Ensembles of ARTMAP-based neural networks: an experimental study
ARTMAP-based models are neural networks which use a match-based learning procedure. The main advantage of ARTMAP-based models over error-based models, such as Multi-Layer Perceptron, is the learning time, which is considered as significantly fast. This ...
An efficient algorithm for solving nonograms
Nonogram is one of logical games popular in Japan and Netherlands. Solving nonogram is a NP-complete problem. There are some related papers proposed. Some use genetic algorithm (GA), but the solution may get stuck in local optima. Some use depth first ...
Mining periodic movement patterns of mobile phone users based on an efficient sampling approach
In m-commerce services, the periodic movement trends of customers at specific periods can be adopted to allocate the resources of telecommunications systems effectively and offer personalized location-based services. This study explores the mining of ...
A novel intrusion detection approach learned from the change of antibody concentration in biological immune response
Inspired by the relationship between the antibody concentration and the intrusion network traffic pattern intensity, we present a Novel Intrusion Detection Approach learned from the change of Antibody Concentration in biological immune response (NIDAAC) ...
Recursive support vector censored regression for monitoring product quality based on degradation profiles
The time-consuming evaluation of a product's lifetime or quality often prevents manufacturers from meeting market requirements within the time allotted for product development. Degradation profiles obtained from harsh testing environments have been ...
Building a qualitative recruitment system via SVM with MCDM approach
Advances in information technology have led to behavioral changes in people and submission of curriculum vitae (CV) via the Internet has become an often-seen phenomenon. Without any technological support for the filtering process, recruitment can be ...
Dealing with limited data in ballistic impact scenarios: an empirical comparison of different neural network approaches
In the domain of high-speed impact between solids, the simulation of one trial entails the use of large resources and an elevated computational cost. The objective of this research is to find the best neural network associated with a new problem of ...
Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation
Skyline computation in databases has been a hot topic in the literature because of its interesting applications. The basic idea is to find non-dominated values within a database. The task is mainly a multi-objective optimization process as described in ...
Shell-neighbor method and its application in missing data imputation
Data preparation is an important step in mining incomplete data. To deal with this problem, this paper introduces a new imputation approach called SN (Shell Neighbors) imputation, or simply SNI. The SNI fills in an incomplete instance (with missing ...
Compositional Bayesian modelling for computation of evidence collection strategies
As forensic science and forensic statistics become increasingly sophisticated, and judges and juries demand more timely delivery of more convincing scientific evidence, crime investigation is becoming progressively more challenging. In particular, this ...