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Agricultural textures are in the interest of classification in image processing. Natural images have unique textural shapes inside which cause a tough problem for classification. This paper tests different feature extraction and classification approaches to serve a benchmarking on several agricultural databases like seeds and leaves. Features are obtained using Local Binary Pattern (LBP), Gray Level...
The aim of this study is to develop a time series classification method based on scale-space theory. Our study has been conducted in three steps: In the first step, scale-space extrema of time series found through using SiZer (SIgnificant ZERo crossings of the derivatives) method and local features set constructed around the determined extreme points, based on interval-widths list entered by the user...
In the growing era of technology, concentration is on the analysis of large amount of structured and unstructured data. The processing applications are inadequate to deal with these data are termed as BigData since in large amounts. In this work, an initial stage for analysing medical informatics using R-studio by R programming is attempted by two algorithms. The biomedical data is used because they...
A novel algorithm is proposed in this study for improving the accuracy and robustness of human biometric identification using electrocardiograms (ECG) from mobile devices. The algorithm combines the advantages of both fiducial and non-fiducial ECG features and implements a fully automated, two-stage cascaded classification system using wavelet analysis coupled with probabilistic random forest machine...
Inflammatory Bowel Disease (IBD) is an autoimmune condition that is observed to be associated with major alterations in the gut microbiome taxonomic composition. Here we classify major changes in microbiome protein family abundances between healthy subjects and IBD patients. We use machine learning to analyze results obtained previously from computing relative abundance of ∼10,000 KEGG orthologous...
This article presents a study conducted in a Brazilian bank, in order to assist the institution account managers in the approach to customers with loans in arrears. This approach is carried out to propose alternatives to customers return to timely payments situation, but the efficiency of this approach is small, accounting for only about 6.8% of customers. A predictive model, using classification...
Nonintrusive load monitoring (NILM) is a procedure for the analysis of the changes in the power (current and voltage) that goes into households and classifying the appliances used in the house according to their individual energy consumption. Utility companies use smart electric meters accompanied with NILM to examine the particular uses of electric power in households. Focus of this paper is on the...
Inventor name disambiguation is the task that distinguishes each unique inventor from all other inventor records in a patent database. This task is essential for processing person name queries in order to get information related to a specific inventor, e.g. a list of all that inventor's patents. Using earlier work on author name disambiguation, we apply it to inventor name disambiguation. A random...
Cancer is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in Malaysia and the world as we know it. It is estimated that statistically one out of every four Malaysians will develop cancer by the age of 75. Conventional methods of diagnosing cancer rely solely on skilled physicians, with the help of medical...
The research of facial beauty is an interdisciplinary topic involved in psychology, aesthetics, computer version and machine learning. In this paper, we propose several methods to assess facial beauty under unconstrained conditions. Our main works are as follows: First, we apply the local binary pattern (LBP) descriptor in different bins for face representation. We tried different types of LBP methods...
This paper addresses the problem of recognizing illogical object juxtaposing in the specific form of classifying digitized paintings in art movements. More precisely we distinguish between realism and surrealism movements. We propose a system based on feature extraction and machine learning that is able to understand the scene in the digitized paintings and to classify the art works from the two movements...
The discrimination of ECG signals is of crucial importance in clinical diagnoses of cardiac diseases. Manual analysis of ECG signals is very complex and time consuming task due to their composite nature. This paper proposes a novel scheme for reliable automatic classification of ECG signals into normal and three different abnormal (arrhythmia affected) categories. The feature extraction is based on...
Massive amount of diagnostic data is generated everyday as a part of daily diagnosis, related to various types of diseases and disorders. For knowledge discovery from this diagnostic data, efficient data mining techniques play a very important role. Ensemble classifier is one of the data classification techniques related to data mining, in which decision of multiple base classifiers is combined for...
Preterm birth brings considerable emotional and economic costs to families and society. However, despite extensive research into understanding the risk factors, the prediction of patient mechanisms and improvements to obstetrical practice, the UK National Health Service still annually spends more than £2.95 billion on this issue. Diagnosis of labour in normal pregnancies is important for minimizing...
In the context of emotional body expression, previous works mainly focused on perceptual studies to identify the most important expressive cues. Only few studies gave insights on which body cues could be relevant for the classification and the characterization of emotions expressed in body movement. In this paper, we present our Random Forest based feature selection approach for the identification...
The hybrid speech synthesis system which combines the hidden Markov model and unit selection method has been widely used and researched in both industry and academia recently due to its naturalness and expressiveness. However, the target duration, which is used to control the duration of selected candidate, is still predicted via the state-based duration model, whose performance is far from satisfactory...
Handwritten digit strings and handwritten signature are used in various applications on a daily basis. Whether one signs a contract, work documents, petition, or wants to approve a cheque payment, one will use personal signature to do all those things. Similar is true for digits strings, where digit recognition is used in postal mail sorting, bank cheque processing, etc. In this paper we use this...
This paper compares the most commonly used ensemble decision tree methods for on-line identification of power system dynamic signature considering the availability of Phasor Measurement Units (PMU) measurements. Since previous work has shown that the surrogate split method included in classification and regression tree is not good enough to handle the unavailability of measurement signals, more effective...
Three types of statistical models have been used to create up to 24h forecasts of the zonal and meridional components of wave energy flux levels at three directional buoys located in the Bay of Biscay. Hourly observations of the mean wave period and the significant height covering the 1999–2012 period have been used for this purpose. Additionally, data from the ocean (WAM model) and atmospheric components...
Evaluation of respiratory activity during sleep is essential in order to reliably diagnose sleep disorder breathing (SDB); a condition associated with serious cardio-vascular morbidity and mortality. In the current study, we developed and validated a robust automatic breathing-sounds (i.e. inspiratory and expiratory sounds) detection system of audio signals acquired during sleep. Random forest classifier...
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