Sequential Dimensionality Reduction for Extracting Localized Features

Linear dimensionality reduction techniques are powerful tools for image analysis as they allow the identification of important features in a data set. In particular, nonnegative matrix factorization (NMF) has become very popular as it is able to extract sparse, localized and easily interpretable features by imposing an additive combination of nonnegative basis elements. Nonnegative matrix underapproximation (NMU) is a closely related technique that has the advantage to identify features sequentially. In this paper, we propose a variant of NMU that is particularly well suited for image analysis as it incorporates the spatial information, that is, it takes into account the fact that neighboring pixels are more likely to be contained in the same features, and favors the extraction of localized features by looking for sparse basis elements. We show that our new approach competes favorably with comparable state-of-the-art techniques on synthetic, facial and hyperspectral image data sets.Comment: 24 pages, 12 figures. New numerical experiments on synthetic data sets, discussion about the convergenc

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic

Using an adaptive neuro-fuzzy inference system for the classification of hypertension

In this work, neuro-fuzzy systems are compared to standard machine learning algorithms to predict the hypertension risk level. Hypertension is a cardiovascular disease, which should be continuously monitored to avoid the worsening of its symptoms. Automatic techniques are useful to support the clinicians in this task, however, most of the machine learning techniques behave like black boxes, thus they are not able to explain how their results have been obtained. In the medical domain, this is a critical factor, and explainability is demanded. Neuro-fuzzy systems, that combine Neural Networks (NNs) and Fuzzy Inference Systems (FISs), are used to obtain explainable results. Moreover, to enhance the explanation, a feature selection method has been used to reduce the number of relevant features and thus the overall number of fuzzy rules. Qualitative analyses have shown comparable results between the machine learning methods and the neuro-fuzzy systems. However, the neuro-fuzzy systems are able to explain the hypertension risk level with only nine fuzzy rules, which are easy to interpret since they use linguistic terms

Computational Intelligence for Digital Health: A brief summary of our research work

In the last few decades, a digitization process has involved various aspects of daily life, and the healthcare sector is one of the fields most heavily affected by this digital transformation. Artificial Intelligence, and in particular Computational Intelligence (CI) techniques, such as Neural Networks and Fuzzy Systems, have proven to be promising methods for extracting meaningful knowledge from medical data and for developing intelligent systems for faster diagnosis, improved monitoring and effective healthcare. CI-based systems can learn models from data that evolve as data changes, taking into account the uncertainty that characterizes health data and processes. Our group working at the Computational Intelligence Laboratory (CILab) of the Department of Computer Science, University of Bari, is currently carrying out scientific research on the application of CI techniques to Digital Health problems

Student oriented subset of the Open University Learning Analytics dataset

The Open University (OU) dataset is an open database containing student demographic and click-stream interaction with the virtual learning platform. The available data are structured in different CSV files. You can find more information about the original dataset at the following link: https://analyse.kmi.open.ac.uk/open_dataset. We extracted a subset of the original dataset that focuses on student information. 25,819 records were collected referring to a specific student, course and semester. Each record is described by the 20 attributes

Deep learning for knowledge tracing in learning analytics: An overview

Learning Analytics (LA) is a recent research branch that refers to methods for measuring, collecting, analyzing, and reporting learners’ data, in order to better understand and optimize the processes and the environments. Knowledge Tracing (KT) deals with the modeling of the evolution, during the time, of the students’ learning process. Particularly its aim is to predict students’ outcomes in order to avoid failures and to support both students and teachers. Recently, KT has been tackled by exploiting Deep Learning (DL) models and generating a new, ongoing, research line that is known as Deep Knowledge Tracing (DKT). This was made possible by the digitalization process that has simplified the gathering of educational data from many different sources such as online learning platforms, intelligent objects, and mainstream IT-based systems for education. DKT predicts the student’s performances by using the information embedded in the collected data. Moreover, it has been shown to be able to outperform the state-of-the-art models for KT. In this paper, we briefly describe the most promising DL models, by focusing on their prominent contribution in solving the KT task

Enabling technologies for urban smart mobility: Recent trends, opportunities and challenges

The increasing population across the globe makes it essential to link smart and sustainable city planning with the logistics of transporting people and goods, which will significantly contribute to how societies will face mobility in the coming years. The concept of smart mobility emerged with the popularity of smart cities and is aligned with the sustainable development goals defined by the United Nations. A reduction in traffic congestion and new route optimizations with reduced ecological footprint are some of the essential factors of smart mobility; however, other aspects must also be taken into account, such as the promotion of active mobility and inclusive mobility, encour-aging the use of other types of environmentally friendly fuels and engagement with citizens. The Internet of Things (IoT), Artificial Intelligence (AI), Blockchain and Big Data technology will serve as the main entry points and fundamental pillars to promote the rise of new innovative solutions that will change the current paradigm for cities and their citizens. Mobility‐as‐a‐service, traffic flow optimization, the optimization of logistics and autonomous vehicles are some of the services and applications that will encompass several changes in the coming years with the transition of existing cities into smart cities. This paper provides an extensive review of the current trends and solutions presented in the scope of smart mobility and enabling technologies that support it. An overview of how smart mobility fits into smart cities is provided by characterizing its main attributes and the key benefits of using smart mobility in a smart city ecosystem. Further, this paper highlights other various opportunities and challenges related to smart mobility. Lastly, the major services and applications that are expected to arise in the coming years within smart mobility are explored with the prospective future trends and scope

Cyber-Physical Systems and Smart Cities in India: Opportunities, Issues, and Challenges

A large section of the population around the globe is migrating towards urban settlements. Nations are working toward smart city projects to provide a better wellbeing for the inhabitants. Cyber-physical systems are at the core of the smart city setups. They are used in almost every system component within a smart city ecosystem. This paper attempts to discuss the key components and issues involved in transforming conventional cities into smart cities with a special focus on cyber-physical systems in the Indian context. The paper primarily focuses on the infrastructural facilities and technical knowhow to smartly convert classical cities that were built haphazardly due to overpopulation and ill planning into smart cities. It further discusses cyber-physical systems as a core component of smart city setups, highlighting the related security issues. The opportunities for businesses, governments, inhabitants, and other stakeholders in a smart city ecosystem in the Indian context are also discussed. Finally, it highlights the issues and challenges concerning technical, financial, and other social and infrastructural bottlenecks in the way of realizing smart city concepts along with future research directions

Explaining Ovarian Cancer Gene Expression Profiles with Fuzzy Rules and Genetic Algorithms

The analysis of gene expression data is a complex task, and many tools and pipelines are available to handle big sequencing datasets for case-control (bivariate) studies. In some cases, such as pilot or exploratory studies, the researcher needs to compare more than two groups of samples consisting of a few replicates. Both standard statistical bioinformatic pipelines and innovative deep learning models are unsuitable for extracting interpretable patterns and information from such datasets. In this work, we apply a combination of fuzzy rule systems and genetic algorithms to analyze a dataset composed of 21 samples and 6 classes, useful for approaching the study of expression profiles in ovarian cancer, compared to other ovarian diseases. The proposed method is capable of performing a feature selection among genes that is guided by the genetic algorithm, and of building a set of if-then rules that explain how classes can be distinguished by observing changes in the expression of selected genes. After testing several parameters, the final model consists of 10 genes involved in the molecular pathways of cancer and 10 rules that correctly classify all samples