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network</title> <meta name="description" content="Search results for: Siamese network"> <meta name="keywords" content="Siamese network"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Siamese network"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 4735</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Siamese network</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4735</span> Person Re-Identification using Siamese Convolutional Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sello%20Mokwena">Sello Mokwena</a>, <a href="https://publications.waset.org/abstracts/search?q=Monyepao%20Thabang"> Monyepao Thabang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, we propose a comprehensive approach to address the challenges in person re-identification models. By combining a centroid tracking algorithm with a Siamese convolutional neural network model, our method excels in detecting, tracking, and capturing robust person features across non-overlapping camera views. The algorithm efficiently identifies individuals in the camera network, while the neural network extracts fine-grained global features for precise cross-image comparisons. The approach's effectiveness is further accentuated by leveraging the camera network topology for guidance. Our empirical analysis on benchmark datasets highlights its competitive performance, particularly evident when background subtraction techniques are selectively applied, underscoring its potential in advancing person re-identification techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=camera%20network" title="camera network">camera network</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network%20topology" title=" convolutional neural network topology"> convolutional neural network topology</a>, <a href="https://publications.waset.org/abstracts/search?q=person%20tracking" title=" person tracking"> person tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=person%20re-identification" title=" person re-identification"> person re-identification</a>, <a href="https://publications.waset.org/abstracts/search?q=siamese" title=" siamese"> siamese</a> </p> <a href="https://publications.waset.org/abstracts/171989/person-re-identification-using-siamese-convolutional-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171989.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">72</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4734</span> Recognition of Gene Names from Gene Pathway Figures Using Siamese Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Azam">Muhammad Azam</a>, <a href="https://publications.waset.org/abstracts/search?q=Micheal%20Olaolu%20Arowolo"> Micheal Olaolu Arowolo</a>, <a href="https://publications.waset.org/abstracts/search?q=Fei%20He"> Fei He</a>, <a href="https://publications.waset.org/abstracts/search?q=Mihail%20Popescu"> Mihail Popescu</a>, <a href="https://publications.waset.org/abstracts/search?q=Dong%20Xu"> Dong Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The number of biological papers is growing quickly, which means that the number of biological pathway figures in those papers is also increasing quickly. Each pathway figure shows extensive biological information, like the names of genes and how the genes are related. However, manually annotating pathway figures takes a lot of time and work. Even though using advanced image understanding models could speed up the process of curation, these models still need to be made more accurate. To improve gene name recognition from pathway figures, we applied a Siamese network to map image segments to a library of pictures containing known genes in a similar way to person recognition from photos in many photo applications. We used a triple loss function and a triplet spatial pyramid pooling network by combining the triplet convolution neural network and the spatial pyramid pooling (TSPP-Net). We compared VGG19 and VGG16 as the Siamese network model. VGG16 achieved better performance with an accuracy of 93%, which is much higher than OCR results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biological%20pathway" title="biological pathway">biological pathway</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20understanding" title=" image understanding"> image understanding</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20name%20recognition" title=" gene name recognition"> gene name recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Siamese%20network" title=" Siamese network"> Siamese network</a>, <a href="https://publications.waset.org/abstracts/search?q=VGG" title=" VGG"> VGG</a> </p> <a href="https://publications.waset.org/abstracts/160723/recognition-of-gene-names-from-gene-pathway-figures-using-siamese-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160723.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">291</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4733</span> Online Pose Estimation and Tracking Approach with Siamese Region Proposal Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cheng%20Fang">Cheng Fang</a>, <a href="https://publications.waset.org/abstracts/search?q=Lingwei%20Quan"> Lingwei Quan</a>, <a href="https://publications.waset.org/abstracts/search?q=Cunyue%20Lu"> Cunyue Lu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Human pose estimation and tracking are to accurately identify and locate the positions of human joints in the video. It is a computer vision task which is of great significance for human motion recognition, behavior understanding and scene analysis. There has been remarkable progress on human pose estimation in recent years. However, more researches are needed for human pose tracking especially for online tracking. In this paper, a framework, called PoseSRPN, is proposed for online single-person pose estimation and tracking. We use Siamese network attaching a pose estimation branch to incorporate Single-person Pose Tracking (SPT) and Visual Object Tracking (VOT) into one framework. The pose estimation branch has a simple network structure that replaces the complex upsampling and convolution network structure with deconvolution. By augmenting the loss of fully convolutional Siamese network with the pose estimation task, pose estimation and tracking can be trained in one stage. Once trained, PoseSRPN only relies on a single bounding box initialization and producing human joints location. The experimental results show that while maintaining the good accuracy of pose estimation on COCO and PoseTrack datasets, the proposed method achieves a speed of 59 frame/s, which is superior to other pose tracking frameworks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=pose%20estimation" title=" pose estimation"> pose estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=pose%20tracking" title=" pose tracking"> pose tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=Siamese%20network" title=" Siamese network"> Siamese network</a> </p> <a href="https://publications.waset.org/abstracts/112839/online-pose-estimation-and-tracking-approach-with-siamese-region-proposal-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112839.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">153</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4732</span> Multi-Objective Optimal Threshold Selection for Similarity Functions in Siamese Networks for Semantic Textual Similarity Tasks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kriuk%20Boris">Kriuk Boris</a>, <a href="https://publications.waset.org/abstracts/search?q=Kriuk%20Fedor"> Kriuk Fedor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a comparative study of fundamental similarity functions for Siamese networks in semantic textual similarity (STS) tasks. We evaluate various similarity functions using the STS Benchmark dataset, analyzing their performance and stability. Additionally, we introduce a multi-objective approach for optimal threshold selection. Our findings provide insights into the effectiveness of different similarity functions and offer a straightforward method for threshold selection optimization, contributing to the advancement of Siamese network architectures in STS applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=siamese%20networks" title="siamese networks">siamese networks</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20textual%20similarity" title=" semantic textual similarity"> semantic textual similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20functions" title=" similarity functions"> similarity functions</a>, <a href="https://publications.waset.org/abstracts/search?q=STS%20benchmark%20dataset" title=" STS benchmark dataset"> STS benchmark dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=threshold%20selection" title=" threshold selection"> threshold selection</a> </p> <a href="https://publications.waset.org/abstracts/187407/multi-objective-optimal-threshold-selection-for-similarity-functions-in-siamese-networks-for-semantic-textual-similarity-tasks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/187407.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">38</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4731</span> Thick Data Analytics for Learning Cataract Severity: A Triplet Loss Siamese Neural Network Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jinan%20Fiaidhi">Jinan Fiaidhi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sabah%20Mohammed"> Sabah Mohammed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Diagnosing cataract severity is an important factor in deciding to undertake surgery. It is usually conducted by an ophthalmologist or through taking a variety of fundus photography that needs to be examined by the ophthalmologist. This paper carries out an investigation using a Siamese neural net that can be trained with small anchor samples to score cataract severity. The model used in this paper is based on a triplet loss function that takes the ophthalmologist best experience in rating positive and negative anchors to a specific cataract scaling system. This approach that takes the heuristics of the ophthalmologist is generally called the thick data approach, which is a kind of machine learning approach that learn from a few shots. Clinical Relevance: The lens of the eye is mostly made up of water and proteins. A cataract occurs when these proteins at the eye lens start to clump together and block lights causing impair vision. This research aims at employing thick data machine learning techniques to rate the severity of the cataract using Siamese neural network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=thick%20data%20analytics" title="thick data analytics">thick data analytics</a>, <a href="https://publications.waset.org/abstracts/search?q=siamese%20neural%20network" title=" siamese neural network"> siamese neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=triplet-loss%20model" title=" triplet-loss model"> triplet-loss model</a>, <a href="https://publications.waset.org/abstracts/search?q=few%20shot%20learning" title=" few shot learning"> few shot learning</a> </p> <a href="https://publications.waset.org/abstracts/159632/thick-data-analytics-for-learning-cataract-severity-a-triplet-loss-siamese-neural-network-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159632.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">111</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4730</span> Gene Names Identity Recognition Using Siamese Network for Biomedical Publications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Micheal%20Olaolu%20Arowolo">Micheal Olaolu Arowolo</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Azam"> Muhammad Azam</a>, <a href="https://publications.waset.org/abstracts/search?q=Fei%20He"> Fei He</a>, <a href="https://publications.waset.org/abstracts/search?q=Mihail%20Popescu"> Mihail Popescu</a>, <a href="https://publications.waset.org/abstracts/search?q=Dong%20Xu"> Dong Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As the quantity of biological articles rises, so does the number of biological route figures. Each route figure shows gene names and relationships. Annotating pathway diagrams manually is time-consuming. Advanced image understanding models could speed up curation, but they must be more precise. There is rich information in biological pathway figures. The first step to performing image understanding of these figures is to recognize gene names automatically. Classical optical character recognition methods have been employed for gene name recognition, but they are not optimized for literature mining data. This study devised a method to recognize an image bounding box of gene name as a photo using deep Siamese neural network models to outperform the existing methods using ResNet, DenseNet and Inception architectures, the results obtained about 84% accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biological%20pathway" title="biological pathway">biological pathway</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20identification" title=" gene identification"> gene identification</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Siamese%20network" title=" Siamese network"> Siamese network</a> </p> <a href="https://publications.waset.org/abstracts/160725/gene-names-identity-recognition-using-siamese-network-for-biomedical-publications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160725.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">292</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4729</span> Scaling Siamese Neural Network for Cross-Domain Few Shot Learning in Medical Imaging</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jinan%20Fiaidhi">Jinan Fiaidhi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sabah%20Mohammed"> Sabah Mohammed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cross-domain learning in the medical field is a research challenge as many conditions, like in oncology imaging, use different imaging modalities. Moreover, in most of the medical learning applications, the sample training size is relatively small. Although few-shot learning (FSL) through the use of a Siamese neural network was able to be trained on a small sample with remarkable accuracy, FSL fails to be effective for use in multiple domains as their convolution weights are set for task-specific applications. In this paper, we are addressing this problem by enabling FSL to possess the ability to shift across domains by designing a two-layer FSL network that can learn individually from each domain and produce a shared features map with extra modulation to be used at the second layer that can recognize important targets from mix domains. Our initial experimentations based on mixed medical datasets like the Medical-MNIST reveal promising results. We aim to continue this research to perform full-scale analytics for testing our cross-domain FSL learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Siamese%20neural%20network" title="Siamese neural network">Siamese neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=few-shot%20learning" title=" few-shot learning"> few-shot learning</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-learning" title=" meta-learning"> meta-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=metric-based%20learning" title=" metric-based learning"> metric-based learning</a>, <a href="https://publications.waset.org/abstracts/search?q=thick%20data%20transformation%20and%20analytics" title=" thick data transformation and analytics"> thick data transformation and analytics</a> </p> <a href="https://publications.waset.org/abstracts/185914/scaling-siamese-neural-network-for-cross-domain-few-shot-learning-in-medical-imaging" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185914.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">56</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4728</span> Programmed Speech to Text Summarization Using Graph-Based Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamsini%20Pulugurtha">Hamsini Pulugurtha</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20V.%20S.%20L.%20Jagadamba"> P. V. S. L. Jagadamba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Programmed Speech to Text and Text Summarization Using Graph-based Algorithms can be utilized in gatherings to get the short depiction of the gathering for future reference. This gives signature check utilizing Siamese neural organization to confirm the personality of the client and convert the client gave sound record which is in English into English text utilizing the discourse acknowledgment bundle given in python. At times just the outline of the gathering is required, the answer for this text rundown. Thus, the record is then summed up utilizing the regular language preparing approaches, for example, solo extractive text outline calculations <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Siamese%20neural%20network" title="Siamese neural network">Siamese neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=English%20speech" title=" English speech"> English speech</a>, <a href="https://publications.waset.org/abstracts/search?q=English%20text" title=" English text"> English text</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title=" natural language processing"> natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20extractive%20text%20summarization" title=" unsupervised extractive text summarization"> unsupervised extractive text summarization</a> </p> <a href="https://publications.waset.org/abstracts/143079/programmed-speech-to-text-summarization-using-graph-based-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143079.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">218</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4727</span> Mammographic Multi-View Cancer Identification Using Siamese Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alisher%20Ibragimov">Alisher Ibragimov</a>, <a href="https://publications.waset.org/abstracts/search?q=Sofya%20Senotrusova"> Sofya Senotrusova</a>, <a href="https://publications.waset.org/abstracts/search?q=Aleksandra%20Beliaeva"> Aleksandra Beliaeva</a>, <a href="https://publications.waset.org/abstracts/search?q=Egor%20Ushakov"> Egor Ushakov</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuri%20Markin"> Yuri Markin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mammography plays a critical role in screening for breast cancer in women, and artificial intelligence has enabled the automatic detection of diseases in medical images. Many of the current techniques used for mammogram analysis focus on a single view (mediolateral or craniocaudal view), while in clinical practice, radiologists consider multiple views of mammograms from both breasts to make a correct decision. Consequently, computer-aided diagnosis (CAD) systems could benefit from incorporating information gathered from multiple views. In this study, the introduce a method based on a Siamese neural network (SNN) model that simultaneously analyzes mammographic images from tri-view: bilateral and ipsilateral. In this way, when a decision is made on a single image of one breast, attention is also paid to two other images – a view of the same breast in a different projection and an image of the other breast as well. Consequently, the algorithm closely mimics the radiologist's practice of paying attention to the entire examination of a patient rather than to a single image. Additionally, to the best of our knowledge, this research represents the first experiments conducted using the recently released Vietnamese dataset of digital mammography (VinDr-Mammo). On an independent test set of images from this dataset, the best model achieved an AUC of 0.87 per image. Therefore, this suggests that there is a valuable automated second opinion in the interpretation of mammograms and breast cancer diagnosis, which in the future may help to alleviate the burden on radiologists and serve as an additional layer of verification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=computer-aided%20diagnosis" title=" computer-aided diagnosis"> computer-aided diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-view%20mammogram" title=" multi-view mammogram"> multi-view mammogram</a>, <a href="https://publications.waset.org/abstracts/search?q=siamese%20neural%20network" title=" siamese neural network"> siamese neural network</a> </p> <a href="https://publications.waset.org/abstracts/173794/mammographic-multi-view-cancer-identification-using-siamese-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173794.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">138</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4726</span> Adaptive Few-Shot Deep Metric Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wentian%20Shi">Wentian Shi</a>, <a href="https://publications.waset.org/abstracts/search?q=Daming%20Shi"> Daming Shi</a>, <a href="https://publications.waset.org/abstracts/search?q=Maysam%20Orouskhani"> Maysam Orouskhani</a>, <a href="https://publications.waset.org/abstracts/search?q=Feng%20Tian"> Feng Tian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Whereas currently the most prevalent deep learning methods require a large amount of data for training, few-shot learning tries to learn a model from limited data without extensive retraining. In this paper, we present a loss function based on triplet loss for solving few-shot problem using metric based learning. Instead of setting the margin distance in triplet loss as a constant number empirically, we propose an adaptive margin distance strategy to obtain the appropriate margin distance automatically. We implement the strategy in the deep siamese network for deep metric embedding, by utilizing an optimization approach by penalizing the worst case and rewarding the best. Our experiments on image recognition and co-segmentation model demonstrate that using our proposed triplet loss with adaptive margin distance can significantly improve the performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=few-shot%20learning" title="few-shot learning">few-shot learning</a>, <a href="https://publications.waset.org/abstracts/search?q=triplet%20network" title=" triplet network"> triplet network</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20margin" title=" adaptive margin"> adaptive margin</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/132975/adaptive-few-shot-deep-metric-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132975.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">171</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4725</span> Deep Supervision Based-Unet to Detect Buildings Changes from VHR Aerial Imagery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shimaa%20Holail">Shimaa Holail</a>, <a href="https://publications.waset.org/abstracts/search?q=Tamer%20Saleh"> Tamer Saleh</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiongwu%20Xiao"> Xiongwu Xiao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Building change detection (BCD) from satellite imagery is an essential topic in urbanization monitoring, agricultural land management, and updating geospatial databases. Recently, methods for detecting changes based on deep learning have made significant progress and impressive results. However, it has the problem of being insensitive to changes in buildings with complex spectral differences, and the features being extracted are not discriminatory enough, resulting in incomplete buildings and irregular boundaries. To overcome these problems, we propose a dual Siamese network based on the Unet model with the addition of a deep supervision strategy (DS) in this paper. This network consists of a backbone (encoder) based on ImageNet pre-training, a fusion block, and feature pyramid networks (FPN) to enhance the step-by-step information of the changing regions and obtain a more accurate BCD map. To train the proposed method, we created a new dataset (EGY-BCD) of high-resolution and multi-temporal aerial images captured over New Cairo in Egypt to detect building changes for this purpose. The experimental results showed that the proposed method is effective and performs well with the EGY-BCD dataset regarding the overall accuracy, F1-score, and mIoU, which were 91.6 %, 80.1 %, and 73.5 %, respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=building%20change%20detection" title="building change detection">building change detection</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20supervision" title=" deep supervision"> deep supervision</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20segmentation" title=" semantic segmentation"> semantic segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=EGY-BCD%20dataset" title=" EGY-BCD dataset"> EGY-BCD dataset</a> </p> <a href="https://publications.waset.org/abstracts/154993/deep-supervision-based-unet-to-detect-buildings-changes-from-vhr-aerial-imagery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154993.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">120</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4724</span> Network Security Attacks and Defences</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ranbir%20Singh">Ranbir Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Deepinder%20Kaur"> Deepinder Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Network security is an important aspect in every field like government offices, Educational Institute and any business organization. Network security consists of the policies adopted to prevent and monitor forbidden access, misuse, modification, or denial of a computer network. Network security is very complicated subject and deal by only well trained and experienced people. However, as more and more people become wired, an increasing number of people need to understand the basics of security in a networked world. The history of the network security included an introduction to the TCP/IP and interworking. Network security starts with authenticating, commonly with a username and a password. In this paper, we study about various types of attacks on network security and how to handle or prevent this attack. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=network%20security" title="network security">network security</a>, <a href="https://publications.waset.org/abstracts/search?q=attacks" title=" attacks"> attacks</a>, <a href="https://publications.waset.org/abstracts/search?q=denial" title=" denial"> denial</a>, <a href="https://publications.waset.org/abstracts/search?q=authenticating" title=" authenticating"> authenticating</a> </p> <a href="https://publications.waset.org/abstracts/53007/network-security-attacks-and-defences" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/53007.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">404</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4723</span> Enhancing the Network Security with Gray Code</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Adi%20Purnomo%20Sidhi">Thomas Adi Purnomo Sidhi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, network is an essential need in almost every part of human daily activities. People now can seamlessly connect to others through the Internet. With advanced technology, our personal data now can be more easily accessed. One of many components we are concerned for delivering the best network is a security issue. This paper is proposing a method that provides more options for security. This research aims to improve network security by focusing on the physical layer which is the first layer of the OSI model. The layer consists of the basic networking hardware transmission technologies of a network. With the use of observation method, the research produces a schematic design for enhancing the network security through the gray code converter. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=network" title="network">network</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20security" title=" network security"> network security</a>, <a href="https://publications.waset.org/abstracts/search?q=grey%20code" title=" grey code"> grey code</a>, <a href="https://publications.waset.org/abstracts/search?q=physical%20layer" title=" physical layer"> physical layer</a> </p> <a href="https://publications.waset.org/abstracts/41361/enhancing-the-network-security-with-gray-code" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41361.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">504</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4722</span> Network Functions Virtualization-Based Virtual Routing Function Deployment under Network Delay Constraints</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kenichiro%20Hida">Kenichiro Hida</a>, <a href="https://publications.waset.org/abstracts/search?q=Shin-Ichi%20Kuribayashi"> Shin-Ichi Kuribayashi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> NFV-based network implements a variety of network functions with software on general-purpose servers, and this allows the network operator to select any capabilities and locations of network functions without any physical constraints. In this paper, we evaluate the influence of the maximum tolerable network delay on the virtual routing function deployment guidelines which the authors proposed previously. Our evaluation results have revealed the following: (1) the more the maximum tolerable network delay condition becomes severe, the more the number of areas where the route selection function is installed increases and the total network cost increases, (2) the higher the routing function cost relative to the circuit bandwidth cost, the increase ratio of total network cost becomes larger according to the maximum tolerable network delay condition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=NFV%20%28Network%20Functions%20Virtualization%29" title="NFV (Network Functions Virtualization)">NFV (Network Functions Virtualization)</a>, <a href="https://publications.waset.org/abstracts/search?q=resource%20allocation" title=" resource allocation"> resource allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20routing%20function" title=" virtual routing function"> virtual routing function</a>, <a href="https://publications.waset.org/abstracts/search?q=minimum%20total%20network%20cost" title=" minimum total network cost"> minimum total network cost</a> </p> <a href="https://publications.waset.org/abstracts/79667/network-functions-virtualization-based-virtual-routing-function-deployment-under-network-delay-constraints" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79667.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">247</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4721</span> SiamMask++: More Accurate Object Tracking through Layer Wise Aggregation in Visual Object Tracking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hyunbin%20Choi">Hyunbin Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jihyeon%20Noh"> Jihyeon Noh</a>, <a href="https://publications.waset.org/abstracts/search?q=Changwon%20Lim"> Changwon Lim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose SiamMask++, an architecture that performs layer-wise aggregation and depth-wise cross-correlation and introduce multi-RPN module and multi-MASK module to improve EAO (Expected Average Overlap), a representative performance evaluation metric for Visual Object Tracking (VOT) challenge. The proposed architecture, SiamMask++, has two versions, namely, bi_SiamMask++, which satisfies the real time (56fps) on systems equipped with GPUs (Titan XP), and rf_SiamMask++, which combines mask refinement modules for EAO improvements. Tests are performed on VOT2016, VOT2018 and VOT2019, the representative datasets of Visual Object Tracking tasks labeled as rotated bounding boxes. SiamMask++ perform better than SiamMask on all the three datasets tested. SiamMask++ is achieved performance of 62.6% accuracy, 26.2% robustness and 39.8% EAO, especially on the VOT2018 dataset. Compared to SiamMask, this is an improvement of 4.18%, 37.17%, 23.99%, respectively. In addition, we do an experimental in-depth analysis of how much the introduction of features and multi modules extracted from the backbone affects the performance of our model in the VOT task. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=visual%20object%20tracking" title="visual object tracking">visual object tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=video" title=" video"> video</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=layer%20wise%20aggregation" title=" layer wise aggregation"> layer wise aggregation</a>, <a href="https://publications.waset.org/abstracts/search?q=Siamese%20network" title=" Siamese network"> Siamese network</a> </p> <a href="https://publications.waset.org/abstracts/151563/siammask-more-accurate-object-tracking-through-layer-wise-aggregation-in-visual-object-tracking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151563.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">159</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4720</span> Survivable IP over WDM Network Design Based on 1 ⊕ 1 Network Coding</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nihed%20Bahria%20El%20Asghar">Nihed Bahria El Asghar</a>, <a href="https://publications.waset.org/abstracts/search?q=Imen%20Jouili"> Imen Jouili</a>, <a href="https://publications.waset.org/abstracts/search?q=Mounir%20Frikha"> Mounir Frikha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Inter-datacenter transport network is very bandwidth and delay demanding. The data transferred over such a network is also highly QoS-exigent mostly because a huge volume of data should be transported transparently with regard to the application user. To avoid the data transfer failure, a backup path should be reserved. No re-routing delay should be observed. A dedicated 1+1 protection is however not applicable in inter-datacenter transport network because of the huge spare capacity. In this context, we propose a survivable virtual network with minimal backup based on network coding (1 ⊕ 1) and solve it using a modified Dijkstra-based heuristic. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=network%20coding" title="network coding">network coding</a>, <a href="https://publications.waset.org/abstracts/search?q=dedicated%20protection" title=" dedicated protection"> dedicated protection</a>, <a href="https://publications.waset.org/abstracts/search?q=spare%20capacity" title=" spare capacity"> spare capacity</a>, <a href="https://publications.waset.org/abstracts/search?q=inter-datacenters%20transport%20network" title=" inter-datacenters transport network"> inter-datacenters transport network</a> </p> <a href="https://publications.waset.org/abstracts/44625/survivable-ip-over-wdm-network-design-based-on-1-1-network-coding" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44625.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">447</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4719</span> Study on Energy Performance Comparison of Information Centric Network Based on Difference of Network Architecture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Takumi%20Shindo">Takumi Shindo</a>, <a href="https://publications.waset.org/abstracts/search?q=Koji%20Okamura"> Koji Okamura</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The first generation of the wide area network was circuit centric network. How the optimal circuit can be signed was the most important issue to get the best performance. This architecture had succeeded for line based telephone system. The second generation was host centric network and Internet based on this architecture has very succeeded world widely. And Internet became as new social infrastructure. Currently the architecture of the network is based on the location of the information. This future network is called Information centric network (ICN). The information-centric network (ICN) has being researched by many projects and different architectures for implementation of ICN have been proposed. The goal of this study is to compare performances of those ICN architectures. In this paper, the authors propose general ICN model which can represent two typical ICN architectures and compare communication performances using request routing. Finally, simulation results are shown. Also, we assume that this network architecture should be adapt to energy on-demand routing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ICN" title="ICN">ICN</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20centric%20network" title=" information centric network"> information centric network</a>, <a href="https://publications.waset.org/abstracts/search?q=CCN" title=" CCN"> CCN</a>, <a href="https://publications.waset.org/abstracts/search?q=energy" title=" energy"> energy</a> </p> <a href="https://publications.waset.org/abstracts/68439/study-on-energy-performance-comparison-of-information-centric-network-based-on-difference-of-network-architecture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68439.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">337</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4718</span> Secure Content Centric Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Syed%20Umair%20Aziz">Syed Umair Aziz</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Faheem"> Muhammad Faheem</a>, <a href="https://publications.waset.org/abstracts/search?q=Sameer%20Hussain"> Sameer Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Faraz%20Idris"> Faraz Idris</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Content centric network is the network based on the mechanism of sending and receiving the data based on the interest and data request to the specified node (which has cached data). In this network, the security is bind with the content not with the host hence making it host independent and secure. In this network security is applied by taking content’s MAC (message authentication code) and encrypting it with the public key of the receiver. On the receiver end, the message is first verified and after verification message is saved and decrypted using the receiver's private key. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=content%20centric%20network" title="content centric network">content centric network</a>, <a href="https://publications.waset.org/abstracts/search?q=client-server" title=" client-server"> client-server</a>, <a href="https://publications.waset.org/abstracts/search?q=host%20security%20threats" title=" host security threats"> host security threats</a>, <a href="https://publications.waset.org/abstracts/search?q=message%20authentication%20code" title=" message authentication code"> message authentication code</a>, <a href="https://publications.waset.org/abstracts/search?q=named%20data%20network" title=" named data network"> named data network</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20caching" title=" network caching"> network caching</a>, <a href="https://publications.waset.org/abstracts/search?q=peer-to-peer" title=" peer-to-peer"> peer-to-peer</a> </p> <a href="https://publications.waset.org/abstracts/32149/secure-content-centric-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32149.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">644</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4717</span> Survey on Securing the Optimized Link State Routing (OLSR) Protocol in Mobile Ad-hoc Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kimaya%20Subhash%20Gaikwad">Kimaya Subhash Gaikwad</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20B.%20Waykar"> S. B. Waykar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The mobile ad-hoc network (MANET) is collection of various types of nodes. In MANET various protocols are used for communication. In OLSR protocol, a node is selected as multipoint relay (MPR) node which broadcast the messages. As the MANET is open kind of network any malicious node can easily enter into the network and affect the performance of the network. The performance of network mainly depends on the components which are taking part into the communication. If the proper nodes are not selected for the communication then the probability of network being attacked is more. Therefore, it is important to select the more reliable and secure components in the network. MANET does not have any filtering so that only selected nodes can be used for communication. The openness of the MANET makes it easier to attack the communication. The most of the attack are on the Quality of service (QoS) of the network. This paper gives the overview of the various attacks that are possible on OLSR protocol and some solutions. The papers focus mainly on the OLSR protocol. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=communication" title="communication">communication</a>, <a href="https://publications.waset.org/abstracts/search?q=MANET" title=" MANET"> MANET</a>, <a href="https://publications.waset.org/abstracts/search?q=OLSR" title=" OLSR"> OLSR</a>, <a href="https://publications.waset.org/abstracts/search?q=QoS" title=" QoS"> QoS</a> </p> <a href="https://publications.waset.org/abstracts/43772/survey-on-securing-the-optimized-link-state-routing-olsr-protocol-in-mobile-ad-hoc-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43772.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">450</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4716</span> A Social Network Analysis of the Palestinian Feminist Network Tal3at</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maath%20M.%20Musleh">Maath M. Musleh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research aims to study recent trends in the Palestinian feminist movement through the case study of Tal3at. The study uses social network analysis as its primary method to analyze Twitter data. It attempts to interpret results through the lens of network theories and Parson’s AGIL paradigm. The study reveals major structural weaknesses in the Tal3at network. Our findings suggest that the movement will decline soon as sentiments of alienation amongst Palestinian women increases. These findings were validated by a couple of central actors in the network. This study contributes an SNA approach to the understanding of the understudied Palestinian feminism. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feminism" title="feminism">feminism</a>, <a href="https://publications.waset.org/abstracts/search?q=Palestine" title=" Palestine"> Palestine</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20network%20analysis" title=" social network analysis"> social network analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=Tal3at" title=" Tal3at"> Tal3at</a> </p> <a href="https://publications.waset.org/abstracts/136124/a-social-network-analysis-of-the-palestinian-feminist-network-tal3at" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136124.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">264</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4715</span> Design a Network for Implementation a Hospital Information System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdulqader%20Rasool%20Feqi%20Mohammed">Abdulqader Rasool Feqi Mohammed</a>, <a href="https://publications.waset.org/abstracts/search?q=Ergun%20Er%C3%A7elebi%CC%87"> Ergun Erçelebi̇</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A large number of hospitals from developed countries are adopting hospital information system to bring efficiency in hospital information system. The purpose of this project is to research on new network security techniques in order to enhance the current network security structure of save a hospital information system (HIS). This is very important because, it will avoid the system from suffering any attack. Security architecture was optimized but there are need to keep researching on best means to protect the network from future attacks. In this final project research, security techniques were uncovered to produce best network security results when implemented in an integrated framework. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hospital%20information%20system" title="hospital information system">hospital information system</a>, <a href="https://publications.waset.org/abstracts/search?q=HIS" title=" HIS"> HIS</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20security%20techniques" title=" network security techniques"> network security techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=internet%20protocol" title=" internet protocol"> internet protocol</a>, <a href="https://publications.waset.org/abstracts/search?q=IP" title=" IP"> IP</a>, <a href="https://publications.waset.org/abstracts/search?q=network" title=" network"> network</a> </p> <a href="https://publications.waset.org/abstracts/44356/design-a-network-for-implementation-a-hospital-information-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44356.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">440</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4714</span> Monitoring and Prediction of Intra-Crosstalk in All-Optical Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Jedidi">Ahmed Jedidi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mesfer%20Mohammed%20Alshamrani"> Mesfer Mohammed Alshamrani</a>, <a href="https://publications.waset.org/abstracts/search?q=Alwi%20Mohammad%20A.%20Bamhdi"> Alwi Mohammad A. Bamhdi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Optical performance monitoring and optical network management are essential in building a reliable, high-capacity, and service-differentiation enabled all-optical network. One of the serious problems in this network is the fact that optical crosstalk is additive, and thus the aggregate effect of crosstalk over a whole AON may be more nefarious than a single point of crosstalk. As results, we note a huge degradation of the Quality of Service (QoS) in our network. For that, it is necessary to identify and monitor the impairments in whole network. In this way, this paper presents new system to identify and monitor crosstalk in AONs in real-time fashion. particular, it proposes a new technique to manage intra-crosstalk in objective to relax QoS of the network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=all-optical%20networks" title="all-optical networks">all-optical networks</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20crosstalk" title=" optical crosstalk"> optical crosstalk</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20cross-connect" title=" optical cross-connect"> optical cross-connect</a>, <a href="https://publications.waset.org/abstracts/search?q=crosstalk" title=" crosstalk"> crosstalk</a>, <a href="https://publications.waset.org/abstracts/search?q=monitoring%20crosstalk" title=" monitoring crosstalk"> monitoring crosstalk</a> </p> <a href="https://publications.waset.org/abstracts/40796/monitoring-and-prediction-of-intra-crosstalk-in-all-optical-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40796.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">463</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4713</span> Software Quality Assurance in Network Security using Cryptographic Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sidra%20Shabbir">Sidra Shabbir</a>, <a href="https://publications.waset.org/abstracts/search?q=Ayesha%20Manzoor"> Ayesha Manzoor</a>, <a href="https://publications.waset.org/abstracts/search?q=Mehreen%20Sirshar"> Mehreen Sirshar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of the network communication has imposed serious threats to the security of assets over the network. Network security is getting more prone to active and passive attacks which may result in serious consequences to data integrity, confidentiality and availability. Various cryptographic techniques have been proposed in the past few years to combat with the concerned problem by ensuring quality but in order to have a fully secured network; a framework of new cryptosystem was needed. This paper discusses certain cryptographic techniques which have shown far better improvement in the network security with enhanced quality assurance. The scope of this research paper is to cover the security pitfalls in the current systems and their possible solutions based on the new cryptosystems. The development of new cryptosystem framework has paved a new way to the widespread network communications with enhanced quality in network security. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cryptography" title="cryptography">cryptography</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20security" title=" network security"> network security</a>, <a href="https://publications.waset.org/abstracts/search?q=encryption" title=" encryption"> encryption</a>, <a href="https://publications.waset.org/abstracts/search?q=decryption" title=" decryption"> decryption</a>, <a href="https://publications.waset.org/abstracts/search?q=integrity" title=" integrity"> integrity</a>, <a href="https://publications.waset.org/abstracts/search?q=confidentiality" title=" confidentiality"> confidentiality</a>, <a href="https://publications.waset.org/abstracts/search?q=security%20algorithms" title=" security algorithms"> security algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=elliptic%20curve%20cryptography" title=" elliptic curve cryptography"> elliptic curve cryptography</a> </p> <a href="https://publications.waset.org/abstracts/26324/software-quality-assurance-in-network-security-using-cryptographic-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26324.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">733</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4712</span> Air Cargo Network Structure Characteristics and Robustness Analysis under the Belt and Road Area</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Feng-jie%20Xie">Feng-jie Xie</a>, <a href="https://publications.waset.org/abstracts/search?q=Jian-hong%20Yan"> Jian-hong Yan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Based on the complex network theory, we construct the air cargo network of the Belt and Road area, analyze its regional distribution and structural characteristics, measure the robustness of the network. The regional distribution results show that Southeast Asia and China have the most prominent development in the air cargo network of the Belt and Road area, Central Asia is the least developed. The structure characteristics found that the air cargo network has obvious small-world characteristics; the degree distribution has single-scale property; it shows a significant rich-club phenomenon simultaneously. The network robustness is measured by two attack strategies of degree and betweenness, but the betweenness of network nodes has a greater impact on network connectivity. And identified 24 key cities that have a large impact on the robustness of the network under the two attack strategies. Based on these results, recommendations are given to maintain the air cargo network connectivity in the Belt and Road area. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=air%20cargo" title="air cargo">air cargo</a>, <a href="https://publications.waset.org/abstracts/search?q=complex%20network" title=" complex network"> complex network</a>, <a href="https://publications.waset.org/abstracts/search?q=robustness" title=" robustness"> robustness</a>, <a href="https://publications.waset.org/abstracts/search?q=structure%20properties" title=" structure properties"> structure properties</a>, <a href="https://publications.waset.org/abstracts/search?q=The%20Belt%20and%20Road" title=" The Belt and Road"> The Belt and Road</a> </p> <a href="https://publications.waset.org/abstracts/129714/air-cargo-network-structure-characteristics-and-robustness-analysis-under-the-belt-and-road-area" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129714.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">196</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4711</span> An Intelligent Cloud Radio Access Network (RAN) Architecture for Future 5G Heterogeneous Wireless Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jin%20Xu">Jin Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 5G network developers need to satisfy the necessary requirements of additional capacity from massive users and spectrally efficient wireless technologies. Therefore, the significant amount of underutilized spectrum in network is motivating operators to combine long-term evolution (LTE) with intelligent spectrum management technology. This new LTE intelligent spectrum management in unlicensed band (LTE-U) has the physical layer topology to access spectrum, specifically the 5-GHz band. We proposed a new intelligent cloud RAN for 5G. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cloud%20radio%20access%20network" title="cloud radio access network">cloud radio access network</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20network" title=" wireless network"> wireless network</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent" title=" multi-agent"> multi-agent</a> </p> <a href="https://publications.waset.org/abstracts/50489/an-intelligent-cloud-radio-access-network-ran-architecture-for-future-5g-heterogeneous-wireless-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50489.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">424</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4710</span> Network Automation in Lab Deployment Using Ansible and Python</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=V.%20Andal%20Priyadharshini">V. Andal Priyadharshini</a>, <a href="https://publications.waset.org/abstracts/search?q=Anumalasetty%20Yashwanth%20Nath"> Anumalasetty Yashwanth Nath</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Network automation has evolved into a solution that ensures efficiency in all areas. The age-old technique to configure common software-defined networking protocols is inefficient as it requires a box-by-box approach that needs to be repeated often and is prone to manual errors. Network automation assists network administrators in automating and verifying the protocol configuration to ensure consistent configurations. This paper implemented network automation using Python and Ansible to configure different protocols and configurations in the container lab virtual environment. Ansible can help network administrators minimize human mistakes, reduce time consumption, and enable device visibility across the network environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Python%20network%20automation" title="Python network automation">Python network automation</a>, <a href="https://publications.waset.org/abstracts/search?q=Ansible%20configuration" title=" Ansible configuration"> Ansible configuration</a>, <a href="https://publications.waset.org/abstracts/search?q=container%20lab%20deployment" title=" container lab deployment"> container lab deployment</a>, <a href="https://publications.waset.org/abstracts/search?q=software-defined%20networking" title=" software-defined networking"> software-defined networking</a>, <a href="https://publications.waset.org/abstracts/search?q=networking%20lab" title=" networking lab"> networking lab</a> </p> <a href="https://publications.waset.org/abstracts/149854/network-automation-in-lab-deployment-using-ansible-and-python" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149854.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">164</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4709</span> Using Mixed Methods in Studying Classroom Social Network Dynamics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nashrawan%20Naser%20Taha">Nashrawan Naser Taha</a>, <a href="https://publications.waset.org/abstracts/search?q=Andrew%20M.%20Cox"> Andrew M. Cox</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In a multi-cultural learning context, where ties are weak and dynamic, combining qualitative with quantitative research methods may be more effective. Such a combination may also allow us to answer different types of question, such as about people’s perception of the network. In this study the use of observation, interviews and photos were explored as ways of enhancing data from social network questionnaires. Integrating all of these methods was found to enhance the quality of data collected and its accuracy, also providing a richer story of the network dynamics and the factors that shaped these changes over time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mixed%20methods" title="mixed methods">mixed methods</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20network%20analysis" title=" social network analysis"> social network analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-cultural%20learning" title=" multi-cultural learning"> multi-cultural learning</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20network%20dynamics" title=" social network dynamics"> social network dynamics</a> </p> <a href="https://publications.waset.org/abstracts/15500/using-mixed-methods-in-studying-classroom-social-network-dynamics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15500.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">510</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4708</span> Increasing of Resiliency by Using Gas Storage in Iranian Gas Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Dourandish">Mohsen Dourandish</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Iran has a huge pipeline network in every state of country which is the longest and vastest pipeline network after Russia and USA (360,000 Km high pressure pipelines and 250,000 Km distribution networks). Furthermore in recent years National Iranian Gas Company is planning to develop natural gas network to cover all cities and villages above 20 families, in a way that 97 percent of Iran population will be gas consumer by 2020. In this condition, network resiliency will be the first priority of NIGC and due to that several planning for increasing resiliency of gas network is under construction. The most important strategy of NIGC is converting tree form pattern network to loop gas networks and developing underground gas storage near main gas consuming centers. In this regard NIGC is planning for construction of over 3500 km high-pressure pipeline and also 10 TCM gas storage capacities in UGSs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Iranian%20gas%20network" title="Iranian gas network">Iranian gas network</a>, <a href="https://publications.waset.org/abstracts/search?q=peak%20shaving" title=" peak shaving"> peak shaving</a>, <a href="https://publications.waset.org/abstracts/search?q=resiliency" title=" resiliency"> resiliency</a>, <a href="https://publications.waset.org/abstracts/search?q=underground%20gas%20storage" title=" underground gas storage"> underground gas storage</a> </p> <a href="https://publications.waset.org/abstracts/46485/increasing-of-resiliency-by-using-gas-storage-in-iranian-gas-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46485.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">325</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4707</span> Dual-Network Memory Model for Temporal Sequences</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Motonobu%20Hattori">Motonobu Hattori</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In neural networks, when new patters are learned by a network, they radically interfere with previously stored patterns. This drawback is called catastrophic forgetting. We have already proposed a biologically inspired dual-network memory model which can much reduce this forgetting for static patterns. In this model, information is ﬁrst stored in the hippocampal network, and thereafter, it is transferred to the neocortical network using pseudo patterns. Because, temporal sequence learning is more important than static pattern learning in the real world, in this study, we improve our conventional dual-network memory model so that it can deal with temporal sequences without catastrophic forgetting. The computer simulation results show the effectiveness of the proposed dual-network memory model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=catastrophic%20forgetting" title="catastrophic forgetting">catastrophic forgetting</a>, <a href="https://publications.waset.org/abstracts/search?q=dual-network" title=" dual-network"> dual-network</a>, <a href="https://publications.waset.org/abstracts/search?q=temporal%20sequences" title=" temporal sequences"> temporal sequences</a>, <a href="https://publications.waset.org/abstracts/search?q=hippocampal" title=" hippocampal "> hippocampal </a> </p> <a href="https://publications.waset.org/abstracts/2908/dual-network-memory-model-for-temporal-sequences" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2908.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">270</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">4706</span> Integrating Knowledge Distillation of Multiple Strategies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Min%20Jindong">Min Jindong</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Mingxia"> Wang Mingxia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the widespread use of artificial intelligence in life, computer vision, especially deep convolutional neural network models, has developed rapidly. With the increase of the complexity of the real visual target detection task and the improvement of the recognition accuracy, the target detection network model is also very large. The huge deep neural network model is not conducive to deployment on edge devices with limited resources, and the timeliness of network model inference is poor. In this paper, knowledge distillation is used to compress the huge and complex deep neural network model, and the knowledge contained in the complex network model is comprehensively transferred to another lightweight network model. Different from traditional knowledge distillation methods, we propose a novel knowledge distillation that incorporates multi-faceted features, called M-KD. In this paper, when training and optimizing the deep neural network model for target detection, the knowledge of the soft target output of the teacher network in knowledge distillation, the relationship between the layers of the teacher network and the feature attention map of the hidden layer of the teacher network are transferred to the student network as all knowledge. in the model. At the same time, we also introduce an intermediate transition layer, that is, an intermediate guidance layer, between the teacher network and the student network to make up for the huge difference between the teacher network and the student network. Finally, this paper adds an exploration module to the traditional knowledge distillation teacher-student network model. The student network model not only inherits the knowledge of the teacher network but also explores some new knowledge and characteristics. Comprehensive experiments in this paper using different distillation parameter configurations across multiple datasets and convolutional neural network models demonstrate that our proposed new network model achieves substantial improvements in speed and accuracy performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title="object detection">object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20distillation" title=" knowledge distillation"> knowledge distillation</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20network" title=" convolutional network"> convolutional network</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20compression" title=" model compression"> model compression</a> </p> <a href="https://publications.waset.org/abstracts/148652/integrating-knowledge-distillation-of-multiple-strategies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148652.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">278</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Siamese%20network&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Siamese%20network&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Siamese%20network&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Siamese%20network&page=5">5</a></li> 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