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The presence of a large number of irrelevant features degrades the classifier accuracy, reduces the understanding of data, and increases the overall time needed for training and classification. Hence, Feature selection is a critical step in the machine learning process. The role of feature selection is to select a subset of size ‘d’ (d<n) from the given set of ‘n’ features that leads to the smallest...
This paper presents the development of a Neuro-genetic model for the prediction of coronary heart diseases. The novelty of this work is feature subset selection using multi-objective genetic algorithm without sacrificing the accuracy of ANN based heart disease predictor. Subsequently, the selected feature subset is used to predict the level of angiographic coronary heart disease using neural networks...
Optimally solving large scale Ready Mixed Concrete Dispatching Problems (RMCDPs) in polynomial time is a crucial issue and, in the absence of automated solutions, experts are hired to handle resource allocation tasks in concrete dispatching centres. Therefore, in the Ready Mixed Concrete (RMC) industry, the performance of experts is accepted as the only practical solution, although there is no benchmark...
Automatic image annotation (AIA) for a huge number of images is one of the most difficult challenging topics for researchers in the last two decades. For labeling images accurately, more various features containing low-level image features, textual tags of images have been extracted so far; however, not whole features give useful information for each conception. Feature selection as one of the important...
Dimensionality reduction continues to be a challenging problem with huge amounts of data being generated in the domains of bio-informatics, social networks etc. We propose a novel dimensionality reduction algorithm based on the idea of consensus clustering using genetic algorithms. Classification is used as validation and the algorithm is evaluated on benchmark data sets of dimensionality ranging...
The paper proposes FRECCA algorithm to find out the clusters of sentences having the inter relation among them. The system uses the fuzzy clustering algorithm that compares the sentences and find out the similarity value and forms a cluster. The fuzzy clustering algorithm checks the possibility of the sentence to which cluster it belongs to. In this paper a Genetic algorithm approach is also suggested...
Prediction time series of economic indicator optimized by Algorithm Genetic (AG) is able in getting best individual with the accuracy around 97%. The parameter of AG are maximum population is 100; Ra are 5 and 10, while Rb are −5 and −10; probability mutation (Pmut) is 0.3; and probability crossover (Pc) is 0.9. It was caused by AG had longer opportunity in fitting data using scenario data from 1961...
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...
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 this paper, a novel method for detecting the onset of Alzheimer's disease (AD) from Magnetic Resonance Imaging (MRI) scans is presented. It uses a combination of three different machine learning algorithms in order to get improved results and is based on a three-class classification problem. The three classes for classification considered in this study are normal, very mild AD and mild and moderate...
In the present study we investigate the evolutionary feature subset selection using wrapper based genetic algorithms on Multi-temporal datasets. Feature subset selection helps in reducing the original feature dimension and also yields high performance. The evolutionary strategy attains a global optimum by reducing the computations iteratively and by traversing intelligently in the entire feature space...
This paper presents a novel method for facial expression classification that employs the combination of two different feature sets in an ensemble approach. A pool of base classifiers is created using two feature sets: Gabor filters and local binary patterns (LBP). Then a multi-objective genetic algorithm is used to search for the best ensemble using as objective functions the accuracy and the size...
We use genetic algorithms and pattern matching to generate a morphological analyzer for Arabic verbs. Our approach consisted of developing general verb patterns and then applying these patterns to derive morphological rules. Except for some rare ambiguous cases, the resulting morphological analyzer is capable of recognizing all instances of verbs.
Due to the rise and rapid growth of E-Commerce, use of credit cards for online purchases has dramatically increased and it caused an explosion in the credit card fraud. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In real life, fraudulent transactions are scattered with genuine transactions and...
Recognizing faces with altered appearances is a challenging task and is only now beginning to be addressed by researchers. The paper presents an evolutionary granular approach for matching face images that have been altered by plastic surgery procedures. The algorithm extracts discriminating information from non-disjoint face granules obtained at different levels of granularity. At the first level...
Recently, the most popular research in the field of emotion recognition on human-computer interaction is to recognize human's feeling using various physiological signals. In the psycho-physiological research, it is known that there is strong correlation between human emotion state and physiological reaction. In this study, seven kinds of emotion (happiness, sadness, anger, fear, disgust, surprise,...
A high rate of expression of Endothelin protein in the placental cell is very much regulated by inhalation of tobacco smoke and leads to placental abnormalities subjected to birth failure. Our application developed using Image Processing, Nearest Neighbor algorithm (NN) and Genetic Algorithms (GA), automates the study of these proteins to assist pathologists and lab technicians in achieving a more...
In Computer Vision, problem of identifying or classifying the objects present in an image is called Object Categorization. It is challenging problem, especially when the images have clutter background, occlusions or different lighting conditions. Many vision features have been proposed which aid object categorization even in such adverse conditions. Past research has shown that, employing multiple...
In this paper a novel algorithm, called Counterpoint Harmony Search (CHS), is presented for the simultaneous denoising and deconvolution of binary images. No prior information about the noise or blur shape and size is required, which makes so called blind decon-volution of binary images possible using CHS. CHS is based on the Harmony Search algorithm and inspired by the island model parallel genetic...
The paper deals with the design and development of classifiers and, in particular, with the problem of selecting the most relevant input variables to be used as inputs for classification purpose in practical applications. In many real problems the selection of input variables is a very important task: often real datasets used for developing a classifier contain a high number of inputs but no a priori...
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