An artificial intelligence tool for heterogeneous team formation in the classroom
Nowadays, there is increasing interest in the development of teamwork skills in the educational context. This growing interest is motivated by its pedagogical effectiveness and the fact that, in labour contexts, enterprises organise their employees in ...
A sensitivity study of seismicity indicators in supervised learning to improve earthquake prediction
The use of different seismicity indicators as input for systems to predict earthquakes is becoming increasingly popular. Nevertheless, the values of these indicators have not been systematically obtained so far. This is mainly due to the gap of ...
An automated system for grammatical analysis of Twitter messages. A learning task application
This paper describes an educational study involving the use of Twitter as a way to enhance High School students' interaction while improving the linguistic quality of their messages. For this purpose, an interactive system has been developed for Twitter ...
Molten steel temperature prediction model based on bootstrap Feature Subsets Ensemble Regression Trees
Large-scale and noise data impose strong restrictions on building temperature models.To solve these two issues, the BFSE-RTs method is proposed in this paper.First, feature subsets are constructed based on multivariate fuzzy Taylor theorem.Second, ...
Identification of mammography anomalies for breast cancer detection by an ensemble of classification models based on artificial immune system
- Gabriele Magna,
- Paola Casti,
- Sowmya Velappa Jayaraman,
- Marcello Salmeri,
- Arianna Mencattini,
- Eugenio Martinelli,
- Corrado Di Natale
The interpretation of diagnostic images is often conditioned by the specific properties of the instrument that generated the image. This makes particularly complicated to develop universal recognition algorithms that can facilitate the diagnosis in case ...
Hierarchical anonymization algorithms against background knowledge attack in data releasing
We define a privacy model based on k-anonymity and one of its strong refinements to prevent the background knowledge attack.We propose two hierarchical anonymization algorithm to satisfy our privacy model.Our algorithms outperform the state-of the art ...
A new quantum-behaved particle swarm optimization based on cultural evolution mechanism for multiobjective problems
The application of quantum-behaved particle swarm optimization to multiobjective problems has attracted more and more attention recently. However, in order to extend quantum-behaved particle swarm optimization to multiobjective context, two major ...
From numeric data to information granules
Designing information granules used intensively in Granular Computing is of paramount relevance to the fundamentals of the discipline. Information granules are key functional components in granular models, granular classifiers, and granular decision-...
A fast scheme for multilevel thresholding based on a modified bees algorithm
Image segmentation is one of the most important tasks in image processing and pattern recognition. One of the most efficient and popular techniques for image segmentation is image thresholding. Among several thresholding methods, Kapur's (maximum ...
A method for constructing the Composite Indicator of business cycles based on information granulation and Dynamic Time Warping
Composite indicators of business cycles play a paramount role in the analysis of macroeconomy, which provide decision makers with much meaningful information. This paper develops a novel constructing method of the business cycle composite indicator ...
Modelling high-frequency FX rate dynamics
We develop a zero-delay hidden Markov model (HMM) to capture the evolution of multivariate foreign exchange (FX) rate data under a frequent trading environment. Recursive filters for the Markov chain and pertinent quantities are derived, and ...