Search results for "Knowledge extraction"
showing 10 items of 58 documents
Knowledge Discovery from the Programme for International Student Assessment
2017
The Programme for International Student Assessment (PISA) is a worldwide study that assesses the proficiencies of 15-year-old students in reading, mathematics, and science every three years. Despite the high quality and open availability of the PISA data sets, which call for big data learning analytics, academic research using this rich and carefully collected data is surprisingly sparse. Our research contributes to reducing this deficit by discovering novel knowledge from the PISA through the development and use of appropriate methods. Since Finland has been the country of most international interest in the PISA assessment, a relevant review of the Finnish educational system is provided. T…
Feature Ranking of Large, Robust, and Weighted Clustering Result
2017
A clustering result needs to be interpreted and evaluated for knowledge discovery. When clustered data represents a sample from a population with known sample-to-population alignment weights, both the clustering and the evaluation techniques need to take this into account. The purpose of this article is to advance the automatic knowledge discovery from a robust clustering result on the population level. For this purpose, we derive a novel ranking method by generalizing the computation of the Kruskal-Wallis H test statistic from sample to population level with two different approaches. Application of these enlargements to both the input variables used in clustering and to metadata provides a…
Application of machine learning techniques to analyse the effects of physical exercise in ventricular fibrillation
2014
This work presents the application of machine learning techniques to analyse the influence of physical exercise in the physiological properties of the heart, during ventricular fibrillation. To this end, different kinds of classifiers (linear and neural models) are used to classify between trained and sedentary rabbit hearts. The use of those classifiers in combination with a wrapper feature selection algorithm allows to extract knowledge about the most relevant features in the problem. The obtained results show that neural models outperform linear classifiers (better performance indices and a better dimensionality reduction). The most relevant features to describe the benefits of physical …
General method for automated feature extraction and selection and its application for gender classification and biomechanical knowledge discovery of …
2020
Modern technologies enable to capture multiple biomechanical parameters often resulting in relational data. The current work proposes a generally applicable method comprising automated feature extraction, ensemble feature selection and classification to best capture the potentials of the data also for generating new biomechanical knowledge. Its benefits are demonstrated in the concrete biomechanically and medically relevant use case of gender classification based on spinal data for stance and gait. Very good results for accuracy were obtained using gait data. Dynamic movements of the lumbar spine in sagittal and frontal plane and of the pelvis in frontal plane best map gender differences.
Structural Knowledge Extraction from Mobility Data
2016
Knowledge extraction has traditionally represented one of the most interesting challenges in AI; in recent years, however, the availability of large collections of data has increased the awareness that “measuring” does not seamlessly translate into “understanding”, and that more data does not entail more knowledge. We propose here a formulation of knowledge extraction in terms of Grammatical Inference (GI), an inductive process able to select the best grammar consistent with the samples. The aim is to let models emerge from data themselves, while inference is turned into a search problem in the space of consistent grammars, induced by samples, given proper generalization operators. We will …
A Context-Aware System for Ambient Assisted Living
2017
In the near future, the world's population will be characterized by an increasing average age, and consequently, the number of people requiring for a special household assistance will dramatically rise. In this scenario, smart homes will significantly help users to increase their quality of life, while maintaining a great level of autonomy. This paper presents a system for Ambient Assisted Living (AAL) capable of understanding context and user's behavior by exploiting data gathered by a pervasive sensor network. The knowledge inferred by adopting a Bayesian knowledge extraction approach is exploited to disambiguate the collected observations, making the AAL system able to detect and predict…
The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration
2014
Background: In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications. Results: This work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a …
Analysis of computer user behavior, security incidents and fraud using Self-Organizing Maps
2019
Abstract This paper addresses several topics of great interest in computer security in recent years: computer users’ behavior, security incidents and fraud exposure on the Internet, due to their high economic and social cost. Traditional research has been based mainly on gathering information about security incidents and fraud through surveys. The novelty of the present study is given by the use of Self-Organizing Maps (SOMs), a visual data mining technique. SOMs are applied to two data sets acquired using two different methodologies for collecting data about computer security. First, a traditional online survey about fraud exposure, security and user behavior was used. Second, in addition …
An Ambient Intelligence Architecture for Extracting Knowledge from Distributed Sensors
2009
Precisely monitoring the environmental conditions is an essential requirement for AmI projects, but the wealth of data generated by the sensing equipment may easily overwhelm the modules devoted to higher-level reasoning, clogging them with irrelevant details. The present work proposes a new approach to knowledge extraction from raw data that addresses this issue at different levels of abstraction. Wireless sensor networks are used as the pervasive sensory tool, and their computational capabilities are exploited to remotely perform preliminary data processing. A central intelligent unit subsequently extracts higher-level concepts represented in a geometrical space and carries on symbolic re…
Knowledge Discovery and Digital Cartography for the ALS (Linguistic Atlas of Sicily) Project
2009
In this paper the latest developments of the ALS (Linguistic Atlas of Sicily) project are presented. The ALS project has the purpose to define methodologies and tools to allow researches in the socio-linguistic field. Different types of variables (both quantitative and qualitative) are involved. The whole framework is based on the definition of ontology-based applications for the creation, retrieval, manipulation and browsing of related data. To this aim, some mapping processes have been defined. The framework eventually shows the result in many ways including spatial maps. The on-going collaboration process is a perfect example a domain hybridizing process, enabling the training on-the-fie…