Search results for " image processing."
showing 10 items of 2265 documents
The regression Tsetlin machine: a novel approach to interpretable nonlinear regression
2019
Relying simply on bitwise operators, the recently introduced Tsetlin machine (TM) has provided competitive pattern classification accuracy in several benchmarks, including text understanding. In this paper, we introduce the regression Tsetlin machine (RTM), a new class of TMs designed for continuous input and output, targeting nonlinear regression problems. In all brevity, we convert continuous input into a binary representation based on thresholding, and transform the propositional formula formed by the TM into an aggregated continuous output. Our empirical comparison of the RTM with state-of-the-art regression techniques reveals either superior or on par performance on five datasets. Thi…
On Detection of Network-Based Co-residence Verification Attacks in SDN-Driven Clouds
2017
Modern cloud environments allow users to consume computational and storage resources in the form of virtual machines. Even though machines running on the same cloud server are logically isolated from each other, a malicious customer can create various side channels to obtain sensitive information from co-located machines. In this study, we concentrate on timely detection of intentional co-residence attempts in cloud environments that utilize software-defined networking. SDN enables global visibility of the network state which allows the cloud provider to monitor and extract necessary information from each flow in every virtual network in online mode. We analyze the extracted statistics on d…
Image-Evoked Affect and its Impact on Eeg-Based Biometrics
2019
Electroencephalography (EEG) signals provide a representation of the brain’s activity patterns and have been recently exploited for user identification and authentication due to their uniqueness and their robustness to interception and artificial replication. Nevertheless, such signals are commonly affected by the individual’s emotional state. In this work, we examine the use of images as stimulus for acquiring EEG signals and study whether the use of images that evoke similar emotional responses leads to higher identification accuracy compared to images that evoke different emotional responses. Results show that identification accuracy increases when the system is trained with EEG recordin…
ES1D: A Deep Network for EEG-Based Subject Identification
2017
Security systems are starting to meet new technologies and new machine learning techniques, and a variety of methods to identify individuals from physiological signals have been developed. In this paper, we present ESID, a deep learning approach to identify subjects from electroencephalogram (EEG) signals captured by using a low cost device. The system consists of a Convolutional Neural Network (CNN), which is fed with the power spectral density of different EEG recordings belonging to different individuals. The network is trained for a period of one million iterations, in order to learn features related to local patterns in the spectral domain of the original signal. The performance of the…
Infrared microspectroscopic determination of collagen cross-links in articular cartilage
2017
Collagen forms an organized network in articular cartilage to give tensile stiffness to the tissue. Due to its long half-life, collagen is susceptible to cross-links caused by advanced glycation end-products. The current standard method for determination of cross-link concentrations in tissues is the destructive high-performance liquid chromatography (HPLC). The aim of this study was to analyze the cross-link concentrations nondestructively from standard unstained histological articular cartilage sections by using Fourier transform infrared (FTIR) microspectroscopy. Half of the bovine articular cartilage samples ( n = 27 ) were treated with threose to increase the collagen cross-linking whi…
Quantum clustering in non-spherical data distributions: Finding a suitable number of clusters
2017
Quantum Clustering (QC) provides an alternative approach to clustering algorithms, several of which are based on geometric relationships between data points. Instead, QC makes use of quantum mechanics concepts to find structures (clusters) in data sets by finding the minima of a quantum potential. The starting point of QC is a Parzen estimator with a fixed length scale, which significantly affects the final cluster allocation. This dependence on an adjustable parameter is common to other methods. We propose a framework to find suitable values of the length parameter σ by optimising twin measures of cluster separation and consistency for a given cluster number. This is an extension of the Se…
Revealing community structures by ensemble clustering using group diffusion
2018
We propose an ensemble clustering approach using group diffusion to reveal community structures in data. We represent data points as a directed graph and assume each data point belong to single cluster membership instead of multiple memberships. The method is based on the concept of ensemble group diffusion with a parameter to represent diffusion depth in clustering. The ability to modulate the diffusion-depth parameter by varying it within a certain interval allows for more accurate construction of clusters. Depending on the value of the diffusion-depth parameter, the presented approach can determine very well both local clusters and global structure of data. At the same time, the ability …
Automatic detection of hemangiomas using unsupervised segmentation of regions of interest
2016
In this paper we compare the performances of three automatic methods of identifying hemangioma regions in images: 1) unsupervised segmentation using the Otsu method, 2) Fuzzy C-means clustering (FCM) and 3) an improved region growing algorithm based on FCM (RG-FCM). For each image, the starting point of the algorithms is a rectangular region of interest (ROI) containing the hemangioma. For computing the performances of each method, the ROIs had been manually labeled in 2 classes: pixels of hemangioma and pixels of non-hemangioma. The computed scores are given separately for each image, as well as global performances across all ROIs for both classes. The best classification of non-hemangioma…
Deep learning network for exploiting positional information in nucleosome related sequences
2017
A nucleosome is a DNA-histone complex, wrapping about 150 pairs of double-stranded DNA. The role of nucleosomes is to pack the DNA into the nucleus of the Eukaryote cells to form the Chromatin. Nucleosome positioning genome wide play an important role in the regulation of cell type-specific gene activities. Several biological studies have shown sequence specificity of nucleosome presence, clearly underlined by the organization of precise nucleotides substrings. Taking into consideration such advances, the identification of nucleosomes on a genomic scale has been successfully performed by DNA sequence features representation and classical supervised classification methods such as Support Vec…
Deep Learning Architectures for DNA Sequence Classification
2017
DNA sequence classification is a key task in a generic computational framework for biomedical data analysis, and in recent years several machine learning technique have been adopted to successful accomplish with this task. Anyway, the main difficulty behind the problem remains the feature selection process. Sequences do not have explicit features, and the commonly used representations introduce the main drawback of the high dimensionality. For sure, machine learning method devoted to supervised classification tasks are strongly dependent on the feature extraction step, and in order to build a good representation it is necessary to recognize and measure meaningful details of the items to cla…