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Traditional probabilistic neural network (PNN) uses identical smooth factor, which easily leads to low recognition rate and misclassification. When the number of training samples increases the number of pattern layer neurons is large, which will lead to complex network structure. Due to these shortcomings, this paper proposes an algorithm using PNN with particle fish swarm algorithm (PFSA-PNN). The...
According to the characteristics of post evaluation for the productive capacity construction project of oilfield and the actual situation of oilfield, detailed analyzed the evaluation index, the relationship and the impact to the Comprehensive Post Evaluation of the post evaluation for the productive capacity construction project of oilfield, proposed the comprehensive post evaluation model based...
To improve the adaptability of vehicle detection algorithms in complex traffic circumstances, a robust detection algorithm based on LBP features of Haar-like Characteristics was proposed. The image texture feature reflects some characteristics of the degree of gray distribution, contrast and spatial distribution, Haar-like was inducted into LBP, then this method calculate the local texture features...
At the initial stage of the mechanism, the collected samples are always in actual state, and the signals in fault conditions are gathered after a certain running time, so the general fault diagnosis model cannot be trained effectively. In this paper, a hybrid fault diagnosis scheme for pump in truck crane was proposed based on particle swarm optimization (PSO) SVDD and DBI K-Cluster method. Firstly,...
Paraphrase detection has several important applications in natural language processing. Examples of such applications include language translation, text summarization, question answering, plagiarism detection, and online information retrieval. A number of metrics have been proposed in the literature to quantify the textual similarity between two sentences. However, the accuracy of utilizing each similarity...
Network intrusion is recognized as a chronic and recurring problem. Hacking techniques continually change and several countermeasure methods have been suggested in the literature including statistical and machine learning approaches. However, no single solution can be claimed as a rule of thumb for the wide spectrum of attacks. In this paper, a novel methodology is proposed for network intrusion detection...
Based on electrical capacitance tomography (ECT) technology and support vector machine (SVM) algorithm, a new method for all-around identification of two-phase flow pattern is proposed combining radial information with axial information of the gas-solid two-phase flow. Compared with the conventional sectional identification method, this method adds another axial one and thus providing more information...
To study the effectiveness of classification algorithms in cigarette sensory quality evaluation, chemical components such as total sugar, protein, potassium, etc. are taken as condition attributes, and ID3, C4.5, rough set, BP neural network, support vector machine, and k-nearest-neighbor are adopted to predict cigarette sensory quality index, such as luster, aroma, harmony, offensive odor, irritation...
Predicting the success of students is a topic which has been studied for a long time in different scientific fields. Evaluation of importance of the features used in the prediction and their subsequent selection is an immensely important step in the process of classification and data mining. This paper presents a study on the importance of student demographic features in the process of predicting...
In controlling biological diseases, it is often more potent to use a combination of agents than using individual ones. However, the number of possible combinations increases exponentially with the number of agents and their concentrations. It is prohibitive to search for effective agent combinations by trial and error as biological systems are complex and their responses to agents are often a slow...
One-class support vector algorithms such as OCSVM and SVDD have been successfully applied to many One-Class Classification (OCC) problems. Many authors assume that kernels like the ones used in standard binary SVM classification are also appropriate to one-class classification. However, a review of the literature indicated that in general, only the Gaussian RBF kernel gives satisfactory results in...
The task of One-Class Classification (OCC) is to characterise a single class that is well described by the training data and distinguish it from all others; this is in contrast to the more common approach of binary classification or multi-class classification, in which all classes are well described by the training data. One-class support vector machine algorithms such as OCSVM and SVDD have been...
Mild cognitive impairment (MCI) is a neurological condition that is often the early stage of Alzheimer's disease (AD). This pilot study explores event-related multiscale entropy (MSE) measures as features for effectively discriminating between normal aging, MCI, and AD participants. Thirty two-channel scalp EEG records recorded during a working memory task from 43 age-matched participants (mean age...
Hyperspectral images provide huge volume of spectral information for classification of land cover classes. Feature reduction plays an important role as a pre-processing step in classification of high dimensional data. Because of limited available training samples, unsupervised feature extraction is a proper selection for reduction of feature space. We propose an unsupervised feature extraction method...
Automatic kinship verification methods are conventionally based on facial appearance. In contrast to all published material, in this paper, we explore the use of facial expression dynamics and spatio-temporal features for kinship verification from smile videos. It is shown that the combined use of dynamic and spatio-temporal features extracted from spontaneous smiles significantly improves the state...
The goal of this study is to explore the advantages of representing natural images with the cortical Layer-4 processing, which is the first step in visual information processing performed by the cerebral cortex of the brain. A cortical module, a macrocolumn, receives input from a small visual field and its Layer 4 performs a nonlinear transform of this input to generate its pluripotent representation...
Auscultation and analysing of lung sound is widely used in clinical area for diagnosis of lung diseases. Due to the non-stationary nature of lung sounds conventional frequency analysis technique is not a successful method for respiratory sound analysis. In this paper, classification of normal and abnormal lung sound using wavelet coefficient intended. Respiratory sounds are decomposed into the frequency...
Facial emotions are the most expressive way to display emotions. Many algorithms have been proposed which employ a particular set of people (usually a database) to both train and test their model. This paper focuses on the challenging task of database independent emotion recognition, which is a generalized case of subject-independent emotion recognition. The emotion recognition system employed in...
In this paper, we propose a novel semi-supervised classification method for four types of lung nodules, i.e., well-circumscribed, vascularized, juxta-pleural and pleural-tail, in low dose computed tomography (LDCT) scans. The proposed method focuses on classifier design by incorporating the knowledge extracted from both training and testing datasets, and contains two stages: (1) bipartite graph construction,...
Mitosis detection poses a major challenge in cell tracking as mitoses are crucial events in the construction of genealogical trees. Making use of typical mitotic patterns that can be seen in phase contrast images of time lapse experiments, we propose a new benchmark data set CeTReS.B-MI consisting of mitotic and non-mitotic cells from the publicly accessible, fully labeled data set CeTReS.B. Using...
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