Neta Rabin

Neta Rabin
Tel Aviv University | TAU · Department of Industrial Engineering

PhD

About

47
Publications
6,508
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498
Citations

Publications

Publications (47)
Article
Thermal imaging is a non-invasive and portable technique with growing use in medical and authentication applications. This research utilizes thermal images for hand identification. Existing hand identification methods mainly extract geometric features, such as the palm’s and fingers’ absolute sizes and ratios. In this work, subject identification b...
Article
Full-text available
Rapid and sensitive screening tools for SARS-CoV-2 infection are essential to limit the spread of COVID-19 and to properly allocate national resources. Here, we developed a new point-of-care, non-contact thermal imaging tool to detect COVID-19, based on advanced image processing algorithms. We captured thermal images of the backs of individuals wit...
Article
Full-text available
In this work, an advanced machine learning technique named diffusion maps is applied for array-based earthquake-explosion discrimination. We rely on prior work that utilizes the diffusion map-based discrimination approach for data collected from a single seismometer. The discrimination task is an essential component of the Comprehensive Nuclear-Tes...
Article
Full-text available
Automatic speech recognition is the main form of man–machine communication. Recently, several studies have shown the ability to automatically recognize speech based on electromyography (EMG) signals of the facial muscles using machine learning methods. The objective of this study was to utilize machine learning methods for automatic identification...
Article
The distal ischemic steal syndrome (ISS) is a complication following the construction of an arteriovenous (A-V) access for hemodialysis. The ability to non-invasively monitor changes in skin microcirculation improves both the diagnosis and treatment of vascular diseases. In this study, we propose a novel technique for evaluating the palms' blood di...
Conference Paper
Full-text available
A common pre-processing task in machine learning is handling missing data entries, also known as imputation. Standard techniques use mean values, regression or optimization based techniques for predicting the missing data values. In this paper, a kernel based technique is utilized for imputing data in a multi-scale manner. The construction is based...
Preprint
Rapid and sensitive screening tools for SARS-CoV-2 infection are essential to limit the spread of COVID-19 and to properly allocate national resources. Here, we developed a new point-of-care, non-contact thermal imaging tool to detect COVID-19, based on image-processing algorithms and machine learning analysis. We captured thermal images of the bac...
Article
Full-text available
Objective: The goal of this study was to characterize the changes in the palm’s blood distribution in response to a decrease in blood pressure due to gravity-induced changes, using thermal imaging. Methods: Thermal hands images were taken from ten healthy volunteers, without any known vascular pathologies, in three different stages: baseline, gravi...
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Non-alcoholic fatty liver disease (NAFLD) comprises a spectrum of progressive liver pathologies, ranging from simple steatosis to non-alcoholic steatohepatitis (NASH), fibrosis and cirrhosis. A liver biopsy is currently required to stratify high-risk patients, and predicting the degree of liver inflammation and fibrosis using non-invasive tests rem...
Article
Kernel-based techniques have become a common way for describing the local and global relationships of data samples that are generated in real-world processes. In this research, we focus on a multi-scale kernel based technique named Auto-adaptive Laplacian Pyramids (ALP). This method can be useful for function approximation and interpolation. ALP is...
Article
Background and objectives: Photobiomodulation (PBM), a non-ionizing, non-thermal irradiation, used clinically to accelerate wound healing and inhibit pain, was previously shown to increase blood flow. However, some individuals respond to PBM, but others do not. The purpose of this study was to investigate factors affecting this patient-specific re...
Article
Surface electromyography (EMG) is non-invasive signal acquisition technique that plays a central role in many application, including clinical diagnostics, control for prosthetic devices and for human-machine interactions. The processing typically begins with a feature extraction step, which may be followed by the application of a dimensionality red...
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Background and Objective: Infrared Thermal Imaging (ITI) is a noninvasive method to measure skin temperature (ST). The latter is determined by the microcirculatory blood flow and ambient temperature. Photobiostimulation has been shown to increase blood flow. The objective was to characterize the spatial and temporal changes in ST, in response to ph...
Article
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In this study, we suggest combining the monitoring of actual examination time used with grades in order to assess examination time extensions in terms of access provision and expected outcome. Using naturally-occurring data collected from a large sample (N = 2315) of undergraduate engineering students, we argue that extended examination time may be...
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Detection and discrimination of seismic events have important implications. Precise detection of earthquakes may help prevent collateral damage and even save lives. On the other hand, the ability to identify explosions reliably not only helps prevent false alarms but also is crucial for monitoring nuclear experiments. In this work, we present a met...
Article
Full-text available
Thermal infrared imaging has been suggested as a non-invasive alternative to monitor physiological processes and disease. However, the use of this technique to image internal organs, such as the heart, has not yet been investigated. We sought to determine the ability of our novel thermal image-processing algorithm to detect structural and functiona...
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The purpose of this study is to evaluate how learning disabilities (LDs), in combination with accommodations, affect the performance of a decision-tree to predict the stability of academic behaviour of undergraduate engineering students. Additionally, this study presents several examples to illustrate how a college could use the resultant model to...
Article
Full-text available
Modeling and analyzing high-dimensional data has become a common task in various fields and applications. Often, it is of interest to learn a function that is defined on the data and then to extend its values to newly arrived data points. The Laplacian pyramids approach invokes kernels of decreasing widths to learns a given dataset and a function d...
Article
Modeling and analysis of students’ performance is a common task that is aimed at identifying important factors that affect the learning process. Typically, the analysis uses one-dimensional input parameters. However, with the advancement of data collections tools, many of the gathered educational datasets have become high-dimensional. Hence, the us...
Article
The aim of this article is to automatically identify repeating seismic events such as an aftershock sequence by utilizing a machine learning technique named diffusion maps. In previous work, the diffusion maps approach was applied for earthquakeexplosion discrimination and for characterizing explosions by their origin quarries. Diffusion maps, whic...
Article
Background: The peripheral microcirculation supplies fresh blood to the small blood vessels, providing oxygen and nutrients to the tissues, removing waste, and maintaining normal homeostatic conditions. The goal of this study was to characterize the response of the peripheral microcirculation, in terms of blood flow and tissue oxygenation variable...
Article
Full-text available
The problem of learning from seismic recordings has been studied for years. There is a growing interest in developing automatic mechanisms for identifying the properties of a seismic event. One main motivation is the ability have a reliable identification of man-made explosions. The availability of multiple high-dimensional observations has increas...
Article
The aim of this paper is to investigate the impact of strategic planning on service small and medium size enterprises' (SMEs) performance. A machine learning methodology, based on an alternating diffusion process, is applied for organizing the SMEs into a network/graph, and generating management profiles. The method relies on ideas from non-linear...
Conference Paper
A challenging problem in machine learning is handling missing data, also known as imputation. Simple imputation techniques complete the missing data by the mean or the median values. A more sophisticated approach is to use regression to predict the missing data from the complete input columns. In case the dimension of the input data is high, dimens...
Article
Nonlinear dimensionality reduction methods often include the construction of kernels for embedding the high-dimensional data points. Standard methods for extending the embedding coordinates (such as the Nyström method) also rely on spectral decomposition of kernels. It is desirable that these kernels capture most of the data sets’ information using...
Conference Paper
Full-text available
Automatic detection and identification of seismic events is an important task that is carried out constantly for seismic monitoring. This monitoring process results in a seismic event bulletin that contains information about the detected events, their locations and, magnitudes and type (natural or man made event). Current automatic seismic bulletin...
Article
Full-text available
Discrimination between earthquakes and explosions is an essential component of nuclear test monitoring and it is also important for maintaining the quality of earthquake catalogs. Currently used discrimination methods provide a partial solution to the problem. In this work, we apply advanced machine learning methods and in particular diffusion maps...
Conference Paper
Full-text available
We present a method for clustering short push-to-talk speech segments in the presence of different numbers of speakers. Iterative Mean Shift algorithm based on the cosine distance is used to perform speaker clustering on i-vectors generated from many short speech segments. We report results as measured by the Accuracy, the average number of detecte...
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Non-linear dimensionality reduction techniques such as manifold learning algorithms have become a common way for processing and analyzing high-dimensional patterns that often have a target value attached. Their application to new points consists in two steps: first, embedding the new data point into the low dimensional space and then, estimating th...
Article
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Finding informative low-dimensional descriptions of high-dimensional simulation data (like the ones arising in molecular dynamics or kinetic Monte Carlo simulations of physical and chemical processes) is crucial to understanding physical phenomena, and can also dramatically assist in accelerating the simulations themselves. In this paper, we discus...
Conference Paper
Chemical and molecular systems are inherently high-dimensional: reactors can contain tens or hundreds of chemical species participating in a reaction network, while macromolecules can contain hundreds or thousands of atoms. The dynamics of such systems can often be well described in fewer dimensions, and obtaining accurate reduced models can signif...
Article
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The adoption of detailed mechanisms for chemical kinetics often poses two types of severe challenges: First, the number of degrees of freedom is large; and second, the dynamics is characterized by widely disparate time scales. As a result, reactive flow solvers with detailed chemistry often become intractable even for large clusters of CPUs, especi...
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Researchers generating new genome-wide data in an exploratory sequencing study can gain biological insights by comparing their data with well-annotated data sets possessing similar genomic patterns. Data compression techniques are needed for efficient comparisons of a new genomic experiment with large repositories of publicly available profiles. Fu...
Conference Paper
Full-text available
The diffusion maps together with the geometric harmonics provide a method for describing the geometry of high di-mensional data and for extending these descriptions to new data points and to functions, which are defined on the data. This method suffers from two limitations. First, even though real-life data is often heterogeneous , the assumption i...
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Automatic acoustic-based vehicle detection is a common task in security and surveillance systems. Usually, a recording device is placed in a designated area and a hardware/software system processes the sounds that are intercepted by this recording device to identify vehicles only as they pass by. An algorithm, which is suitable for online automatic...
Conference Paper
We propose a learning framework, which is based on diffusion methodology, that performs data fusion and anomaly detection in multi-dimensional time series data. Real life applications and processes usually contain a large number of sensors that generate parameters (features), where each sensor collects partial information about the running process....
Article
We introduce a novel real-time algorithm for automatic acoustic-based vehicle detection. Commonly, surveillance systems for this task use a microphone that is placed in a target area. The recorded sounds are processed in order to detect vehicles as they pass by.The proposed algorithm uses the wavelet-packet transform in order to extract spatio-temp...
Conference Paper
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This paper shows how to compress (encode) losslessly, search and decompress (decode) textual data in a machine/device that has a limited memory (several kilobytes).
Article
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We propose a robust algorithm to detect the arrival of a vehicle of arbitrary type when other noises are present. It is done via analysis of its acoustic signature against an existing database of recorded and processed acoustic signals to detect the arrival of a vehicle of arbitrary type when other noises are present. To achieve it with minimum num...
Article
We present a method to enhance, by postprocessing, the performance of gradient-based edge detectors. It improves the performance of the edge detector by adding terms which are similar to the artificial dissipation that appear in the numerical solution of hyperbolic PDEs. This term is added to the output of the edge detector. The edges that are miss...
Article
Full-text available
In many fields including economics, collection of time series such as stocks or energy prices are governed by a similar non-linear dynamical process. These time series are often measured hourly, thus, each day can be viewed as a high-dimensional data point. In this paper, we apply a spectral method, which based on anisotropic diffusion kernels to t...

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