Abstract

Abstract

Abstract
A new CMOS VLSI implementation of an asymmetric programmable sigmoid neural activation function, as well as of its derivative, is presented. It consists of two coupled PMOS and NMOS differential pairs with different programmable bias currents that set the upper and lower limits of the sigmoid. The circuit works in the weak inversion region, for low power consumption and exponential envelope, or in strong inversion to achieve higher speeds. The results obtained from the theoretical transfer function, and from the simulations of the circuit implemented in AMI's 0.35 mu m technology, show a very good match.

Abstract
Many real life problems require the classification of items in naturally ordered classes. These problems are traditionally handled by conventional methods for nominal classes, ignoring the order. This paper introduces a new training model for feedforward neural networks, for multiclass classification problems, where the classes are ordered. The proposed model has just one output unit which takes values in the interval [0,1]; this interval is then subdivided into K subintervals (one for each class), according to a specific probabilistic model. A comparison is made with conventional approaches, as well as with other architectures specific for ordinal data proposed in the literature. The new model compares favourably with the other methods under study, in the synthetic dataset used for evaluation.

Abstract
The cosmetic result is an important endpoint for breast cancer conservative treatment (BCCT), but the verification of this outcome remains without a standard. Objective assessment methods are preferred to overcome the drawbacks of subjective evaluation. In this paper a novel algorithm is proposed, based on support vector machines, for the classification of ordinal categorical data. This classifier is then applied as a new methodology for the objective assessment of the aesthetic result of BCCT. Based on the new classifier, a semi-objective score for quantification of the aesthetic results of BCCT was developed, allowing the discrimination of patients into four classes.

Abstract
Cosmetic assessment or conservative breast cancer treatment plays a major role in the study of breast cancer techniques. Objective assessment methods are being preferred to overcome the drawbacks of subjective evaluation. In this paper a methodology for the objective assessment of conservative breast cancer treatment is proposed. The quantitative measures used in this research provide an objective way to calculate the overall cosmetic result. We report experiments using support vector machines to derive an optimal assessment rule. The results seem to indicate that it is possible to construct an algorithm for a complete objective classification of the aesthetic result of breast conservative treatment.