81 research outputs found
A Study on Super-Resolution Image Reconstruction Techniques
With the rapid development of space technology and its related technologies, more and more remote sensing platforms are sent to outer space to survey our earth. Recognizing and positioning all these space objects is the basis of knowing about the space, but there are no other effective methods in space target recognition except orbit and radio signal recognition. Super-resolution image reconstruction, which is based on the image of space objects, provides an effective way of solving this problem. In this paper, the principle of super-resolution image reconstruction and several typical reconstruction methods were introduced. By comparison, Nonparametric Finite Support Restoration Techniques were analyzed in details. At last, several aspects of super-resolution image reconstruction that should be studied further more were put forward
Managing Service-Heterogeneity using Osmotic Computing
Computational resource provisioning that is closer to a user is becoming
increasingly important, with a rise in the number of devices making continuous
service requests and with the significant recent take up of latency-sensitive
applications, such as streaming and real-time data processing. Fog computing
provides a solution to such types of applications by bridging the gap between
the user and public/private cloud infrastructure via the inclusion of a "fog"
layer. Such approach is capable of reducing the overall processing latency, but
the issues of redundancy, cost-effectiveness in utilizing such computing
infrastructure and handling services on the basis of a difference in their
characteristics remain. This difference in characteristics of services because
of variations in the requirement of computational resources and processes is
termed as service heterogeneity. A potential solution to these issues is the
use of Osmotic Computing -- a recently introduced paradigm that allows division
of services on the basis of their resource usage, based on parameters such as
energy, load, processing time on a data center vs. a network edge resource.
Service provisioning can then be divided across different layers of a
computational infrastructure, from edge devices, in-transit nodes, and a data
center, and supported through an Osmotic software layer. In this paper, a
fitness-based Osmosis algorithm is proposed to provide support for osmotic
computing by making more effective use of existing Fog server resources. The
proposed approach is capable of efficiently distributing and allocating
services by following the principle of osmosis. The results are presented using
numerical simulations demonstrating gains in terms of lower allocation time and
a higher probability of services being handled with high resource utilization.Comment: 7 pages, 4 Figures, International Conference on Communication,
Management and Information Technology (ICCMIT 2017), At Warsaw, Poland, 3-5
April 2017, http://www.iccmit.net/ (Best Paper Award
Sensor-AssistedWeighted Average Ensemble Model for Detecting Major Depressive Disorder
The present methods of diagnosing depression are entirely dependent on self-report
ratings or clinical interviews. Those traditional methods are subjective, where the individual may
or may not be answering genuinely to questions. In this paper, the data has been collected using
self-report ratings and also using electronic smartwatches. This study aims to develop a weighted
average ensemble machine learning model to predict major depressive disorder (MDD) with superior
accuracy. The data has been pre-processed and the essential features have been selected using a
correlation-based feature selection method. With the selected features, machine learning approaches
such as Logistic Regression, Random Forest, and the proposedWeighted Average Ensemble Model are
applied. Further, for assessing the performance of the proposed model, the Area under the Receiver
Optimization Characteristic Curves has been used. The results demonstrate that the proposed
Weighted Average Ensemble model performs with better accuracy than the Logistic Regression and
the Random Forest approaches
Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues
The essential use of natural language processing is to analyze the sentiment of the author
via the context. This sentiment analysis (SA) is said to determine the exactness of the underlying
emotion in the context. It has been used in several subject areas such as stock market prediction, social
media data on product reviews, psychology, judiciary, forecasting, disease prediction, agriculture, etc.
Many researchers have worked on these areas and have produced significant results. These outcomes
are beneficial in their respective fields, as they help to understand the overall summary in a short
time. Furthermore, SA helps in understanding actual feedback shared across di erent platforms such
as Amazon, TripAdvisor, etc. The main objective of this thorough survey was to analyze some of
the essential studies done so far and to provide an overview of SA models in the area of emotion
AI-driven SA. In addition, this paper o ers a review of ontology-based SA and lexicon-based SA
along with machine learning models that are used to analyze the sentiment of the given context.
Furthermore, this work also discusses di erent neural network-based approaches for analyzing
sentiment. Finally, these di erent approaches were also analyzed with sample data collected from
Twitter. Among the four approaches considered in each domain, the aspect-based ontology method
produced 83% accuracy among the ontology-based SAs, the term frequency approach produced 85%
accuracy in the lexicon-based analysis, and the support vector machine-based approach achieved
90% accuracy among the other machine learning-based approaches.Ministerio de Educación (MOE) en Taiwán N/
Deep Learning Assisted Neonatal Cry Classification via Support Vector Machine Models
Neonatal infants communicate with us through cries. The infant cry signals have distinct patterns depending on the purpose of the cries. Preprocessing, feature extraction, and feature selection need expert attention and take much effort in audio signals in recent days. In deep learning techniques, it automatically extracts and selects the most important features. For this, it requires an enormous amount of data for effective classification. This work mainly discriminates the neonatal cries into pain, hunger, and sleepiness. The neonatal cry auditory signals are transformed into a spectrogram image by utilizing the short-time Fourier transform (STFT) technique. The deep convolutional neural network (DCNN) technique takes the spectrogram images for input. The features are obtained from the convolutional neural network and are passed to the support vector machine (SVM) classifier. Machine learning technique classifies neonatal cries. This work combines the advantages of machine learning and deep learning techniques to get the best results even with a moderate number of data samples. The experimental result shows that CNN-based feature extraction and SVM classifier provides promising results. While comparing the SVM-based kernel techniques, namely radial basis function (RBF), linear and polynomial, it is found that SVM-RBF provides the highest accuracy of kernel-based infant cry classification system provides 88.89% accuracy
An Efficient Hybrid Fuzzy-Clustering Driven 3D-Modeling of Magnetic Resonance Imagery for Enhanced Brain Tumor Diagnosis
Brain tumor detection and its analysis are essential in medical diagnosis. The proposed
work focuses on segmenting abnormality of axial brain MR DICOM slices, as this format holds
the advantage of conserving extensive metadata. The axial slices presume the left and right part
of the brain is symmetric by a Line of Symmetry (LOS). A semi-automated system is designed to
mine normal and abnormal structures from each brain MR slice in a DICOM study. In this work,
Fuzzy clustering (FC) is applied to the DICOM slices to extract various clusters for di erent k. Then,
the best-segmented image that has high inter-class rigidity is obtained using the silhouette fitness
function. The clustered boundaries of the tissue classes further enhanced by morphological operations.
The FC technique is hybridized with the standard image post-processing techniques such as marker
controlled watershed segmentation (MCW), region growing (RG), and distance regularized level sets
(DRLS). This procedure is implemented on renowned BRATS challenge dataset of di erent modalities
and a clinical dataset containing axial T2 weighted MR images of a patient. The sequential analysis of
the slices is performed using the metadata information present in the DICOM header. The validation
of the segmentation procedures against the ground truth images authorizes that the segmented objects
of DRLS through FC enhanced brain images attain maximum scores of Jaccard and Dice similarity
coe cients. The average Jaccard and dice scores for segmenting tumor part for ten patient studies of
the BRATS dataset are 0.79 and 0.88, also for the clinical study 0.78 and 0.86, respectively. Finally, 3D
visualization and tumor volume estimation are done using accessible DICOM information.Ministerio de Desarrollo de Recursos Humanos, India SPARC/2018-2019/P145/SLUniversidad Politécnica de Tomsk, Rusia RRSG/19/500
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A Defined Antigen Skin Test That Enables Implementation of BCG Vaccination for Control of Bovine Tuberculosis: Proof of Concept.
A Defined Antigen Skin Test That Enables Implementation of BCG Vaccination for Control of Bovine Tuberculosis:Proof of Concept
In most low- and middle-income countries (LMICs), bovine tuberculosis (bTB) remains endemic due to the absence of control programs. This is because successful bTB control and eradication programs have relied on test-and-slaughter strategies that are socioeconomically unfeasible in LMICs. While Bacillus Calmette–Guérin (BCG) vaccine-induced protection for cattle has long been documented in experimental and field trials, its use in control programs has been precluded by the inability to differentiate BCG-vaccinated from naturally infected animals using the OIE-prescribed purified protein derivative (PPD)-based tuberculin skin tests. In the current study, the diagnostic specificity and capability for differentiating infected from vaccinated animals (DIVA) of a novel defined antigen skin test (DST) in BCG-vaccinated (Bos taurus ssp. taurus x B. t. ssp. indicus) calves were compared with the performance of traditional PPD-tuberculin in both the skin test and in vitro interferon-gamma release assay (IGRA). The IFN-γ production from whole blood cells stimulated with both PPDs increased significantly from the 0 week baseline levels, while DST induced no measurable IFN-γ production in BCG-vaccinated calves. None of the 15 BCG-vaccinated calves were reactive with the DST skin test (100% specificity; one-tailed lower 95% CI: 82). In contrast, 10 of 15 BCG-vaccinated calves were classified as reactors with the PPD-based single intradermal test (SIT) (specificity in vaccinated animals = 33%; 95% CI: 12, 62). Taken together, the results provide strong evidence that the DST is highly specific and enables DIVA capability in both skin and IGRA assay format, thereby enabling the implementation of BCG vaccine-based bTB control, particularly in settings where test and slaughter remain unfeasible
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