Paraconsistent Annotated Logic Algorithms Applied in Management and Control of Communication Network Routes.

This paper presents a computational method based on non-classical logic dedicated to routing management and information stream control in communication networks. Paraconsistent logic (PL) was used to create an algorithmic structure whose main property is to accept contradiction. Moreover, a computational structure, the denominated paraconsistent data analyzer (PDAPAL2v), was constructed to perform routing management in communication networks. Direct comparisons of PDAPAL2v with a classical logic system that simulates routing conditions were made in the laboratory. In the conventional system, the paraconsistent algorithms were considered as binary logic gates, and in the tests, the same adjustment limits of PDAPAL2v were applied. Using a database with controlled insertion of noise, we obtained an efficacy of 97% in the detection of deteriorated packets with PDAPAL2v and 72% with the conventional simulation system. Functional tests were carried out, showing that PDAPAL2v is able to assess the conditions and degradation of links and perform the analysis and correlation of various inputs and variables, even if the signals have contradictory values. From practical tests in the laboratory, the proposed method represents a new way of managing and controlling communication network routes with good performance.

Paraconsistent analysis network applied in the treatment of Raman spectroscopy data to support medical diagnosis of skin cancer.

Paraconsistent logic (PL) is a type of non-classical logic that accepts contradiction as a fundamental concept and has produced valuable results in the analysis of uncertainties. In this work, algorithms based on a type of PL-paraconsistent annotated logic of two values (PAL2v)-are interconnected into a network of paraconsistent analysis (PANnet). PANnet was applied to a dataset comprising 146 Raman spectra of skin tissue biopsy fragments of which 30 spectra were determined to represent normal skin tissue (N), 96 were determined to represent tissue with basal cell carcinoma, and 19 were determined to be tissue with melanoma (MEL). In this database, paraconsistent analysis was able to correctly discriminate 136 out of a total of 145 fragments, obtaining a 93.793 % correct diagnostic accuracy. The application of PAL2v in the analysis of Raman spectroscopy signals produces better discrimination of cells than conventional statistical processes and presents a good graphical overview through its associated lattice structure. The technique of PAL2v-based data processing can be fundamental in the development of a computational tool dedicated to support the diagnosis of skin cancer using Raman spectroscopy.

Modelo de análise de variáveis craniométricas através das redes neurais artificiais paraconsistentes / Analysis of craniometric variables throughout paraconsistent logic neural network

Este trabalho desenvolve um método para análise de variáveis craniométricas que utiliza as Redes Neurais Artificiais Paraconsistentes. O modelo desenvolvido recebe as medidas das variáveis craniométricas de um determinado paciente e as compara com as médias das variáveis craniométricas normais de uma amostra da população brasileira. Variáveis craniométricas de 120 pacientes foram processadas pelo modelo proposto e avaliadas por três especialistas em Ortodontia...

This work shows the development of an unequal craniometric analysis method, which uses Paraconsistent Neural Network. The developed model processes craniometric measures of a specific person, and compares to the expected means drawn from a Brazilian sample. Cephalometric values of a sample of 120 patients were processed by the paraconsistent model and analyzed by three specialists in Orthodontics...