Fast non-technical losses identification through Optimum-Path Forest

Fraud detection in energy systems by illegal consumers is the most actively pursued study in non-technical losses by electric power companies. Commonly used supervised pattern recognition techniques, such as Artificial Neural Networks and Support Vector Machines have been applied for automatic commercial frauds identification, however they suffer from slow convergence and high computational burden. We introduced here the Optimum-Path Forest classifier for a fast non-technical losses recognition, which has been demonstrated to be superior than neural networks and similar to Support Vector Machines, but much faster. Comparisons among these classifiers are also presented. © 2009 IEEE.

Parkinson's disease identification through Optimum-Path Forest

Artificial intelligence techniques have been extensively used for the identification of several disorders related with the voice signal analysis, such as Parkinson's disease (PD). However, some of these techniques flaw by assuming some separability in the original feature space or even so in the one induced by a kernel mapping. In this paper we propose the PD automatic recognition by means of Optimum-Path Forest (OPF), which is a new recently developed pattern recognition technique that does not assume any shape/separability of the classes/feature space. The experiments showed that OPF outperformed Support Vector Machines, Artificial Neural Networks and other commonly used supervised classification techniques for PD identification. © 2010 IEEE.

Prediction of Oncogenic Interactions and Cancer-Related Signaling Networks Based on Network Topology

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Isolation and Identification of Environmental Mycobacteria in the Waters of a Hemodialysis Center

The use of poorly treated water during hemodialysis may lead to contamination with nontuberculous mycobacteria (NTM). This study aimed to isolate and identify NTM species in the water of a Brazilian hemodialysis center. We collected 210 samples of water from the hydric system of the unit (post-osmosis system, hemodialysis rooms, reuse system, and hemodialysis equipment) and from the municipal supply network; we isolated the NTM by a classic microbiological technique and identified them by the PCR restriction enzyme pattern of the hsp65 gene (PRA). Fifty-one (24.3 %) of the collected samples tested positive for NTM; both the municipal supply network (2 samples, 3.2 %) and the hydric system of the hemodialysis center (49 samples, 96.1 %) contained NTM. We isolated and identified potentially pathogenic bacteria such as Mycobacterium lentiflavum (59.0 %) and M. kansasii (5.0 %), as well as rarely pathogenic bacteria like M. gordonae (24.0 %), M. gastri (8.0 %), and M. szulgai (4.0 %). The ability of NTM to cause diseases is well documented in the literature. Therefore, the identification of NTM in the water of a Brazilian hemodialysis center calls for more effective water disinfection procedures in this unit.

Analysis of Structural Integrity Using an ARTMAP-Fuzzy Artificial Neural Network

This paper presents the application of artificial neural networks in the analysis of the structural integrity of a building. The main objective is to apply an artificial neural network based on adaptive resonance theory, called ARTMAP-Fuzzy neural network and apply it to the identification and characterization of structural failure. This methodology can help professionals in the inspection of structures, to identify and characterize flaws in order to conduct preventative maintenance to ensure the integrity of the structure and decision-making. In order to validate the methodology was modeled a building of two walk, and from this model were simulated various situations (base-line condition and improper conditions), resulting in a database of signs, which were used as input data for ARTMAP-Fuzzy network. The results show efficiency, robustness and accuracy.