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Due to the high variability in tumor morphology and the low signal-to-noise ratio inherent to mammography, manual classification of mammogram yields a significant number of patients being called back, and subsequent large number of biopsies performed to reduce the risk of missing cancer. The convolutional neural network (CNN) is a popular deep-learning construct used in image classification. This...
In the literature, a number of methods have been proposed for semi-supervised learning. Recently, graph-based methods of semi-supervised learning have become popular because of their capability of handling large amounts of unlabeled data. However, the existing graph based semi-supervised learning algorithms do not optimize the process of selecting better labeled data. We have developed a new selective...
We propose EC3, a novel algorithm that merges classification and clustering together in order to support both binary and multi-class classification. EC3 is based on a principled combination of multiple classification and multiple clustering methods using a convex optimization function. We additionally propose iEC3, a variant of EC3 that handles imbalanced training data. We perform an extensive experimental...
Antimicrobial peptides might become crucial in fighting antibiotic resistant bacteria and other infections. Next Generation Sequencing technologies are generating a large amount of data where peptides with antimicrobial activity could be found. Therefore, algorithms that can efficiently determine whether or not a short sequence of amino acids is antimicrobial are needed. In this context, Quantitative...
Linear Discriminant Analysis (LDA) is widely-used for supervised dimension reduction and linear classification. Classical LDA, however, suffers from the ill-posed estimation problem on data with high dimension and low sample size (HDLSS). To cope with this problem, in this paper, we propose an Adaptive Wishart Discriminant Analysis (AWDA) for classification, that makes predictions in an ensemble way...
The availability of intelligent embedded system to assist the classification application is a great challenge in machine learning field in last few decades. Extreme Learning Machine (ELM) is one of the best learning methods for the implementation due to its classification accuracy and speed. The main computational effort of ELM is to compute the pseudo-inverse of hidden layers output. This work presents...
Identification of the correct medicinal plants that goes in to the preparation of a medicine is very important in ayurvedic medicinal industry. The main features required to identify a medicinal plant is its leaf shape, colour and texture. Colour and texture from both sides of the leaf contain deterministic parameters to identify the species. This paper explores feature vectors from both the front...
Traditional vehicle detectors always utilize singletemplate model to represent the vehicle which can not encircle vehicles with different aspect ratios. In this paper, we propose a fast and accurate approach for detecting vehicles which joints classification and aspect ratio regression. The key idea is extending the boosting decision trees method to estimate vehicle's aspect ratio during vehicle detection,...
Feature selection represents a key stage in electroencephalogram (EEG) classifications, because these applications involve numerous, high-dimensional samples. In recent literature, a multitude of supervised embedded feature selection procedures has been proposed. Regardless if they are configured as Single Objective (SOO) or Multi-Objective Optimizations (MOO), the embedded methods assess the quality...
The method of approximating a discriminant functions of the training set is proposed. The sign of the discriminant functions allows us to classify the point in one or another class. The approximation is constructed with greater precision in the neighborhood of zero values of the discriminant function. To estimate a posterior probability of a class of a point two methods are proposed: based on a series...
One of the major causes of death in the world is Heart Failure. This disease affects directly the heart's pumping job. Because of this perturbation, nutriments and oxygen are not well circulated and distributed. The New York Heart Association has classified this disease into four different classes based on patient symptoms. In this paper, we are using a data mining technique, more precisely a sequential...
Deep learning (deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures or otherwise composed of multiple non-linear transformations. In this paper, we present the results of testing neural networks architectures...
Motor function rehabilitation is very urgent for patients. Motor imagery is an efficient way for rehabilitation. To achieve the supervision of multiple rehabilitation targets simultaneously, the promotion of multi-class motor imagery classification accuracy is critical. In this paper, a multi-class classification method is proposed by utilizing singular value decomposition and deep boltzmann machine...
Speech feature learning is very important for the design of classification algorithm of Parkinson's disease (PD). Existing speech feature learning method for classification of PD just pays attention to the speech feature. This paper proposed a novel hybrid feature learning algorithm which puts the features of all the speech segments of each subject together, thereby obtaining new and high efficient...
With the availability of medical data for large number of patients in hospitals, early detection of diseases has been made easier in the recent past. Conditions like Infertility which are hard to detect or diagnose can be now diagnosed with greater precision with the help of predictive modeling. One of the key challenges for early detection and timely treatment is in identifying and recording key...
Autonomous Underwater Vehicle (AUV) has limited energy capacity due to it being an embedded system. To overcome this limitation, the AUV can home into a docking station to recharge its battery. Several research has been conducted on the docking of AUV using vision. In some literatures, docking would fail if the target placed at the docking station is missing or disoriented from the camera view. This...
The aim of this study is to compare some classifiers' performance related to the tuples amount. The different metrics of performance has been considered, such as: Accuracy, Mean Absolute Error (MAE), and Kappa Statistic. In this research, the different numbers of tuples are considered as well. The readmission process dataset of Diabetic patients, which has been experimented, consists of 47 features...
Roller element bearing fault diagnosis is crucial in industry to maintain that the machine is in good condition so that there is no delay of work due to machine breakdown. This paper discusses the use of Extreme Learning Machine (ELM) algorithm to classify bearing faults. The performance of ELM is compared with Back Propagation (BP) algorithm. It was found that the results show that the ELM has smaller...
Existing work on identifying security requirements relies on training binary classification models using domain-specific data sets to achieve a high accuracy. Considering that domain-specific data sets are often not readily available, we propose a domain-independent model for classifying security requirements based on two key ideas. First, we train our model on the description of weaknesses from the...
In this paper, we take up the second RE17 data challenge: the identification of requirements types using the "Quality attributes (NFR)" dataset provided. We studied how accurately we can automatically classify requirements as functional (FR) and non-functional (NFR) in the dataset with supervised machine learning. Furthermore, we assessed how accurately we can identify various types of NFRs,...
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