Jorge Ribeiro

Bio: Jorge Ribeiro is an academic researcher from Instituto Politécnico Nacional. The author has contributed to research in topics: Knowledge representation and reasoning & Information system. The author has an hindex of 9, co-authored 63 publications receiving 281 citations. Previous affiliations of Jorge Ribeiro include Polytechnic Institute of Viana do Castelo & University of Santiago de Compostela.

IT governance using COBIT implemented in a high public educational institution: a case study

Abstract: The organizations to be competitive have to provide services and delineate strategies to maintain high levels of profitability and efficiency to cope with changes in markets. Increasingly, organizations based their operational services through its Information Systems (IS) and Information Technology (IT) that need to be managed, controlled and monitored constantly. Although there are several guidelines oriented to the management and control of certain sectors of IT like COSO, ITIL, PMBok, CMM, ISO 27001 and Six Sigma. The COBIT - Control Objectives for Information and Technology is the framework that covers all activities related to IT for the IT Governance. Under the applicability of the quality services certification (ISO 9001 standard) in all IT and IS services of a High Public Portuguese Educational Institution, this paper presents a case study of the implementation and use of COBIT for IT Governance in that Institution. With the implementation of the framework the Institution could ensure the requirements for the quality services certification and manage and control efficiently there IS and IT and the results were very positive. The Institution has improved significantly the quality of services, reduced the execution time of tasks in about 25%, monitor and control more efficiency the technological infrastructure, reduced 30% in the number of incidents resolved and finalized by the various informatics departments and reduced 10% in the number of reopened incidents.

Evolutionary intelligence in asphalt pavement modeling and quality-of-information

Abstract: The analysis and development of a novel approach to asphalt pavement modeling, able to attend the need to predict the failure according to technical and non-technical criteria in a highway, is a hard task, namely in terms of the huge amount of possible scenarios. Indeed, the current state-of-the-art for service-life prediction is at empiric and empiric–mechanistic levels, and does not provide any suitable answer even for a single failure criteria. Consequently, it is imperative to achieve qualified models and qualitative reasoning methods, in particular due to the need to have first-class environments at our disposal where defective information is at hand. To fulfill this goal, this paper presents a dynamic and formal model oriented to fulfill the task of making predictions for multi-failure criteria, in particular in scenarios with incomplete information; it is an intelligence tool that advances according to the quality-of-information of the extensions of the predicates that model the universe of discourse. On the other hand, it is also considered the degree-of-confidence factor, a parameter that measures one‘s confidence on the list of characteristics presented by an asphalt pavement, set in terms of the attributes or variables that make the argument of the predicates referred to above.

A Deep-Big Data Approach to Health Care in the AI Age

Abstract: The intersection of these two trends is what we call The Issue and it is helping businesses in every industry to become more efficient and productive. One's aim is to have an insight into the development and maintenance of comprehensive and integrated health information systems that enable sound policy and effective health system management in order to improve health and health care. Undeniably, different sorts of technologies have been developed, each with their own advantages and disadvantages, which will be sorted out by attending at the impact that Artificial Intelligence and Decision Support Systems have to everyone in the healthcare sector engaged to quality-of-care, i.e., making sure that doctors, nurses, and staff have the training and tools they need to do their jobs.

Do we need hundreds of classifiers to solve real world classification problems

Abstract: We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearest-neighbors, partial least squares and principal component regression, logistic and multinomial regression, multiple adaptive regression splines and other methods), implemented in Weka, R (with and without the caret package), C and Matlab, including all the relevant classifiers available today. We use 121 data sets, which represent the whole UCI data base (excluding the large-scale problems) and other own real problems, in order to achieve significant conclusions about the classifier behavior, not dependent on the data set collection. The classifiers most likely to be the bests are the random forest (RF) versions, the best of which (implemented in R and accessed via caret) achieves 94.1% of the maximum accuracy overcoming 90% in the 84.3% of the data sets. However, the difference is not statistically significant with the second best, the SVM with Gaussian kernel implemented in C using LibSVM, which achieves 92.3% of the maximum accuracy. A few models are clearly better than the remaining ones: random forest, SVM with Gaussian and polynomial kernels, extreme learning machine with Gaussian kernel, C5.0 and avNNet (a committee of multi-layer perceptrons implemented in R with the caret package). The random forest is clearly the best family of classifiers (3 out of 5 bests classifiers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in the top-20, respectively).

An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels

Abstract: Extreme learning machines (ELMs) basically give answers to two fundamental learning problems: (1) Can fundamentals of learning (i.e., feature learning, clus- tering, regression and classification) be made without tuning hidden neurons (including biological neurons) even when the output shapes and function modeling of these neurons are unknown? (2) Does there exist unified frame- work for feedforward neural networks and feature space methods? ELMs that have built some tangible links between machine learning techniques and biological learning mechanisms have recently attracted increasing attention of researchers in widespread research areas. This paper provides an insight into ELMs in three aspects, viz: random neurons, random features and kernels. This paper also shows that in theory ELMs (with the same kernels) tend to outperform support vector machine and its variants in both regression and classification applications with much easier implementation.

Doing Your Research Project: A Guide for First-time Researchers in Education, Health and Social Science [Book Review]

Abstract: Review(s) of: Doing your Research Project: A Guide for First-time Researchers in Education, Health and Social Science, 4th Edition, by Judith Bell, Softcover, 267 pages, 2 Figures, 8 Tables, 7 Pictorial Examples, Checklists, ISBN: 13: 9780335215041, A$46.95.