains a challenging task, particularly in specific scenarios and domains. Multimodality can help tap into richer information sources and construct more refined user/item profiles for recommendations. However, existing literature lacks a shared and universal schema for modeling and solving the recommendation problem through the lens of multimodality. This work aims to formalize a general multimodal schema for multimedia recommendation. It provides a comprehensive literature review of multimodal approaches for multimedia recommendation from the last eight years, outlines the theoretical foundations of a multimodal pipeline, and demonstrates its rationale by applying it to selected state-of-the-art approaches. The work also conducts a benchmarking analysis of recent algorithms for multimedia recommendation within Elliot, a rigorous framework for evaluating recommender systems. The main aim is to provide guidelines for designing and implementing the next generation of multimodal approaches in multimedia recommendation