Utilizando comitês de classificadores para predição de rendimento escolar

Educational Data Mining is an application domain in artificial intelligence area that has been extensively explored nowadays. Technological advances and in particular, the increasing use of virtual learning environments have allowed the generation of considerable amounts of data to be investigated. Among the activities to be treated in this context exists the prediction of school performance of the students, which can be accomplished through the use of machine learning techniques. Such techniques may be used for student’s classification in predefined labels. One of the strategies to apply these techniques consists in their combination to design multi-classifier systems, which efficiency can be proven by results achieved in other studies conducted in several areas, such as medicine, commerce and biometrics. The data used in the experiments were obtained from the interactions between students in one of the most used virtual learning environments called Moodle. In this context, this paper presents the results of several experiments that include the use of specific multi-classifier systems systems, called ensembles, aiming to reach better results in school performance prediction that is, searching for highest accuracy percentage in the student’s classification. Therefore, this paper presents a significant exploration of educational data and it shows analyzes of relevant results about these experiments.

Adaptive Brain-Computer Interface Systems For Communication in People with Severe Neuromuscular Disabilities

Brain-computer interfaces (BCI) have the potential to restore communication or control abilities in individuals with severe neuromuscular limitations, such as those with amyotrophic lateral sclerosis (ALS). The role of a BCI is to extract and decode relevant information that conveys a user's intent directly from brain electro-physiological signals and translate this information into executable commands to control external devices. However, the BCI decision-making process is error-prone due to noisy electro-physiological data, representing the classic problem of efficiently transmitting and receiving information via a noisy communication channel.

This research focuses on P300-based BCIs which rely predominantly on event-related potentials (ERP) that are elicited as a function of a user's uncertainty regarding stimulus events, in either an acoustic or a visual oddball recognition task. The P300-based BCI system enables users to communicate messages from a set of choices by selecting a target character or icon that conveys a desired intent or action. P300-based BCIs have been widely researched as a communication alternative, especially in individuals with ALS who represent a target BCI user population. For the P300-based BCI, repeated data measurements are required to enhance the low signal-to-noise ratio of the elicited ERPs embedded in electroencephalography (EEG) data, in order to improve the accuracy of the target character estimation process. As a result, BCIs have relatively slower speeds when compared to other commercial assistive communication devices, and this limits BCI adoption by their target user population. The goal of this research is to develop algorithms that take into account the physical limitations of the target BCI population to improve the efficiency of ERP-based spellers for real-world communication.

In this work, it is hypothesised that building adaptive capabilities into the BCI framework can potentially give the BCI system the flexibility to improve performance by adjusting system parameters in response to changing user inputs. The research in this work addresses three potential areas for improvement within the P300 speller framework: information optimisation, target character estimation and error correction. The visual interface and its operation control the method by which the ERPs are elicited through the presentation of stimulus events. The parameters of the stimulus presentation paradigm can be modified to modulate and enhance the elicited ERPs. A new stimulus presentation paradigm is developed in order to maximise the information content that is presented to the user by tuning stimulus paradigm parameters to positively affect performance. Internally, the BCI system determines the amount of data to collect and the method by which these data are processed to estimate the user's target character. Algorithms that exploit language information are developed to enhance the target character estimation process and to correct erroneous BCI selections. In addition, a new model-based method to predict BCI performance is developed, an approach which is independent of stimulus presentation paradigm and accounts for dynamic data collection. The studies presented in this work provide evidence that the proposed methods for incorporating adaptive strategies in the three areas have the potential to significantly improve BCI communication rates, and the proposed method for predicting BCI performance provides a reliable means to pre-assess BCI performance without extensive online testing.

Moderating effect of ear preference on personality in the prediction of sales performance

This study examined the relationship between ear preference, personality, and performance ratings on 203 telesales staff. Social desirability scores were a significant predictor of two relatively independent sets of supervisor ratings (actual performance and developmental potential) in interaction with ear preference. It was found that the social desirability scale was a significant positive predictor for staff preferring a right ear headset, but a negative predictor for staff preferring a left ear headset. These results were interpreted in terms of different strategies used to achieve successful sales.

The use of clinical risk factors enhances the performance of BMD in the prediction of hip and osteoporotic fractures in men and women.

SUMMARY: BMD and clinical risk factors predict hip and other osteoporotic fractures. The combination of clinical risk factors and BMD provide higher specificity and sensitivity than either alone. INTRODUCTION AND HYPOTHESES: To develop a risk assessment tool based on clinical risk factors (CRFs) with and without BMD. METHODS: Nine population-based studies were studied in which BMD and CRFs were documented at baseline. Poisson regression models were developed for hip fracture and other osteoporotic fractures, with and without hip BMD. Fracture risk was expressed as gradient of risk (GR, risk ratio/SD change in risk score). RESULTS: CRFs alone predicted hip fracture with a GR of 2.1/SD at the age of 50 years and decreased with age. The use of BMD alone provided a higher GR (3.7/SD), and was improved further with the combined use of CRFs and BMD (4.2/SD). For other osteoporotic fractures, the GRs were lower than for hip fracture. The GR with CRFs alone was 1.4/SD at the age of 50 years, similar to that provided by BMD (GR = 1.4/SD) and was not markedly increased by the combination (GR = 1.4/SD). The performance characteristics of clinical risk factors with and without BMD were validated in eleven independent population-based cohorts. CONCLUSIONS: The models developed provide the basis for the integrated use of validated clinical risk factors in men and women to aid in fracture risk prediction.

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.

Prediction of human adaptation and performance in underwater environments

Resumen tomado de la publicaci??n

Performance and prediction: Bayesian modelling of fallible choice in chess

Evaluating agents in decision-making applications requires assessing their skill and predicting their behaviour. Both are well developed in Poker-like situations, but less so in more complex game and model domains. This paper addresses both tasks by using Bayesian inference in a benchmark space of reference agents. The concepts are explained and demonstrated using the game of chess but the model applies generically to any domain with quantifiable options and fallible choice. Demonstration applications address questions frequently asked by the chess community regarding the stability of the rating scale, the comparison of players of different eras and/or leagues, and controversial incidents possibly involving fraud. The last include alleged under-performance, fabrication of tournament results, and clandestine use of computer advice during competition. Beyond the model world of games, the aim is to improve fallible human performance in complex, high-value tasks.

Prediction of the thermal performance of horizontal-coupled ground source heat exchangers

The thermal performance of a horizontal-coupled ground-source heat pump system has been assessed both experimentally and numerically in a UK climate. A numerical simulation of thermal behaviour of the horizontal-coupled heat exchanger for combinations of different ambient air temperatures, wind speeds, refrigerant temperature and soil thermal properties was studied using a validated 2D transient model. The specific heat extraction by the heat exchanger increased with ambient temperature and soil thermal conductivity, however it decreased with increasing refrigerant temperature. The effect of wind speed was negligible.

Use of ridge regression for the prediction of early growth performance in crossbred calves

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Prediction of Hevea progeny performance in the presence of genotype-environment interaction

Twenty two open-pollinated Hevea progenies from different parental clones of the Asian origin were tested at five sites in the Northwestern São Paulo State Brazil to investigate the progeny girth growth, rubber yield, bark thickness and plant height. Except for the rubber yield, the analysis of variance indicated highly significant (p<0.01) genotype x environment interaction and heterogeneity of regressions among the progenies. However, the regression stability analysis identified only a few interacting progenies which had regression coefficients significantly different from the expected value of one. The linear regressions of the progeny mean performance at each test on an environmental index (mean of all the progenies in each test) showed the general stability and adaptability of most selected Hevea progenies over the test environments. The few progenies which were responsive and high yielding on different test sites could be used to maximize the rubber cultivars productivity and to obtain the best use of the genetically improved stock under different environmental conditions.

Prediction of culture performance of juvenile Litopenaeus vannamei by in vitro (pH-stat) degree of feed protein hydrolysis with species-specific enzymes

Rapid in vitro methods for measuring digestibility may be useful in analysing aqua feeds if the extent and limits of their application are clearly defined. The pH-stat protein digestibility routine with shrimp hepatopancreas enzymes was previously related to apparent protein digestibility with juvenile Litopenaeus vannamei fed diets containing different protein ingredients. The potential of the method to predict culture performance of shrimp fed six commercial feeds (T3, T4, T5, T6, T7 and T8) with 350 g kg(-1) declared crude-protein content was assessed. The consistency of results obtained using hepatopancreas enzyme extracts from either pond or clear water-raised shrimp was further verified in terms of reproducibility and possible diet history effects upon in vitro outputs. Shrimps were previously acclimated and then maintained over 56 days (initial mean weight 3.28 g) on each diet in 500-L tanks at 114 ind m(-2), clear water closed system with continuous renewal and mechanical filtering (50 mu m), with four replicates per treatment. Feeds were offered four times daily (six days a week) delivered in trays at feeding rates ranging from 4.0% to 7.0% of stocked shrimp biomass. Feed was accessible to shrimp 4 h daily for 1-h feeding period after which uneaten feed was recovered. Growth and survival were determined every 14 days from a sample of 16 individuals per tank. Water quality was monitored daily (pH, temperature and salinity) and managed by water back flushing filter cleaning every 7-10 days. Feeds were analysed for crude protein, gross energy, amino acids and pepsin digestibility. In vitro pH-stat degree of protein hydrolysis (DH%) was determined for each feed using hepatopancreas enzyme extracts from experimental (clear water) or pond-raised shrimp. Feeds resulted in significant differences in shrimp performance (P < 0.05) as seen by the differences in growth rates (0.56-0.98 g week(-1)), final weight and feed conversion ratio (FCR). Shrimp performance and in vitro DH% with pond-raised shrimp enzymes showed significant correlation (P < 0.05) for yield (R-2 = 0.72), growth rates (R-2 = 0.72-0.80) and FCR (R-2 = -0.67). Other feed attributes (protein : energy ratio, amino acids, true protein, non-protein nitrogen contents and in vitro pepsin digestibility) showed none or limited correlation with shrimp culture performance. Additional correlations were found between growth rates and methionine (R-2 = 0.73), FCR and histidine (R-2 = -0.60), and DH% and methionine or methionine+cystine feed contents (R-2 = 0.67-0.92). pH-stat assays with shrimp enzymes generated reproducible DH% results with either pond (CV <= 6.5%) or clear water (CV <= 8.5%) hepatopancreas enzyme sources. Moreover, correlations between shrimp growth rates and feed DH% were significant regardless of the enzyme origin (pond or clear water-raised shrimp) and showed consistent R-2 values. Results suggest the feasibility of using standardized hepatopancreas enzyme extracts for in vitro protein digestibility.

Poor performance of microbiological sampling in the prediction of recurrent arthroplasty infection

During a two-stage revision for prosthetic joint infections (PJI), joint aspirations, open tissue sampling and serum inflammatory markers are performed before re-implantation to exclude ongoing silent infection. We investigated the performance of these diagnostic procedures on the risk of recurrence of PJI among asymptomatic patients undergoing a two-stage revision. A total of 62 PJI were found in 58 patients. All patients had intra-operative surgical exploration during re-implantation, and 48 of them had intra-operative microbiological swabs. Additionally, 18 joint aspirations and one open biopsy were performed before second-stage reimplantation. Recurrence or persistence of PJI occurred in 12 cases with a mean delay of 218 days after re-implantation, but only four pre- or intraoperative invasive joint samples had grown a pathogen in cultures. In at least seven recurrent PJIs (58%), patients had a normal C-reactive protein (CRP, < 10 mg/l) level before re-implantation. The sensitivity, specificity, positive predictive and negative predictive values of pre-operative invasive joint aspiration and CRP for the prediction of PJI recurrence was 0.58, 0.88, 0.5, 0.84 and 0.17, 0.81, 0.13, 0.86, respectively. As a conclusion, pre-operative joint aspiration, intraoperative bacterial sampling, surgical exploration and serum inflammatory markers are poor predictors of PJI recurrence. The onset of reinfection usually occurs far later than reimplantation.

Probabilistic models for the prediction of a ship performance in dynamic ice

We introduce two probabilistic, data-driven models that predict a ship's speed and the situations where a ship is probable to get stuck in ice based on the joint effect of ice features such as the thickness and concentration of level ice, ice ridges, rafted ice, moreover ice compression is considered. To develop the models to datasets were utilized. First, the data from the Automatic Identification System about the performance of a selected ship was used. Second, a numerical ice model HELMI, developed in the Finnish Meteorological Institute, provided information about the ice field. The relations between the ice conditions and ship movements were established using Bayesian learning algorithms. The case study presented in this paper considers a single and unassisted trip of an ice-strengthened bulk carrier between two Finnish ports in the presence of challenging ice conditions, which varied in time and space. The obtained results show good prediction power of the models. This means, on average 80% for predicting the ship's speed within specified bins, and above 90% for predicting cases where a ship may get stuck in ice. We expect this new approach to facilitate the safe and effective route selection problem for ice-covered waters where the ship performance is reflected in the objective function.