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A force fields-based multi-scale docking method is proposed in this paper. Molecular docking problem has been divided into three sub problems: rigid-rigid phase, flexible-flexible phase and flexible-rigid phase. Residue groups of protein have been adopted to describe the conformation of protein. K-mean clustering algorithm and genetic algorithm have been developed to solve the optimization problem...
The rapidly growing amount of data being produced in the world has become a challenging problem for decision support systems. These data are located in disparate sites while time, cost and privacy concerns makes it impossible to aggregate them into one location. Information fusion systems aim to make decisions by getting the outputs of the distributed sources. Since each source is making its local...
This paper proposes an application of Wireless Sensor Network (WSN) for indoor localization using IEEE 802.15.4 standard. Proposed algorithm applies K-means clustering and Genetic Algorithm (GA) as engine to prepare offline information which result in increasing accuracy and decreasing computational cost of fingerprint technique for indoor localization. K-means clustering will be applied to cluster...
In the automotive industry the issue of safety remains a major priority. This aspect is not focused just on the driver but also on the other participants of the traffic like the pedestrians. This paper describes a pedestrian detection system where three different classification methods are used for detecting pedestrians with a far infrared camera. The three methods are tested and compared on variable...
This paper presents a genetic algorithm (GA)-support vector machine (SVM) hybrid classifier for multiclass fault identification of drivetrain gearboxes in variable-speed operational conditions. An adaptive feature extraction algorithm is employed to effectively extract the features of gearbox faults from the stator current signal of an AC machine connected to the gearbox. The multiclass GA-SVM classifier...
In this paper, we propose the optimal parameters of local binary patterns such as type of pattern, size of blocks in the feature space and distance measure for face recognition using a genetic algorithm. The genetic algorithm is able to optimize all these parameters quickly and to improve the recognition accuracy. We provide a comparative study of three types of local binary patterns (LBP, LGP and...
In order to increase the accuracy of abnormal event detection in crowd video surveillance, this paper proposes a novel hybrid optimization of feature selection and support vector machine (SVM) training model based on genetic algorithm. For reducing dimensions of multi-feature, we propose an adaptive genetic simulated annealing algorithm (ASAGA) feature selection method. The ASAGA takes advantage of...
Detection and delineation of Electrocardiogram has played a vital role in cardiovascular monitoring systems. The enormous database of heart beats which characterize the heart disease, uncertainity, randomness in occurrence of these beats necessitate the use of Rough set theory. Over the years Rough set theory has been effectively used for removal of uncertainties and reduction of dataset. This paper...
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...
In the past years, the Web has become a huge source of opinionative data. Social media, such as Twitter, are regarded as public diaries, where millions of people express their sentiments and opinions in their daily interaction. One of the biggest challenges in the analysis of such data, is the classification of their polarity, that is, whether they carry a positive or negative connotation. For this...
Outbreak of debt crisis in Europe has made the issue of corporate failure prediction, known as financial distress prediction (FDP) as well, a significant topic in the field of management science. The purpose of this paper is to propose five hybrid classifiers to tackle corporate failure prediction problem. Principle component analysis (PCA),information gain (IG) and relief (Re) methods as representatives...
Emotional speech recognition is an interesting application that is able to recognize different emotional states from speech signal. In Human-Robot Interaction (HRI), emotion recognition is being applied on intelligent robots so that they can understand emotional states of user and interact in a more human-like manner. However, it is not easy to apply emotion recognition algorithms in real applications...
Depression is a disorder that has a huge impact on both the patient and its environment. An effective treatment of depression is of crucial importance. Currently, Internet-based self-help therapies are the state-of-the-art among therapies that do not involve a human therapist. However, these interventions are not tailored towards individual patient needs. The utilization of pervasive technology, including...
The bump hunting, proposed by Friedman and Fisher, has become important in many fields such as marketing and medical fields, and etc. Among them, to answer the unresolved question of molecular heterogeneity and of tumoral phenotype in cancer, the local sparse bump hunting algorithm, such as CART (Classification and Regression Trees) and PRIM (Patient Rule Induction Method), is useful. In the bump...
Due to the high dimensionality of hyperspectral data, dimension reduction is becoming an important problem in hyperspectral image classification. Band selection can retain the information which is capable of keeping the original meaning of the data, and thus has attracted more attention. This paper tackles the band selection problem from the perspective of multiple classifiers combination, which can...
This paper presents a new algorithm to improve the speed of threshold searching process in C4.5 by using the technique of genetic algorithms. In the threshold searching process in C4.5, the values in a numerical attribute are sorted first and then the mid-point between every two consecutive values is calculated and designated as a candidate threshold. This process can be time consuming and it is not...
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...
Credit scoring is becoming a competitive issue with rapid growth and significant advance. Building a satisfactory credit model has attracted lots of researchers in the past decades and it is still one of the hottest research topics in the field of credit industry. This paper emphasizes on surveying the development of the artificial intelligence technologies for solving credit scoring. It covers algorithms...
Comparing with the traditional method of credit evaluation, this paper presents a classification evaluation method based on the projection pursuit and the fuzzy rules. Firstly, we use projection pursuit technology to reducing the dimensionality of the training sample, and use genetic algorithm to optimize the projection direction to find the best projection value, classification in accordance with...
Approximate nearest neighbor (ANN) search provides computationally viable option for retrieval from large document collection. Hashing based techniques are widely regarded as most efficient methods for ANN based retrieval. It has been established that by combination of multiple features in a multiple kernel learning setup can significantly improve the effectiveness of hash codes. The paper presents...
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