Network intrusion detection has been an area of rapid advancement in recent times. Similar advances in the field of intelligent computing have led to the introduction of several classification techniques for accurately identifying and differentiating network traffic into normal and anomalous. Group Method for Data Handling (GMDH) is one such supervised inductive learning approach for the synthe...

CtBP is a transcriptional corepressor with tumorigenic potential that targets the promoter of the tumor suppressor gene E-cadherin. Pnn/DRS (Pnn) is a "nuclear speckle"-associated protein involved in mRNA processing as well as transcriptional regulation of E-cadherin via its binding to CtBP. Here, we show that CtBP can recruit Pnn to CtBP-associated complexes, resulting in Pnn-dependent chromat...

Increasing emphasis has been put on the influence of desmosome related proteins on progress of colorectal cancer (CRC). Pinin (PNN) is a desmosome-associated molecule that has been reported its overexpression could increase desmoglein 2 (DSG2) and E-cadherin (E-ca) levels. However, it was documented that DSG2 and E-ca had opposite functions in CRC. Thus, we attempted to elucidate function and m...

In this paper the major principles to effectively design a parameter-less, multi-objective evolutionary algorithm that optimizes a population of probabilistic neural network (PNN) classifier models are articulated; PNN is an example of an exemplar-based classifier. These design principles are extracted from experiences, discussed in this paper, which guided the creation of the parameter-less mu...

Phoneme recognition can be viewed as classifying multivariate observations. Multi-layer perceptrons (MLP) and probabilistic neural networks (PNN) approach the decision problem using two complementary models. The MLP models the discriminant surfaces between different phoneme categories, essentially by piece-wise planar approximations, while the PNN approximates class conditional probability dens...

Parallel system with distributed memory is a promising platform to achieve a high performance computing with less construction cost. Applications with less communications, such as a kind of parameter sweep applications (PSA), can be efficiently carried out on such a parallel system, but some applications are not suitable for the parallel system due to a large communication cost. We focus on PNN...

BACKGROUND Statistical learning (SL) techniques can address non-linear relationships and small datasets but do not provide an output that has an epidemiologic interpretation. METHODS A small set of clinical variables (CVs) for stage-1 non-small cell lung cancer patients was used to evaluate an approach for using SL methods as a preprocessing step for survival analysis. A stochastic method of ...

this study employs a gmdh neural network model, which has high capability in recognition of complicated non-linear trends especially with small samples, for modeling and predicting iranian gdp growth. first a fundamental model containing 7 independent variables together with dependent variable is designed and then by using deductive process and omission of one variable at a time, a total of 18 ...

Coagulation-flocculation is the most important parts of water treatment process. Traditionally, optimum pre coagulant dosage is determined by used jar tests in laboratory. However; jar tests are time-consuming, expensive, and less adaptive to changes in raw water quality in real time. Soft computing can be used to overcome these limitations. In this paper, multi-objective evolutionary Pareto op...