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The cutting stock problem (CSP) is an important problem in class of combinatorial optimization problems because of its NP-hard nature. Cutting of the required material from available stock with minimum wastage is a challenging process in many manufacturing industries such as rod industry, paper industry, textile industry, wood industry, plastic and leather manufacturing industry etc. The objective...
Software development cost estimation is an important activity in the early software design phases. The input datasets are primarily taken from the promise repository. Data mining and soft computing techniques are used to assess the software development cost estimation. Each feature in the input dataset is divided, the linguistic terms along with the membership are identified using trapezoidal membership...
Image registration is one of the crucial steps in the analysis of remotely sensed data. A new acquired image must be transformed, using image registration techniques, to match the orientation and scale of the related reference images. Image registration requires intensive computational effort not only because of its computational complexity, but also due to the continuous increase in image resolution...
Cluster is bunch of similar items. Unsupervised classification of patterns into clusters is known as clustering. It is useful in knowledge discovery in data. Clustering is able to deal with different data types. Fuzzy rules are used for data intelligence illustration purpose. User gets highly interpretable discovered clusters using fuzzy rules. To generate accurate fuzzy rules triangular membership...
Multi-objective genetic algorithm (MOGA) has been used for more than a decade to solve real-world optimization problems that have several, and often conflicting objectives. In this research, the conflicting objectives of achieving the maximum accuracy of the solution and at the same time minimizing the redundancy of the optimal solutions in retrieving the best set of exam questions for academicians...
Manufacturing data is an important source of knowledge that can be used to enhance the production capability. The detection of the causes of defects may possibly lead to an improvement in production. However, the production records generally contain an enormous set of features. It is almost impossible in practice to monitor all features at once. This research proposes the feature reduction technique,...
Face detection is a technique of detecting any face from a set of images. Face can be detected on the basis of features of the face such as pose, height, width etc. Although there are various techniques implemented for the detection of faces such as face detection using neural networks, but the features extracted using neural network is not sufficient and has low accuracy. Hence in this paper an efficient...
The accuracy of the power system model is important in investigating the transient phenomena of load frequency control (LFC). In this paper, Segmentation Particle Swarm Optimization (SePSO) method is proposed for governor-turbine model identification of single area power plant. The method is acquired based on a combination of segmentation and Particle Swarm Optimization (PSO) algorithms, in which...
In order to solve the problems of traditional SVM classifier for software defect prediction, this paper proposes a novel dynamic SVM method based on improved cost-sensitive SVM (CSSVM) which is optimized by the Genetic Algorithm (GA). Through selecting the geometric classification accuracy as the fitness function, the GA method could improve the performance of CSSVM by enhancing the accuracy of defective...
Prototype selection aims at reducing the scale of datasets to improve prediction accuracy and operation efficiency by removing noisy or redundant patterns via the nearest neighbor classification algorithms. Genetic algorithms have been used recently for prototype selection and showed good performance, however, they have some drawbacks such as the deteriorated running effect, slow convergence for the...
Biomedical cloud computing offers on-demand healthcare services. A sensor fusion method is developed based on non-parametric density estimation on genetic evolution computing. Our method provides a potential solution for decision making on flicking features when not all measurements of sensors appear at the input end. The method was applied on major depressive disorder detection as an application...
Ensembles of classifiers were shown to provide better accuracy than single classifiers. However, the classification robustness is an important performance measure for classifiers and ensembles, besides accuracy, that should be considered. Increasing the robustness of classification systems results in reducing the probability of over-fitting. The robustness, as defined in this study, has not been studied...
Spirometry is the most commonly performed Pulmonary Function Test (PFT) which is used to distinguish obstructive from restrictive lung diseases. This paper presents the basic system requirements for an automatic pulmonary disease classification system based on spirometric signal using a novel algorithm. The software of the system extracted features from the digitized spirogram waveform values and...
We describe how a task in computer vision can be effectively resolved by employing Genetic Algorithm. This paper focuses on the problem of semantic segmentation of digital images. We propose to use an improved genetic algorithm for the learning parameters of weak classifiers in a boosting learning set up. We propose a new encoding and genetic operators in accordance with this problem. Beside that,...
An improved probability positioning algorithm is proposed to enhance the accuracy of location estimation for outdoors under cellular network. The traditional probability algorithm models the received signal strength (RSS) by the standard Gaussian model from a base station. However, the propagation of the radio signal is based on a log-loss propagation model [1], which explains the relationship of...
Ensemble systems are composed of a set of individual classifiers, organized in a parallel way, that receive the input patterns and send their output to a combination method, which is responsible for providing the final output of the system. The use of feature selection methods in ensemble systems has been shown to be efficient, since it reduces the dimensionality while increases the diversity among...
Hyper networks consist of a large number of hyper edges that represent high-order features sampled from training sets. The order of hyper edges is an important parameter of a hyper network model and influences the performance of the hyper network classification system. Previous studies determine the parameter by the artificial exhaustive search method before evolutionary learning. Not only is the...
This paper presents a method for extracting automatically classification rules via multi-objective genetic algorithms. The paper also proposes a novel objective measure to quantify the similarity of the rules. The other objectives of the rules are average support value and accuracy. We experimentally evaluate our approach on socio-demographics and biochemistry datasets of schizophrenia patients and...
In this paper, we present a weight learning method introduced to learn weights on each individual classifier to construct an ensemble. Genetic algorithm is applied to search for an optimal combination of weights for each individual classifier on which classifier ensemble is expected to give best performance. Our proposed ensemble approach can combine heterogeneous classifiers and/or classifier ensembles...
In this paper, we introduced a classifier ensemble approach to combine heterogeneous classifiers in the presence of class label noise in the datasets. To enhance the performance of classifier ensemble, we give a preprocessing approach to filter out this class label noise. The filtered data is then used to learn individual classifier model. After that, a weight learning method is introduced to learn...
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