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Face recognition on a tilted face with expression poses challenging tasks. This paper presents an investigation of face recognition based on a Gabor Filter and Oriented Gabor Phase Congruency Image with Random Forest. Gabor Filter (GF) gives the magnitude information and Oriented Gabor Phase Congruency Image (OGPCI) gives the phase information of the Gabor response. Random Forest (RF) is used as an...
Electroencephalography (EEG) have been receiving a lot of attention due to its recent use in the field of biometrics. Signals traced from the different parts of the brain has become an upsurge area of interest for the researchers. Evidences have been provided by the research communities where the uniqueness of neuro-signals can possibly be used for building a robust biometric identification system...
Wind energy integration research generally relies on complex sensors located at remote sites. The procedure for generating high-level synthetic information from databases containing large amounts of low-level data must therefore account for possible sensor failures and imperfect input data. Data-mining methods are widely used for recognizing the relationship between wind farm power output and wind...
One of the major reasons for the poor recognition rate of Devanagari OCR is the inadequate handling of dika, vowel modifiers and half forms of consonants, conjuncts, and touching characters during segmentation. We attempted to minimize the segmentation errors by reducing the segmentation tasks. In this work, we propose a hybrid OCR system for printed Nepali text using the Random Forest (RF) Machine...
Systemic-to-Pulmonary Artery (SPA) shunt surgery, one of the most common cardiac surgical procedures in the newborn period, provides a means to palliate children with limited pulmonary blood flow, such as in Tetralogy of Fallot. Despite the simplicity of the procedure, it is associated with significant morbidity (such as need for extracorporeal membrane oxygenation (ECMO), and long post-operative...
Positron Emission Tomography (PET) is a 3-D functional imaging modality which help physicians to diagnose neurodegenerative diseases like Alzheimer's Disease (AD). Computer-aided detection and diagnosis, based on medical imaging techniques is of importance for a quantitative evaluation. A novel method of ranking the effectiveness of brain regions to separate AD from healthy brains images is presented...
Class imbalance is a significant challenge that practitioners in the field of bioinformatics are faced with on a daily basis. It is a phenomenon that occurs when number of instances of one class is much greater than number of instances of the other class(es) and it has adverse effects on the performance of classification models built on this skewed data. Random Forest as a robust classifier has been...
Bioinformatics datasets contain a number of characteristics, such as noisy data and difficult to learn class boundaries, which make it challenge to build effective predictive models. One option for improving results is the use of ensemble learning methods, which involve combining the results of multiple predictive models into a single decision. Since we do not rely on a single model, we reduce the...
Ensemble learning is a powerful tool that has shown promise when applied towards bioinformatics datasets. In particular, the Random Forest classifier has been an effective and popular algorithm due to its relatively good classification performance and its ease of use. However, Random Forest does not account for class imbalance which is known for decreasing classification performance and increasing...
Urban land cover classification has always been crucial due to its ability to link many elements of human and physical environments. In this paper, random forest is explored for urban areas. Lidar data and aerial imagery with 0.5-m resolution were used to classify four land categories in the study area located in the City of Niagara Falls (ON, Canada). Based on the experiment results, RF based classification...
The aim of this paper is to develop an effective classification approach based on Random Forest (RF) algorithm. Three fruits; i.e., apples, Strawberry, and oranges were analysed and several features were extracted based on the fruits' shape, colour characteristics as well as Scale Invariant Feature Transform (SIFT). A preprocessing stages using image processing to prepare the fruit images dataset...
This work aims to develop an effective flower classification approach using machine learning algorithms. Eight flower categories were analyzed in order to extract their features. Scale Invariant Feature Transform (SIFT) and Segmentation-based Fractal Texture Analysis (SFTA) algorithms are used to extract flower features. The proposed approach consists of three phases namely: segmentation, feature...
In the United States, the number of Phasor Measurement Units (PMU) will increase from 166 networked devices in 2010 to 1043 in 2014. According to the Department of Energy, they are being installed in order to “evaluate and visualize reliability margin (which describes how close the system is to the edge of its stability boundary).” However, there is still a lot of debate in academia and industry around...
We propose using filter bank common spatial pattern (FBCSP) feature extraction algorithm, and random forest (RF) technique for classification of EEG motor imagery signals. FBCSP algorithm allows extracting features and dealing with subject variability by automatic selection of frequency bands. Performing random forest in the classification avoid the use of feature selection step, since RF combine...
Machine-learning techniques such as decision support systems (DSS) are of great help in various fields. Medicine is one of the fields that can benefit from the application of data mining and pattern recognition techniques. The evolution of computational intelligence can improve many areas in health care including diagnosis, prognosis, screening, etc. The multiclass classification problem is important...
One of the most successful types of brain computer interfaces (BCI) is based on the P300 evoked potential (EP) elicited by oddball type of paradigms. Given a particular paradigm the main challenge is to obtain an efficient and robust classification. This paper proposes the use of Random Forest (RF), a tree based ensemble learning method providing state-of-the-art generalization performance, for P300...
Software defect prediction has recently attracted attention of many software quality researchers. One of the major areas in current project management software is to effectively utilize resources to make meaningful impact on time and cost. A pragmatic assessment of metrics is essential in order to comprehend the quality of software and to ensure corrective measures. Software defect prediction methods...
Protein-RNA interactions play important role in a variety of biological processes in cells. An improved method is proposed for predicting RNA-binding residues from amino acids sequences which combines a novel hybrid feature with a random forest (RF) algorithm. The hybrid feature contains the evolutionary information, the secondary structure information and two novel features reflected the information...
We describe a flexible and efficient architecture for generic object recognition system based on ensemble classifier in a field programmable gate array (FPGA) environment. We have shown previously utilizing a bag of covariance matrices as object descriptor improves the object recognition accuracy while speed up the learning process. We extend this technique, and present its hardware architecture,...
We describe an efficient architecture for generic object recognition system based on an ensemble classifier in a field programmable gate array (FPGA) environment. Utilization of a bag of covariance matrices as object descriptor improves the object recognition accuracy while speed up the learning process. We extend this technique, and present its hardware architecture, as well as object classifier...
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