Non-invasive estimation of chlorophyll content in plants plays an important role in precision agriculture. This task may be tackled using hyperspectral imaging that acquires numerous narrow bands of the electromagneti...
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We formally introduce integrity constraints for probabilistic spatio-temporal knowledgebases. We start by defining the syntax and semantics of PST knowledgebases. This definition generalizes the SPOT framework which i...
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Vision-based human action recognition (HAR) is a hot topic of research from the decade due to a few popular applications such as visual surveillance and robotics. For correct action recognition, various local and glob...
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Vision-based human action recognition (HAR) is a hot topic of research from the decade due to a few popular applications such as visual surveillance and robotics. For correct action recognition, various local and global points are requires known as features. These features modified during the variation in human movement. But due to a bit change in several human actions, the features of these actions are mixed that degrade the recognition performance. In this article, we design a new 26-layered Convolutional Neural Network (CNN) architecture for accurate complex action recognition. The features are extracted from the global average pooling layer and fully connected (FC) layer, and fused by a proposed high entropy-based approach. Further, we propose a feature selection method name Poisson distribution along with Univariate Measures (PDaUM). Few of fused CNN features are irrelevant, and few of them are redundant that makes the incorrect prediction among complex human actions. Therefore, the proposed PDaUM based approach selects only the strongest features that later passed to the Extreme Learning Machine (ELM) and Softmax for final recognition. Four datasets are using for experimental analysis - HMDB51 (51 classes), UCF Sports (10 classes), KTH (6 classes), and Weizmann (10 classes). On these datasets, the ELM classifier gives an improved performance as compared to a Softmax classifier. The achieved accuracy on each dataset is 81.4%, 99.2%, 98.3%, and 98.7%, respectively. Comparison with existing techniques, it is shown that the proposed architecture gives better performance in terms of accuracy and testing time.
With the new technology of 3D light field (LF) imaging, fundus photography can be expanded to provide depth information. This increases the diagnostic possibilities and additionally improves image quality by digitally...
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Indonesia is one of the countries in South East Asia has significant forest fire with dangerous impact to neighboring countries of the emission of haze and carbon. In this research aims to do plotting and mapping loca...
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Protein-protein interaction networks provide important information about functions of proteins. There are various studies which analyze interaction networks and predict functions of novel proteins based on their netwo...
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Protein-protein interaction networks provide important information about functions of proteins. There are various studies which analyze interaction networks and predict functions of novel proteins based on their network connectivity. However, all of these methods are sequential methods that do not utilize high performance computing. Functional flow is one of these methods that uses network connectivity, distance effect, and topology of the network with local and global views to predict protein function. With these advantages, the functional flow algorithm produces more accurate results compared to other techniques. However, due to lack of a parallelized version of the algorithm, the method cannot be practically applied on large scale networks of complex species. In this paper, we provide a parallel implementation of functional flow. We use Hadoop which is one of the open source map/reduce environments. For our experiments, we installed Hadoop on 18 hosts with eight cores each. The first map/reduce job distributes the protein interaction network as a format which allows parallel distributed computing on all the worker nodes. The other map/reduce jobs generate flows for each known protein function and the function of novel proteins are predicted by accumulating all of these generated flows. Our experiments show that the method can be distributed on worker nodes efficiently and the application can provide better performance as the number of resources increases.
This volume collects and presents the fundamentals, tools, and processes of utilizing geospatial information technologies to process remotely sensed data for use in agricultural monitoring and management. The issues r...
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ISBN:
(数字)9783030663872
ISBN:
(纸本)9783030663865;9783030663896
This volume collects and presents the fundamentals, tools, and processes of utilizing geospatial information technologies to process remotely sensed data for use in agricultural monitoring and management. The issues related to handling digital agro-geoinformation, such as collecting (including field visits and remote sensing), processing, storing, archiving, preservation, retrieving, transmitting, accessing, visualization, analyzing, synthesizing, presenting, and disseminating agro-geoinformation have never before been systematically documented in one volume. The book is edited by International Conference on Agro-Geoinformatics organizers Dr. Liping Di (George Mason University), who coined the term “Agro-Geoinformatics” in 2012, and Dr. Berk Üstündağ (Istanbul Technical University) and are uniquely positioned to curate and edit this foundational text.;The book is composed of eighteen chapters that can each stand alone but also build on each other to give the reader a comprehensive understanding of agro-geoinformatics and what the tools and processes that compose the field can accomplish. Topics covered include land parcel identification, image processing in agricultural observation systems, databasing and managing agricultural data, crop status monitoring, moisture and evapotranspiration assessment, flood damage monitoring, agricultural decision support systems and more.
The growth of e-commerce has altered how consumers shop, providing a digital space where convenience, vast product offerings, and competitive pricing converge. In today's world, e-commerce websites are transitioni...
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Background: Ensemble selection is one of the most researched topics for ensemble learning. Researchers have been attracted to selecting a subset of base classifiers that may perform more helpful than the whole ensembl...
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