Computer-aided diagnosis in clinical endoscopy using neuro-fuzzy systems

In this paper, an innovative detection system to support medical diagnosis and detection of abnormal lesions by processing endoscopic images is presented. The images used in this study have been obtained using the new M2A Swallowable Imaging Capsule - a patented, video colourimaging disposable capsule. Schemes have been developed to extract new texture features from the texture spectra in the hromatic and achromatic domains for a selected region of nterest from each colour component histogram of endoscopic images. The implementation of an advanced fuzzy inference neural network which combines fuzzy systems and artificial neural networks and the concept of fusion of multiple classifiers dedicated to specific feature parameters have been also adopted in this paper. The detection accuracy of the proposed system has reached to loo%, providing thus an indication that such intelligent schemes could be used as a supplementary diagnostic tool in endoscopy

Development of Neurofuzzy Architectures for Electricity Price Forecasting

In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models

A Fuzzy-Wavelet Neural Network Model for the Detection of Meat Spoilage using an Electronic Nose

Food product safety is one of the most promising areas for the application of electronic noses. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). This paper proposes a fuzzy-wavelet neural network model which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modeling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results indicated that the proposed modeling scheme could be considered as a valuable detection methodology in food microbiolog

Day ahead hourly Price Forecast in ISO New England Market using Neuro-Fuzzy Systems

Accurate electricity price forecasting is an alarming challenge for market participants and managers owing to high volatility of the electricity prices. Price forecasting is also the most important management goal for market participants since it forms the basis of maximizing profits. These markets are usually organized in power pools and administrated by the independent system operator (ISO). The aim of this study is to examine the performance of asymmetric neuro-fuzzy network models for day-ahead electricity price forecasting in the ISO New England market. The implemented model has been developed with two alternative defuzzification models. The first model follows the Takagi–Sugeno–Kang scheme, while the second the traditional centre of average method. A clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of rules in the network. Simulation results corresponding to the minimum and maximum electricity price indicate that the proposed network architectures could provide a considerable improvement for the forecasting accuracy compared to alternative learning-based scheme

An Extended NRBF Model for the Detection of Meat Spoilage

A fast non-invasive detection of spoilage microorganisms in meat, using Fourier transform infrared spectroscopy (FTIR) and Extended Normalized Radial Basis Function neural networks has been proposed in this paper. The aim is to associate spectral data with microbiological data (log counts), for Total Viable Counts, Pseudomonas spp., Lactic Acid Bacteria and Enterobacteriaceae by predicting their micro-biological population from FTIR spectra. The dimensionality reduction of spectral data has been explored by the implementation of a fuzzy principal component algorithm, while produced results confirmed the superiority of the proposed method compared to multilayer perceptron neural networks used recently in the area of food microbiology

Development of a clinical data warehouse

There is increasing worldwide awareness that bionics and artificial intelligence will play an important role in microbial analysis. An intelligent data-warehouse system consisting of an odour generation mechanism, rapid volatile delivery and recovery system, and a classifier system based on Neural Networks and Genetic Algorithms have been applied as part of a microbial analysis. The microbiological warehouse environment has, also adopted the concept of fusion of multiple classifiers dedicated to specific feature parameters. The experimental results confirm the soundness of the presented method

Neuro-Fuzzy based Identification of Meat Spoilage using an Electronic Nose

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). An adaptive fuzzy logic system model that utilizes a prototype defuzzification scheme has been developed to classify beef samples in their respective quality class and to predict their associated microbiological population directly from volatile compounds fingerprints. Results confirmed the superiority of the adopted methodology and indicated that volatile information in combination with an efficient choice of a modeling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilag

An Asymmetric Neuro-Fuzzy Model for the Detection of Meat Spoilage

In food industry, quality and safety parameters are direct related with consumers’ health condition. There is a growing interest in developing non-invasive sensorial techniques that have the capability of predicting quality attributes in realtime operation. Among other detection methodologies, Fourier transform infrared (FTIR) spectroscopy has been widely used for rapid inspection of various food products. In this paper, an advanced clustering-based neurofuzzy identification model has been developed to detect meat spoilage microorganisms during aerobic storage at various temperatures, utilizing FTIR spectra. A clustering scheme has been utilized as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been used in the fuzzification part of the model. The proposed model not only classifies meat samples in their respective quality class (i.e. fresh, semi-fresh and spoiled), but also predicts their associated microbiological population directly from FTIR spectra. Results verified the superiority of the proposed scheme against the adaptive neurofuzzy inference system, multilayer perceptron and partial least squares in terms of prediction accuracy

A rule based approach to classification of EEG datasets: a comparison between ANFIS and rough sets

This paper compares two different rule based classification methods in order to evaluate their relative efficiacy with respect to classification accuracy and the caliber of the resulting rules. Specifically, the application of Adaptive Neuro-Fuzzy Inference System (ANFIS) and rough sets were deployed on a complete dataset consisting of electroencephalogram (EEG) data. The results indicate that both were able to classify this dataset accurately and the number of rules were similar in both cases, provided the dataset was pre-processed using PCA in the case of ANFIS

Predictive modeling in food mycology using adaptive neuro-fuzzy systems

Fungal growth leads to spoilage of food and animal feeds and to formation of mycotoxins and potentially allergenic spores. There is a growing interest in predictive modeling microbial growth as an alternative to time consuming traditional, microbiological enumeration techniques. Several statistical models have been accounted to describe the growth of different micro-organisms. However neural networks, as highly nonlinear approximator scheme, have the potential of modeling some complex, phenomena better than the others. The application of adaptive neuro-fuzzy systems in predictive microbiology is presented in this paper. This technique is used to build up a model of the joint effect of water-activity, pH level and temperature to predict the maximum specific growth rate of the Ascomycetous Fungus Monascus Ruber. The proposed scheme is compared against standard neural network approaches. Neuro-fuzzy systems offer an alternative and powerful technique to model microbial kinetic parameters and could thus become an efficient tool in predictive mycology