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Extraction of relevant features from high-dimensional multi-way functional MRI (fMRI) data is essential for the classification of a cognitive task. In general, fMRI records a combination of neural activation signals and several other noisy components. Alternatively, fMRI data is represented as a high dimensional array using a number of voxels, time instants, and snapshots. The organisation of fMRI...
The unprecedented interest in big data has paved way for augmented technologies. One of the major usefulness of big data is found in the field of healthcare analytics. The healthcare data come from varied sources. Specifically EHR data provide a comprehensive view of patient's health. People are paying more attention to their health and want the best possible healthcare especially with new technologies...
In these days, there are a growing interest in pattern recognition for tasks as prediction of weather events, recommendation of the best route, intrusion detection or face detection. Each of these tasks can be modelled as classification problem, where a common alternative is to use an ensemble model of classification. A well-known example is given by Mixture-of-Experts model, which represents a probabilistic...
Over the past few years, the dimensionality of functional MRI (fMRI) effects the analysis of brain data. In the field of machine learning and statistical analysis, classification of objects plays a significant role. Machine learning classifiers are used to discover the class of new data points from a set of data points. The application of learning techniques on fMRI data alleviates to cognitive state...
Patten recognition techniques are widely used for image processing in medical imaging. It provides assistance to physicians and scientists in large scale diagnosis. In this paper, we have proposed an automated system for detecting melanoma from dermoscopic images. We detected melanoma by extracting information from region of interest (ROI) rather than the whole image composed of lesion and background...
To improve the automation of metal sheet production these sheets have to be tracked during the processing steps. This is preferably done by video optical tracking. To recognize the metal sheets each of them has an unique ID imprinted. This paper describes a method to automatically detect the characters on metal sheets, to classify them and to combine them to a string. First the image is preprocessed...
In the article we present a practical study on methods for numerical feature selection. We compare quality of classification models built on different sets of features. In particular, we consider the problem of handwritten digits recognition and printed musical notation recognition. We apply a suite of index-based and wrapper methods for feature selection. Experiments show that both on regular data...
Convolutional neural network (CNN) has been successfully used in many fields including image recognition. CNN is composed of input, convolution, pooling, hidden and output layers, and the weights and biases between layers except the ones between convolution and pooling layers are acquired by learning. In comparison to the conventional neural networks, the learning cost of CNN is higher, and the learning...
Proteomic analysis is a rapidly developing research field that has recently been used in the diagnosis and treatment of various diseases by analyzing the structure and functions of protein patterns in the cell. Numerous computer based decision support mechanisms implemented in this context have mostly used special image processing techniques until now. Recently, high performance self-learning deep...
Image classification is a crucial problem for many image processing problems. Images that have close textures are challenging to be classified with high accuracy rate. Especially in natural images, classification is a difficult problem when considered independently from the color. In this study, seeds are classified based on textural features obtained from a database with 22 grades of seed. Feature...
In this paper, we propose a system that is capable of automatically differentiating between normal and abnormal heartbeats of patients using signals acquired from electrocardiography (ECG). The components of the ECG signals, that are PQRST intervals, were studied to acquire features for classification. Different time intervals of p-wave, QRS complex and t-wave were used as features. These features...
Classification is one of the most researched issues in Machine Learning. In this study, the Lorentzian Support Vector Machine (LSVM) method is proposed that performs classification in Lorentzian space. This proposed new classifier forms a hyperplane separating the classes based on the Lorentzian metric and maximize margins between nearest points to the hyperplane according to the Lorentzian distance...
In this study, classification of Normal and Extra systolic heart sounds (HS) have been carried out using in PASCAL Heart Sounds (HS) data base. The extrasystole is the HS that is produced by performing an extra beat in each heart cycle, unlike the heartbeat normal cycle. It can be felt by people as palpitations. Occurrence of these sounds in certain age groups may be the indication of tachycardia...
By passing of time, the size of data such as fMRI scans, speech signals and digital photographs becomes very high and it takes large amount of time for data processing. To overcome this problem, the dimensionality of data should be reduced. Whereas graph embedding introduces a successful framework for dimensionality reduction, we use it as the base of our proposed method. In this framework, similarity...
Human posture recognition is gaining increasing attention in the field of computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computer-interaction and surveillance systems. With the development of image processing and computer vision techniques, it is possible to analysis human behavior automatically by recognition the posture of human...
Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet...
Reliable automatic system for Human Epithelial-2 (HEp-2) cell image classification can facilitate the diagnosis of systemic autoimmune diseases. In this paper, an automatic pattern recognition system using fully convolutional network (FCN) was proposed to address the HEp-2 specimen classification problem. The FCN in the proposed framework was adapted from VGG-16, which was trained with ICPR 2016 dataset...
As different staining patterns of HEp-2 cells indicate different diseases, the classification of Indirect Immune Fluorescence (IIF) images on Human Epithelial-2 (HEp-2) cell is important for clinical applications. Different from traditional pattern recognition techniques, we use CNN to extract more high-level features for cell images classification. Compared to the existing CNN based HEp-2 classification...
Nowadays multidimensional data as a part of Big Data are collected in every organization and feature selection is one of the main approaches in terms of processing them with machine learning methods. In this study, firstly, a Feature Selection based on Lorentzian Metric (FSLM) is developed. The proposed method unlike from matrix multiplication in Euclidean space uses the Lorentzian analogue to calculate...
We describe a novel unsupervised method for classifying diabetes patients using laboratory data, which can potentially generalize to other diseases. 2,365 diabetic patients were clustered using discrete wavelet transforms of Glycated Hemoglobin (HbA1C). Latent class growth analysis classified HbA1C trends. The clusters were compared using ICD-9 codes, creatinine, and blood glucose, and were evaluated...
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