Millions of people are affected by the disease Knee Osteoarthritis, and prevalence condition is steadily increasing. osteoarthritis has a significant impact on people's lives generating increased worry, mental health disorders, physical problems. Early detection knee critical for decreasing consequences, numerous studies being conducted to classify osteoarthritis. In this study, deep CNN classi...

Classifier combination is now an established pattern recognition subdiscipline. Despite the strong aspiration for theoretical studies, classifier combination relies mainly on heuristic and empirical solutions. Assuming that “soft computing” encompasses neural networks, evolutionary computation, and fuzzy sets, we explain how each of the three components has been used in classifier combination.

the quality evaluation of agricultural products is one of the key factors in promoting their quality. in this study, a method based on combined image processing technique and artificial neural network was presented. separation of touching almonds under different positions is a very important step in design of grading devices. in this study, an image processing algorithm based on extracting crit...

The early detection of lung cancer is a challenging problem, due to the structure of the cancer cells, where most of the cells are overlapped with each other. This paper presents the feature extraction process and neural network classifier to check the state of a patient in its early stage whether it is normal or abnormal. After that we predict the survival rate of a patient by extracted featur...

Impetuous development of artificial neural networks makes it possible to transfer many ideas from this area into adjacent areas. This work investigates an opportunity of mapping learning classifier systems (LCS) into artificial neural networks (ANN). Possible learning types for hybrid connectionist classifier system (CLCS) for multi-step problems are derived. Transformation’s opportunity of the...

Preprocessing and feature extraction can significantly enhance the performance of a neural network based classifier. In this paper several feature extraction techniques including edge filters, local features and distance transformation are selected for image preprocessing in order to improve the recognition accuracy in combination with a neural network classifier. The visual object recognition ...

Non-stationarity is inherent in EEG data. We propose a concept for an adaptive brain computer interface (BCI) that adapts a classifier to the changes in EEG data. It combines labeled and unlabeled data acquired during normal operation of the system. The classifier is based on Fuzzy Neural Gas (FNG), a prototype-based classifier. Based on four data sets we show that retraining the classifier sig...

A bstract We leverage representation learning and the inductive bias in neural-net-based Standard Model jet classification tasks, to detect non-QCD signal jets. In establishing framework for classification-based anomaly detection physics, we demonstrate that, with a well-calibrated powerful enough feature extractor , well-trained mass-decorrelated supervised neural classifier can serve as stron...

Artificial neural networks have been recognized as a powerful tool for pattern classification problems, but a number of researchers have also suggested that straightforward neural-network approaches to pattern recognition are largely inadequate for difficult problems such as handwritten numeral recognition. In this paper, we present three sophisticated neural-network classifiers to solve comple...