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neighbor</title> <meta name="description" content="Search results for: K-nearest neighbor"> <meta name="keywords" content="K-nearest neighbor"> <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="K-nearest neighbor"> <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> 192</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: K-nearest neighbor</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">192</span> Closest Possible Neighbor of a Different Class: Explaining a Model Using a Neighbor Migrating Generator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hassan%20Eshkiki">Hassan Eshkiki</a>, <a href="https://publications.waset.org/abstracts/search?q=Benjamin%20Mora"> Benjamin Mora</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Neighbor Migrating Generator is a simple and efficient approach to finding the closest potential neighbor(s) with a different label for a given instance and so without the need to calibrate any kernel settings at all. This allows determining and explaining the most important features that will influence an AI model. It can be used to either migrate a specific sample to the class decision boundary of the original model within a close neighborhood of that sample or identify global features that can help localising neighbor classes. The proposed technique works by minimizing a loss function that is divided into two components which are independently weighted according to three parameters α, β, and ω, α being self-adjusting. Results show that this approach is superior to past techniques when detecting the smallest changes in the feature space and may also point out issues in models like over-fitting. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=explainable%20AI" title="explainable AI">explainable AI</a>, <a href="https://publications.waset.org/abstracts/search?q=EX%20AI" title=" EX AI"> EX AI</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20importance" title=" feature importance"> feature importance</a>, <a href="https://publications.waset.org/abstracts/search?q=counterfactual%20explanations" title=" counterfactual explanations"> counterfactual explanations</a> </p> <a href="https://publications.waset.org/abstracts/156369/closest-possible-neighbor-of-a-different-class-explaining-a-model-using-a-neighbor-migrating-generator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156369.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">190</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">191</span> Nearest Neighbor Investigate Using R+ Tree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rutuja%20Desai">Rutuja Desai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Search engine is fundamentally a framework used to search the data which is pertinent to the client via WWW. Looking close-by spot identified with the keywords is an imperative concept in developing web advances. For such kind of searching, extent pursuit or closest neighbor is utilized. In range search the forecast is made whether the objects meet to query object. Nearest neighbor is the forecast of the focuses close to the query set by the client. Here, the nearest neighbor methodology is utilized where Data recovery R+ tree is utilized rather than IR2 tree. The disadvantages of IR2 tree is: The false hit number can surpass the limit and the mark in Information Retrieval R-tree must have Voice over IP bit for each one of a kind word in W set is recouped by Data recovery R+ tree. The inquiry is fundamentally subordinate upon the key words and the geometric directions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=information%20retrieval" title="information retrieval">information retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=nearest%20neighbor%20search" title=" nearest neighbor search"> nearest neighbor search</a>, <a href="https://publications.waset.org/abstracts/search?q=keyword%20search" title=" keyword search"> keyword search</a>, <a href="https://publications.waset.org/abstracts/search?q=R%2B%20tree" title=" R+ tree"> R+ tree</a> </p> <a href="https://publications.waset.org/abstracts/33680/nearest-neighbor-investigate-using-r-tree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33680.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">289</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">190</span> Feature Extraction Technique for Prediction the Antigenic Variants of the Influenza Virus</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Majid%20Forghani">Majid Forghani</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20Khachay"> Michael Khachay</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In genetics, the impact of neighboring amino acids on a target site is referred as the nearest-neighbor effect or simply neighbor effect. In this paper, a new method called wavelet particle decomposition representing the one-dimensional neighbor effect using wavelet packet decomposition is proposed. The main idea lies in known dependence of wavelet packet sub-bands on location and order of neighboring samples. The method decomposes the value of a signal sample into small values called particles that represent a part of the neighbor effect information. The results have shown that the information obtained from the particle decomposition can be used to create better model variables or features. As an example, the approach has been applied to improve the correlation of test and reference sequence distance with titer in the hemagglutination inhibition assay. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=antigenic%20variants" title="antigenic variants">antigenic variants</a>, <a href="https://publications.waset.org/abstracts/search?q=neighbor%20effect" title=" neighbor effect"> neighbor effect</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20packet" title=" wavelet packet"> wavelet packet</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelet%20particle%20decomposition" title=" wavelet particle decomposition"> wavelet particle decomposition</a> </p> <a href="https://publications.waset.org/abstracts/96149/feature-extraction-technique-for-prediction-the-antigenic-variants-of-the-influenza-virus" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/96149.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">154</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">189</span> Hybrid Fuzzy Weighted K-Nearest Neighbor to Predict Hospital Readmission for Diabetic Patients </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Soha%20A.%20Bahanshal">Soha A. Bahanshal</a>, <a href="https://publications.waset.org/abstracts/search?q=Byung%20G.%20Kim"> Byung G. Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20fuzzy%20weighted%20k-nearest%20neighbor" title=" hybrid fuzzy weighted k-nearest neighbor"> hybrid fuzzy weighted k-nearest neighbor</a>, <a href="https://publications.waset.org/abstracts/search?q=diabetic%20hospital%20readmission" title=" diabetic hospital readmission"> diabetic hospital readmission</a> </p> <a href="https://publications.waset.org/abstracts/129397/hybrid-fuzzy-weighted-k-nearest-neighbor-to-predict-hospital-readmission-for-diabetic-patients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129397.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">186</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">188</span> Determination of Neighbor Node in Consideration of the Imaging Range of Cameras in Automatic Human Tracking System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kozo%20Tanigawa">Kozo Tanigawa</a>, <a href="https://publications.waset.org/abstracts/search?q=Tappei%20Yotsumoto"> Tappei Yotsumoto</a>, <a href="https://publications.waset.org/abstracts/search?q=Kenichi%20Takahashi"> Kenichi Takahashi</a>, <a href="https://publications.waset.org/abstracts/search?q=Takao%20Kawamura"> Takao Kawamura</a>, <a href="https://publications.waset.org/abstracts/search?q=Kazunori%20Sugahara"> Kazunori Sugahara</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An automatic human tracking system using mobile agent technology is realized because a mobile agent moves in accordance with a migration of a target person. In this paper, we propose a method for determining the neighbor node in consideration of the imaging range of cameras. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=human%20tracking" title="human tracking">human tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20agent" title=" mobile agent"> mobile agent</a>, <a href="https://publications.waset.org/abstracts/search?q=Pan%2FTilt%2FZoom" title=" Pan/Tilt/Zoom"> Pan/Tilt/Zoom</a>, <a href="https://publications.waset.org/abstracts/search?q=neighbor%20relation" title=" neighbor relation"> neighbor relation</a> </p> <a href="https://publications.waset.org/abstracts/11821/determination-of-neighbor-node-in-consideration-of-the-imaging-range-of-cameras-in-automatic-human-tracking-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11821.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">516</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">187</span> Urban Land Cover from GF-2 Satellite Images Using Object Based and Neural Network Classifications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lamyaa%20Gamal%20El-Deen%20Taha">Lamyaa Gamal El-Deen Taha</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashraf%20Sharawi"> Ashraf Sharawi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> China launched satellite GF-2 in 2014. This study deals with comparing nearest neighbor object-based classification and neural network classification methods for classification of the fused GF-2 image. Firstly, rectification of GF-2 image was performed. Secondly, a comparison between nearest neighbor object-based classification and neural network classification for classification of fused GF-2 was performed. Thirdly, the overall accuracy of classification and kappa index were calculated. Results indicate that nearest neighbor object-based classification is better than neural network classification for urban mapping. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GF-2%20images" title="GF-2 images">GF-2 images</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction-rectification" title=" feature extraction-rectification"> feature extraction-rectification</a>, <a href="https://publications.waset.org/abstracts/search?q=nearest%20neighbour%20object%20based%20classification" title=" nearest neighbour object based classification"> nearest neighbour object based classification</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation%20algorithms" title=" segmentation algorithms"> segmentation algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network%20classification" title=" neural network classification"> neural network classification</a>, <a href="https://publications.waset.org/abstracts/search?q=multilayer%20perceptron" title=" multilayer perceptron"> multilayer perceptron</a> </p> <a href="https://publications.waset.org/abstracts/84243/urban-land-cover-from-gf-2-satellite-images-using-object-based-and-neural-network-classifications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84243.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">389</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">186</span> Identity Verification Using k-NN Classifiers and Autistic Genetic Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fuad%20M.%20Alkoot">Fuad M. Alkoot</a> </p> <p class="card-text"><strong>Abstract:</strong></p> DNA data have been used in forensics for decades. However, current research looks at using the DNA as a biometric identity verification modality. The goal is to improve the speed of identification. We aim at using gene data that was initially used for autism detection to find if and how accurate is this data for identification applications. Mainly our goal is to find if our data preprocessing technique yields data useful as a biometric identification tool. We experiment with using the nearest neighbor classifier to identify subjects. Results show that optimal classification rate is achieved when the test set is corrupted by normally distributed noise with zero mean and standard deviation of 1. The classification rate is close to optimal at higher noise standard deviation reaching 3. This shows that the data can be used for identity verification with high accuracy using a simple classifier such as the k-nearest neighbor (k-NN).  <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biometrics" title="biometrics">biometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20data" title=" genetic data"> genetic data</a>, <a href="https://publications.waset.org/abstracts/search?q=identity%20verification" title=" identity verification"> identity verification</a>, <a href="https://publications.waset.org/abstracts/search?q=k%20nearest%20neighbor" title=" k nearest neighbor"> k nearest neighbor</a> </p> <a href="https://publications.waset.org/abstracts/75552/identity-verification-using-k-nn-classifiers-and-autistic-genetic-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75552.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">257</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">185</span> Lightweight Cryptographically Generated Address for IPv6 Neighbor Discovery </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amjed%20Sid%20Ahmed">Amjed Sid Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Rosilah%20Hassan"> Rosilah Hassan</a>, <a href="https://publications.waset.org/abstracts/search?q=Nor%20Effendy%20Othman"> Nor Effendy Othman </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Limited functioning of the Internet Protocol version 4 (IPv4) has necessitated the development of the Internetworking Protocol next generation (IPng) to curb the challenges. Indeed, the IPng is also referred to as the Internet Protocol version 6 (IPv6) and includes the Neighbor Discovery Protocol (NDP). The latter performs the role of Address Auto-configuration, Router Discovery (RD), and Neighbor Discovery (ND). Furthermore, the role of the NDP entails redirecting the service, detecting the duplicate address, and detecting the unreachable services. Despite the fact that there is an NDP’s assumption regarding the existence of trust the links’ nodes, several crucial attacks may affect the Protocol. Internet Engineering Task Force (IETF) therefore has recommended implementation of Secure Neighbor Discovery Protocol (SEND) to tackle safety issues in NDP. The SEND protocol is mainly used for validation of address rights, malicious response inhibiting techniques and finally router certification procedures. For routine running of these tasks, SEND utilizes on the following options, Cryptographically Generated Address (CGA), RSA Signature, Nonce and Timestamp option. CGA is produced at extra high costs making it the most notable disadvantage of SEND. In this paper a clear description of the constituents of CGA, its operation and also recommendations for improvements in its generation are given. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CGA" title="CGA">CGA</a>, <a href="https://publications.waset.org/abstracts/search?q=IPv6" title=" IPv6"> IPv6</a>, <a href="https://publications.waset.org/abstracts/search?q=NDP" title=" NDP"> NDP</a>, <a href="https://publications.waset.org/abstracts/search?q=SEND" title=" SEND"> SEND</a> </p> <a href="https://publications.waset.org/abstracts/31309/lightweight-cryptographically-generated-address-for-ipv6-neighbor-discovery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31309.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">385</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">184</span> Economics of Conflict: Core Economic Dimensions of the Georgian-South Ossetian Context</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=V.%20Charaia">V. Charaia </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article presents SWOT analysis for Georgian - South Ossetian conflict. The research analyzes socio-economic aspects and considers future prospects for all sides including neighbor countries and regions. Also it includes the possibilities of positive intervention of neighbor countries to solve the conflict or to mitigate its negative results. The main question of the article is: What will it take to award Georgians and South Ossetians with a peace dividend? <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=conflict%20economics" title="conflict economics">conflict economics</a>, <a href="https://publications.waset.org/abstracts/search?q=investments" title=" investments"> investments</a>, <a href="https://publications.waset.org/abstracts/search?q=trade" title=" trade"> trade</a>, <a href="https://publications.waset.org/abstracts/search?q=remittances" title=" remittances"> remittances</a> </p> <a href="https://publications.waset.org/abstracts/55987/economics-of-conflict-core-economic-dimensions-of-the-georgian-south-ossetian-context" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55987.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">235</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">183</span> Performance Comparison of Different Regression Methods for a Polymerization Process with Adaptive Sampling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Florin%20Leon">Florin Leon</a>, <a href="https://publications.waset.org/abstracts/search?q=Silvia%20Curteanu"> Silvia Curteanu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Developing complete mechanistic models for polymerization reactors is not easy, because complex reactions occur simultaneously; there is a large number of kinetic parameters involved and sometimes the chemical and physical phenomena for mixtures involving polymers are poorly understood. To overcome these difficulties, empirical models based on sampled data can be used instead, namely regression methods typical of machine learning field. They have the ability to learn the trends of a process without any knowledge about its particular physical and chemical laws. Therefore, they are useful for modeling complex processes, such as the free radical polymerization of methyl methacrylate achieved in a batch bulk process. The goal is to generate accurate predictions of monomer conversion, numerical average molecular weight and gravimetrical average molecular weight. This process is associated with non-linear gel and glass effects. For this purpose, an adaptive sampling technique is presented, which can select more samples around the regions where the values have a higher variation. Several machine learning methods are used for the modeling and their performance is compared: support vector machines, k-nearest neighbor, k-nearest neighbor and random forest, as well as an original algorithm, large margin nearest neighbor regression. The suggested method provides very good results compared to the other well-known regression algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=batch%20bulk%20methyl%20methacrylate%20polymerization" title="batch bulk methyl methacrylate polymerization">batch bulk methyl methacrylate polymerization</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20sampling" title=" adaptive sampling"> adaptive sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=large%20margin%20nearest%20neighbor%20regression" title=" large margin nearest neighbor regression"> large margin nearest neighbor regression</a> </p> <a href="https://publications.waset.org/abstracts/54074/performance-comparison-of-different-regression-methods-for-a-polymerization-process-with-adaptive-sampling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54074.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">304</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">182</span> Improving Cryptographically Generated Address Algorithm in IPv6 Secure Neighbor Discovery Protocol through Trust Management </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Moslehpour">M. Moslehpour</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Khorsandi"> S. Khorsandi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As transition to widespread use of IPv6 addresses has gained momentum, it has been shown to be vulnerable to certain security attacks such as those targeting Neighbor Discovery Protocol (NDP) which provides the address resolution functionality in IPv6. To protect this protocol, Secure Neighbor Discovery (SEND) is introduced. This protocol uses Cryptographically Generated Address (CGA) and asymmetric cryptography as a defense against threats on integrity and identity of NDP. Although SEND protects NDP against attacks, it is computationally intensive due to Hash2 condition in CGA. To improve the CGA computation speed, we parallelized CGA generation process and used the available resources in a trusted network. Furthermore, we focused on the influence of the existence of malicious nodes on the overall load of un-malicious ones in the network. According to the evaluation results, malicious nodes have adverse impacts on the average CGA generation time and on the average number of tries. We utilized a Trust Management that is capable of detecting and isolating the malicious node to remove possible incentives for malicious behavior. We have demonstrated the effectiveness of the Trust Management System in detecting the malicious nodes and hence improving the overall system performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CGA" title="CGA">CGA</a>, <a href="https://publications.waset.org/abstracts/search?q=ICMPv6" title=" ICMPv6"> ICMPv6</a>, <a href="https://publications.waset.org/abstracts/search?q=IPv6" title=" IPv6"> IPv6</a>, <a href="https://publications.waset.org/abstracts/search?q=malicious%20node" title=" malicious node"> malicious node</a>, <a href="https://publications.waset.org/abstracts/search?q=modifier" title=" modifier"> modifier</a>, <a href="https://publications.waset.org/abstracts/search?q=NDP" title=" NDP"> NDP</a>, <a href="https://publications.waset.org/abstracts/search?q=overall%20load" title=" overall load"> overall load</a>, <a href="https://publications.waset.org/abstracts/search?q=SEND" title=" SEND"> SEND</a>, <a href="https://publications.waset.org/abstracts/search?q=trust%20management" title=" trust management"> trust management</a> </p> <a href="https://publications.waset.org/abstracts/41739/improving-cryptographically-generated-address-algorithm-in-ipv6-secure-neighbor-discovery-protocol-through-trust-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41739.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">184</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">181</span> Diabetes Diagnosis Model Using Rough Set and K- Nearest Neighbor Classifier</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Usiobaifo%20Agharese%20Rosemary">Usiobaifo Agharese Rosemary</a>, <a href="https://publications.waset.org/abstracts/search?q=Osaseri%20Roseline%20Oghogho"> Osaseri Roseline Oghogho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Diabetes is a complex group of disease with a variety of causes; it is a disorder of the body metabolism in the digestion of carbohydrates food. The application of machine learning in the field of medical diagnosis has been the focus of many researchers and the use of recognition and classification model as a decision support tools has help the medical expert in diagnosis of diseases. Considering the large volume of medical data which require special techniques, experience, and high diagnostic skill in the diagnosis of diseases, the application of an artificial intelligent system to assist medical personnel in order to enhance their efficiency and accuracy in diagnosis will be an invaluable tool. In this study will propose a diabetes diagnosis model using rough set and K-nearest Neighbor classifier algorithm. The system consists of two modules: the feature extraction module and predictor module, rough data set is used to preprocess the attributes while K-nearest neighbor classifier is used to classify the given data. The dataset used for this model was taken for University of Benin Teaching Hospital (UBTH) database. Half of the data was used in the training while the other half was used in testing the system. The proposed model was able to achieve over 80% accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classifier%20algorithm" title="classifier algorithm">classifier algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=diabetes" title=" diabetes"> diabetes</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnostic%20model" title=" diagnostic model"> diagnostic model</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/43090/diabetes-diagnosis-model-using-rough-set-and-k-nearest-neighbor-classifier" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43090.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">336</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">180</span> Microstructure Evolution and Pre-transformation Microstructure Reconstruction in Ti-6Al-4V Alloy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shreyash%20Hadke">Shreyash Hadke</a>, <a href="https://publications.waset.org/abstracts/search?q=Manendra%20Singh%20Parihar"> Manendra Singh Parihar</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajesh%20Khatirkar"> Rajesh Khatirkar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the present investigation, the variation in the microstructure with the changes in the heat treatment conditions i.e. temperature and time was observed. Ti-6Al-4V alloy was subject to solution annealing treatments in β (1066C) and α+β phase (930C and 850C) followed by quenching, air cooling and furnace cooling to room temperature respectively. The effect of solution annealing and cooling on the microstructure was studied by using optical microscopy (OM), scanning electron microscopy (SEM), electron backscattered diffraction (EBSD) and x-ray diffraction (XRD). The chemical composition of the β phase for different conditions was determined with the help of energy dispersive spectrometer (EDS) attached to SEM. Furnace cooling resulted in the development of coarser structure (α+β), while air cooling resulted in much finer structure with widmanstatten morphology of α at the grain boundaries. Quenching from solution annealing temperature formed α’ martensite, their proportion being dependent on the temperature in β phase field. It is well known that the transformation of β to α follows Burger orientation relationship (OR). In order to reconstruct the microstructure of parent β phase, a MATLAB code was written using neighbor-to-neighbor, triplet method and Tari’s method. The code was tested on the annealed samples (1066C solution annealing temperature followed by furnace cooling to room temperature). The parent phase data thus generated was then plotted using the TSL-OIM software. The reconstruction results of the above methods were compared and analyzed. The Tari’s approach (clustering approach) gave better results compared to neighbor-to-neighbor and triplet method but the time taken by the triplet method was least compared to the other two methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ti-6Al-4V%20alloy" title="Ti-6Al-4V alloy">Ti-6Al-4V alloy</a>, <a href="https://publications.waset.org/abstracts/search?q=microstructure" title=" microstructure"> microstructure</a>, <a href="https://publications.waset.org/abstracts/search?q=electron%20backscattered%20diffraction" title=" electron backscattered diffraction"> electron backscattered diffraction</a>, <a href="https://publications.waset.org/abstracts/search?q=parent%20phase%20reconstruction" title=" parent phase reconstruction"> parent phase reconstruction</a> </p> <a href="https://publications.waset.org/abstracts/24632/microstructure-evolution-and-pre-transformation-microstructure-reconstruction-in-ti-6al-4v-alloy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24632.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">446</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">179</span> Performance of Environmental Efficiency of Energy Iran and Other Middle East Countries</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bahram%20Fathi">Bahram Fathi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20Khodaparast%20Mashhadi"> Mahdi Khodaparast Mashhadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Masuod%20Homayounifar"> Masuod Homayounifar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> According to 1404 forecasting documentation, among the most fundamental ways of Iran’s success in competition with other regional countries are innovations, efficiency enhancements and domestic productivity. Therefore, in this study, the energy consumption efficiency of Iran and the neighbor countries has been measured in the period between 2007-2012 considering the simultaneous economic activities, CO2 emission, and consumption of energy through data envelopment analysis of undesirable output. The results of the study indicated that the energy efficiency changes in both Iran and the average neighbor countries has been on a descending trend and Iran’s energy efficiency status is not desirable compared to the other countries in the region. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy%20efficiency" title="energy efficiency">energy efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental" title=" environmental"> environmental</a>, <a href="https://publications.waset.org/abstracts/search?q=undesirable%20output" title=" undesirable output"> undesirable output</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20envelopment%20analysis" title=" data envelopment analysis"> data envelopment analysis</a> </p> <a href="https://publications.waset.org/abstracts/39885/performance-of-environmental-efficiency-of-energy-iran-and-other-middle-east-countries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39885.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">448</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">178</span> A Selection Approach: Discriminative Model for Nominal Attributes-Based Distance Measures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fang%20Gong">Fang Gong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Distance measures are an indispensable part of many instance-based learning (IBL) and machine learning (ML) algorithms. The value difference metrics (VDM) and inverted specific-class distance measure (ISCDM) are among the top-performing distance measures that address nominal attributes. VDM performs well in some domains owing to its simplicity and poorly in others that exist missing value and non-class attribute noise. ISCDM, however, typically works better than VDM on such domains. To maximize their advantages and avoid disadvantages, in this paper, a selection approach: a discriminative model for nominal attributes-based distance measures is proposed. More concretely, VDM and ISCDM are built independently on a training dataset at the training stage, and the most credible one is recorded for each training instance. At the test stage, its nearest neighbor for each test instance is primarily found by any of VDM and ISCDM and then chooses the most reliable model of its nearest neighbor to predict its class label. It is simply denoted as a discriminative distance measure (DDM). Experiments are conducted on the 34 University of California at Irvine (UCI) machine learning repository datasets, and it shows DDM retains the interpretability and simplicity of VDM and ISCDM but significantly outperforms the original VDM and ISCDM and other state-of-the-art competitors in terms of accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distance%20measure" title="distance measure">distance measure</a>, <a href="https://publications.waset.org/abstracts/search?q=discriminative%20model" title=" discriminative model"> discriminative model</a>, <a href="https://publications.waset.org/abstracts/search?q=nominal%20attributes" title=" nominal attributes"> nominal attributes</a>, <a href="https://publications.waset.org/abstracts/search?q=nearest%20neighbor" title=" nearest neighbor"> nearest neighbor</a> </p> <a href="https://publications.waset.org/abstracts/119343/a-selection-approach-discriminative-model-for-nominal-attributes-based-distance-measures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/119343.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">114</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">177</span> A Distributed Cryptographically Generated Address Computing Algorithm for Secure Neighbor Discovery Protocol in IPv6</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Moslehpour">M. Moslehpour</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Khorsandi"> S. Khorsandi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to shortage in IPv4 addresses, transition to IPv6 has gained significant momentum in recent years. Like Address Resolution Protocol (ARP) in IPv4, Neighbor Discovery Protocol (NDP) provides some functions like address resolution in IPv6. Besides functionality of NDP, it is vulnerable to some attacks. To mitigate these attacks, Internet Protocol Security (IPsec) was introduced, but it was not efficient due to its limitation. Therefore, SEND protocol is proposed to automatic protection of auto-configuration process. It is secure neighbor discovery and address resolution process. To defend against threats on NDP’s integrity and identity, Cryptographically Generated Address (CGA) and asymmetric cryptography are used by SEND. Besides advantages of SEND, its disadvantages like the computation process of CGA algorithm and sequentially of CGA generation algorithm are considerable. In this paper, we parallel this process between network resources in order to improve it. In addition, we compare the CGA generation time in self-computing and distributed-computing process. We focus on the impact of the malicious nodes on the CGA generation time in the network. According to the result, although malicious nodes participate in the generation process, CGA generation time is less than when it is computed in a one-way. By Trust Management System, detecting and insulating malicious nodes is easier. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=NDP" title="NDP">NDP</a>, <a href="https://publications.waset.org/abstracts/search?q=IPsec" title=" IPsec"> IPsec</a>, <a href="https://publications.waset.org/abstracts/search?q=SEND" title=" SEND"> SEND</a>, <a href="https://publications.waset.org/abstracts/search?q=CGA" title=" CGA"> CGA</a>, <a href="https://publications.waset.org/abstracts/search?q=modifier" title=" modifier"> modifier</a>, <a href="https://publications.waset.org/abstracts/search?q=malicious%20node" title=" malicious node"> malicious node</a>, <a href="https://publications.waset.org/abstracts/search?q=self-computing" title=" self-computing"> self-computing</a>, <a href="https://publications.waset.org/abstracts/search?q=distributed-computing" title=" distributed-computing"> distributed-computing</a> </p> <a href="https://publications.waset.org/abstracts/45747/a-distributed-cryptographically-generated-address-computing-algorithm-for-secure-neighbor-discovery-protocol-in-ipv6" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45747.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> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">176</span> Thailand’s Education Cooperation with Neighboring Countries: The Key Factors to Strengthen the “Soft Power” Relationship</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rungrot%20Trongsakul">Rungrot Trongsakul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper was aimed to study the model of education cooperation during Thailand and neighbor countries, especially the countries which the territory-cohesion border with Thailand used “Soft Power” to enhance the good relationship. This research employed qualitative method, analyzed and synthesized the content of cooperation projects, policies, laws, relevant theories, relevant research papers and documents and used SWOT analysis. The research findings revealed that Thailand’s education cooperation projects with neighbor countries had two characteristics: 1) education cooperation projects/programs were a part in economic cooperation projects, and 2) there were directly education cooperation projects. The suggested education cooperation model was based on the concept of “Soft Power”, thus the determination of action plans or projects as key factors of public and private organizations should be based on sincere participation among people, communities and relevant organizations of the neighbor countries. Adoption of education-cultural exchange, learning and sharing process is a key to strengthen good relationship of the countries’ cooperation. The roles of education in this included sharing and acceptance of culture and local wisdom, human resource development, knowledge management, integration and networking building could enhance relationship between agents of related organizations of Thailand and neighbors countries. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=education" title="education">education</a>, <a href="https://publications.waset.org/abstracts/search?q=soft-power" title=" soft-power"> soft-power</a>, <a href="https://publications.waset.org/abstracts/search?q=relationship" title=" relationship"> relationship</a>, <a href="https://publications.waset.org/abstracts/search?q=cooperation" title=" cooperation"> cooperation</a>, <a href="https://publications.waset.org/abstracts/search?q=Thailand%20neighboring%20countries" title=" Thailand neighboring countries"> Thailand neighboring countries</a> </p> <a href="https://publications.waset.org/abstracts/14473/thailands-education-cooperation-with-neighboring-countries-the-key-factors-to-strengthen-the-soft-power-relationship" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14473.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">359</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">175</span> Measuring Multi-Class Linear Classifier for Image Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatma%20Susilawati%20Mohamad">Fatma Susilawati Mohamad</a>, <a href="https://publications.waset.org/abstracts/search?q=Azizah%20Abdul%20Manaf"> Azizah Abdul Manaf</a>, <a href="https://publications.waset.org/abstracts/search?q=Fadhillah%20Ahmad"> Fadhillah Ahmad</a>, <a href="https://publications.waset.org/abstracts/search?q=Zarina%20Mohamad"> Zarina Mohamad</a>, <a href="https://publications.waset.org/abstracts/search?q=Wan%20Suryani%20Wan%20Awang"> Wan Suryani Wan Awang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A simple and robust multi-class linear classifier is proposed and implemented. For a pair of classes of the linear boundary, a collection of segments of hyper planes created as perpendicular bisectors of line segments linking centroids of the classes or part of classes. Nearest Neighbor and Linear Discriminant Analysis are compared in the experiments to see the performances of each classifier in discriminating ripeness of oil palm. This paper proposes a multi-class linear classifier using Linear Discriminant Analysis (LDA) for image identification. Result proves that LDA is well capable in separating multi-class features for ripeness identification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-class" title="multi-class">multi-class</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20classifier" title=" linear classifier"> linear classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=nearest%20neighbor" title=" nearest neighbor"> nearest neighbor</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20discriminant%20analysis" title=" linear discriminant analysis"> linear discriminant analysis</a> </p> <a href="https://publications.waset.org/abstracts/51310/measuring-multi-class-linear-classifier-for-image-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51310.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">538</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">174</span> Study on the Efficient Routing Algorithms in Delay-Tolerant Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Si-Gwan%20Kim">Si-Gwan Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Delay Tolerant Networks (DTN), there may not exist an end-to-end path between source and destination at the time of message transmission. Employing ‘Store Carry and Forward’ delivery mechanism for message transmission in such networks usually incurs long message delays. In this paper, we present the modified Binary Spray and Wait (BSW) routing protocol that enhances the performance of the original one. Our proposed algorithm adjusts the number of forward messages depending on the number of neighbor nodes. By using beacon messages periodically, the number of neighbor nodes can be managed. The simulation using ONE simulator results shows that our modified version gives higher delivery ratio and less latency as compared to BSW. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=delay%20tolerant%20networks" title="delay tolerant networks">delay tolerant networks</a>, <a href="https://publications.waset.org/abstracts/search?q=store%20carry%20and%20forward" title=" store carry and forward"> store carry and forward</a>, <a href="https://publications.waset.org/abstracts/search?q=one%20simulator" title=" one simulator"> one simulator</a>, <a href="https://publications.waset.org/abstracts/search?q=binary%20spray%20and%20wait" title=" binary spray and wait"> binary spray and wait</a> </p> <a href="https://publications.waset.org/abstracts/97723/study-on-the-efficient-routing-algorithms-in-delay-tolerant-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97723.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">123</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">173</span> The Influence of Noise on Aerial Image Semantic Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pengchao%20Wei">Pengchao Wei</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiangzhong%20Fang"> Xiangzhong Fang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Noise is ubiquitous in this world. Denoising is an essential technology, especially in image semantic segmentation, where noises are generally categorized into two main types i.e. feature noise and label noise. The main focus of this paper is aiming at modeling label noise, investigating the behaviors of different types of label noise on image semantic segmentation tasks using K-Nearest-Neighbor and Convolutional Neural Network classifier. The performance without label noise and with is evaluated and illustrated in this paper. In addition to that, the influence of feature noise on the image semantic segmentation task is researched as well and a feature noise reduction method is applied to mitigate its influence in the learning procedure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title="convolutional neural network">convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=denoising" title=" denoising"> denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20noise" title=" feature noise"> feature noise</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20semantic%20segmentation" title=" image semantic segmentation"> image semantic segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=k-nearest-neighbor" title=" k-nearest-neighbor"> k-nearest-neighbor</a>, <a href="https://publications.waset.org/abstracts/search?q=label%20noise" title=" label noise"> label noise</a> </p> <a href="https://publications.waset.org/abstracts/141479/the-influence-of-noise-on-aerial-image-semantic-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141479.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">220</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">172</span> Minimization of Propagation Delay in Multi Unmanned Aerial Vehicle Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Purva%20Joshi">Purva Joshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Rohit%20Thanki"> Rohit Thanki</a>, <a href="https://publications.waset.org/abstracts/search?q=Omar%20Hanif"> Omar Hanif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Unmanned aerial vehicles (UAVs) are becoming increasingly important in various industrial applications and sectors. Nowadays, a multi UAV network is used for specific types of communication (e.g., military) and monitoring purposes. Therefore, it is critical to reducing propagation delay during communication between UAVs, which is essential in a multi UAV network. This paper presents how the propagation delay between the base station (BS) and the UAVs is reduced using a searching algorithm. Furthermore, the iterative-based K-nearest neighbor (k-NN) algorithm and Travelling Salesmen Problem (TSP) algorthm were utilized to optimize the distance between BS and individual UAV to overcome the problem of propagation delay in multi UAV networks. The simulation results show that this proposed method reduced complexity, improved reliability, and reduced propagation delay in multi UAV networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi%20UAV%20network" title="multi UAV network">multi UAV network</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20distance" title=" optimal distance"> optimal distance</a>, <a href="https://publications.waset.org/abstracts/search?q=propagation%20delay" title=" propagation delay"> propagation delay</a>, <a href="https://publications.waset.org/abstracts/search?q=K%20-%20nearest%20neighbor" title=" K - nearest neighbor"> K - nearest neighbor</a>, <a href="https://publications.waset.org/abstracts/search?q=traveling%20salesmen%20problem" title=" traveling salesmen problem"> traveling salesmen problem</a> </p> <a href="https://publications.waset.org/abstracts/150423/minimization-of-propagation-delay-in-multi-unmanned-aerial-vehicle-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150423.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">200</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">171</span> Security in Resource Constraints Network Light Weight Encryption for Z-MAC</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mona%20Almansoori">Mona Almansoori</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Mustafa"> Ahmed Mustafa</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Elshamy"> Ahmad Elshamy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wireless sensor network was formed by a combination of nodes, systematically it transmitting the data to their base stations, this transmission data can be easily compromised if the limited processing power and the data consistency from these nodes are kept in mind; there is always a discussion to address the secure data transfer or transmission in actual time. This will present a mechanism to securely transmit the data over a chain of sensor nodes without compromising the throughput of the network by utilizing available battery resources available in the sensor node. Our methodology takes many different advantages of Z-MAC protocol for its efficiency, and it provides a unique key by sharing the mechanism using neighbor node MAC address. We present a light weighted data integrity layer which is embedded in the Z-MAC protocol to prove that our protocol performs well than Z-MAC when we introduce the different attack scenarios. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hybrid%20MAC%20protocol" title="hybrid MAC protocol">hybrid MAC protocol</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20integrity" title=" data integrity"> data integrity</a>, <a href="https://publications.waset.org/abstracts/search?q=lightweight%20encryption" title=" lightweight encryption"> lightweight encryption</a>, <a href="https://publications.waset.org/abstracts/search?q=neighbor%20based%20key%20sharing" title=" neighbor based key sharing"> neighbor based key sharing</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20node%20dataprocessing" title=" sensor node dataprocessing"> sensor node dataprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=Z-MAC" title=" Z-MAC"> Z-MAC</a> </p> <a href="https://publications.waset.org/abstracts/128077/security-in-resource-constraints-network-light-weight-encryption-for-z-mac" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128077.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">143</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">170</span> Message Authentication Scheme for Vehicular Ad-Hoc Networks under Sparse RSUs Environment </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wen%20Shyong%20Hsieh">Wen Shyong Hsieh</a>, <a href="https://publications.waset.org/abstracts/search?q=Chih%20Hsueh%20Lin"> Chih Hsueh Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we combine the concepts of chameleon hash function (CHF) and identification based cryptography (IBC) to build a message authentication environment for VANET under sparse RSUs. Based on the CHF, TA keeps two common secrets that will be embedded to all identities to be as the evidence of mutual trusting. TA will issue one original identity to every RSU and vehicle. An identity contains one public ID and one private key. The public ID, includes three components: pseudonym, random key, and public key, is used to present one entity and can be verified to be a legal one. The private key is used to claim the ownership of the public ID. Based on the concept of IBC, without any negotiating process, a CHF pairing key multiplied by one private key and other’s public key will be used for mutually trusting and to be utilized as the session key of secure communicating between RSUs and vehicles. To help the vehicles to do message authenticating, the RSUs are assigned to response the vehicle’s temple identity request using two short time secretes that are broadcasted by TA. To light the loading of request information, one day is divided into M time slots. At every time slot, TA will broadcast two short time secretes to all valid RSUs for that time slot. Any RSU can response the temple identity request from legal vehicles. With the collected announcement of public IDs from the neighbor vehicles, a vehicle can set up its neighboring set, which includes the information about the neighbor vehicle’s temple public ID and temple CHF pairing key that can be derived by the private key and neighbor’s public key and will be used to do message authenticating or secure communicating without the help of RSU. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Internet%20of%20Vehicles%20%28IOV%29" title="Internet of Vehicles (IOV)">Internet of Vehicles (IOV)</a>, <a href="https://publications.waset.org/abstracts/search?q=Vehicular%20Ad-hoc%20Networks%20%28VANETs%29" title=" Vehicular Ad-hoc Networks (VANETs)"> Vehicular Ad-hoc Networks (VANETs)</a>, <a href="https://publications.waset.org/abstracts/search?q=Chameleon%20Hash%20Function%20%28CHF%29" title=" Chameleon Hash Function (CHF)"> Chameleon Hash Function (CHF)</a>, <a href="https://publications.waset.org/abstracts/search?q=message%20authentication" title=" message authentication"> message authentication</a> </p> <a href="https://publications.waset.org/abstracts/58528/message-authentication-scheme-for-vehicular-ad-hoc-networks-under-sparse-rsus-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58528.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">391</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">169</span> Towards a Balancing Medical Database by Using the Least Mean Square Algorithm </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kamel%20Belammi">Kamel Belammi</a>, <a href="https://publications.waset.org/abstracts/search?q=Houria%20Fatrim"> Houria Fatrim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> imbalanced data set, a problem often found in real world application, can cause seriously negative effect on classification performance of machine learning algorithms. There have been many attempts at dealing with classification of imbalanced data sets. In medical diagnosis classification, we often face the imbalanced number of data samples between the classes in which there are not enough samples in rare classes. In this paper, we proposed a learning method based on a cost sensitive extension of Least Mean Square (LMS) algorithm that penalizes errors of different samples with different weight and some rules of thumb to determine those weights. After the balancing phase, we applythe different classifiers (support vector machine (SVM), k- nearest neighbor (KNN) and multilayer neuronal networks (MNN)) for balanced data set. We have also compared the obtained results before and after balancing method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multilayer%20neural%20networks" title="multilayer neural networks">multilayer neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=k-%20nearest%20neighbor" title=" k- nearest neighbor"> k- nearest neighbor</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=imbalanced%20medical%20data" title=" imbalanced medical data"> imbalanced medical data</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20mean%20square%20algorithm" title=" least mean square algorithm"> least mean square algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=diabetes" title=" diabetes"> diabetes</a> </p> <a href="https://publications.waset.org/abstracts/33277/towards-a-balancing-medical-database-by-using-the-least-mean-square-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33277.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">532</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">168</span> Artificial Intelligence-Based Detection of Individuals Suffering from Vestibular Disorder</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dua%20Hi%C5%9Fam">Dua Hişam</a>, <a href="https://publications.waset.org/abstracts/search?q=Serhat%20%C4%B0kizo%C4%9Flu"> Serhat İkizoğlu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Identifying the problem behind balance disorder is one of the most interesting topics in the medical literature. This study has considerably enhanced the development of artificial intelligence (AI) algorithms applying multiple machine learning (ML) models to sensory data on gait collected from humans to classify between normal people and those suffering from Vestibular System (VS) problems. Although AI is widely utilized as a diagnostic tool in medicine, AI models have not been used to perform feature extraction and identify VS disorders through training on raw data. In this study, three machine learning (ML) models, the Random Forest Classifier (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbor (KNN), have been trained to detect VS disorder, and the performance comparison of the algorithms has been made using accuracy, recall, precision, and f1-score. With an accuracy of 95.28 %, Random Forest Classifier (RF) was the most accurate model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=vestibular%20disorder" title="vestibular disorder">vestibular disorder</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest%20classifier" title=" random forest classifier"> random forest classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=k-nearest%20neighbor" title=" k-nearest neighbor"> k-nearest neighbor</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20gradient%20boosting" title=" extreme gradient boosting"> extreme gradient boosting</a> </p> <a href="https://publications.waset.org/abstracts/162312/artificial-intelligence-based-detection-of-individuals-suffering-from-vestibular-disorder" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162312.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">69</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">167</span> FCNN-MR: A Parallel Instance Selection Method Based on Fast Condensed Nearest Neighbor Rule</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lu%20Si">Lu Si</a>, <a href="https://publications.waset.org/abstracts/search?q=Jie%20Yu"> Jie Yu</a>, <a href="https://publications.waset.org/abstracts/search?q=Shasha%20Li"> Shasha Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Jun%20Ma"> Jun Ma</a>, <a href="https://publications.waset.org/abstracts/search?q=Lei%20Luo"> Lei Luo</a>, <a href="https://publications.waset.org/abstracts/search?q=Qingbo%20Wu"> Qingbo Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yongqi%20Ma"> Yongqi Ma</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhengji%20Liu"> Zhengji Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Instance selection (IS) technique is used to reduce the data size to improve the performance of data mining methods. Recently, to process very large data set, several proposed methods divide the training set into some disjoint subsets and apply IS algorithms independently to each subset. In this paper, we analyze the limitation of these methods and give our viewpoint about how to divide and conquer in IS procedure. Then, based on fast condensed nearest neighbor (FCNN) rule, we propose a large data sets instance selection method with MapReduce framework. Besides ensuring the prediction accuracy and reduction rate, it has two desirable properties: First, it reduces the work load in the aggregation node; Second and most important, it produces the same result with the sequential version, which other parallel methods cannot achieve. We evaluate the performance of FCNN-MR on one small data set and two large data sets. The experimental results show that it is effective and practical. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=instance%20selection" title="instance selection">instance selection</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20reduction" title=" data reduction"> data reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=MapReduce" title=" MapReduce"> MapReduce</a>, <a href="https://publications.waset.org/abstracts/search?q=kNN" title=" kNN"> kNN</a> </p> <a href="https://publications.waset.org/abstracts/71156/fcnn-mr-a-parallel-instance-selection-method-based-on-fast-condensed-nearest-neighbor-rule" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71156.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">253</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">166</span> Spatial Data Mining by Decision Trees</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sihem%20Oujdi">Sihem Oujdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hafida%20Belbachir"> Hafida Belbachir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Existing methods of data mining cannot be applied on spatial data because they require spatial specificity consideration, as spatial relationships. This paper focuses on the classification with decision trees, which are one of the data mining techniques. We propose an extension of the C4.5 algorithm for spatial data, based on two different approaches Join materialization and Querying on the fly the different tables. Similar works have been done on these two main approaches, the first - Join materialization - favors the processing time in spite of memory space, whereas the second - Querying on the fly different tables- promotes memory space despite of the processing time. The modified C4.5 algorithm requires three entries tables: a target table, a neighbor table, and a spatial index join that contains the possible spatial relationship among the objects in the target table and those in the neighbor table. Thus, the proposed algorithms are applied to a spatial data pattern in the accidentology domain. A comparative study of our approach with other works of classification by spatial decision trees will be detailed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=C4.5%20algorithm" title="C4.5 algorithm">C4.5 algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20trees" title=" decision trees"> decision trees</a>, <a href="https://publications.waset.org/abstracts/search?q=S-CART" title=" S-CART"> S-CART</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20data%20mining" title=" spatial data mining"> spatial data mining</a> </p> <a href="https://publications.waset.org/abstracts/11935/spatial-data-mining-by-decision-trees" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11935.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">612</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">165</span> Analysis of Genetic Variations in Camel Breeds (Camelus dromedarius) </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yasser%20M.%20Saad">Yasser M. Saad</a>, <a href="https://publications.waset.org/abstracts/search?q=Amr%20A.%20El%20Hanafy"> Amr A. El Hanafy</a>, <a href="https://publications.waset.org/abstracts/search?q=Saleh%20A.%20Alkarim"> Saleh A. Alkarim</a>, <a href="https://publications.waset.org/abstracts/search?q=Hussein%20A.%20Almehdar"> Hussein A. Almehdar</a>, <a href="https://publications.waset.org/abstracts/search?q=Elrashdy%20M.%20Redwan"> Elrashdy M. Redwan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Camels are substantial providers of transport, milk, sport, meat, shelter, security and capital in many countries, particularly in Saudi Arabia. Inter simple sequence repeat technique was used to detect the genetic variations among some camel breeds (Majaheim, Safra, Wadah, and Hamara). Actual number of alleles, effective number of alleles, gene diversity, Shannon’s information index and polymorphic bands were calculated for each evaluated camel breed. Neighbor-joining tree that re-constructed for evaluated these camel breeds showed that, Hamara breed is distantly related from the other evaluated camels. In addition, the polymorphic sites, haplotypes and nucleotide diversity were identified for some camelidae <em>cox1</em> gene sequences (obtained from NCBI). The distance value between <em>C. bactrianus</em> and <em>C. dromedarius</em> (0.072) was relatively low. Analysis of genetic diversity is an important way for conserving <em>Camelus dromedarius</em> genetic resources. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=camel" title="camel">camel</a>, <a href="https://publications.waset.org/abstracts/search?q=genetics" title=" genetics"> genetics</a>, <a href="https://publications.waset.org/abstracts/search?q=ISSR" title=" ISSR"> ISSR</a>, <a href="https://publications.waset.org/abstracts/search?q=neighbor-joining" title=" neighbor-joining"> neighbor-joining</a> </p> <a href="https://publications.waset.org/abstracts/73254/analysis-of-genetic-variations-in-camel-breeds-camelus-dromedarius" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73254.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">472</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">164</span> Comparison of Different k-NN Models for Speed Prediction in an Urban Traffic Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyoung%20Kim">Seyoung Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeongmin%20Kim"> Jeongmin Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Kwang%20Ryel%20Ryu"> Kwang Ryel Ryu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (<em>k</em>-NN) as predictive models is that it does not require any explicit model building. Instead, <em>k</em>-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up <em>k</em>-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different <em>k</em>-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=k-NN" title=" k-NN"> k-NN</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20speed%20prediction" title=" traffic speed prediction"> traffic speed prediction</a> </p> <a href="https://publications.waset.org/abstracts/43415/comparison-of-different-k-nn-models-for-speed-prediction-in-an-urban-traffic-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43415.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">363</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">163</span> Molecular Survey and Genetic Diversity of Bartonella henselae Strains Infecting Stray Cats from Algeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Naouelle%20Azzag">Naouelle Azzag</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadia%20Haddad"> Nadia Haddad</a>, <a href="https://publications.waset.org/abstracts/search?q=Benoit%20Durand"> Benoit Durand</a>, <a href="https://publications.waset.org/abstracts/search?q=Elisabeth%20Petit"> Elisabeth Petit</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Ammouche"> Ali Ammouche</a>, <a href="https://publications.waset.org/abstracts/search?q=Bruno%20Chomel"> Bruno Chomel</a>, <a href="https://publications.waset.org/abstracts/search?q=Henri%20J.%20Boulouis"> Henri J. Boulouis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Bartonella henselae is a small, gram negative, arthropod-borne bacterium that has been shown to cause multiple clinical manifestations in humans including cat scratch disease, bacillary angiomatosis, endocarditis, and bacteremia. In this research, we report the results of a cross sectional study of Bartonella henselae bacteremia in stray cats from Algiers. Whole blood of 227 stray cats from Algiers was tested for the presence of Bartonella species by culture and for the evaluation of the genetic diversity of B. henselae strains by multi-locus variable number of tandem repeats assay (MLVA). Bacteremia prevalence was 17% and only B. henselae was identified. Type I was the predominant type (64%). MLVA typing of 259 strains from 30 bacteremic cats revealed 52 different profiles. 51 of these profiles were specific to Algerian cats/identified for the first time. 20/30 cats (67%) harbored 2 to 7 MLVA profiles simultaneously. The similarity of MLVA profiles obtained from the same cat, neighbor-joining clustering and structure-neighbor clustering showed that such a diversity likely results from two different mechanisms occurring either independently or simultaneously independent infections and genetic drift from a primary strain. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bartonella" title="Bartonella">Bartonella</a>, <a href="https://publications.waset.org/abstracts/search?q=cat" title=" cat"> cat</a>, <a href="https://publications.waset.org/abstracts/search?q=MLVA" title=" MLVA"> MLVA</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic" title=" genetic"> genetic</a> </p> <a href="https://publications.waset.org/abstracts/108213/molecular-survey-and-genetic-diversity-of-bartonella-henselae-strains-infecting-stray-cats-from-algeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108213.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">149</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=K-nearest%20neighbor&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=K-nearest%20neighbor&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=K-nearest%20neighbor&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=K-nearest%20neighbor&page=5">5</a></li> <li class="page-item"><a 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