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classification</title> <meta name="description" content="Search results for: Meyer classification"> <meta name="keywords" content="Meyer classification"> <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 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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="Meyer classification"> <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> 2232</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Meyer classification</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2232</span> Evaluating Classification with Efficacy Metrics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guofan%20Shao">Guofan Shao</a>, <a href="https://publications.waset.org/abstracts/search?q=Lina%20Tang"> Lina Tang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hao%20Zhang"> Hao Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The values of image classification accuracy are affected by class size distributions and classification schemes, making it difficult to compare the performance of classification algorithms across different remote sensing data sources and classification systems. Based on the term efficacy from medicine and pharmacology, we have developed the metrics of image classification efficacy at the map and class levels. The novelty of this approach is that a baseline classification is involved in computing image classification efficacies so that the effects of class statistics are reduced. Furthermore, the image classification efficacies are interpretable and comparable, and thus, strengthen the assessment of image data classification methods. We use real-world and hypothetical examples to explain the use of image classification efficacies. The metrics of image classification efficacy meet the critical need to rectify the strategy for the assessment of image classification performance as image classification methods are becoming more diversified. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accuracy%20assessment" title="accuracy assessment">accuracy assessment</a>, <a href="https://publications.waset.org/abstracts/search?q=efficacy" title=" efficacy"> efficacy</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20classification" title=" image classification"> image classification</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=uncertainty" title=" uncertainty"> uncertainty</a> </p> <a href="https://publications.waset.org/abstracts/142555/evaluating-classification-with-efficacy-metrics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142555.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">210</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">2231</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">2230</span> Arabic Text Representation and Classification Methods: Current State of the Art</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rami%20Ayadi">Rami Ayadi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Maraoui"> Mohsen Maraoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Mounir%20Zrigui"> Mounir Zrigui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we have presented a brief current state of the art for Arabic text representation and classification methods. We decomposed Arabic Task Classification into four categories. First we describe some algorithms applied to classification on Arabic text. Secondly, we cite all major works when comparing classification algorithms applied on Arabic text, after this, we mention some authors who proposing new classification methods and finally we investigate the impact of preprocessing on Arabic TC. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=text%20classification" title="text classification">text classification</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabic" title=" Arabic"> Arabic</a>, <a href="https://publications.waset.org/abstracts/search?q=impact%20of%20preprocessing" title=" impact of preprocessing"> impact of preprocessing</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20algorithms" title=" classification algorithms"> classification algorithms</a> </p> <a href="https://publications.waset.org/abstracts/10277/arabic-text-representation-and-classification-methods-current-state-of-the-art" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10277.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">469</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">2229</span> Sensitive Analysis of the ZF Model for ABC Multi Criteria Inventory Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Makram%20Ben%20Jeddou">Makram Ben Jeddou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The ABC classification is widely used by managers for inventory control. The classical ABC classification is based on the Pareto principle and according to the criterion of the annual use value only. Single criterion classification is often insufficient for a closely inventory control. Multi-criteria inventory classification models have been proposed by researchers in order to take into account other important criteria. From these models, we will consider the ZF model in order to make a sensitive analysis on the composite score calculated for each item. In fact, this score based on a normalized average between a good and a bad optimized index can affect the ABC items classification. We will then focus on the weights assigned to each index and propose a classification compromise. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ABC%20classification" title="ABC classification">ABC classification</a>, <a href="https://publications.waset.org/abstracts/search?q=multi%20criteria%20inventory%20%20classification%20models" title=" multi criteria inventory classification models"> multi criteria inventory classification models</a>, <a href="https://publications.waset.org/abstracts/search?q=ZF-model" title=" ZF-model"> ZF-model</a> </p> <a href="https://publications.waset.org/abstracts/22613/sensitive-analysis-of-the-zf-model-for-abc-multi-criteria-inventory-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22613.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">508</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">2228</span> A New Approach for Improving Accuracy of Multi Label Stream Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kunal%20Shah">Kunal Shah</a>, <a href="https://publications.waset.org/abstracts/search?q=Swati%20Patel"> Swati Patel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many real world problems involve data which can be considered as multi-label data streams. Efficient methods exist for multi-label classification in non streaming scenarios. However, learning in evolving streaming scenarios is more challenging, as the learners must be able to adapt to change using limited time and memory. Classification is used to predict class of unseen instance as accurate as possible. Multi label classification is a variant of single label classification where set of labels associated with single instance. Multi label classification is used by modern applications, such as text classification, functional genomics, image classification, music categorization etc. This paper introduces the task of multi-label classification, methods for multi-label classification and evolution measure for multi-label classification. Also, comparative analysis of multi label classification methods on the basis of theoretical study, and then on the basis of simulation was done on various data sets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binary%20relevance" title="binary relevance">binary relevance</a>, <a href="https://publications.waset.org/abstracts/search?q=concept%20drift" title=" concept drift"> concept drift</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20stream%20mining" title=" data stream mining"> data stream mining</a>, <a href="https://publications.waset.org/abstracts/search?q=MLSC" title=" MLSC"> MLSC</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20window%20with%20buffer" title=" multiple window with buffer"> multiple window with buffer</a> </p> <a href="https://publications.waset.org/abstracts/33035/a-new-approach-for-improving-accuracy-of-multi-label-stream-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33035.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">584</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">2227</span> Classification of Attacks Over Cloud Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karim%20Abouelmehdi">Karim Abouelmehdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Loubna%20Dali"> Loubna Dali</a>, <a href="https://publications.waset.org/abstracts/search?q=Elmoutaoukkil%20Abdelmajid"> Elmoutaoukkil Abdelmajid</a>, <a href="https://publications.waset.org/abstracts/search?q=Hoda%20Elsayed"> Hoda Elsayed</a>, <a href="https://publications.waset.org/abstracts/search?q=Eladnani%20Fatiha"> Eladnani Fatiha</a>, <a href="https://publications.waset.org/abstracts/search?q=Benihssane%20Abderahim"> Benihssane Abderahim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The security of cloud services is the concern of cloud service providers. In this paper, we will mention different classifications of cloud attacks referred by specialized organizations. Each agency has its classification of well-defined properties. The purpose is to present a high-level classification of current research in cloud computing security. This classification is organized around attack strategies and corresponding defenses. <p class="card-text"><strong>Keywords:</strong> <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=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=risk" title=" risk"> risk</a>, <a href="https://publications.waset.org/abstracts/search?q=security" title=" security"> security</a> </p> <a href="https://publications.waset.org/abstracts/31849/classification-of-attacks-over-cloud-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31849.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">548</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">2226</span> Review and Comparison of Associative Classification Data Mining Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Suzan%20Wedyan">Suzan Wedyan </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data mining is one of the main phases in the Knowledge Discovery Database (KDD) which is responsible of finding hidden and useful knowledge from databases. There are many different tasks for data mining including regression, pattern recognition, clustering, classification, and association rule. In recent years a promising data mining approach called associative classification (AC) has been proposed, AC integrates classification and association rule discovery to build classification models (classifiers). This paper surveys and critically compares several AC algorithms with reference of the different procedures are used in each algorithm, such as rule learning, rule sorting, rule pruning, classifier building, and class allocation for test cases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=associative%20classification" title="associative classification">associative classification</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=learning" title=" learning"> learning</a>, <a href="https://publications.waset.org/abstracts/search?q=rule%20ranking" title=" rule ranking"> rule ranking</a>, <a href="https://publications.waset.org/abstracts/search?q=rule%20pruning" title=" rule pruning"> rule pruning</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a> </p> <a href="https://publications.waset.org/abstracts/4191/review-and-comparison-of-associative-classification-data-mining-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4191.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">537</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">2225</span> Meta-Learning for Hierarchical Classification and Applications in Bioinformatics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fabio%20Fabris">Fabio Fabris</a>, <a href="https://publications.waset.org/abstracts/search?q=Alex%20A.%20Freitas"> Alex A. Freitas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hierarchical classification is a special type of classification task where the class labels are organised into a hierarchy, with more generic class labels being ancestors of more specific ones. Meta-learning for classification-algorithm recommendation consists of recommending to the user a classification algorithm, from a pool of candidate algorithms, for a dataset, based on the past performance of the candidate algorithms in other datasets. Meta-learning is normally used in conventional, non-hierarchical classification. By contrast, this paper proposes a meta-learning approach for more challenging task of hierarchical classification, and evaluates it in a large number of bioinformatics datasets. Hierarchical classification is especially relevant for bioinformatics problems, as protein and gene functions tend to be organised into a hierarchy of class labels. This work proposes meta-learning approach for recommending the best hierarchical classification algorithm to a hierarchical classification dataset. This work’s contributions are: 1) proposing an algorithm for splitting hierarchical datasets into new datasets to increase the number of meta-instances, 2) proposing meta-features for hierarchical classification, and 3) interpreting decision-tree meta-models for hierarchical classification algorithm recommendation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithm%20recommendation" title="algorithm recommendation">algorithm recommendation</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=bioinformatics" title=" bioinformatics"> bioinformatics</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20classification" title=" hierarchical classification"> hierarchical classification</a> </p> <a href="https://publications.waset.org/abstracts/81005/meta-learning-for-hierarchical-classification-and-applications-in-bioinformatics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81005.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">314</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">2224</span> Review on Effective Texture Classification Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sujata%20S.%20Kulkarni">Sujata S. Kulkarni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Effective and efficient texture feature extraction and classification is an important problem in image understanding and recognition. This paper gives a review on effective texture classification method. The objective of the problem of texture representation is to reduce the amount of raw data presented by the image, while preserving the information needed for the task. Texture analysis is important in many applications of computer image analysis for classification include industrial and biomedical surface inspection, for example for defects and disease, ground classification of satellite or aerial imagery and content-based access to image databases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title="compressed sensing">compressed sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20classification" title=" image classification"> image classification</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20analysis" title=" texture analysis"> texture analysis</a> </p> <a href="https://publications.waset.org/abstracts/24461/review-on-effective-texture-classification-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24461.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">434</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">2223</span> Research on Ultrafine Particles Classification Using Hydrocyclone with Annular Rinse Water</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tao%20Youjun">Tao Youjun</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhao%20Younan"> Zhao Younan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The separation effect of fine coal can be improved by the process of pre-desliming. It was significantly enhanced when the fine coal was processed using Falcon concentrator with the removal of -45um coal slime. Ultrafine classification tests using Krebs classification cyclone with annular rinse water showed that increasing feeding pressure can effectively avoid the phenomena of heavy particles passing into overflow and light particles slipping into underflow. The increase of rinse water pressure could reduce the content of fine-grained particles while increasing the classification size. The increase in feeding concentration had a negative effect on the efficiency of classification, meanwhile increased the classification size due to the enhanced hindered settling caused by high underflow concentration. As a result of optimization experiments with response indicator of classification efficiency which based on orthogonal design using Design-Expert software indicated that the optimal classification efficiency reached 91.32% with the feeding pressure of 0.03MPa, the rinse water pressure of 0.02MPa and the feeding concentration of 12.5%. Meanwhile, the classification size was 49.99 μm which had a good agreement with the predicted value. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hydrocyclone" title="hydrocyclone">hydrocyclone</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrafine%20classification" title=" ultrafine classification"> ultrafine classification</a>, <a href="https://publications.waset.org/abstracts/search?q=slime" title=" slime"> slime</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20efficiency" title=" classification efficiency"> classification efficiency</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20size" title=" classification size "> classification size </a> </p> <a href="https://publications.waset.org/abstracts/99752/research-on-ultrafine-particles-classification-using-hydrocyclone-with-annular-rinse-water" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99752.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">167</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">2222</span> Radical Web Text Classification Using a Composite-Based Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kolade%20Olawande%20Owoeye">Kolade Olawande Owoeye</a>, <a href="https://publications.waset.org/abstracts/search?q=George%20R.%20S.%20Weir"> George R. S. Weir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The widespread of terrorism and extremism activities on the internet has become a major threat to the government and national securities due to their potential dangers which have necessitated the need for intelligence gathering via web and real-time monitoring of potential websites for extremist activities. However, the manual classification for such contents is practically difficult or time-consuming. In response to this challenge, an automated classification system called composite technique was developed. This is a computational framework that explores the combination of both semantics and syntactic features of textual contents of a web. We implemented the framework on a set of extremist webpages dataset that has been subjected to the manual classification process. Therein, we developed a classification model on the data using J48 decision algorithm, this is to generate a measure of how well each page can be classified into their appropriate classes. The classification result obtained from our method when compared with other states of arts, indicated a 96% success rate in classifying overall webpages when matched against the manual classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extremist" title="extremist">extremist</a>, <a href="https://publications.waset.org/abstracts/search?q=web%20pages" title=" web pages"> web pages</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=semantics" title=" semantics"> semantics</a>, <a href="https://publications.waset.org/abstracts/search?q=posit" title=" posit"> posit</a> </p> <a href="https://publications.waset.org/abstracts/98432/radical-web-text-classification-using-a-composite-based-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98432.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">145</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">2221</span> Speech Emotion Recognition: A DNN and LSTM Comparison in Single and Multiple Feature Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thiago%20Spilborghs%20Bueno%20Meyer">Thiago Spilborghs Bueno Meyer</a>, <a href="https://publications.waset.org/abstracts/search?q=Plinio%20Thomaz%20Aquino%20Junior"> Plinio Thomaz Aquino Junior</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Through speech, which privileges the functional and interactive nature of the text, it is possible to ascertain the spatiotemporal circumstances, the conditions of production and reception of the discourse, the explicit purposes such as informing, explaining, convincing, etc. These conditions allow bringing the interaction between humans closer to the human-robot interaction, making it natural and sensitive to information. However, it is not enough to understand what is said; it is necessary to recognize emotions for the desired interaction. The validity of the use of neural networks for feature selection and emotion recognition was verified. For this purpose, it is proposed the use of neural networks and comparison of models, such as recurrent neural networks and deep neural networks, in order to carry out the classification of emotions through speech signals to verify the quality of recognition. It is expected to enable the implementation of robots in a domestic environment, such as the HERA robot from the RoboFEI@Home team, which focuses on autonomous service robots for the domestic environment. Tests were performed using only the Mel-Frequency Cepstral Coefficients, as well as tests with several characteristics of Delta-MFCC, spectral contrast, and the Mel spectrogram. To carry out the training, validation and testing of the neural networks, the eNTERFACE’05 database was used, which has 42 speakers from 14 different nationalities speaking the English language. The data from the chosen database are videos that, for use in neural networks, were converted into audios. It was found as a result, a classification of 51,969% of correct answers when using the deep neural network, when the use of the recurrent neural network was verified, with the classification with accuracy equal to 44.09%. The results are more accurate when only the Mel-Frequency Cepstral Coefficients are used for the classification, using the classifier with the deep neural network, and in only one case, it is possible to observe a greater accuracy by the recurrent neural network, which occurs in the use of various features and setting 73 for batch size and 100 training epochs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=emotion%20recognition" title="emotion recognition">emotion recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=speech" title=" speech"> speech</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=human-robot%20interaction" title=" human-robot interaction"> human-robot interaction</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a> </p> <a href="https://publications.waset.org/abstracts/145908/speech-emotion-recognition-a-dnn-and-lstm-comparison-in-single-and-multiple-feature-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145908.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">170</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">2220</span> Hyperspectral Image Classification Using Tree Search Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shreya%20Pare">Shreya Pare</a>, <a href="https://publications.waset.org/abstracts/search?q=Parvin%20Akhter"> Parvin Akhter</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Remotely sensing image classification becomes a very challenging task owing to the high dimensionality of hyperspectral images. The pixel-wise classification methods fail to take the spatial structure information of an image. Therefore, to improve the performance of classification, spatial information can be integrated into the classification process. In this paper, the multilevel thresholding algorithm based on a modified fuzzy entropy function is used to perform the segmentation of hyperspectral images. The fuzzy parameters of the MFE function have been optimized by using a new meta-heuristic algorithm based on the Tree-Search algorithm. The segmented image is classified by a large distribution machine (LDM) classifier. Experimental results are shown on a hyperspectral image dataset. The experimental outputs indicate that the proposed technique (MFE-TSA-LDM) achieves much higher classification accuracy for hyperspectral images when compared to state-of-art classification techniques. The proposed algorithm provides accurate segmentation and classification maps, thus becoming more suitable for image classification with large spatial structures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperspectral%20images" title=" hyperspectral images"> hyperspectral images</a>, <a href="https://publications.waset.org/abstracts/search?q=large%20distribution%20margin" title=" large distribution margin"> large distribution margin</a>, <a href="https://publications.waset.org/abstracts/search?q=modified%20fuzzy%20entropy%20function" title=" modified fuzzy entropy function"> modified fuzzy entropy function</a>, <a href="https://publications.waset.org/abstracts/search?q=multilevel%20thresholding" title=" multilevel thresholding"> multilevel thresholding</a>, <a href="https://publications.waset.org/abstracts/search?q=tree%20search%20algorithm" title=" tree search algorithm"> tree search algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperspectral%20image%20classification%20using%20tree%20search%20algorithm" title=" hyperspectral image classification using tree search algorithm"> hyperspectral image classification using tree search algorithm</a> </p> <a href="https://publications.waset.org/abstracts/143284/hyperspectral-image-classification-using-tree-search-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143284.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">177</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">2219</span> Pose Normalization Network for Object Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bingquan%20Shen">Bingquan Shen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Convolutional Neural Networks (CNN) have demonstrated their effectiveness in synthesizing 3D views of object instances at various viewpoints. Given the problem where one have limited viewpoints of a particular object for classification, we present a pose normalization architecture to transform the object to existing viewpoints in the training dataset before classification to yield better classification performance. We have demonstrated that this Pose Normalization Network (PNN) can capture the style of the target object and is able to re-render it to a desired viewpoint. Moreover, we have shown that the PNN improves the classification result for the 3D chairs dataset and ShapeNet airplanes dataset when given only images at limited viewpoint, as compared to a CNN baseline. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title="convolutional neural networks">convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20classification" title=" object classification"> object classification</a>, <a href="https://publications.waset.org/abstracts/search?q=pose%20normalization" title=" pose normalization"> pose normalization</a>, <a href="https://publications.waset.org/abstracts/search?q=viewpoint%20invariant" title=" viewpoint invariant"> viewpoint invariant</a> </p> <a href="https://publications.waset.org/abstracts/56852/pose-normalization-network-for-object-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56852.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">352</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">2218</span> Lean Models Classification: Towards a Holistic View</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Y.%20Tiamaz">Y. Tiamaz</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Souissi"> N. Souissi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this paper is to present a classification of Lean models which aims to capture all the concepts related to this approach and thus facilitate its implementation. This classification allows the identification of the most relevant models according to several dimensions. From this perspective, we present a review and an analysis of Lean models literature and we propose dimensions for the classification of the current proposals while respecting among others the axes of the Lean approach, the maturity of the models as well as their application domains. This classification allowed us to conclude that researchers essentially consider the Lean approach as a toolbox also they design their models to solve problems related to a specific environment. Since Lean approach is no longer intended only for the automotive sector where it was invented, but to all fields (IT, Hospital, ...), we consider that this approach requires a generic model that is capable of being implemented in all areas. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lean%20approach" title="lean approach">lean approach</a>, <a href="https://publications.waset.org/abstracts/search?q=lean%20models" title=" lean models"> lean models</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=dimensions" title=" dimensions"> dimensions</a>, <a href="https://publications.waset.org/abstracts/search?q=holistic%20view" title=" holistic view"> holistic view</a> </p> <a href="https://publications.waset.org/abstracts/65716/lean-models-classification-towards-a-holistic-view" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65716.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">435</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">2217</span> A Summary-Based Text Classification Model for Graph Attention Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shuo%20Liu">Shuo Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Chinese text classification tasks, redundant words and phrases can interfere with the formation of extracted and analyzed text information, leading to a decrease in the accuracy of the classification model. To reduce irrelevant elements, extract and utilize text content information more efficiently and improve the accuracy of text classification models. In this paper, the text in the corpus is first extracted using the TextRank algorithm for abstraction, the words in the abstract are used as nodes to construct a text graph, and then the graph attention network (GAT) is used to complete the task of classifying the text. Testing on a Chinese dataset from the network, the classification accuracy was improved over the direct method of generating graph structures using text. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chinese%20natural%20language%20processing" title="Chinese natural language processing">Chinese natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20classification" title=" text classification"> text classification</a>, <a href="https://publications.waset.org/abstracts/search?q=abstract%20extraction" title=" abstract extraction"> abstract extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20attention%20network" title=" graph attention network"> graph attention network</a> </p> <a href="https://publications.waset.org/abstracts/158060/a-summary-based-text-classification-model-for-graph-attention-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158060.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">100</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">2216</span> Real-Time Classification of Marbles with Decision-Tree Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20S.%20Parlak">K. S. Parlak</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Turan"> E. Turan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The separation of marbles according to the pattern quality is a process made according to expert decision. The classification phase is the most critical part in terms of economic value. In this study, a self-learning system is proposed which performs the classification of marbles quickly and with high success. This system performs ten feature extraction by taking ten marble images from the camera. The marbles are classified by decision tree method using the obtained properties. The user forms the training set by training the system at the marble classification stage. The system evolves itself in every marble image that is classified. The aim of the proposed system is to minimize the error caused by the person performing the classification and achieve it quickly. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title="decision tree">decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means%20clustering" title=" k-means clustering"> k-means clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=marble%20classification" title=" marble classification"> marble classification</a> </p> <a href="https://publications.waset.org/abstracts/76038/real-time-classification-of-marbles-with-decision-tree-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76038.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">382</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">2215</span> Analysis of Different Classification Techniques Using WEKA for Diabetic Disease </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Usama%20Ahmed">Usama Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Data mining is the process of analyze data which are used to predict helpful information. It is the field of research which solve various type of problem. In data mining, classification is an important technique to classify different kind of data. Diabetes is most common disease. This paper implements different classification technique using Waikato Environment for Knowledge Analysis (WEKA) on diabetes dataset and find which algorithm is suitable for working. The best classification algorithm based on diabetic data is Naïve Bayes. The accuracy of Naïve Bayes is 76.31% and take 0.06 seconds to build the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=diabetes" title=" diabetes"> diabetes</a>, <a href="https://publications.waset.org/abstracts/search?q=WEKA" title=" WEKA"> WEKA</a> </p> <a href="https://publications.waset.org/abstracts/127192/analysis-of-different-classification-techniques-using-weka-for-diabetic-disease" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127192.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">147</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">2214</span> Arabic Text Classification: Review Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Hijazi">M. Hijazi</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Zeki"> A. Zeki</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Ismail"> A. Ismail</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An enormous amount of valuable human knowledge is preserved in documents. The rapid growth in the number of machine-readable documents for public or private access requires the use of automatic text classification. Text classification can be defined as assigning or structuring documents into a defined set of classes known in advance. Arabic text classification methods have emerged as a natural result of the existence of a massive amount of varied textual information written in the Arabic language on the web. This paper presents a review on the published researches of Arabic Text Classification using classical data representation, Bag of words (BoW), and using conceptual data representation based on semantic resources such as Arabic WordNet and Wikipedia. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arabic%20text%20classification" title="Arabic text classification">Arabic text classification</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabic%20WordNet" title=" Arabic WordNet"> Arabic WordNet</a>, <a href="https://publications.waset.org/abstracts/search?q=bag%20of%20words" title=" bag of words"> bag of words</a>, <a href="https://publications.waset.org/abstracts/search?q=conceptual%20representation" title=" conceptual representation"> conceptual representation</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20relations" title=" semantic relations"> semantic relations</a> </p> <a href="https://publications.waset.org/abstracts/42905/arabic-text-classification-review-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/42905.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">426</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">2213</span> Image Classification with Localization Using Convolutional Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bhuyain%20Mobarok%20Hossain">Bhuyain Mobarok Hossain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20classification" title="image classification">image classification</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=localization" title=" localization"> localization</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20filter" title=" particle filter"> particle filter</a> </p> <a href="https://publications.waset.org/abstracts/139288/image-classification-with-localization-using-convolutional-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139288.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">305</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">2212</span> Mapping of Arenga Pinnata Tree Using Remote Sensing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zulkiflee%20Abd%20Latif">Zulkiflee Abd Latif</a>, <a href="https://publications.waset.org/abstracts/search?q=Sitinor%20Atikah%20Nordin"> Sitinor Atikah Nordin</a>, <a href="https://publications.waset.org/abstracts/search?q=Alawi%20Sulaiman"> Alawi Sulaiman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Different tree species possess different and various benefits. Arenga Pinnata tree species own several potential uses that is valuable for the economy and the country. Mapping vegetation using remote sensing technique involves various process, techniques and consideration. Using satellite imagery, this method enables the access of inaccessible area and with the availability of near infra-red band; it is useful in vegetation analysis, especially in identifying tree species. Pixel-based and object-based classification technique is used as a method in this study. Pixel-based classification technique used in this study divided into unsupervised and supervised classification. Object based classification technique becomes more popular another alternative method in classification process. Using spectral, texture, color and other information, to classify the target make object-based classification is a promising technique for classification. Classification of Arenga Pinnata trees is overlaid with elevation, slope and aspect, soil and river data and several other data to give information regarding the tree character and living environment. This paper will present the utilization of remote sensing technique in order to map Arenga Pinnata tree species <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arenga%20Pinnata" title="Arenga Pinnata">Arenga Pinnata</a>, <a href="https://publications.waset.org/abstracts/search?q=pixel-based%20classification" title=" pixel-based classification"> pixel-based classification</a>, <a href="https://publications.waset.org/abstracts/search?q=object-based%20classification" title=" object-based classification"> object-based classification</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a> </p> <a href="https://publications.waset.org/abstracts/13681/mapping-of-arenga-pinnata-tree-using-remote-sensing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13681.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">380</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">2211</span> Vehicle Type Classification with Geometric and Appearance Attributes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghada%20S.%20Moussa">Ghada S. Moussa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increase in population along with economic prosperity, an enormous increase in the number and types of vehicles on the roads occurred. This fact brings a growing need for efficiently yet effectively classifying vehicles into their corresponding categories, which play a crucial role in many areas of infrastructure planning and traffic management. This paper presents two vehicle-type classification approaches; 1) geometric-based and 2) appearance-based. The two classification approaches are used for two tasks: multi-class and intra-class vehicle classifications. For the evaluation purpose of the proposed classification approaches’ performance and the identification of the most effective yet efficient one, 10-fold cross-validation technique is used with a large dataset. The proposed approaches are distinguishable from previous research on vehicle classification in which: i) they consider both geometric and appearance attributes of vehicles, and ii) they perform remarkably well in both multi-class and intra-class vehicle classification. Experimental results exhibit promising potentials implementations of the proposed vehicle classification approaches into real-world applications. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=appearance%20attributes" title="appearance attributes">appearance attributes</a>, <a href="https://publications.waset.org/abstracts/search?q=geometric%20attributes" title=" geometric attributes"> geometric attributes</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=vehicle%20classification" title=" vehicle classification"> vehicle classification</a> </p> <a href="https://publications.waset.org/abstracts/2688/vehicle-type-classification-with-geometric-and-appearance-attributes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2688.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">338</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">2210</span> A Reliable Multi-Type Vehicle Classification System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghada%20S.%20Moussa">Ghada S. Moussa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Vehicle classification is an important task in traffic surveillance and intelligent transportation systems. Classification of vehicle images is facing several problems such as: high intra-class vehicle variations, occlusion, shadow, illumination. These problems and others must be considered to develop a reliable vehicle classification system. In this study, a reliable multi-type vehicle classification system based on Bag-of-Words (BoW) paradigm is developed. Our proposed system used and compared four well-known classifiers; Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-Nearest Neighbour (KNN), and Decision Tree to classify vehicles into four categories: motorcycles, small, medium and large. Experiments on a large dataset show that our approach is efficient and reliable in classifying vehicles with accuracy of 95.7%. The SVM outperforms other classification algorithms in terms of both accuracy and robustness alongside considerable reduction in execution time. The innovativeness of developed system is it can serve as a framework for many vehicle classification systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=vehicle%20classification" title="vehicle classification">vehicle classification</a>, <a href="https://publications.waset.org/abstracts/search?q=bag-of-words%20technique" title=" bag-of-words technique"> bag-of-words technique</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM%20classifier" title=" SVM classifier"> SVM classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=LDA%20classifier" title=" LDA classifier"> LDA classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=KNN%20classifier" title=" KNN classifier"> KNN classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree%20classifier" title=" decision tree classifier"> decision tree classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=SIFT%20algorithm" title=" SIFT algorithm"> SIFT algorithm</a> </p> <a href="https://publications.waset.org/abstracts/7262/a-reliable-multi-type-vehicle-classification-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7262.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">358</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">2209</span> Optimal Mother Wavelet Function for Shoulder Muscles of Upper Limb Amputees</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amanpreet%20Kaur">Amanpreet Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Wavelet transform (WT) is a powerful statistical tool used in applied mathematics for signal and image processing. The different mother, wavelet basis function, has been compared to select the optimal wavelet function that represents the electromyogram signal characteristics of upper limb amputees. Four different EMG electrode has placed on different location of shoulder muscles. Twenty one wavelet functions from different wavelet families were investigated. These functions included Daubechies (db1-db10), Symlets (sym1-sym5), Coiflets (coif1-coif5) and Discrete Meyer. Using mean square error value, the significance of the mother wavelet functions has been determined for teres, pectorals, and infraspinatus around shoulder muscles. The results show that the best mother wavelet is the db3 from the Daubechies family for efficient classification of the signal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daubechies" title="Daubechies">Daubechies</a>, <a href="https://publications.waset.org/abstracts/search?q=upper%20limb%20amputation" title=" upper limb amputation"> upper limb amputation</a>, <a href="https://publications.waset.org/abstracts/search?q=shoulder%20muscles" title=" shoulder muscles"> shoulder muscles</a>, <a href="https://publications.waset.org/abstracts/search?q=Symlets" title=" Symlets"> Symlets</a>, <a href="https://publications.waset.org/abstracts/search?q=Coiflets" title=" Coiflets"> Coiflets</a> </p> <a href="https://publications.waset.org/abstracts/103654/optimal-mother-wavelet-function-for-shoulder-muscles-of-upper-limb-amputees" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/103654.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">2208</span> A Generalized Weighted Loss for Support Vextor Classification and Multilayer Perceptron</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Filippo%20Portera">Filippo Portera</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Usually standard algorithms employ a loss where each error is the mere absolute difference between the true value and the prediction, in case of a regression task. In the present, we present several error weighting schemes that are a generalization of the consolidated routine. We study both a binary classification model for Support Vextor Classification and a regression net for Multylayer Perceptron. Results proves that the error is never worse than the standard procedure and several times it is better. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=loss" title="loss">loss</a>, <a href="https://publications.waset.org/abstracts/search?q=binary-classification" title=" binary-classification"> binary-classification</a>, <a href="https://publications.waset.org/abstracts/search?q=MLP" title=" MLP"> MLP</a>, <a href="https://publications.waset.org/abstracts/search?q=weights" title=" weights"> weights</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/163661/a-generalized-weighted-loss-for-support-vextor-classification-and-multilayer-perceptron" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163661.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">95</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">2207</span> Multilabel Classification with Neural Network Ensemble Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sezin%20Ek%C5%9Fio%C4%9Flu">Sezin Ekşioğlu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multilabel classification has a huge importance for several applications, it is also a challenging research topic. It is a kind of supervised learning that contains binary targets. The distance between multilabel and binary classification is having more than one class in multilabel classification problems. Features can belong to one class or many classes. There exists a wide range of applications for multi label prediction such as image labeling, text categorization, gene functionality. Even though features are classified in many classes, they may not always be properly classified. There are many ensemble methods for the classification. However, most of the researchers have been concerned about better multilabel methods. Especially little ones focus on both efficiency of classifiers and pairwise relationships at the same time in order to implement better multilabel classification. In this paper, we worked on modified ensemble methods by getting benefit from k-Nearest Neighbors and neural network structure to address issues within a beneficial way and to get better impacts from the multilabel classification. Publicly available datasets (yeast, emotion, scene and birds) are performed to demonstrate the developed algorithm efficiency and the technique is measured by accuracy, F1 score and hamming loss metrics. Our algorithm boosts benchmarks for each datasets with different metrics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multilabel" title="multilabel">multilabel</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=KNN" title=" KNN"> KNN</a> </p> <a href="https://publications.waset.org/abstracts/148169/multilabel-classification-with-neural-network-ensemble-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148169.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">155</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">2206</span> Classification of High Order Thinking Skills (HOTS)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Alkiyumi">Mohammed Alkiyumi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Educational systems are currently paying special attention to developing learners' higher thinking skills to develop the capabilities of human resources to deal with contemporary challenges. Although psychologists disagree about the concept of higher-order thinking skills and the skills they include, there is unlimited effort in designing them and building strategies for their implementation. The most important factor helping to develop these skills is their classification according to specific criteria, and the most important of these classifications is Bloom's classification, which is dominant in most educational systems at all levels. Previous classifications have many limitations, including the comprehensiveness of the skills they contain, the logical structure of their hierarchy, and classification criteria. Therefore, this article puts another step in this area by providing a new classification of higher-order thinking skills that includes five categories: the first response stage, transformative stage, application, reasoning stage, and the production stage with a logical justification for this classification, with some techniques to developing it among learners. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=high-order%20thinking%20skills" title="high-order thinking skills">high-order thinking skills</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=teaching" title=" teaching"> teaching</a>, <a href="https://publications.waset.org/abstracts/search?q=education" title=" education"> education</a> </p> <a href="https://publications.waset.org/abstracts/187723/classification-of-high-order-thinking-skills-hots" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/187723.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">42</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">2205</span> Application of Rapid Eye Imagery in Crop Type Classification Using Vegetation Indices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sunita%20Singh">Sunita Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajani%20Srivastava"> Rajani Srivastava</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For natural resource management and in other applications about earth observation revolutionary remote sensing technology plays a significant role. One of such application in monitoring and classification of crop types at spatial and temporal scale, as it provides latest, most precise and cost-effective information. Present study emphasizes the use of three different vegetation indices of Rapid Eye imagery on crop type classification. It also analyzed the effect of each indices on classification accuracy. Rapid Eye imagery is highly demanded and preferred for agricultural and forestry sectors as it has red-edge and NIR bands. The three indices used in this study were: the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) and all of these incorporated the Red Edge band. The study area is Varanasi district of Uttar Pradesh, India and Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Classification was performed with these three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 85% was obtained using three vegetation indices. The study concluded that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the Rapid Eye imagery can get satisfactory results of classification accuracy without original bands. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GNDVI" title="GNDVI">GNDVI</a>, <a href="https://publications.waset.org/abstracts/search?q=NDRE" title=" NDRE"> NDRE</a>, <a href="https://publications.waset.org/abstracts/search?q=NDVI" title=" NDVI"> NDVI</a>, <a href="https://publications.waset.org/abstracts/search?q=rapid%20eye" title=" rapid eye"> rapid eye</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetation%20indices" title=" vegetation indices"> vegetation indices</a> </p> <a href="https://publications.waset.org/abstracts/79921/application-of-rapid-eye-imagery-in-crop-type-classification-using-vegetation-indices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79921.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">362</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">2204</span> Performance Analysis of Artificial Neural Network Based Land Cover Classification </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Najam%20Aziz">Najam Aziz</a>, <a href="https://publications.waset.org/abstracts/search?q=Nasru%20Minallah"> Nasru Minallah</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Junaid"> Ahmad Junaid</a>, <a href="https://publications.waset.org/abstracts/search?q=Kashaf%20Gul"> Kashaf Gul </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Landcover classification using automated classification techniques, while employing remotely sensed multi-spectral imagery, is one of the promising areas of research. Different land conditions at different time are captured through satellite and monitored by applying different classification algorithms in specific environment. In this paper, a SPOT-5 image provided by SUPARCO has been studied and classified in Environment for Visual Interpretation (ENVI), a tool widely used in remote sensing. Then, Artificial Neural Network (ANN) classification technique is used to detect the land cover changes in Abbottabad district. Obtained results are compared with a pixel based Distance classifier. The results show that ANN gives the better overall accuracy of 99.20% and Kappa coefficient value of 0.98 over the Mahalanobis Distance Classifier. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=landcover%20classification" title="landcover classification">landcover classification</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title=" remote sensing"> remote sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=SPOT%205" title=" SPOT 5"> SPOT 5</a> </p> <a href="https://publications.waset.org/abstracts/61063/performance-analysis-of-artificial-neural-network-based-land-cover-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61063.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">546</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">2203</span> Scene Classification Using Hierarchy Neural Network, Directed Acyclic Graph Structure, and Label Relations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Po-Jen%20Chen">Po-Jen Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Jian-Jiun%20Ding"> Jian-Jiun Ding</a>, <a href="https://publications.waset.org/abstracts/search?q=Hung-Wei%20Hsu"> Hung-Wei Hsu</a>, <a href="https://publications.waset.org/abstracts/search?q=Chien-Yao%20Wang"> Chien-Yao Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jia-Ching%20Wang"> Jia-Ching Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A more accurate scene classification algorithm using label relations and the hierarchy neural network was developed in this work. In many classification algorithms, it is assumed that the labels are mutually exclusive. This assumption is true in some specific problems, however, for scene classification, the assumption is not reasonable. Because there are a variety of objects with a photo image, it is more practical to assign multiple labels for an image. In this paper, two label relations, which are exclusive relation and hierarchical relation, were adopted in the classification process to achieve more accurate multiple label classification results. Moreover, the hierarchy neural network (hierarchy NN) is applied to classify the image and the directed acyclic graph structure is used for predicting a more reasonable result which obey exclusive and hierarchical relations. Simulations show that, with these techniques, a much more accurate scene classification result can be achieved. <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=label%20relation" title=" label relation"> label relation</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchy%20neural%20network" title=" hierarchy neural network"> hierarchy neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=scene%20classification" title=" scene classification"> scene classification</a> </p> <a href="https://publications.waset.org/abstracts/66516/scene-classification-using-hierarchy-neural-network-directed-acyclic-graph-structure-and-label-relations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66516.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">458</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=Meyer%20classification&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Meyer%20classification&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Meyer%20classification&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=Meyer%20classification&page=5">5</a></li> <li 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