Browsing by Issue Date, starting with "2010-07"
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- Comparison of attitudes towards statistics in graduate and undergraduate health sciences' studentsPublication . Pimenta, Rui; Faria, Brigida Mónica; Pereira, Ilídio; Costa, Elísio; Vieira, MargaridaThe ultimate goal of statistical education is to provide an appropriate use of statistical thinking (Schau et al., 1995). This is particularly pertinent in the context of the health sciences since these professionals need to carry out their own investigations and make decisions under uncertainty integrating the best practices of evidence based medicine. Health professionals develop, at times, anxiety towards statistics due to their fear of mathematics. However, biostatistics courses can, nowadays, be conducted without a high level of calculus. Students’ attitudes might influence their learning of statistical concepts as much as their cognitive abilities. The influence of attitudes towards statistics on the development of statistical reasoning and thinking has been studied in different ways (Carmona, 2004; Blanco, 2008). However, the attitudes towards statistics in health sciences’ students are a new kind of research particularly pertinent due to an increasing number of students in this field. The difficulties in the use of statistics by different groups of professionals, particularly by health professionals, are well documented mainly when carrying out research. Health professionals need to promote their statistical abilities in order to be able to recognize when additional knowledge and skills are required, to obtain this additional statistical understanding or, better yet, to claim the consultation of a statistician. Improvement of positive attitudes towards statistics is a critical goal in statistics education. Positive attitudes contribute to a better use of statistical knowledge and to a better understanding of the variation inherent to data, enabling better decision-making under uncertainty when dealing with statistics. In this work, we evaluate, compare and present the first study which analyses the attitudes towards statistics in Portuguese health sciences students enrolling in postgraduate or undergraduate programs.
- Maximin Spreading AlgorithmPublication . Pires, E. J. Solteiro; Mendes, Luís; Lopes, António M.; Oliveira, P. B. de Moura; Tenreiro Machado, J. A.; Vaz, João; Rosário, Maria J.This paper presents a genetic algorithm to optimize uni-objective problems with an infinite number of optimal solutions. The algorithm uses the maximin concept and -dominance to promote diversity over the admissible space. The proposed algorithm is tested with two well-known functions. The practical results of the algorithm are in good agreement with the optimal solutions of these functions. Moreover, the proposed optimization method is also applied in two practical real-world engineering optimization problems, namely, in radio frequency circuit design and in kinematic optimization of a parallel robot. In all the cases, the algorithm draws a set of optimal solutions. This means that each problem can be solved in several different ways, all with the same maximum performance.
- Influence of birthweight, socioeconomic status, cardio-respiratory fitness in body mass indexPublication . Vale, S.; Soares-Miranda, L.; Santos, R.; Moreira, C.; Marques, A. I.; Santos, P. C.; Aleixo, I.; Mota, J.The increasing overweight and obesity prevalence among children and adolescents may not only result in life quality prejudice but also increase obesity indexes at adult age. Perinatal factors such birthweight (BW) as well as socioeconomic factors (SES) and cardio respiratory fitness (CRF) have shown different associations with adolescents’ obesity level. Thus, the aims of the present study were to describe the prevalence of overweight/obesity (Ov/ Ob) and, to evaluate the associations of BW, SES and CRF on adolescents body mass index (BMI).
- Machine Learning algorithms applied to the classification of robotic soccer formations and opponent teamsPublication . Faria, Brígida Mónica; Reis, Luís Paulo; Lau, Nuno; Castillo, GladysMachine Learning (ML) and Knowledge Discovery (KD) are research areas with several different applications but that share a common objective of acquiring more and new information from data. This paper presents an application of several ML techniques in the identification of the opponent team and also on the classification of robotic soccer formations in the context of RoboCup international robotic soccer competition. RoboCup international project includes several distinct leagues were teams composed by different types of real or simulated robots play soccer games following a set of pre-established rules. The simulated 2D league uses simulated robots encouraging research on artificial intelligence methodologies like high-level coordination and machine learning techniques. The experimental tests performed, using four distinct datasets, enabled us to conclude that the Support Vector Machines (SVM) technique has higher accuracy than the k-Nearest Neighbor, Neural Networks and Kernel Naïve Bayes in terms of adaptation to a new kind of data. Also, the experimental results enable to conclude that using the Principal Component Analysis SVM achieves worse results than using simpler methods that have as primary assumption the distance between samples, like k-NN.