Search results for "Machine"

ML-Based Radiomics Analysis for Breast Cancer Classification in DCE-MRI

2022

Breast cancer is the most common malignancy that threatening women’s health. Although Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) for breast lesions characterization is widely used in the clinical practice, physician grading performance is still not optimal, showing a specificity of about 72%. In this work Radiomics was used to analyze a dataset acquired with two different protocols in order to train Machine-Learning algorithms for breast cancer classification. Original radiomic features were expanded considering Laplacian of Gaussian filtering and Wavelet Transform images to evaluate whether they can improve predictive performance. A Multi-Instant features selection invo…

Resilient hexapod robot

2017

In this paper, we present a method of learning desired behaviour of the specific robotic system and transfer of the existing knowledge in the event of partial system failure. Six-legged robot (hexapod) built on top of the Bioloid platform is used for the method verification. We use genetic algorithms to optimize the hexapod's gait, after which we simulate physical damage caused to the robot. The goal of this method is to optimize the gait in accordance with the actual robot morphology, instead of the assumed one. Also, knowledge that was previously gained will be transferred in order to improve the results. Nonstandard genetic algorithm with the specific mixed population is used for this.

Fast Training of Self Organizing Maps for the Visual Exploration of Molecular Compounds

2007

Visual exploration of scientific data in life science\ud area is a growing research field due to the large amount of\ud available data. The Kohonen’s Self Organizing Map (SOM) is\ud a widely used tool for visualization of multidimensional data.\ud In this paper we present a fast learning algorithm for SOMs\ud that uses a simulated annealing method to adapt the learning\ud parameters. The algorithm has been adopted in a data analysis\ud framework for the generation of similarity maps. Such maps\ud provide an effective tool for the visual exploration of large and\ud multi-dimensional input spaces. The approach has been applied\ud to data generated during the High Throughput Screening\ud of mo…

On Representing Concepts in High-dimensional Linear Spaces

2017

Producing a mathematical model of concepts is a very important issue in artificial intelligence, because if such a model were found this, besides being a very interesting result in its own right, would also contribute to the emergence of what we could call the ‘mathematics of thought.’ One of the most interesting attempts made in this direction is P. Gardenfors’ theory of conceptual spaces, a ¨ theory which is mostly presented by its author in an informal way. The main aim of the present article is contributing to Gardenfors’ theory of conceptual spaces ¨ by discussing some of the advantages which derive from the possibility of representing concepts in high-dimensional linear spaces.

Design Space Exploration of Parallel Embedded Architectures for Native Clifford Algebra Operations

2012

In the past few decades, Geometric or Clifford algebra (CA) has received a growing attention in many research fields, such as robotics, machine vision and computer graphics, as a natural and intuitive way to model geometric objects and their transformations. At the same time, the high dimensionality of Clifford algebra and its computational complexity demand specialized hardware architectures for the direct support of Clifford data types and operators. This paper presents the design space exploration of parallel embedded architectures for native execution of four-dimensional (4D) and five-dimensional (5D) Clifford algebra operations. The design space exploration has been described along wit…

Structural Knowledge Extraction and Representation in Sensory Data

During the last decades the availability of increasingly cheaper technology for pervasive monitoring has boosted the creation of systems able to automatically comprehend the events occurring in the monitored area, in order to plan a set of actions to bring the environment closer to the user's preferences. These systems must inevitably process a great amount of raw data - sensor measurements - and need to summarize them in a high-level representation to accomplish their tasks. An implicit requirement is the need to learn from experience, in order to be able to capture the hidden structure of the data, in terms of relations between its key components. The availability of large collections of …

High-dimensional perceptual signals and synthetic phenomenology

2011

Synthetic phenomenology, in the sense of Chrisley, mainly focuses on the analysis of simplified perceptual signals with small or reduced dimensionality. Instead, we claim that synthetic phenomenology should be analysed in terms of dynamic perceptual signals with huge dimensionality. We claim that forms of dimensionality reduction of the perceptual signals, as done e.g. in typical robot vision applications, are characteristics of automatic “unconscious” processing. An effective “conscious” process actually deals with and must exploit the richness of the perceptual signals coming from the retina. We explore the hypothesis of a high-resolution buffer for the visual process and we discuss an ap…

Text localization from photos

2009

In this paper a new text extraction algorithm is proposed. In real scenes the text is usually overlapped or is part of the background. To identify the text regions, in complex conditions, a method exploiting a “multi-resolution feature based method” for extracting text with undefined dimension has been developed. Once identified, the multi-resolution information are merged and skimmed through a set of Support Vector Machines (SVM). The tests and the comparisons with other techniques, performed on heterogeneous images, have shown the effectiveness of the proposed.

Fake News Spreaders Detection: Sometimes Attention Is Not All You Need

2022

Guided by a corpus linguistics approach, in this article we present a comparative evaluation of State-of-the-Art (SotA) models, with a special focus on Transformers, to address the task of Fake News Spreaders (i.e., users that share Fake News) detection. First, we explore the reference multilingual dataset for the considered task, exploiting corpus linguistics techniques, such as chi-square test, keywords and Word Sketch. Second, we perform experiments on several models for Natural Language Processing. Third, we perform a comparative evaluation using the most recent Transformer-based models (RoBERTa, DistilBERT, BERT, XLNet, ELECTRA, Longformer) and other deep and non-deep SotA models (CNN,…

Macchine, media, pensieri e visioni... appunti (molto) sparsi

2020

Immersive art proposes a vision that is the result of a machinic utopia