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Finding the molecular features causes the halophilicity in the halostable organisms is helpful to understand the halophilic adaption. In this study, we proposed a prediction method for halophilic proteins by using a machine learning method. The stages of this study are six-fold. First, we establish a non-redundant dataset of the halophilic proteins, collected from NCBI, Uniprotkb and EMBL-EBI databases...
The main difficulty faced by a learning algorithm is to find the appropriate knowledge inside of the huge search space of possible solutions. Typically, the researchers try to solve this problem developing more efficient search algorithms, defining “ad-hoc” heuristic for the specific problem or reducing the expressiveness of the knowledge representation. This work explores an alternative way that...
Predicting RNA secondary structure is a significant challenge in Bioinformatics especially including pseudoknots. There are so many researches proposed that pseudoknots have their own biological functions inside human body, so it is important to predict this kind of RNA secondary structures. There are several methods to predict RNA secondary structure, and the most common one is using minimum free...
In this paper, a hybrid approach incorporating the Nearest Shrunken Centroid (NSC) and Genetic Algorithm (GA) is proposed to automatically search for an optimal range of shrinkage threshold values for the NSC to improve feature selection and classification accuracy for high dimensional data. The selection of a threshold value is crucial as it is the key factor in the NSC to find significant relative...
Data mining techniques have been widely used in clinical decision support systems for prediction and diagnosis of various diseases with good accuracy. These techniques have been very effective in designing clinical support systems because of their ability to discover hidden patterns and relationships in medical data. One of the most important applications of such systems is in diagnosis of heart diseases...
Intrusion detection systems have been around for quite some time, to protect systems from inside ad outside threats. Researchers and scientists are concerned on how to enhance the intrusion detection performance, to be able to deal with real-time attacks and detect them fast from quick response. One way to improve performance is to use minimal number of features to define a model in a way that it...
This paper presents a new approach to the problem of semantic segmentation of digital images. We aim to improve the performance of some state-of-the-art approaches for the task. We exploit a new version of texton feature [28], which can encode image texture and object layout for learning a robust classifier. We propose to use a genetic algorithm for the learning parameters of weak classifiers in a...
The issue of studying the effect of fixing the length of the selected feature subsets using ant colony optimization (ACO) has not yet been studied. This paper addresses this concern by demonstrating four points that are: 1) determining the optimal feature subset, 2) determining the length of the subsets in ACO for subset selection problems, 3) different stopping criteria when solving feature selection...
Classification is a data mining method that assigns items in a collection to target classes with the goal to accurately predict the target class for each item in the data. Genetic programming (GP) is one of the effective evolutionary computation techniques to solve classification problems, however, it suffers from a long run time. In addition, there are many parameters that need to be set before the...
Ensembles of classifiers have recently proved their efficiency in cancer diagnosis based on microarray datasets. The main performance indicators, namely, accuracy and diversity, present the main focus of study when designing an ensemble. One other important performance indicator is classification robustness. In an attempt to improve the performance of an ensemble, the proposed algorithm presents a...
Presence of TCSC (Thyristor-Controlled Series Compensator) compensated transmission lines is increasing in modern power systems due to their benefits like increased power flow capacity but these benefits come at the cost of difficulty in protection of the transmission line. This paper presents a new method using SVM (Support Vector Machine) for fault classification in such line. This method is compared...
A model is presented for the pressure decline of acid fracturing wells after shut-in, which based on the material balance principle, and comprehensively considered the influences of compressibility and thermal expansion of fluids (wasted acid and supercritical carbon dioxide gas produced by acid-rock reaction), and artificial fracture width change for wasted acid keeping on etching fracture face after...
A novel method based on the concepts of genetic algorithm (GA) is proposed to design a fuzzy controller directly from some gathered input-output data. The proposed method can pick up fuzzy rule models and determine the parameters of membership functions of each input variable automatically from adequate datum. And it can optimize parameters of membership functions using a real coded genetic algorithms...
In this paper we apply two variable elimination algorithms to data obtained from an RF (Radio Frequency) Power Generator Fault Mode for analysis. We use a two wrapper approach using Support Vector Machines (SVM) and Radial Basis Function Networks (RBF) to build an efficient classifier with variable elimination. Comparisons are made for both continuous and discrete datasets.
In Bioinformatics, the prediction of protein function is considered a very important task but also difficult. Using a set of enzymes represented by Hydrolase, Isomerase, Ligase, Lyase, Transferase and Oxidoreductase classes, previously used by Dobson et al., this paper proposes a self-learning process able to predict their classes, based on their primary and secondary structures, through a Support...
It is extremely prerequisite to rotate collected image to the horizontal for automatic identification of PDF417 bar code, which is very sensitive to the skew angle. However, the existing skew angle detection methods are defective because of computationally expensive or high complexity. Here, a new tool mathematical morphology which is useful for image processing is used to extract PDF417 from complex...
The traditional BP neural network training method processes the training dataset serially on one machine, so the efficiency is quite low. The massive data that need to be explored brings great challenge for BP neural network. The traditional serial training method of BP neural network will encounter many problems, such as costing too much time and insufficient memory to finish the training process...
Multiclass classification is an important technique to many complex biomedicine problems. Genetic algorithms (GA) are proven to be effective to select features prior to multiclass classification by support vector machines (SVM). However, their use is computation intensive. Based on SOA (Service Oriented Architecture) design principles, this paper proposes a cloud computing framework that exploits...
This paper proposes a prognostic model for rehabilitating the chronic obstructive pulmonary disease (COPD) patients in real time. The proposed approach applies a comprehensive predictive model employing a time series forecasting using condensed polynomial neural network with swarm intelligence. Discrete particle swarm optimization (DPSO) filters out the relevant neurons and continuous particle swarm...
In view of the classification favors seriously to the most kinds when we use the traditional sorter to classify the imbalanced data set and the errors of classification of minority kind is big, A new minority kind of sample sampling method based on genetic algorithm and K-means cluster is proposed. First the method clusters and groups the minority kind of sample through K-means algorithm, then gains...
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