Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference

A machine learning taxonomic classifier for science publications

Dissertação de mestrado integrado em Engineering and Management of Information SystemsThe evolution in scientific production, associated with the growing interdomain collaboration of knowledge and the increasing co-authorship of scientific works remains supported by processes of manual, highly subjective classification, subject to misinterpretation. The very taxonomy on which this same classification process is based is not consensual, with governmental organizations resorting to taxonomies that do not keep up with changes in scientific areas, and indexers / repositories that seek to keep up with those changes. We find a reality distinct from what is expected and that the domains where scientific work is recorded can easily be misrepresentative of the work itself. The taxonomy applied today by governmental bodies, such as the one that regulates scientific production in Portugal, is not enough, is limiting, and promotes classification in areas close to the desired, therefore with great potential for error. An automatic classification process based on machine learning algorithms presents itself as a possible solution to the subjectivity problem in classification, and while it does not solve the issue of taxonomy mismatch this work shows this possibility with proved results. In this work, we propose a classification taxonomy, as well as we develop a process based on machine learning algorithms to solve the classification problem. We also present a set of directions for future work for an increasingly representative classification of evolution in science, which is not intended as airtight, but flexible and perhaps increasingly based on phenomena and not just disciplines.A evolução na produção de ciência, associada à crescente colaboração interdomínios do conhecimento e à também crescente coautoria de trabalhos permanece suportada por processos de classificação manual, subjetiva e sujeita a interpretações erradas. A própria taxonomia na qual assenta esse mesmo processo de classificação não é consensual, com organismos estatais a recorrerem a taxonomias que não acompanham as alterações nas áreas científicas, e indexadores/repositórios que procuram acompanhar essas mesmas alterações. Verificamos uma realidade distinta do espectável e que os domínios onde são registados os trabalhos científicos podem facilmente estar desenquadrados. A taxonomia hoje aplicada pelos organismos governamentais, como o caso do organismo que regulamenta a produção científica em Portugal, não é suficiente, é limitadora, e promove a classificação em domínios aproximados do desejado, logo com grande potencial para erro. Um processo de classificação automática com base em algoritmos de machine learning apresenta-se como uma possível solução para o problema da subjetividade na classificação, e embora não resolva a questão do desenquadramento da taxonomia utilizada, é apresentada neste trabalho como uma possibilidade comprovada. Neste trabalho propomos uma taxonomia de classificação, bem como nós desenvolvemos um processo baseado em machine learning algoritmos para resolver o problema de classificação. Apresentamos ainda um conjunto de direções para trabalhos futuros para uma classificação cada vez mais representativa da evolução nas ciências, que não pretende ser hermética, mas flexível e talvez cada vez mais baseada em fenómenos e não apenas em disciplinas

Linear programming and development planning models

Historically optimal allocation has been the major concern in the economic analysis. Such problems were dealt with classical optimization techniques such as differential calculus or the calculus of variations. A new class of optimization models has since become of considerable interest, related to problems of optimum allocation of limited resources in a given state of the economy. These new models are different in that they employ new solution techniques to arrive in their solutions. The most flourishing of these methods are linear programming, input-output analysis and game theory. The first to be developed was the game theory by John Von Neumann.1 The theory of games attempts to study economic behaviour by concentrating on individuals or groups with conflicting interests. Neumann showed that under certain assumptions each participant can act so as to be guaranteed at least a certain minimum gain or maximum loss. When each participant acts so as to secure his minimum guaranteed return, then he prevents his opponents from attaining any more than their minimum guaranteeable gains. Thus the minimum gains become the actual gains, and the actions and returns for all the participants are determinate.

A new direction for public understanding of science: toward a participant-centered model of science engagement.

Engaging the public with science is not an easy task. When presented, scientific findings, public health recommendations, and other scientific information filter through the personal values, beliefs, and biases of members of the public. Science communicators must contend with these differences in order to be effective in cultivating a public understanding of science. Given the importance of scientific understanding for living well in a complex world, increasing science understanding through science engagement is imperative. The field of public engagement with science is dichotomized by a public information deficit approach and a contextualist approach. The deficit approach prizes the factual content of science, its epistemic authority, and its communication to the public while the contextualist approach recognizes the sociocultural embeddedness of science in society, how science is received by publics, and how local knowledges intersect with science. I contend both approaches are incomplete, and I put forth a synthesis. My approach, the participant-centered model of science engagement, incorporates the factual content of science and its epistemic authority, but in a way that is sensitive to context. I argue for a deliberative democratic approach to public engagement with science and articulate a model inspired by learner-centered approaches to teaching in the formal education literature. I outline and assess six participant-centered strategies along with recommendations for particular practices associated with each

Comparative Analysis of Student Learning: Technical, Methodological and Result Assessing of PISA-OECD and INVALSI-Italian Systems .

PISA is the most extensive international survey promoted by the OECD in the field of education, which measures the skills of fifteen-year-old students from more than 80 participating countries every three years. INVALSI are written tests carried out every year by all Italian students in some key moments of the school cycle, to evaluate the levels of some fundamental skills in Italian, Mathematics and English. Our comparison is made up to 2018, the last year of the PISA-OECD survey, even if INVALSI was carried out for the last edition in 2022. Our analysis focuses attention on the common part of the reference populations, which are the 15-year-old students of the 2nd class of secondary schools of II degree, where both sources give a similar picture of the students