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Detecting smoke during the initial stages is vital for preventing fire events. This study proposes a video-based approach for alarm systems that detects smoke based on temporal features extracted from optical smoke flow pattern analysis and spatial-temporal energy analysis. To do this, it considers various optical characteristics such as the diffusion, color, and semi-transparency of smoke. In the...
Electroencephalogram (EEG) data is a set of brain signals recorded by special EEG headsets. These signals reflect the cortical electrical activity. The technique for utilization of EEG data has emerged to be a safe and portable non-invasive Brain Computer Interface (BCI) that can easily be used for studying the human cognitive states. In this paper we have focused on studying the pilot's cortical...
This paper addresses the problem of automatic classification of OCT images for identification of patients with DME versus normal subjects. In this paper a relativity simple and practical approach is proposed to exploit the information in OCT images for a robust classification of Diabetic Macular Edema (DME) using coherent tensors. From the retinal OCT scan top and bottom layers are extracted to find...
Accurate prediction of clinical changes of Mild Cognitive Impairment (MCI) patients at future time points is important for early diagnosis and possible prevention of Alzheimer's disease (AD). In this paper, future clinical changes in Neuropsychological Measures (NM) of MCI patients are estimated via three different predictive models employing linear regression and extrapolation. The completed time...
It is of significant importance for any classification and recognition system, which claims near or better than human performance to be immune to small perturbations in the dataset. Researchers found out that neural networks are not very robust to small perturbations and can easily be fooled to persistently misclassify by adding a particular class of noise in the test data. This, so-called adversarial...
Plant disease analysis is one of the critical tasks in the field of agriculture. Automatic identification and classification of plant diseases can be supportive to agriculture yield maximization. In this paper we compare performance of several Machine Learning techniques for identifying and classifying plant disease patterns from leaf images. A three-phase framework has been implemented for this purpose...
Microaneurysms (MAs) are the first visible sign of diabetic retinopathy (DR), a retinal abnormality which may lead to blindness in diabetes patients. In time and precise MAs detection is very important for early diagnosis of DR and can save patient's vision. In this paper, we present an automated system for accurate and reliable detection of MAs. The proposed system consists of preprocessing, feature...
In medical imaging, digital images are analyzed to develop computer aided diagnostic (CAD) systems using state of the art image processing and pattern recognition techniques. Diabetic maculopathy is one of the retinal abnormalities in which diabetic patient suffers from severe vision loss due to affected macula. In this paper, we propose an automated system for the grading of diabetic maculopathy...
Accurate load forecasting is essential for energy planning and load management. This paper presents long term industrial load forecasting (LTLF) using Nonlinear Autoregressive Exogenous model (NARX) based Feed-Forward Neural Network (FFNN) method, Support Vector Regression (SVR) and Neural Network models. It is applied to data sets obtained from National Transmission and Dispatch Company (NTDC) of...
In Pattern recognition, ensembles of classifiers are used to increase the performance and accuracy of classification systems. The creation of ensembles, selection of base classifiers and combining the decisions of the classifiers is an active research area. In this paper we propose a method of ensemble creation that is based on fuzzy clustering (Fuzzy C Mean) and fuzzy entropy; and named as Fuzzy...
In this paper, we propose a novel approach for identity verification based on the directional analysis of velocity-based partitions of an on-line signature. First, inter-feature dependencies in a signature are exploited by decomposing the shape (horizontal trajectory, vertical trajectory) into two partitions based on the velocity profile of the base-signature for each signer, which offers the flexibility...
In protein fold recognition problem an effort is made to assign a fold to given proteins, this is of practical importance and has diverse application in the field of bioinformatics such as the discovery of new drugs, the individual implication of amino acid in a protein and bringing improvement in a specific protein function. In this paper, we have studied various machine learning techniques for protein...
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