Search results for "machine learning."
showing 10 items of 1455 documents
Integration of a structural features-based preclassifier and a man-machine interactive classifier for a fast multi-stroke character recognition
2003
A transputer-based parallel machine for handwritten character recognition is proposed. An algorithm based on structural features and on a tree classifier was used to accomplish the pre-classification of the unknown sample in order to speed up the recognition process. The algorithm for the final classification is based on the description of the strokes through Fourier descriptors. The learning phase is accomplished through a man-machine interactive process. The proposed system can expand its knowledge base. A special representation of this knowledge base is proposed in order to record a great amount of data in a suitable way. A fast multistroke handwritten isolated character recognition syst…
A Medium Level Language for Pyramid Architectures
1989
In the paper a Parallel C Languages for pyramid architectures is described. The concept of context is introduced in order to handle concurrence between processes in massive parallel machines. Feature implementation on the PAPIA-machine are given.
Algorithmic paradigms for stability-based cluster validity and model selection statistical methods, with applications to microarray data analysis
2012
AbstractThe advent of high throughput technologies, in particular microarrays, for biological research has revived interest in clustering, resulting in a plethora of new clustering algorithms. However, model selection, i.e., the identification of the correct number of clusters in a dataset, has received relatively little attention. Indeed, although central for statistics, its difficulty is also well known. Fortunately, a few novel techniques for model selection, representing a sharp departure from previous ones in statistics, have been proposed and gained prominence for microarray data analysis. Among those, the stability-based methods are the most robust and best performing in terms of pre…
An Active Learning Approach for Classifying Explosion Quakes
2022
In this work, an Active Learning approach for improving the classification of passed seismo-volcanic events is proposed. Here we study the specific case of Explosion Quakes from Stromboli Volcano versus other seismo-volcanic events, recorded as seismograms, and the use of Random Forest as a Classification method. In conformity with the active learning paradigm, the approach recalls the human intervention for the annotation of uncertain data. The uncertainty is established by the event probabilities, predicted by a trained random forest classifier. The human intervention consists of editing and relabelling the data into these main three classes: Explosion Quakes, Non-Explosion Quakes or Non-…
M-VIF: A machine-vision based on information fusion
2002
The authors describe a new architecture for machine vision, which is based on information fusion approach. Its general design has been developed by using a formal computation model that integrates three main ingredients of the visual computation: the data, the models, and the algorithms. The hardware design and the software environment of M-VIF are also given. The simulation of M-VIF is under development on the HERMIA-machine.
Graph Comparison and Artificial Models for Simulating Real Criminal Networks
2021
Network Science is an active research field, with numerous applications in areas like computer science, economics, or sociology. Criminal networks, in particular, possess specific topologies which allow them to exhibit strong resilience to disruption. Starting from a dataset related to meetings between members of a Mafia organization which operated in Sicily during 2000s, we here aim to create artificial models with similar properties. To this end, we use specific tools of Social Network Analysis, including network models (Barabási-Albert identified to be the most promising) and metrics which allow us to quantify the similarity between two networks. To the best of our knowledge, the DeltaCo…
Face Expression Recognition through Broken Symmetries
2008
Security systems, criminology, physical access control and man-machine interactions are examples of applications where recognition of human faces may be crucial. In the present paper a new signature, based on a measure of axial symmetry called DST, is proposed as a significant feature to analyze facial expressions. The measure of symmetry is an elaborate difference between the internal and external symmetry kernels of an object. The idea here is to use the evolution of the symmetry measure of a face over an ordered set of its sub-images. We claim that different evolutionary trends will represent different face expressions. The proposed signature has been tested on several face databases (ps…
Adversarial Machine Learning in e-Health: Attacking a Smart Prescription System
2022
Machine learning (ML) algorithms are the basis of many services we rely on in our everyday life. For this reason, a new research line has recently emerged with the aim of investigating how ML can be misled by adversarial examples. In this paper we address an e-health scenario in which an automatic system for prescriptions can be deceived by inputs forged to subvert the model's prediction. In particular, we present an algorithm capable of generating a precise sequence of moves that the adversary has to take in order to elude the automatic prescription service. Experimental analyses performed on a real dataset of patients' clinical records show that a minimal alteration of the clinical record…
Short-Term Sensory Data Prediction in Ambient Intelligence Scenarios
2014
Predicting data is a crucial ability for resource-constrained devices like the nodes of a Wireless Sensor Network. In the context of Ambient Intelligence scenarios, in particular, short-term sensory data prediction becomes a key enabler for more difficult tasks such as prolonging network lifetime, reducing the amount of communication required and improving user-environment interaction. In this chapter we propose a software module designed for clustered wireless sensor networks, able to predict various environmental quantities, namely temperature, humidity and light. The software module is supported by an ontology that describes the topology of the AmI scenario and the effects of the actuato…
Mimicking biological mechanisms for sensory information fusion
2013
Current Artificial Intelligence systems are bound to become increasingly interconnected to their surrounding environment in the view of the newly rising Ambient Intelligence (AmI) perspective. In this paper, we present a comprehensive AmI framework for performing fusion of raw data, perceived by sensors of different nature, in order to extract higher-level information according to a model structured so as to resemble the perceptual signal processing occurring in the human nervous system. Following the guidelines of the greater BICA challenge, we selected the specific task of user presence detection in a locality of the system as a representative application clarifying the potentialities of …