Search Results for author:
Found 13 papers, 2 papers with code
GANsemble for Small and Imbalanced Data Sets: A Baseline for Synthetic Microplastics Data
We experiment with the GANsemble framework on a small and imbalanced microplastics data set.
Machine learning applications using diffusion tensor imaging of human brain: A PubMed literature review
We performed a PubMed search to find 148 papers published between January 2010 and December 2019 related to human brain, Diffusion Tensor Imaging (DTI), and Machine Learning (ML).
A Hierarchical Genetic Optimization of a Fuzzy Logic System for Flow Control in Micro Grids
In particular, this study focuses on the application of a Hierarchical Genetic Algorithm (HGA) for tuning the Rule Base (RB) of a Fuzzy Inference System (FIS), trying to discover a minimal fuzzy rules set in a Fuzzy Logic Controller (FLC) adopted to perform decision making in the microgrid.
Position paper: a general framework for applying machine learning techniques in operating room
In this position paper we describe a general framework for applying machine learning and pattern recognition techniques in healthcare.
Data-driven detrending of nonstationary fractal time series with echo state networks
We assume that such a dynamical process is predictable to a certain degree by means of a class of recurrent networks called Echo State Network (ESN), which are capable to model a generic dynamical process.
Discrimination and characterization of Parkinsonian rest tremors by analyzing long-term correlations and multifractal signatures
The subjects belong to two different groups, formed by four and eight subjects with, respectively, high- and low-amplitude rest tremors.
On the impact of topological properties of smart grids in power losses optimization problems
In this paper, we face the problem of joint optimization of both topology and network parameters in a real smart grid.
An Agent-Based Algorithm exploiting Multiple Local Dissimilarities for Clusters Mining and Knowledge Discovery
We propose a multi-agent algorithm able to automatically discover relevant regularities in a given dataset, determining at the same time the set of configurations of the adopted parametric dissimilarity measure yielding compact and separated clusters.
Classifying sequences by the optimized dissimilarity space embedding approach: a case study on the solubility analysis of the E. coli proteome
We evaluate a version of the recently-proposed classification system named Optimized Dissimilarity Space Embedding (ODSE) that operates in the input space of sequences of generic objects.
Characterization of graphs for protein structure modeling and recognition of solubility
This paper deals with the relations among structural, topological, and chemical properties of the E. Coli proteome from the vantage point of the solubility/aggregation propensity of proteins.
Entropic one-class classifiers
The one-class classification problem is a well-known research endeavor in pattern recognition.
Data granulation by the principles of uncertainty
The proposed framework is conceived (i) to offer a guideline for the synthesis of information granules and (ii) to build a groundwork to compare and quantitatively judge over different data granulation procedures.
Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification
Modern electric power grids (smart grids) are equipped with smart sensors that allow to gather real-time information regarding the physical status of all the component elements belonging to the whole infrastructure (e. g., cables and related insulation, transformers, breakers and so on).
