10182888
Nonlinear feature extraction using a neuro genetic hybrid
Feature extraction is a process that extracts salient features from observed variables. It is considered a promising alternative to overcome the problems of weight and structure optimization in artificial neural networks. There were many nonlinear feature extraction methods using neural networks but they still have the same difficulties arisen from the fixed network topology. In this paper, we propose a novel combination of genetic algorithm and feedforward neural networks for nonlinear feature extraction. The genetic algorithm evolves the feature space by utilizing characteristics of hidden neurons. It improved remarkably the performance of neural networks on a number of real world regression and classification problems.
18194762
Daily Stock Prediction Using Neuro-genetic Hybrids
We propose a neuro-genetic daily stock prediction model. Traditional indicators of stock prediction are utilized to produce useful input features of neural networks. The genetic algorithm optimizes the neural networks under a 2D encoding and crossover. To reduce the time in processing mass data, a parallel genetic algorithm was used on a Linux cluster system. It showed notable improvement on the average over the buy-and-hold strategy. We also observed that some companies were more predictable than others.
3218907
Assignment Copy Detection Using Neuro-genetic Hybrids
It is difficult for teachers to find out copies in their program assignments. We propose a method which detects the similarity of assignments. We first transform a program into a sequence of tokens and provide it to an artificial neural network. The neural network is optimized by a hybrid genetic algorithm. Experimental results showed considerable improvement on artificial neural networks.
493776
Evolutionary Ensemble for Stock Prediction
We propose a genetic ensemble of recurrent neural networks for stock prediction model. The genetic algorithm tunes neural networks in a two-dimensional and parallel framework. The ensemble makes the decision of buying or selling more conservative. It showed notable improvement on the average over not only the buy-and-hold strategy but also other traditional ensemble approaches.
15507852
Testing of concurrent programs based on message sequence charts
Most prior work on specification-based testing of concurrent programs have postulated an equivalence relation between specifications and programs. However the equivalence relation, which requires a specification and a program to encompass the same set of synchronization sequences, is not well-suited for resting concurrent programs against partial and nondeterministic specifications. We present a new specification-based approach to testing concurrent programs against Message Sequence Charts (MSCs) with partial and nondeterministic semantics. New conformance relations, named behavioral conformance and nondeterminacy conformance, are defined and the details of the proposed testing methodology based on the conformance relations are given. The effectiveness of our testing approach is investigated using a telephone switching system example.
42278030
Hybrid knowledge-based architecture for building an intelligent nondestructive signal inspection system
The paper deals with the methods of implementing an intelligent inspection system for monitoring the health of any device or material using a nondestructive signal. A hybrid knowledge representation and processing architecture is proposed as a solution to problems encountered in the processes of the nondestructive inspection. In the proposed system, to modularize knowledge elicitation and to make knowledge processing reliable, the task of inspection is delegated to two subsystems each of which has a proper knowledge processing scheme to match the properties of its own task. The front-end subsystem which detects the signal patterns (events) for any harmful flaw is built by integrating a fuzzified syntactic pattern recognition concept and a neural network concept. The back-end subsystem which evaluates the characteristics of the events is based upon object-oriented rule base system concepts. The methods of integrating the approaches in the proposed architecture are also proposed in the paper. The proposed architecture is verified by developing and evaluating a prototype which automatically interprets eddy current Lissajous signals to inspect the state of tubes used in nuclear power plants.
61822837
Resilient Localization for Sensor Networks in Outdoor Environments
The process of computing the physical locations of nodes in a wireless sensor network is known as localization. Self-localization is critical for large-scale sensor networks because manual or assisted localization is often impractical due to time requirements, economic constraints or inherent limitations of deployment scenarios. We have developed a service for reliably localizing wireless sensor networks in environments conducive to ranging errors by using a custom hardware-software solution for acoustic ranging and a family of self-localization algorithms. The ranging solution improves on previous work, extending the practical measurement range threefold (20-30m) while maintaining a distance-invariant median measurement error of about 1% of maximum range (33cm). The localization scheme is based on least squares scaling with soft constraints. Evaluation using ranging results obtained from sensor network field experiments shows that the localization scheme is resilient against large-magnitude ranging errors and sparse range measurements, both of which are common in large-scale outdoor sensor network deployments
10587534
Assessing and restructuring of classes based on cohesion
Classes are the fundamental concepts in the object-oriented paradigm. They are the basic units of object-oriented programs, and serve as the units of encapsulation, which promotes their modifiability and reusability. However, improper modeling in the design phase or uncontrolled changes during the maintenance phase can degrade the quality of classes, which leads to programs which are cumbersome to maintain or extend. The paper proposes a method for assessing the quality of classes and restructuring them. The authors define an abstraction as the most cohesive form of a class, and the cohesion indicates the extent to which a class approaches the abstraction. Assessment and restructuring are both based on the notion of class cohesion. Less cohesive classes can be transformed into a form of an abstraction by restructuring them.
9037240
Empirical evaluation of a fuzzy logic-based software quality prediction model
Software inspection, due to its repeated success on industrial applications, has now become an industry standard practice. Recently, researchers began analyzing inspection data to obtain insights on how software processes can be improved. For example, project managers need to identify potentially error-prone software components so that limited project resource may be optimally allocated. This paper proposes an automated and fuzzy logic-based approach to satisfy such a need. Fuzzy logic offers significant advantages over other approaches due to its ability to naturally represent qualitative aspect of inspection data and apply flexible inference rules. In order to empirically evaluate the effectiveness of our approach, we have analyzed published inspection data and the ones collected from two separate inspection experiments which we had conducted. χ2 analysis is applied to statistically demonstrate validity of the proposed quality prediction model.