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Protein secondary structure prediction is an important problem in bioinformatics. For this task, a method based on the SVM-PSSM Classifier combined by sequence feature (SF) is proposed in this paper. Protein sequence data is represented by a hybrid formation which combines the Position-Specific Scoring Matrix (PSSM) with the Hydrophobicity Sequence Feature (HSF), and the Structural Sequence Feature(SSF)...
Gait has been used in many research area including medical and health. One of the ways to capture gait signal is by using the accelerometer sensor in the smartphone. In this work, gait signal is used to identify a person. The accuracy of the gait recognition while the phone held in the palm is evaluated. Besides that, the factor of linear interpolation is examined. Lastly, k-NN, MLP and SVM algorithm...
In order to identify a large number of very similar objects, a novel recognition approach is proposed by mean of combination of two dynamic grouping algorithms, the visual processing mechanism, PCA and multi-pathway SVM. The samples have been segmented to appropriate groups by grouping features, and then features with rotation invariance and translation invariance of each group are extracted. Finally,...
P300-speller which relies on P300-event related potential (ERP) is an important application of the BCI system. However, the accuracy and information transmission rate were relatively low for practical use. To solve the problem, researchers focused on two aspects of paradigms and classifiers. P300-speller with familiar face paradigm achieved a better performance. In addition, Bayesian linear discriminate...
The complexity and viariability of the Arabic handwriting makes difficult the implementation of an efficient recognition system through the use of a unique recognition engine. In this paper, two handwriting word recognition systems are combined in order to take advantage of their complementarities. The first one is a segmentation free based system that uses the generative classifier HMM. The second...
In the present world, there is a need of emails communication but unsolicited emails hamper such communications. The present research emphasises to build a spam classification model with/without the use of ensemble of classifiers methods have been incorporated. Through this study, the aim is to distinguish between ham emails and spam emails by making an efficient and sensitive classification model...
In this Globalized world, the Call Centers and BPOsare increasing at an exponential rate. There is stiff competitionamong various companies and every company wants to have itsclients happy and satisfied with the resolution of the problems. For this purpose, Agent Quality Monitoring is an importantrequirement. Since in a typical Call Centre, thousands of calls aremade by agents in a single day, it...
In this paper, we focused on the problem of automatic modulation classification of digital signals. Several useful characteristic parameters which can be used for modulation analysis are extracted from spectral correlation, for different types of modulated signals have different power spectral density functions. A density estimation approach based on Support Vector Machine (SVM) is developed. Also,...
Multivariate methods of pattern recognition, classification and discriminant analysis have been found most useful in many types of chemical and biological problems. Predicting the biological activity of molecules from their chemical structures is a principal problem in drug discovery. Pattern recognition has gained attention as methods covering this need. In the present study classification models...
Deep neural networks yield positive object detection results in aerial imaging. To deal with the massive computational time required, we propose to connect an SVM Network to the different feature maps of a CNN. After the training of this SVM Network, we use an activation path to cross the network in a predefined order. We stop the crossing as quickly as possible. This early exit from the CNN allows...
With the increasing size of big data, classifiers usually suffer from intractable computing and storage issues. Moreover, decision boundaries in complex classification problems are usually complicated and circuitous. Modeling on too many instances can sometimes cause oversensitivity to noise and degrade the learning accuracies. Instance selection offers an effective way to improve classification performance...
In this paper, we propose a new framework for facial expression classification. This framework utilizes random forest as the classifier based on the features extracted from improved principal component analysis (PCA). Traditional PCA has two drawbacks: it is difficult to estimate the covariance matrix, and it is computational prohibitive to get the eigenvectors. In order to solve the two problems,...
Artificial neural networks (ANNs) have rarely been used in the field of medical education, especially in the prediction of learning performances. This study aims to evaluate the potential application of ANN models for predicting learning performances, in comparison with multivariate logistic regression models. The predictor variables included demographics, high-school backgrounds, first-year grade-point...
Human depicts their emotions through facial expression or their way of speech. In order to make this process possible for a machine, a training mechanism is needed to give machine the ability to recognize human expression. This paper compare and analyse the performance of three machine learning algorithm to do the task of classifying human facial expression. The total of 23 variables calculated from...
With the development of stone processing and sales, effective stone surface texture image recognition methods are needed. We proposed a new stone surface texture image recognition method based on texture and colour. We combine the following visual features: Gabor features which can well simulate the single cell sensing profile of mammalian visual neurons, The Grey-level Co-occurrence Matrices(GLCM)...
Named entities in a text are the atomic elements that represent the name of something, and the name can be a person name, name of an organization, name of a place or location etc. In the field of information extraction the identification and classification of named entities are quite an important task. The identification and classification of the named entities in a text into some pre-defined classes...
In this paper, we present a novel authentication method based on image feature matching to make intelligent lock key can be defined by user which will improve the security and ease-use of intelligent electronic lock. It is a new unlock scheme for intelligent lock that may replaces the text passwords and biological features which are typically used in intelligent lock system. In this method, an object...
This paper implements a feature extraction technique for recognizing online handwritten Gurmukhi characters. For attaining high recognition accuracy in such a system, computation of suitable features is an important task. DFT (Discrete Fourier Transform) based feature extraction technique is employed in this work. In this paper, we have considered 86 stroke classes of Gurmukhi script. We have taken...
Within the supervised machine learning framework, classifier performance is significantly affected by the size of training datasets. One of the ways to improve classification accuracy with small training datasets is to utilize additional knowledge about training data that is not present in testing data. In the Learning Using Privileged Information (LUPI) learning paradigm, this additional knowledge...
Support vector machines (SVM), originally introduced as powerful binary classifiers, can also be used for multi-class recognition with the help of creative meta-learning strategies such as commonly used one-vs-rest, one-vs-one and majority voting. In this paper, we explore the potential of creating informed nested dichotomies based on clustering pseudo-labels and probability estimates generated a...
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