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This study explored the hidden biomedical information from knee MR images for osteoarthritis prediction. We have computed the Cartilage Damage Index (CDI) information from 36 informative locations on tibiofemoral compartment from 3D MR imaging reconstruction and used PCA analysis to process the feature set. The processed feature set and original raw feature set were severed as input to four machine...
Machine learning classifiers help physicians to make near-perfect diagnoses, minimizing costs and time. Since medical data usually contains a high degree of uncertainty and ambiguity, proper ordering and classification require a proper comparative performance analysis of machine learning classifiers. Machine learning classifiers are applied on the Ovarian Cancer Dataset. Ovarian cancer is the fifth...
A variety of methods exists for electroencephalographic (EEG) signals classification. In this paper, we briefly review selected methods developed for such a purpose. First, a short description of the EEG signal characteristics is provided. Then, a comparison between the selected EEG signal classification methods, based on the overview of research studies on this topic, is presented. Examples of methods...
Convolutional neural network (CNN) is more and more important in pattern recognition. In this work, we adopt label relations and long short-term memory (LSTM) to develop an accurate CNN-based scene classification algorithm. Traditional scene classification algorithms assume that labels are mutually exclusive. However, this is not reasonable when an image has a variety of objects and hence has multiple...
This study investigates the discrimination between calm, exciting positive and exciting negative emotional states using EEG signals. Towards this direction, a publicly available dataset from eNTERFACE Workshop 2006 was used having as stimuli emotionally evocative images. At first, EEG features were extracted based on literature review. Then, a computational framework is proposed using machine learning...
The breast cancer is one of the most popular cause of death among women. It is also one of the diseases that can be cured and has high healing chances when it is detected in the early stages [1]. Detecting the cancer and differentiating between the diagnosis that affirm whether a patient has breast cancer or not has been considered as a big challenge. In order to have an accurate diagnosis, Support...
This paper evaluated three methods of atrial fibrillation (AF) detection in Korean patients using 149 records of photoplethysmography signals from 148 participants: the k-nearest neighbor (kNN), neural network (NN), and support vector machine (SVM) methods. The 149 records are preprocessed to calculate the root-mean square of the successive differences in the R-R intervals and Shannon entropy which...
With the large volume of network traffic flow, it is necessary to preprocess raw data before classification to gain the accurate results speedily. Feature selection is an essential approach in preprocessing phase. The Principal Component Analysis (PCA) is recognized as an effective and efficient method. In this paper, we classify network traffic by using the PCA technique together with six machine...
The demand of text classification is growing significantly in web searching, data mining, web ranking, recommendation systems and so many other fields of information and technology. This paper illustrates the text classification process on different dataset using some standard supervised machine learning techniques. Text documents can be classified through various kinds of classifiers. Labeled text...
Dengue virus infection or dengue fever is caused by the dengue virus (DENV). It is transmitted to humans by mosquitoes. There are four serotypes classified together based on their surface antigens. Each serotype can provide specific immunity and short-term cross-immunity in human. Several studies have examined the classification of dengue molecules into four major classes including methods such as...
Classification performances of the supervised machine learning techniques such as support vector machines, neural networks and logistic regression are compared for modulation recognition purposes. The simple and robust features are used to distinguish continuous-phase FSK from QAM-PSK signals. Signals having root-raised-cosine shaped pulses are simulated in extreme noisy conditions having joint impurities...
In the modern society, energy consumption such as gas and electricity is closely related to the weather condition because of the large share of weather-sensitive electrical appliances. Investigating how weather influences the energy consumption is of great significance for energy demand forecasting. This paper proposes an optimum regression approach for analyzing weather influence on the energy consumption...
In a wind farm, where several wind turbines are arranged in rows and columns, the wind speed available for the downstream turbines are significantly reduced by the wake effect. The wake losses can reduce the total productivity of a wind farm up to 20 per cent. Understanding the wake pattern in an existing wind farm is essential for the short-term wind power forecast. In this paper, we propose the...
In this paper a universal, coarse-grained reconfigurable architecture for hardware acceleration of decision trees (DTs), artificial neural networks (ANNs), and support vector machines (SVMs) is proposed. Using proposed architecture, two versions of DTs (Functional DT and Axis-Parallel DT), two versions of SVMs (with polynomial and radial kernels) and two versions of ANNs (Multi Layer Perceptron and...
Estimating age at death of cadavers is an important ability in various subfields of forensic science and bioarchaeology. It can allow investigators to pinpoint someone's identity, more accurately locate an event of interest in time and clarify other societal or legal issues concerning a given skeletal collection. There are two main categories of methods for estimating age at death: biochemical methods...
Machine Learning (ML) provides a theoretical and methodological framework that allows to quantify the relationship between the user's Quality of Experience (OoE) and the network's Quality of Service (QoS). In the literature, several ML-based QoS/QoE correlation models have been proposed. All of those models use inductive supervised learning techniques and most of them are built in an offline batch...
In this paper a survey on fault diagnosing techniques of electronic circuits are presented which are related mainly to industrial applications. Diagnozing the faults in circuit boards is very essential for achieving better reliability and easy maintainance of electronic systems. The circuit fault finding diagnosis is treated as the pattern recognition case and uses machine learning methodology. Increasing...
Given recent advances in sensing technology, including performance improvements, as well as reduction in size and cost, prognostics and health management is gaining increased popularity as a method to ensure reliability and safety of engineered systems. While many methods have been developed for a wide range of systems, relatively little research has examined the potential benefits of concepts from...
After the subprime crisis in 2008, an efficient Financial Distress Prediction (FDP) model has become necessary. Many research works have attempted to provide a model using statistical or intelligent methods. In this respect, this paper adopts a two-stage hybrid model that integrates Deep Learning and Support Vector Machine as a FDP modeling method. Local receptive fields is a technique used in order...
This research aimed at integrating data from remote sensing resources and machine learning for developing a forecasting model of successful royal rainmaking operation in the upper north provinces of Thailand. The Support Vector Machine (SVM), neuron network method, and decision tree (C4.5) were used for data integration and forecast modeling. The data were collected between 1 January 2012 to 31 December...
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