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classification method</title> <meta name="description" content="Search results for: classification method"> <meta name="keywords" content="classification method"> <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 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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="classification method"> <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> 20429</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: classification method</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20429</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">211</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">20428</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">20427</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">20426</span> Sentiment Classification Using Enhanced Contextual Valence Shifters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vo%20Ngoc%20Phu">Vo Ngoc Phu</a>, <a href="https://publications.waset.org/abstracts/search?q=Phan%20Thi%20Tuoi"> Phan Thi Tuoi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We have explored different methods of improving the accuracy of sentiment classification. The sentiment orientation of a document can be positive (+), negative (-), or neutral (0). We combine five dictionaries from [2, 3, 4, 5, 6] into the new one with 21137 entries. The new dictionary has many verbs, adverbs, phrases and idioms, that are not in five ones before. The paper shows that our proposed method based on the combination of Term-Counting method and Enhanced Contextual Valence Shifters method has improved the accuracy of sentiment classification. The combined method has accuracy 68.984% on the testing dataset, and 69.224% on the training dataset. All of these methods are implemented to classify the reviews based on our new dictionary and the Internet Movie data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sentiment%20classification" title="sentiment classification">sentiment classification</a>, <a href="https://publications.waset.org/abstracts/search?q=sentiment%20orientation" title=" sentiment orientation"> sentiment orientation</a>, <a href="https://publications.waset.org/abstracts/search?q=valence%20shifters" title=" valence shifters"> valence shifters</a>, <a href="https://publications.waset.org/abstracts/search?q=contextual" title=" contextual"> contextual</a>, <a href="https://publications.waset.org/abstracts/search?q=valence%20shifters" title=" valence shifters"> valence shifters</a>, <a href="https://publications.waset.org/abstracts/search?q=term%20counting" title=" term counting"> term counting</a> </p> <a href="https://publications.waset.org/abstracts/11410/sentiment-classification-using-enhanced-contextual-valence-shifters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11410.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">504</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20425</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">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">20424</span> Semi-Automatic Method to Assist Expert for Association Rules Validation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amdouni%20Hamida">Amdouni Hamida</a>, <a href="https://publications.waset.org/abstracts/search?q=Gammoudi%20Mohamed%20Mohsen"> Gammoudi Mohamed Mohsen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to help the expert to validate association rules extracted from data, some quality measures are proposed in the literature. We distinguish two categories: objective and subjective measures. The first one depends on a fixed threshold and on data quality from which the rules are extracted. The second one consists on providing to the expert some tools in the objective to explore and visualize rules during the evaluation step. However, the number of extracted rules to validate remains high. Thus, the manually mining rules task is very hard. To solve this problem, we propose, in this paper, a semi-automatic method to assist the expert during the association rule's validation. Our method uses rule-based classification as follow: (i) We transform association rules into classification rules (classifiers), (ii) We use the generated classifiers for data classification. (iii) We visualize association rules with their quality classification to give an idea to the expert and to assist him during validation process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=association%20rules" title="association rules">association rules</a>, <a href="https://publications.waset.org/abstracts/search?q=rule-based%20classification" title=" rule-based classification"> rule-based classification</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20quality" title=" classification quality"> classification quality</a>, <a href="https://publications.waset.org/abstracts/search?q=validation" title=" validation"> validation</a> </p> <a href="https://publications.waset.org/abstracts/29443/semi-automatic-method-to-assist-expert-for-association-rules-validation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29443.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">439</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">20423</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">20422</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">20421</span> A Hybrid Fuzzy Clustering Approach for Fertile and Unfertile Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shima%20Soltanzadeh">Shima Soltanzadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Hosain%20Fazel%20Zarandi"> Mohammad Hosain Fazel Zarandi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mojtaba%20Barzegar%20Astanjin"> Mojtaba Barzegar Astanjin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Diagnosis of male infertility by the laboratory tests is expensive and, sometimes it is intolerable for patients. Filling out the questionnaire and then using classification method can be the first step in decision-making process, so only in the cases with a high probability of infertility we can use the laboratory tests. In this paper, we evaluated the performance of four classification methods including naive Bayesian, neural network, logistic regression and fuzzy c-means clustering as a classification, in the diagnosis of male infertility due to environmental factors. Since the data are unbalanced, the ROC curves are most suitable method for the comparison. In this paper, we also have selected the more important features using a filtering method and examined the impact of this feature reduction on the performance of each methods; generally, most of the methods had better performance after applying the filter. We have showed that using fuzzy c-means clustering as a classification has a good performance according to the ROC curves and its performance is comparable to other classification methods like logistic regression. <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=fuzzy%20c-means" title=" fuzzy c-means"> fuzzy c-means</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=Naive%20Bayesian" title=" Naive Bayesian"> Naive Bayesian</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=ROC%20curve" title=" ROC curve"> ROC curve</a> </p> <a href="https://publications.waset.org/abstracts/50838/a-hybrid-fuzzy-clustering-approach-for-fertile-and-unfertile-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50838.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">337</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20420</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">20419</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">20418</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">20417</span> New Approach to Construct Phylogenetic Tree</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ouafae%20Baida">Ouafae Baida</a>, <a href="https://publications.waset.org/abstracts/search?q=Najma%20Hamzaoui"> Najma Hamzaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Maha%20Akbib"> Maha Akbib</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelfettah%20Sedqui"> Abdelfettah Sedqui</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelouahid%20Lyhyaoui"> Abdelouahid Lyhyaoui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Numerous scientific works present various methods to analyze the data for several domains, specially the comparison of classifications. In our recent work, we presented a new approach to help the user choose the best classification method from the results obtained by every method, by basing itself on the distances between the trees of classification. The result of our approach was in the form of a dendrogram contains methods as a succession of connections. This approach is much needed in phylogeny analysis. This discipline is intended to analyze the sequences of biological macro molecules for information on the evolutionary history of living beings, including their relationship. The product of phylogeny analysis is a phylogenetic tree. In this paper, we recommend the use of a new method of construction the phylogenetic tree based on comparison of different classifications obtained by different molecular genes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20classification" title="hierarchical classification">hierarchical classification</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20methods" title=" classification methods"> classification methods</a>, <a href="https://publications.waset.org/abstracts/search?q=structure%20of%20tree" title=" structure of tree"> structure of tree</a>, <a href="https://publications.waset.org/abstracts/search?q=genes" title=" genes"> genes</a>, <a href="https://publications.waset.org/abstracts/search?q=phylogenetic%20analysis" title=" phylogenetic analysis"> phylogenetic analysis</a> </p> <a href="https://publications.waset.org/abstracts/21857/new-approach-to-construct-phylogenetic-tree" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21857.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">510</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">20416</span> Comparative Analysis of Classification Methods in Determining Non-Active Student Characteristics in Indonesia Open University</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dewi%20Juliah%20Ratnaningsih">Dewi Juliah Ratnaningsih</a>, <a href="https://publications.waset.org/abstracts/search?q=Imas%20Sukaesih%20Sitanggang"> Imas Sukaesih Sitanggang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Classification is one of data mining techniques that aims to discover a model from training data that distinguishes records into the appropriate category or class. Data mining classification methods can be applied in education, for example, to determine the classification of non-active students in Indonesia Open University. This paper presents a comparison of three methods of classification: Naïve Bayes, Bagging, and C.45. The criteria used to evaluate the performance of three methods of classification are stratified cross-validation, confusion matrix, the value of the area under the ROC Curve (AUC), Recall, Precision, and F-measure. The data used for this paper are from the non-active Indonesia Open University students in registration period of 2004.1 to 2012.2. Target analysis requires that non-active students were divided into 3 groups: C1, C2, and C3. Data analyzed are as many as 4173 students. Results of the study show: (1) Bagging method gave a high degree of classification accuracy than Naïve Bayes and C.45, (2) the Bagging classification accuracy rate is 82.99 %, while the Naïve Bayes and C.45 are 80.04 % and 82.74 % respectively, (3) the result of Bagging classification tree method has a large number of nodes, so it is quite difficult in decision making, (4) classification of non-active Indonesia Open University student characteristics uses algorithms C.45, (5) based on the algorithm C.45, there are 5 interesting rules which can describe the characteristics of non-active Indonesia Open University students. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=comparative%20analysis" title="comparative analysis">comparative analysis</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=clasiffication" title=" clasiffication"> clasiffication</a>, <a href="https://publications.waset.org/abstracts/search?q=Bagging" title=" Bagging"> Bagging</a>, <a href="https://publications.waset.org/abstracts/search?q=Na%C3%AFve%20Bayes" title=" Naïve Bayes"> Naïve Bayes</a>, <a href="https://publications.waset.org/abstracts/search?q=C.45" title=" C.45"> C.45</a>, <a href="https://publications.waset.org/abstracts/search?q=non-active%20students" title=" non-active students"> non-active students</a>, <a href="https://publications.waset.org/abstracts/search?q=Indonesia%20Open%20University" title=" Indonesia Open University"> Indonesia Open University</a> </p> <a href="https://publications.waset.org/abstracts/8231/comparative-analysis-of-classification-methods-in-determining-non-active-student-characteristics-in-indonesia-open-university" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8231.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">315</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">20415</span> Using Self Organizing Feature Maps for Classification in RGB Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hassan%20Masoumi">Hassan Masoumi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahad%20Salimi"> Ahad Salimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Nazanin%20Barhemmat"> Nazanin Barhemmat</a>, <a href="https://publications.waset.org/abstracts/search?q=Babak%20Gholami"> Babak Gholami</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feed-forward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on self organizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification. <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=SOFM%20algorithm" title=" SOFM algorithm"> SOFM algorithm</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=neighborhood" title=" neighborhood"> neighborhood</a>, <a href="https://publications.waset.org/abstracts/search?q=RGB%20image" title=" RGB image"> RGB image</a> </p> <a href="https://publications.waset.org/abstracts/26819/using-self-organizing-feature-maps-for-classification-in-rgb-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26819.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">478</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">20414</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">20413</span> A Review of Effective Gene Selection Methods for Cancer Classification Using Microarray Gene Expression Profile</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hala%20Alshamlan">Hala Alshamlan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ghada%20Badr"> Ghada Badr</a>, <a href="https://publications.waset.org/abstracts/search?q=Yousef%20Alohali"> Yousef Alohali </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cancer is one of the dreadful diseases, which causes considerable death rate in humans. DNA microarray-based gene expression profiling has been emerged as an efficient technique for cancer classification, as well as for diagnosis, prognosis, and treatment purposes. In recent years, a DNA microarray technique has gained more attraction in both scientific and in industrial fields. It is important to determine the informative genes that cause cancer to improve early cancer diagnosis and to give effective chemotherapy treatment. In order to gain deep insight into the cancer classification problem, it is necessary to take a closer look at the proposed gene selection methods. We believe that they should be an integral preprocessing step for cancer classification. Furthermore, finding an accurate gene selection method is a very significant issue in a cancer classification area because it reduces the dimensionality of microarray dataset and selects informative genes. In this paper, we classify and review the state-of-art gene selection methods. We proceed by evaluating the performance of each gene selection approach based on their classification accuracy and number of informative genes. In our evaluation, we will use four benchmark microarray datasets for the cancer diagnosis (leukemia, colon, lung, and prostate). In addition, we compare the performance of gene selection method to investigate the effective gene selection method that has the ability to identify a small set of marker genes, and ensure high cancer classification accuracy. To the best of our knowledge, this is the first attempt to compare gene selection approaches for cancer classification using microarray gene expression profile. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gene%20selection" title="gene selection">gene selection</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=cancer%20classification" title=" cancer classification"> cancer classification</a>, <a href="https://publications.waset.org/abstracts/search?q=microarray" title=" microarray"> microarray</a>, <a href="https://publications.waset.org/abstracts/search?q=gene%20expression%20profile" title=" gene expression profile"> gene expression profile</a> </p> <a href="https://publications.waset.org/abstracts/8991/a-review-of-effective-gene-selection-methods-for-cancer-classification-using-microarray-gene-expression-profile" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8991.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">454</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">20412</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">20411</span> Improved Classification Procedure for Imbalanced and Overlapped Situations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hankyu%20Lee">Hankyu Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Seoung%20Bum%20Kim"> Seoung Bum Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The issue with imbalance and overlapping in the class distribution becomes important in various applications of data mining. The imbalanced dataset is a special case in classification problems in which the number of observations of one class (i.e., major class) heavily exceeds the number of observations of the other class (i.e., minor class). Overlapped dataset is the case where many observations are shared together between the two classes. Imbalanced and overlapped data can be frequently found in many real examples including fraud and abuse patients in healthcare, quality prediction in manufacturing, text classification, oil spill detection, remote sensing, and so on. The class imbalance and overlap problem is the challenging issue because this situation degrades the performance of most of the standard classification algorithms. In this study, we propose a classification procedure that can effectively handle imbalanced and overlapped datasets by splitting data space into three parts: nonoverlapping, light overlapping, and severe overlapping and applying the classification algorithm in each part. These three parts were determined based on the Hausdorff distance and the margin of the modified support vector machine. An experiments study was conducted to examine the properties of the proposed method and compared it with other classification algorithms. The results showed that the proposed method outperformed the competitors under various imbalanced and overlapped situations. Moreover, the applicability of the proposed method was demonstrated through the experiment with real data. <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=imbalanced%20data%20with%20class%20overlap" title=" imbalanced data with class overlap"> imbalanced data with class overlap</a>, <a href="https://publications.waset.org/abstracts/search?q=split%20data%20space" title=" split data space"> split data space</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/52079/improved-classification-procedure-for-imbalanced-and-overlapped-situations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52079.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">308</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">20410</span> Satellite Imagery Classification Based on Deep Convolution Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhong%20Ma">Zhong Ma</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhuping%20Wang"> Zhuping Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Congxin%20Liu"> Congxin Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiangzeng%20Liu"> Xiangzeng Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Satellite imagery classification is a challenging problem with many practical applications. In this paper, we designed a deep convolution neural network (DCNN) to classify the satellite imagery. The contributions of this paper are twofold — First, to cope with the large-scale variance in the satellite image, we introduced the inception module, which has multiple filters with different size at the same level, as the building block to build our DCNN model. Second, we proposed a genetic algorithm based method to efficiently search the best hyper-parameters of the DCNN in a large search space. The proposed method is evaluated on the benchmark database. The results of the proposed hyper-parameters search method show it will guide the search towards better regions of the parameter space. Based on the found hyper-parameters, we built our DCNN models, and evaluated its performance on satellite imagery classification, the results show the classification accuracy of proposed models outperform the state of the art method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=satellite%20imagery%20classification" title="satellite imagery classification">satellite imagery classification</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20convolution%20network" title=" deep convolution network"> deep convolution network</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=hyper-parameter%20optimization" title=" hyper-parameter optimization"> hyper-parameter optimization</a> </p> <a href="https://publications.waset.org/abstracts/44963/satellite-imagery-classification-based-on-deep-convolution-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44963.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">301</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">20409</span> A Comparison of South East Asian Face Emotion Classification based on Optimized Ellipse Data Using Clustering Technique </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Karthigayan">M. Karthigayan</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Rizon"> M. Rizon</a>, <a href="https://publications.waset.org/abstracts/search?q=Sazali%20Yaacob"> Sazali Yaacob</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Nagarajan"> R. Nagarajan</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Muthukumaran"> M. Muthukumaran</a>, <a href="https://publications.waset.org/abstracts/search?q=Thinaharan%20Ramachandran"> Thinaharan Ramachandran</a>, <a href="https://publications.waset.org/abstracts/search?q=Sargunam%20Thirugnanam"> Sargunam Thirugnanam </a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, using a set of irregular and regular ellipse fitting equations using Genetic algorithm (GA) are applied to the lip and eye features to classify the human emotions. Two South East Asian (SEA) faces are considered in this work for the emotion classification. There are six emotions and one neutral are considered as the output. Each subject shows unique characteristic of the lip and eye features for various emotions. GA is adopted to optimize irregular ellipse characteristics of the lip and eye features in each emotion. That is, the top portion of lip configuration is a part of one ellipse and the bottom of different ellipse. Two ellipse based fitness equations are proposed for the lip configuration and relevant parameters that define the emotions are listed. The GA method has achieved reasonably successful classification of emotion. In some emotions classification, optimized data values of one emotion are messed or overlapped to other emotion ranges. In order to overcome the overlapping problem between the emotion optimized values and at the same time to improve the classification, a fuzzy clustering method (FCM) of approach has been implemented to offer better classification. The GA-FCM approach offers a reasonably good classification within the ranges of clusters and it had been proven by applying to two SEA subjects and have improved the classification rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ellipse%20fitness%20function" title="ellipse fitness function">ellipse fitness function</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <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=fuzzy%20clustering" title=" fuzzy clustering "> fuzzy clustering </a> </p> <a href="https://publications.waset.org/abstracts/16362/a-comparison-of-south-east-asian-face-emotion-classification-based-on-optimized-ellipse-data-using-clustering-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16362.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">20408</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">20407</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">20406</span> Classification of Poverty Level Data in Indonesia Using the Naïve Bayes Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anung%20Style%20Bukhori">Anung Style Bukhori</a>, <a href="https://publications.waset.org/abstracts/search?q=Ani%20Dijah%20Rahajoe"> Ani Dijah Rahajoe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Poverty poses a significant challenge in Indonesia, requiring an effective analytical approach to understand and address this issue. In this research, we applied the Naïve Bayes classification method to examine and classify poverty data in Indonesia. The main focus is on classifying data using RapidMiner, a powerful data analysis platform. The analysis process involves data splitting to train and test the classification model. First, we collected and prepared a poverty dataset that includes various factors such as education, employment, and health..The experimental results indicate that the Naïve Bayes classification model can provide accurate predictions regarding the risk of poverty. The use of RapidMiner in the analysis process offers flexibility and efficiency in evaluating the model's performance. The classification produces several values to serve as the standard for classifying poverty data in Indonesia using Naive Bayes. The accuracy result obtained is 40.26%, with a moderate recall result of 35.94%, a high recall result of 63.16%, and a low recall result of 38.03%. The precision for the moderate class is 58.97%, for the high class is 17.39%, and for the low class is 58.70%. These results can be seen from the graph below. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=poverty" title="poverty">poverty</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=na%C3%AFve%20bayes" title=" naïve bayes"> naïve bayes</a>, <a href="https://publications.waset.org/abstracts/search?q=Indonesia" title=" Indonesia"> Indonesia</a> </p> <a href="https://publications.waset.org/abstracts/179775/classification-of-poverty-level-data-in-indonesia-using-the-naive-bayes-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/179775.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">55</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">20405</span> Composite Approach to Extremism and Terrorism Web Content Classification</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%20Weir"> George Weir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Terrorism and extremism activities on the internet are becoming the most significant threats to national security because of their potential dangers. In response to this challenge, law enforcement and security authorities are actively implementing comprehensive measures by countering the use of the internet for terrorism. To achieve the measures, there is need for intelligence gathering via the internet. This includes real-time monitoring of potential websites that are used for recruitment and information dissemination among other operations by extremist groups. However, with billions of active webpages, real-time monitoring of all webpages become almost impossible. To narrow down the search domain, there is a need for efficient webpage classification techniques. This research proposed a new approach tagged: SentiPosit-based method. SentiPosit-based method combines features of the Posit-based method and the Sentistrenght-based method for classification of terrorism and extremism webpages. The experiment was carried out on 7500 webpages obtained through TENE-webcrawler by International Cyber Crime Research Centre (ICCRC). The webpages were manually grouped into three classes which include the ‘pro-extremist’, ‘anti-extremist’ and ‘neutral’ with 2500 webpages in each category. A supervised learning algorithm is then applied on the classified dataset in order to build the model. Results obtained was compared with existing classification method using the prediction accuracy and runtime. It was observed that our proposed hybrid approach produced a better classification accuracy compared to existing approaches within a reasonable runtime. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sentiposit" title="sentiposit">sentiposit</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=extremism" title=" extremism"> extremism</a>, <a href="https://publications.waset.org/abstracts/search?q=terrorism" title=" terrorism"> terrorism</a> </p> <a href="https://publications.waset.org/abstracts/87450/composite-approach-to-extremism-and-terrorism-web-content-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87450.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">20404</span> Classification of Construction Projects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Safa">M. Safa</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Sabet"> A. Sabet</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20MacGillivray"> S. MacGillivray</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Davidson"> M. Davidson</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Kaczmarczyk"> K. Kaczmarczyk</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20T.%20Haas"> C. T. Haas</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20E.%20Gibson"> G. E. Gibson</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Rayside"> D. Rayside </a> </p> <p class="card-text"><strong>Abstract:</strong></p> To address construction project requirements and specifications, scholars and practitioners need to establish a taxonomy according to a scheme that best fits their need. While existing characterization methods are continuously being improved, new ones are devised to cover project properties which have not been previously addressed. One such method, the Project Definition Rating Index (PDRI), has received limited consideration strictly as a classification scheme. Developed by the Construction Industry Institute (CII) in 1996, the PDRI has been refined over the last two decades as a method for evaluating a project's scope definition completeness during front-end planning (FEP). The main contribution of this study is a review of practical project classification methods, and a discussion of how PDRI can be used to classify projects based on their readiness in the FEP phase. The proposed model has been applied to 59 construction projects in Ontario, and the results are discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=project%20classification" title="project classification">project classification</a>, <a href="https://publications.waset.org/abstracts/search?q=project%20definition%20rating%20index%20%28PDRI%29" title=" project definition rating index (PDRI)"> project definition rating index (PDRI)</a>, <a href="https://publications.waset.org/abstracts/search?q=risk" title=" risk"> risk</a>, <a href="https://publications.waset.org/abstracts/search?q=project%20goals%20alignment" title=" project goals alignment"> project goals alignment</a> </p> <a href="https://publications.waset.org/abstracts/22973/classification-of-construction-projects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22973.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">679</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">20403</span> MhAGCN: Multi-Head Attention Graph Convolutional Network for Web Services Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bing%20Li">Bing Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhi%20Li"> Zhi Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Yilong%20Yang"> Yilong Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Web classification can promote the quality of service discovery and management in the service repository. It is widely used to locate developers desired services. Although traditional classification methods based on supervised learning models can achieve classification tasks, developers need to manually mark web services, and the quality of these tags may not be enough to establish an accurate classifier for service classification. With the doubling of the number of web services, the manual tagging method has become unrealistic. In recent years, the attention mechanism has made remarkable progress in the field of deep learning, and its huge potential has been fully demonstrated in various fields. This paper designs a multi-head attention graph convolutional network (MHAGCN) service classification method, which can assign different weights to the neighborhood nodes without complicated matrix operations or relying on understanding the entire graph structure. The framework combines the advantages of the attention mechanism and graph convolutional neural network. It can classify web services through automatic feature extraction. The comprehensive experimental results on a real dataset not only show the superior performance of the proposed model over the existing models but also demonstrate its potentially good interpretability for graph analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=attention%20mechanism" title="attention mechanism">attention mechanism</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20convolutional%20network" title=" graph convolutional network"> graph convolutional network</a>, <a href="https://publications.waset.org/abstracts/search?q=interpretability" title=" interpretability"> interpretability</a>, <a href="https://publications.waset.org/abstracts/search?q=service%20classification" title=" service classification"> service classification</a>, <a href="https://publications.waset.org/abstracts/search?q=service%20discovery" title=" service discovery"> service discovery</a> </p> <a href="https://publications.waset.org/abstracts/131673/mhagcn-multi-head-attention-graph-convolutional-network-for-web-services-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131673.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">135</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">20402</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">20401</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">20400</span> Comparative Analysis of Spectral Estimation Methods for Brain-Computer Interfaces</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rafik%20Djemili">Rafik Djemili</a>, <a href="https://publications.waset.org/abstracts/search?q=Hocine%20Bourouba"> Hocine Bourouba</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20C.%20Amara%20Korba"> M. C. Amara Korba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a method in order to classify EEG signals for Brain-Computer Interfaces (BCI). EEG signals are first processed by means of spectral estimation methods to derive reliable features before classification step. Spectral estimation methods used are standard periodogram and the periodogram calculated by the Welch method; both methods are compared with Logarithm of Band Power (logBP) features. In the method proposed, we apply Linear Discriminant Analysis (LDA) followed by Support Vector Machine (SVM). Classification accuracy reached could be as high as 85%, which proves the effectiveness of classification of EEG signals based BCI using spectral methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain-computer%20interface" title="brain-computer interface">brain-computer interface</a>, <a href="https://publications.waset.org/abstracts/search?q=motor%20imagery" title=" motor imagery"> motor imagery</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title=" electroencephalogram"> electroencephalogram</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20discriminant%20analysis" title=" linear discriminant analysis"> linear discriminant analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/6971/comparative-analysis-of-spectral-estimation-methods-for-brain-computer-interfaces" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6971.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">499</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=classification%20method&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=classification%20method&page=3">3</a></li> <li class="page-item"><a class="page-link" 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