A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture

Neural Computing and Applications - Support vector machine (SVM) is a well-regarded machine learning algorithm widely applied to classification tasks and regression problems. SVM was founded based...     

Mechanistic Studies on the [2 + 2] Photocycloaddition of Deuterium-Labeled Olefins

The mechanism of intramolecular [2 + 2] photocycloaddition of 1b has been investigated. Structure determination of deuterium-labeled photoadducts was accomplished by ²H NMR.     

Fluorescence spectroscopy coupled with factorial discriminant analysis technique to identify sheep milk from different feeding systems

Rapid measurements of milk properties and discrimination of milk origin are necessary techniques for quality control of milk products. The present study was undertaken to evaluate the potential of using front face fluorescence spectroscopy (FFFS) and synchronous fluorescence spectroscopy (SFS) for monitoring the quality of forty-five ewe’s milk samples originating from different feeding systems. Physico-chemical analyses and fluorescence spectra were conducted on samples during lactation periods (the first 11 weeks). The principal component analysis (PCA) separately applied to the physico-chemical and fluorescence spectral data showed only small discrimination between milk samples based on lactation periods and diet compositions. Similar results were obtained by separately applying factorial discriminant analysis (FDA) on each technique. In a second step, concatenation technique were applied to FFF spectra acquired after excitation set at 250, 290, 380 nm and emission set at 410 nm. Results obtained showed a good discrimination among milk samples with regard to feeding systems given to the ewes throughout the lactation periods. In addition, a better discrimination was observed with FFFS than with SFS.     

Pedestrian detection using multiple feature channels and contour cues with census transform histogram and random forest classifier

Pattern Analysis and Applications - This paper presents a reliable and real-time method to detect pedestrians in image scenes that can vary greatly in appearance. To achieve greater reliability in...     

Siamese convolutional neural network and fusion of the best overlapping blocks for kinship verification

Multimedia Tools and Applications - Analysis of facial images decoding familial features has been attracting the attention of researchers to develop a computerized system interested in determining...     

A nonparametric Bayesian learning model using accelerated variational inference and feature selection

Pattern Analysis and Applications - Developing effective machine learning methods for multimedia data modeling continues to challenge computer vision scientists. The capability of providing...     

Online variational learning of finite inverted Beta-Liouville mixture model for biomedical analysis

Image segmentation is widely applied for biomedical image analysis. However, segmentation of medical images is challenging due to many image modalities, such as, CT, X-ray, MRI, microscopy among others. An additional challenge to this is the high variability, inconsistent regions with missing edges, absence of texture contrast, and high noise in the background of biomedical images. Thus, many segmentation approaches have been investigated to address these issues and to transform medical images into meaningful information. During the past decade, finite mixture models have been revealed to be one of the most flexible and popular approaches in data clustering. In this article, we propose a statistical framework for online variational learning of finite inverted Beta-Liouville mixture model for clustering medical images. The online variational learning framework is used to estimate the parameters and the number of mixture components simultaneously, thus decreasing the computational complexity of the model. To this end, we evaluated our proposed algorithm on five different biomedical image data sets including optic disc detection and localization in diabetic retinopathy, digital imaging in melanoma lesion detection and segmentation, brain tumor detection, colon cancer detection and computer aid detection (CAD) of Malaria. Furthermore, we compared the proposed algorithm with three other popular algorithms. In our results, we analyze that the proposed online variational learning of finite IBL mixture model algorithm performs accurately on multiple modalities of medical images. It detects the disease patterns with high confidence. Computational and statistical approaches like the one presented in this article hold a significant impact on medical image analysis and interpretation in both clinical applications and scientific research. We believe that the proposed algorithm has the capacity to address multi modal biomedical image data sets and can be further applied by researchers to analyze correct disease patterns.     

A multifractal edge detector

Multimedia Tools and Applications - Significant research has been conducted into image edge detection, usually focusing on gradient and higher order derivative approaches. Recent development in the...     

Image and video denoising by combining unsupervised bounded generalized gaussian mixture modeling and spatial information

Multimedia Tools and Applications - In recent years, a great deal of effort has been expended on developing robust solutions for images quality degradation caused mainly by noise. In this paper, we...