Word learning dogs (Canis familiaris) provide an animal model for studying exceptional performance

Exceptional performance is present in various human activities but its origins are debated and challenging to study. We report evidence of exceptional performance and qualitative variation in learning object-names in dogs. 34 naïve family dogs and 6 knowledgeable individuals that knew multiple toy names, found in 2 years of search around the Globe, were exposed to 3 months of training to learn two novel toy-names and were tested in two-way choice tests. Only 1 naïve and all 6 knowledgeable dogs passed the tests. Additionally, only these dogs learned at least 10 new toy names over the 3 months, showing qualitative variation in this capacity. Although previous object-name knowledge could provide an explanation for the superior performance of the knowledgeable dogs, their rarity and the absence of previous training of this skill point to exceptional giftedness in these individuals, providing the basis to establish dogs as a model-species for studying talent.

Media diversity and the analysis of qualitative variation

Diversity is recognised as a significant criterion for appraising the democratic performance of media systems. This article begins by considering key conceptual debates that help differentiate types and levels of diversity. It then addresses a core methodological challenge in measuring diversity: how do we model statistical variation and difference when many measures of source and content diversity only attain the nominal level of measurement? We identify a range of obscure statistical indices developed in other fields that measure the strength of ‘qualitative variation’. Using original data, we compare the performance of five diversity indices and, on this basis, propose the creation of a more effective diversity average measure. The article concludes by outlining innovative strategies for drawing statistical inferences from these measures, using bootstrapping and permutation testing resampling. All statistical procedures are supported by a unique online resource developed for this article.

Ensemble classification technique for heart disease prediction with meta-heuristic-enabled training system

ObjectivesThis research work exclusively aims to develop a novel heart disease prediction framework including three major phases, namely proposed feature extraction, dimensionality reduction, and proposed ensemble-based classification.MethodsAs the novelty, the training of NN is carried out by a new enhanced optimization algorithm referred to as Sea Lion with Canberra Distance (S-CDF) via tuning the optimal weights. The improved S-CDF algorithm is the extended version of the existing “Sea Lion Optimization (SLnO)”. Initially, the statistical and higher-order statistical features are extracted including central tendency, degree of dispersion, and qualitative variation, respectively. However, in this scenario, the “curse of dimensionality” seems to be the greatest issue, such that there is a necessity of dimensionality reduction in the extracted features. Hence, the principal component analysis (PCA)-based feature reduction approach is deployed here. Finally, the dimensional concentrated features are fed as the input to the proposed ensemble technique with “Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN)” with optimized Neural Network (NN) as the final classifier.ResultsAn elaborative analyses as well as discussion have been provided by concerning the parameters, like evaluation metrics, year of publication, accuracy, implementation tool, and utilized datasets obtained by various techniques.ConclusionsFrom the experiment outcomes, it is proved that the accuracy of the proposed work with the proposed feature set is 5, 42.85, and 10% superior to the performance with other feature sets like central tendency + dispersion feature, central tendency qualitative variation, and dispersion qualitative variation, respectively.ResultsFinally, the comparative evaluation shows that the presented work is appropriate for heart disease prediction as it has high accuracy than the traditional works.