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feature points</title> <meta name="description" content="Search results for: important feature points"> <meta name="keywords" content="important feature points"> <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="important feature points"> <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> 17618</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: important feature points</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17618</span> A Robust Digital Image Watermarking Against Geometrical Attack Based on Hybrid Scheme</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Samadzadeh%20Mahabadi">M. Samadzadeh Mahabadi</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Shanbehzadeh"> J. Shanbehzadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a hybrid digital image-watermarking scheme, which is robust against varieties of attacks and geometric distortions. The image content is represented by important feature points obtained by an image-texture-based adaptive Harris corner detector. These feature points are extracted from LL2 of 2-D discrete wavelet transform which are obtained by using the Harris-Laplacian detector. We calculate the Fourier transform of circular regions around these points. The amplitude of this transform is rotation invariant. The experimental results demonstrate the robustness of the proposed method against the geometric distortions and various common image processing operations such as JPEG compression, colour reduction, Gaussian filtering, median filtering, and rotation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20watermarking" title="digital watermarking">digital watermarking</a>, <a href="https://publications.waset.org/abstracts/search?q=geometric%20distortions" title=" geometric distortions"> geometric distortions</a>, <a href="https://publications.waset.org/abstracts/search?q=geometrical%20attack" title=" geometrical attack"> geometrical attack</a>, <a href="https://publications.waset.org/abstracts/search?q=Harris%20Laplace" title=" Harris Laplace"> Harris Laplace</a>, <a href="https://publications.waset.org/abstracts/search?q=important%20feature%20points" title=" important feature points"> important feature points</a>, <a href="https://publications.waset.org/abstracts/search?q=rotation" title=" rotation"> rotation</a>, <a href="https://publications.waset.org/abstracts/search?q=scale%20invariant%20feature" title=" scale invariant feature"> scale invariant feature</a> </p> <a href="https://publications.waset.org/abstracts/6175/a-robust-digital-image-watermarking-against-geometrical-attack-based-on-hybrid-scheme" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6175.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">501</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">17617</span> Barnard Feature Point Detector for Low-Contractperiapical Radiography Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chih-Yi%20Ho">Chih-Yi Ho</a>, <a href="https://publications.waset.org/abstracts/search?q=Tzu-Fang%20Chang"> Tzu-Fang Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chih-Chia%20Huang"> Chih-Chia Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chia-Yen%20Lee"> Chia-Yen Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In dental clinics, the dentists use the periapical radiography image to assess the effectiveness of endodontic treatment of teeth with chronic apical periodontitis. Periapical radiography images are taken at different times to assess alveolar bone variation before and after the root canal treatment, and furthermore to judge whether the treatment was successful. Current clinical assessment of apical tissue recovery relies only on dentist personal experience. It is difficult to have the same standard and objective interpretations due to the dentist or radiologist personal background and knowledge. If periapical radiography images at the different time could be registered well, the endodontic treatment could be evaluated. In the image registration area, it is necessary to assign representative control points to the transformation model for good performances of registration results. However, detection of representative control points (feature points) on periapical radiography images is generally very difficult. Regardless of which traditional detection methods are practiced, sufficient feature points may not be detected due to the low-contrast characteristics of the x-ray image. Barnard detector is an algorithm for feature point detection based on grayscale value gradients, which can obtain sufficient feature points in the case of gray-scale contrast is not obvious. However, the Barnard detector would detect too many feature points, and they would be too clustered. This study uses the local extrema of clustering feature points and the suppression radius to overcome the problem, and compared different feature point detection methods. In the preliminary result, the feature points could be detected as representative control points by the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20detection" title="feature detection">feature detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Barnard%20detector" title=" Barnard detector"> Barnard detector</a>, <a href="https://publications.waset.org/abstracts/search?q=registration" title=" registration"> registration</a>, <a href="https://publications.waset.org/abstracts/search?q=periapical%20radiography%20image" title=" periapical radiography image"> periapical radiography image</a>, <a href="https://publications.waset.org/abstracts/search?q=endodontic%20treatment" title=" endodontic treatment"> endodontic treatment</a> </p> <a href="https://publications.waset.org/abstracts/67658/barnard-feature-point-detector-for-low-contractperiapical-radiography-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67658.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">442</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">17616</span> A Speeded up Robust Scale-Invariant Feature Transform Currency Recognition Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daliyah%20S.%20Aljutaili">Daliyah S. Aljutaili</a>, <a href="https://publications.waset.org/abstracts/search?q=Redna%20A.%20Almutlaq"> Redna A. Almutlaq</a>, <a href="https://publications.waset.org/abstracts/search?q=Suha%20A.%20Alharbi"> Suha A. Alharbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Dina%20M.%20Ibrahim"> Dina M. Ibrahim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> All currencies around the world look very different from each other. For instance, the size, color, and pattern of the paper are different. With the development of modern banking services, automatic methods for paper currency recognition become important in many applications like vending machines. One of the currency recognition architecture’s phases is Feature detection and description. There are many algorithms that are used for this phase, but they still have some disadvantages. This paper proposes a feature detection algorithm, which merges the advantages given in the current SIFT and SURF algorithms, which we call, Speeded up Robust Scale-Invariant Feature Transform (SR-SIFT) algorithm. Our proposed SR-SIFT algorithm overcomes the problems of both the SIFT and SURF algorithms. The proposed algorithm aims to speed up the SIFT feature detection algorithm and keep it robust. Simulation results demonstrate that the proposed SR-SIFT algorithm decreases the average response time, especially in small and minimum number of best key points, increases the distribution of the number of best key points on the surface of the currency. Furthermore, the proposed algorithm increases the accuracy of the true best point distribution inside the currency edge than the other two algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=currency%20recognition" title="currency recognition">currency recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20detection%20and%20description" title=" feature detection and description"> feature detection and description</a>, <a href="https://publications.waset.org/abstracts/search?q=SIFT%20algorithm" title=" SIFT algorithm"> SIFT algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=SURF%20algorithm" title=" SURF algorithm"> SURF algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=speeded%20up%20and%20robust%20features" title=" speeded up and robust features"> speeded up and robust features</a> </p> <a href="https://publications.waset.org/abstracts/94315/a-speeded-up-robust-scale-invariant-feature-transform-currency-recognition-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94315.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">17615</span> Keypoint Detection Method Based on Multi-Scale Feature Fusion of Attention Mechanism</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiaoxiao%20Li">Xiaoxiao Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Shuangcheng%20Jia"> Shuangcheng Jia</a>, <a href="https://publications.waset.org/abstracts/search?q=Qian%20Li"> Qian Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Keypoint detection has always been a challenge in the field of image recognition. This paper proposes a novelty keypoint detection method which is called Multi-Scale Feature Fusion Convolutional Network with Attention (MFFCNA). We verified that the multi-scale features with the attention mechanism module have better feature expression capability. The feature fusion between different scales makes the information that the network model can express more abundant, and the network is easier to converge. On our self-made street sign corner dataset, we validate the MFFCNA model with an accuracy of 97.8% and a recall of 81%, which are 5 and 8 percentage points higher than the HRNet network, respectively. On the COCO dataset, the AP is 71.9%, and the AR is 75.3%, which are 3 points and 2 points higher than HRNet, respectively. Extensive experiments show that our method has a remarkable improvement in the keypoint recognition tasks, and the recognition effect is better than the existing methods. Moreover, our method can be applied not only to keypoint detection but also to image classification and semantic segmentation with good generality. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=keypoint%20detection" title="keypoint detection">keypoint detection</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20fusion" title=" feature fusion"> feature fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=attention" title=" attention"> attention</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20segmentation" title=" semantic segmentation"> semantic segmentation</a> </p> <a href="https://publications.waset.org/abstracts/147796/keypoint-detection-method-based-on-multi-scale-feature-fusion-of-attention-mechanism" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147796.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">119</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">17614</span> Light-Weight Network for Real-Time Pose Estimation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jianghao%20Hu">Jianghao Hu</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongyu%20Wang"> Hongyu Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The effective and efficient human pose estimation algorithm is an important task for real-time human pose estimation on mobile devices. This paper proposes a light-weight human key points detection algorithm, Light-Weight Network for Real-Time Pose Estimation (LWPE). LWPE uses light-weight backbone network and depthwise separable convolutions to reduce parameters and lower latency. LWPE uses the feature pyramid network (FPN) to fuse the high-resolution, semantically weak features with the low-resolution, semantically strong features. In the meantime, with multi-scale prediction, the predicted result by the low-resolution feature map is stacked to the adjacent higher-resolution feature map to intermediately monitor the network and continuously refine the results. At the last step, the key point coordinates predicted in the highest-resolution are used as the final output of the network. For the key-points that are difficult to predict, LWPE adopts the online hard key points mining strategy to focus on the key points that hard predicting. The proposed algorithm achieves excellent performance in the single-person dataset selected in the AI (artificial intelligence) challenge dataset. The algorithm maintains high-precision performance even though the model only contains 3.9M parameters, and it can run at 225 frames per second (FPS) on the generic graphics processing unit (GPU). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=depthwise%20separable%20convolutions" title="depthwise separable convolutions">depthwise separable convolutions</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20pyramid%20network" title=" feature pyramid network"> feature pyramid network</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20pose%20estimation" title=" human pose estimation"> human pose estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=light-weight%20backbone" title=" light-weight backbone "> light-weight backbone </a> </p> <a href="https://publications.waset.org/abstracts/112845/light-weight-network-for-real-time-pose-estimation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112845.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">154</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17613</span> Automated Feature Detection and Matching Algorithms for Breast IR Sequence Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chia-Yen%20Lee">Chia-Yen Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Hao-Jen%20Wang"> Hao-Jen Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jhih-Hao%20Lai"> Jhih-Hao Lai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, infrared (IR) imaging has been considered as a potential tool to assess the efficacy of chemotherapy and early detection of breast cancer. Regions of tumor growth with high metabolic rate and angiogenesis phenomenon lead to the high temperatures. Observation of differences between the heat maps in long term is useful to help assess the growth of breast cancer cells and detect breast cancer earlier, wherein the multi-time infrared image alignment technology is a necessary step. Representative feature points detection and matching are essential steps toward the good performance of image registration and quantitative analysis. However, there is no clear boundary on the infrared images and the subject's posture are different for each shot. It cannot adhesive markers on a body surface for a very long period, and it is hard to find anatomic fiducial markers on a body surface. In other words, it’s difficult to detect and match features in an IR sequence images. In this study, automated feature detection and matching algorithms with two type of automatic feature points (i.e., vascular branch points and modified Harris corner) are developed respectively. The preliminary results show that the proposed method could identify the representative feature points on the IR breast images successfully of 98% accuracy and the matching results of 93% accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Harris%20corner" title="Harris corner">Harris corner</a>, <a href="https://publications.waset.org/abstracts/search?q=infrared%20image" title=" infrared image"> infrared image</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20detection" title=" feature detection"> feature detection</a>, <a href="https://publications.waset.org/abstracts/search?q=registration" title=" registration"> registration</a>, <a href="https://publications.waset.org/abstracts/search?q=matching" title=" matching"> matching</a> </p> <a href="https://publications.waset.org/abstracts/16915/automated-feature-detection-and-matching-algorithms-for-breast-ir-sequence-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16915.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">304</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17612</span> Words Spotting in the Images Handwritten Historical Documents </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Issam%20Ben%20Jami">Issam Ben Jami </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Information retrieval in digital libraries is very important because most famous historical documents occupy a significant value. The word spotting in historical documents is a very difficult notion, because automatic recognition of such documents is naturally cursive, it represents a wide variability in the level scale and translation words in the same documents. We first present a system for the automatic recognition, based on the extraction of interest points words from the image model. The extraction phase of the key points is chosen from the representation of the image as a synthetic description of the shape recognition in a multidimensional space. As a result, we use advanced methods that can find and describe interesting points invariant to scale, rotation and lighting which are linked to local configurations of pixels. We test this approach on documents of the 15th century. Our experiments give important results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20matching" title="feature matching">feature matching</a>, <a href="https://publications.waset.org/abstracts/search?q=historical%20documents" title=" historical documents"> historical documents</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=word%20spotting" title=" word spotting"> word spotting</a> </p> <a href="https://publications.waset.org/abstracts/52183/words-spotting-in-the-images-handwritten-historical-documents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52183.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">274</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">17611</span> Retina Registration for Biometrics Based on Characterization of Retinal Feature Points</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nougrara%20Zineb">Nougrara Zineb</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The unique structure of the blood vessels in the retina has been used for biometric identification. The retina blood vessel pattern is a unique pattern in each individual and it is almost impossible to forge that pattern in a false individual. The retina biometrics’ advantages include high distinctiveness, universality, and stability overtime of the blood vessel pattern. Once the creases have been extracted from the images, a registration stage is necessary, since the position of the retinal vessel structure could change between acquisitions due to the movements of the eye. Image registration consists of following steps: Feature detection, feature matching, transform model estimation and image resembling and transformation. In this paper, we present an algorithm of registration; it is based on the characterization of retinal feature points. For experiments, retinal images from the DRIVE database have been tested. The proposed methodology achieves good results for registration in general. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fovea" title="fovea">fovea</a>, <a href="https://publications.waset.org/abstracts/search?q=optic%20disc" title=" optic disc"> optic disc</a>, <a href="https://publications.waset.org/abstracts/search?q=registration" title=" registration"> registration</a>, <a href="https://publications.waset.org/abstracts/search?q=retinal%20images" title=" retinal images"> retinal images</a> </p> <a href="https://publications.waset.org/abstracts/72438/retina-registration-for-biometrics-based-on-characterization-of-retinal-feature-points" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72438.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">266</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">17610</span> Variability Management of Contextual Feature Model in Multi-Software Product Line</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Fezan%20Afzal">Muhammad Fezan Afzal</a>, <a href="https://publications.waset.org/abstracts/search?q=Asad%20Abbas"> Asad Abbas</a>, <a href="https://publications.waset.org/abstracts/search?q=Imran%20Khan"> Imran Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Salma%20Imtiaz"> Salma Imtiaz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Software Product Line (SPL) paradigm is used for the development of the family of software products that share common and variable features. Feature model is a domain of SPL that consists of common and variable features with predefined relationships and constraints. Multiple SPLs consist of a number of similar common and variable features, such as mobile phones and Tabs. Reusability of common and variable features from the different domains of SPL is a complex task due to the external relationships and constraints of features in the feature model. To increase the reusability of feature model resources from domain engineering, it is required to manage the commonality of features at the level of SPL application development. In this research, we have proposed an approach that combines multiple SPLs into a single domain and converts them to a common feature model. Extracting the common features from different feature models is more effective, less cost and time to market for the application development. For extracting features from multiple SPLs, the proposed framework consists of three steps: 1) find the variation points, 2) find the constraints, and 3) combine the feature models into a single feature model on the basis of variation points and constraints. By using this approach, reusability can increase features from the multiple feature models. The impact of this research is to reduce the development of cost, time to market and increase products of SPL. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=software%20product%20line" title="software product line">software product line</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20model" title=" feature model"> feature model</a>, <a href="https://publications.waset.org/abstracts/search?q=variability%20management" title=" variability management"> variability management</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-SPLs" title=" multi-SPLs"> multi-SPLs</a> </p> <a href="https://publications.waset.org/abstracts/172205/variability-management-of-contextual-feature-model-in-multi-software-product-line" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172205.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">69</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17609</span> Feature Selection of Personal Authentication Based on EEG Signal for K-Means Cluster Analysis Using Silhouettes Score</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jianfeng%20Hu">Jianfeng Hu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Personal authentication based on electroencephalography (EEG) signals is one of the important field for the biometric technology. More and more researchers have used EEG signals as data source for biometric. However, there are some disadvantages for biometrics based on EEG signals. The proposed method employs entropy measures for feature extraction from EEG signals. Four type of entropies measures, sample entropy (SE), fuzzy entropy (FE), approximate entropy (AE) and spectral entropy (PE), were deployed as feature set. In a silhouettes calculation, the distance from each data point in a cluster to all another point within the same cluster and to all other data points in the closest cluster are determined. Thus silhouettes provide a measure of how well a data point was classified when it was assigned to a cluster and the separation between them. This feature renders silhouettes potentially well suited for assessing cluster quality in personal authentication methods. In this study, “silhouettes scores” was used for assessing the cluster quality of k-means clustering algorithm is well suited for comparing the performance of each EEG dataset. The main goals of this study are: (1) to represent each target as a tuple of multiple feature sets, (2) to assign a suitable measure to each feature set, (3) to combine different feature sets, (4) to determine the optimal feature weighting. Using precision/recall evaluations, the effectiveness of feature weighting in clustering was analyzed. EEG data from 22 subjects were collected. Results showed that: (1) It is possible to use fewer electrodes (3-4) for personal authentication. (2) There was the difference between each electrode for personal authentication (p<0.01). (3) There is no significant difference for authentication performance among feature sets (except feature PE). Conclusion: The combination of k-means clustering algorithm and silhouette approach proved to be an accurate method for personal authentication based on EEG signals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=personal%20authentication" title="personal authentication">personal authentication</a>, <a href="https://publications.waset.org/abstracts/search?q=K-mean%20clustering" title=" K-mean clustering"> K-mean clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title=" electroencephalogram"> electroencephalogram</a>, <a href="https://publications.waset.org/abstracts/search?q=EEG" title=" EEG"> EEG</a>, <a href="https://publications.waset.org/abstracts/search?q=silhouettes" title=" silhouettes"> silhouettes</a> </p> <a href="https://publications.waset.org/abstracts/67078/feature-selection-of-personal-authentication-based-on-eeg-signal-for-k-means-cluster-analysis-using-silhouettes-score" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67078.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">285</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">17608</span> Temporally Coherent 3D Animation Reconstruction from RGB-D Video Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salam%20Khalifa">Salam Khalifa</a>, <a href="https://publications.waset.org/abstracts/search?q=Naveed%20Ahmed"> Naveed Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present a new method to reconstruct a temporally coherent 3D animation from single or multi-view RGB-D video data using unbiased feature point sampling. Given RGB-D video data, in form of a 3D point cloud sequence, our method first extracts feature points using both color and depth information. In the subsequent steps, these feature points are used to match two 3D point clouds in consecutive frames independent of their resolution. Our new motion vectors based dynamic alignment method then fully reconstruct a spatio-temporally coherent 3D animation. We perform extensive quantitative validation using novel error functions to analyze the results. We show that despite the limiting factors of temporal and spatial noise associated to RGB-D data, it is possible to extract temporal coherence to faithfully reconstruct a temporally coherent 3D animation from RGB-D video data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D%20video" title="3D video">3D video</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20animation" title=" 3D animation"> 3D animation</a>, <a href="https://publications.waset.org/abstracts/search?q=RGB-D%20video" title=" RGB-D video"> RGB-D video</a>, <a href="https://publications.waset.org/abstracts/search?q=temporally%20coherent%203D%20animation" title=" temporally coherent 3D animation"> temporally coherent 3D animation</a> </p> <a href="https://publications.waset.org/abstracts/12034/temporally-coherent-3d-animation-reconstruction-from-rgb-d-video-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12034.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">373</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">17607</span> Imputation Technique for Feature Selection in Microarray Data Set</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Younies%20Saeed%20Hassan%20Mahmoud">Younies Saeed Hassan Mahmoud</a>, <a href="https://publications.waset.org/abstracts/search?q=Mai%20Mabrouk"> Mai Mabrouk</a>, <a href="https://publications.waset.org/abstracts/search?q=Elsayed%20Sallam"> Elsayed Sallam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Analysing DNA microarray data sets is a great challenge, which faces the bioinformaticians due to the complication of using statistical and machine learning techniques. The challenge will be doubled if the microarray data sets contain missing data, which happens regularly because these techniques cannot deal with missing data. One of the most important data analysis process on the microarray data set is feature selection. This process finds the most important genes that affect certain disease. In this paper, we introduce a technique for imputing the missing data in microarray data sets while performing feature selection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DNA%20microarray" title="DNA microarray">DNA microarray</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=missing%20data" title=" missing data"> missing data</a>, <a href="https://publications.waset.org/abstracts/search?q=bioinformatics" title=" bioinformatics"> bioinformatics</a> </p> <a href="https://publications.waset.org/abstracts/21839/imputation-technique-for-feature-selection-in-microarray-data-set" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21839.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">574</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">17606</span> A Quantitative Evaluation of Text Feature Selection Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20S.%20Harish">B. S. Harish</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20B.%20Revanasiddappa"> M. B. Revanasiddappa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to rapid growth of text documents in digital form, automated text classification has become an important research in the last two decades. The major challenge of text document representations are high dimension, sparsity, volume and semantics. Since the terms are only features that can be found in documents, selection of good terms (features) plays an very important role. In text classification, feature selection is a strategy that can be used to improve classification effectiveness, computational efficiency and accuracy. In this paper, we present a quantitative analysis of most widely used feature selection (FS) methods, viz. Term Frequency-Inverse Document Frequency (tfidf ), Mutual Information (MI), Information Gain (IG), CHISquare (x2), Term Frequency-Relevance Frequency (tfrf ), Term Strength (TS), Ambiguity Measure (AM) and Symbolic Feature Selection (SFS) to classify text documents. We evaluated all the feature selection methods on standard datasets like 20 Newsgroups, 4 University dataset and Reuters-21578. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classifiers" title="classifiers">classifiers</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=text%20classification" title=" text classification "> text classification </a> </p> <a href="https://publications.waset.org/abstracts/28926/a-quantitative-evaluation-of-text-feature-selection-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28926.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> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17605</span> A Survey of Feature Selection and Feature Extraction Techniques in Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Samina%20Khalid">Samina Khalid</a>, <a href="https://publications.waset.org/abstracts/search?q=Shamila%20Nasreen"> Shamila Nasreen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Dimensionality reduction as a preprocessing step to machine learning is effective in removing irrelevant and redundant data, increasing learning accuracy, and improving result comprehensibility. However, the recent increase of dimensionality of data poses a severe challenge to many existing feature selection and feature extraction methods with respect to efficiency and effectiveness. In the field of machine learning and pattern recognition, dimensionality reduction is important area, where many approaches have been proposed. In this paper, some widely used feature selection and feature extraction techniques have analyzed with the purpose of how effectively these techniques can be used to achieve high performance of learning algorithms that ultimately improves predictive accuracy of classifier. An endeavor to analyze dimensionality reduction techniques briefly with the purpose to investigate strengths and weaknesses of some widely used dimensionality reduction methods is presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=age%20related%20macular%20degeneration" title="age related macular degeneration">age related macular degeneration</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection%20feature%20subset%20selection%20feature%20extraction%2Ftransformation" title=" feature selection feature subset selection feature extraction/transformation"> feature selection feature subset selection feature extraction/transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=FSA%E2%80%99s" title=" FSA’s"> FSA’s</a>, <a href="https://publications.waset.org/abstracts/search?q=relief" title=" relief"> relief</a>, <a href="https://publications.waset.org/abstracts/search?q=correlation%20based%20method" title=" correlation based method"> correlation based method</a>, <a href="https://publications.waset.org/abstracts/search?q=PCA" title=" PCA"> PCA</a>, <a href="https://publications.waset.org/abstracts/search?q=ICA" title=" ICA"> ICA</a> </p> <a href="https://publications.waset.org/abstracts/6168/a-survey-of-feature-selection-and-feature-extraction-techniques-in-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6168.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">496</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">17604</span> Video Stabilization Using Feature Point Matching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shamsundar%20Kulkarni">Shamsundar Kulkarni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Video capturing by non-professionals will lead to unanticipated effects. Such as image distortion, image blurring etc. Hence, many researchers study such drawbacks to enhance the quality of videos. In this paper, an algorithm is proposed to stabilize jittery videos .A stable output video will be attained without the effect of jitter which is caused due to shaking of handheld camera during video recording. Firstly, salient points from each frame from the input video are identified and processed followed by optimizing and stabilize the video. Optimization includes the quality of the video stabilization. This method has shown good result in terms of stabilization and it discarded distortion from the output videos recorded in different circumstances. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20stabilization" title="video stabilization">video stabilization</a>, <a href="https://publications.waset.org/abstracts/search?q=point%20feature%20matching" title=" point feature matching"> point feature matching</a>, <a href="https://publications.waset.org/abstracts/search?q=salient%20points" title=" salient points"> salient points</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20quality%20measurement" title=" image quality measurement"> image quality measurement</a> </p> <a href="https://publications.waset.org/abstracts/57341/video-stabilization-using-feature-point-matching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57341.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">313</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">17603</span> Deep Learning Based Fall Detection Using Simplified Human Posture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kripesh%20Adhikari">Kripesh Adhikari</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Bouchachia"> Hamid Bouchachia</a>, <a href="https://publications.waset.org/abstracts/search?q=Hammadi%20Nait-Charif"> Hammadi Nait-Charif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Falls are one of the major causes of injury and death among elderly people aged 65 and above. A support system to identify such kind of abnormal activities have become extremely important with the increase in ageing population. Pose estimation is a challenging task and to add more to this, it is even more challenging when pose estimations are performed on challenging poses that may occur during fall. Location of the body provides a clue where the person is at the time of fall. This paper presents a vision-based tracking strategy where available joints are grouped into three different feature points depending upon the section they are located in the body. The three feature points derived from different joints combinations represents the upper region or head region, mid-region or torso and lower region or leg region. Tracking is always challenging when a motion is involved. Hence the idea is to locate the regions in the body in every frame and consider it as the tracking strategy. Grouping these joints can be beneficial to achieve a stable region for tracking. The location of the body parts provides a crucial information to distinguish normal activities from falls. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fall%20detection" title="fall detection">fall detection</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=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=pose%20estimation" title=" pose estimation"> pose estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=tracking" title=" tracking"> tracking</a> </p> <a href="https://publications.waset.org/abstracts/104451/deep-learning-based-fall-detection-using-simplified-human-posture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104451.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">189</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">17602</span> The Relationship between Human Pose and Intention to Fire a Handgun</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Joshua%20van%20Staden">Joshua van Staden</a>, <a href="https://publications.waset.org/abstracts/search?q=Dane%20Brown"> Dane Brown</a>, <a href="https://publications.waset.org/abstracts/search?q=Karen%20Bradshaw"> Karen Bradshaw</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gun violence is a significant problem in modern-day society. Early detection of carried handguns through closed-circuit television (CCTV) can aid in preventing potential gun violence. However, CCTV operators have a limited attention span. Machine learning approaches to automating the detection of dangerous gun carriers provide a way to aid CCTV operators in identifying these individuals. This study provides insight into the relationship between human key points extracted using human pose estimation (HPE) and their intention to fire a weapon. We examine the feature importance of each keypoint and their correlations. We use principal component analysis (PCA) to reduce the feature space and optimize detection. Finally, we run a set of classifiers to determine what form of classifier performs well on this data. We find that hips, shoulders, and knees tend to be crucial aspects of the human pose when making these predictions. Furthermore, the horizontal position plays a larger role than the vertical position. Of the 66 key points, nine principal components could be used to make nonlinear classifications with 86% accuracy. Furthermore, linear classifications could be done with 85% accuracy, showing that there is a degree of linearity in the data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20engineering" title="feature engineering">feature engineering</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20pose" title=" human pose"> human pose</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=security" title=" security"> security</a> </p> <a href="https://publications.waset.org/abstracts/155235/the-relationship-between-human-pose-and-intention-to-fire-a-handgun" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155235.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">93</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">17601</span> Bioengineering System for Prediction and Early Prenosological Diagnostics of Stomach Diseases Based on Energy Characteristics of Bioactive Points with Fuzzy Logic </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20Alshamasin">Mahdi Alshamasin</a>, <a href="https://publications.waset.org/abstracts/search?q=Riad%20Al-Kasasbeh"> Riad Al-Kasasbeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikolay%20Korenevskiy"> Nikolay Korenevskiy </a> </p> <p class="card-text"><strong>Abstract:</strong></p> We apply mathematical models for the interaction of the internal and biologically active points of meridian structures. Amongst the diseases for which reflex diagnostics are effective are those of the stomach disease. It is shown that use of fuzzy logic decision-making yields good results for the prediction and early diagnosis of gastrointestinal tract diseases, depending on the reaction energy of biologically active points (acupuncture points). It is shown that good results for the prediction and early diagnosis of diseases from the reaction energy of biologically active points (acupuncture points) are obtained by using fuzzy logic decision-making. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=acupuncture%20points" title="acupuncture points">acupuncture points</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20logic" title=" fuzzy logic"> fuzzy logic</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnostically%20important%20points%20%28DIP%29" title=" diagnostically important points (DIP)"> diagnostically important points (DIP)</a>, <a href="https://publications.waset.org/abstracts/search?q=confidence%20factors" title=" confidence factors"> confidence factors</a>, <a href="https://publications.waset.org/abstracts/search?q=membership%20functions" title=" membership functions"> membership functions</a>, <a href="https://publications.waset.org/abstracts/search?q=stomach%20diseases" title=" stomach diseases "> stomach diseases </a> </p> <a href="https://publications.waset.org/abstracts/34901/bioengineering-system-for-prediction-and-early-prenosological-diagnostics-of-stomach-diseases-based-on-energy-characteristics-of-bioactive-points-with-fuzzy-logic" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34901.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">467</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">17600</span> RGB Color Based Real Time Traffic Sign Detection and Feature Extraction System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kay%20Thinzar%20Phu">Kay Thinzar Phu</a>, <a href="https://publications.waset.org/abstracts/search?q=Lwin%20Lwin%20Oo"> Lwin Lwin Oo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In an intelligent transport system and advanced driver assistance system, the developing of real-time traffic sign detection and recognition (TSDR) system plays an important part in recent research field. There are many challenges for developing real-time TSDR system due to motion artifacts, variable lighting and weather conditions and situations of traffic signs. Researchers have already proposed various methods to minimize the challenges problem. The aim of the proposed research is to develop an efficient and effective TSDR in real time. This system proposes an adaptive thresholding method based on RGB color for traffic signs detection and new features for traffic signs recognition. In this system, the RGB color thresholding is used to detect the blue and yellow color traffic signs regions. The system performs the shape identify to decide whether the output candidate region is traffic sign or not. Lastly, new features such as termination points, bifurcation points, and 90’ angles are extracted from validated image. This system uses Myanmar Traffic Sign dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20thresholding%20based%20on%20RGB%20color" title="adaptive thresholding based on RGB color">adaptive thresholding based on RGB color</a>, <a href="https://publications.waset.org/abstracts/search?q=blue%20color%20detection" title=" blue color detection"> blue color detection</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=yellow%20color%20detection" title=" yellow color detection"> yellow color detection</a> </p> <a href="https://publications.waset.org/abstracts/77127/rgb-color-based-real-time-traffic-sign-detection-and-feature-extraction-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77127.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">313</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">17599</span> Image Retrieval Based on Multi-Feature Fusion for Heterogeneous Image Databases</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20W.%20U.%20D.%20Chathurani">N. W. U. D. Chathurani</a>, <a href="https://publications.waset.org/abstracts/search?q=Shlomo%20Geva"> Shlomo Geva</a>, <a href="https://publications.waset.org/abstracts/search?q=Vinod%20Chandran"> Vinod Chandran</a>, <a href="https://publications.waset.org/abstracts/search?q=Proboda%20Rajapaksha"> Proboda Rajapaksha </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Selecting an appropriate image representation is the most important factor in implementing an effective Content-Based Image Retrieval (CBIR) system. This paper presents a multi-feature fusion approach for efficient CBIR, based on the distance distribution of features and relative feature weights at the time of query processing. It is a simple yet effective approach, which is free from the effect of features' dimensions, ranges, internal feature normalization and the distance measure. This approach can easily be adopted in any feature combination to improve retrieval quality. The proposed approach is empirically evaluated using two benchmark datasets for image classification (a subset of the Corel dataset and Oliva and Torralba) and compared with existing approaches. The performance of the proposed approach is confirmed with the significantly improved performance in comparison with the independently evaluated baseline of the previously proposed feature fusion approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20fusion" title="feature fusion">feature fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20retrieval" title=" image retrieval"> image retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=membership%20function" title=" membership function"> membership function</a>, <a href="https://publications.waset.org/abstracts/search?q=normalization" title=" normalization"> normalization</a> </p> <a href="https://publications.waset.org/abstracts/52968/image-retrieval-based-on-multi-feature-fusion-for-heterogeneous-image-databases" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52968.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">345</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">17598</span> A Research and Application of Feature Selection Based on IWO and Tabu Search</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Laicheng%20Cao">Laicheng Cao</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiangqian%20Su"> Xiangqian Su</a>, <a href="https://publications.waset.org/abstracts/search?q=Youxiao%20Wu"> Youxiao Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Feature selection is one of the important problems in network security, pattern recognition, data mining and other fields. In order to remove redundant features, effectively improve the detection speed of intrusion detection system, proposes a new feature selection method, which is based on the invasive weed optimization (IWO) algorithm and tabu search algorithm(TS). Use IWO as a global search, tabu search algorithm for local search, to improve the results of IWO algorithm. The experimental results show that the feature selection method can effectively remove the redundant features of network data information in feature selection, reduction time, and to guarantee accurate detection rate, effectively improve the speed of detection system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection" title="intrusion detection">intrusion detection</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=iwo" title=" iwo"> iwo</a>, <a href="https://publications.waset.org/abstracts/search?q=tabu%20search" title=" tabu search"> tabu search</a> </p> <a href="https://publications.waset.org/abstracts/28884/a-research-and-application-of-feature-selection-based-on-iwo-and-tabu-search" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28884.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">530</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">17597</span> Feature Evaluation Based on Random Subspace and Multiple-K Ensemble</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jaehong%20Yu">Jaehong Yu</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> Clustering analysis can facilitate the extraction of intrinsic patterns in a dataset and reveal its natural groupings without requiring class information. For effective clustering analysis in high dimensional datasets, unsupervised dimensionality reduction is an important task. Unsupervised dimensionality reduction can generally be achieved by feature extraction or feature selection. In many situations, feature selection methods are more appropriate than feature extraction methods because of their clear interpretation with respect to the original features. The unsupervised feature selection can be categorized as feature subset selection and feature ranking method, and we focused on unsupervised feature ranking methods which evaluate the features based on their importance scores. Recently, several unsupervised feature ranking methods were developed based on ensemble approaches to achieve their higher accuracy and stability. However, most of the ensemble-based feature ranking methods require the true number of clusters. Furthermore, these algorithms evaluate the feature importance depending on the ensemble clustering solution, and they produce undesirable evaluation results if the clustering solutions are inaccurate. To address these limitations, we proposed an ensemble-based feature ranking method with random subspace and multiple-k ensemble (FRRM). The proposed FRRM algorithm evaluates the importance of each feature with the random subspace ensemble, and all evaluation results are combined with the ensemble importance scores. Moreover, FRRM does not require the determination of the true number of clusters in advance through the use of the multiple-k ensemble idea. Experiments on various benchmark datasets were conducted to examine the properties of the proposed FRRM algorithm and to compare its performance with that of existing feature ranking methods. The experimental results demonstrated that the proposed FRRM outperformed the competitors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clustering%20analysis" title="clustering analysis">clustering analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple-k%20ensemble" title=" multiple-k ensemble"> multiple-k ensemble</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20subspace-based%20feature%20evaluation" title=" random subspace-based feature evaluation"> random subspace-based feature evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=unsupervised%20feature%20ranking" title=" unsupervised feature ranking"> unsupervised feature ranking</a> </p> <a href="https://publications.waset.org/abstracts/52081/feature-evaluation-based-on-random-subspace-and-multiple-k-ensemble" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52081.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">339</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">17596</span> Triangular Geometric Feature for Offline Signature Verification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zuraidasahana%20Zulkarnain">Zuraidasahana Zulkarnain</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Shafry%20Mohd%20Rahim"> Mohd Shafry Mohd Rahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Nor%20Anita%20Fairos%20Ismail"> Nor Anita Fairos Ismail</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Azhar%20M.%20Arsad"> Mohd Azhar M. Arsad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Handwritten signature is accepted widely as a biometric characteristic for personal authentication. The use of appropriate features plays an important role in determining accuracy of signature verification; therefore, this paper presents a feature based on the geometrical concept. To achieve the aim, triangle attributes are exploited to design a new feature since the triangle possesses orientation, angle and transformation that would improve accuracy. The proposed feature uses triangulation geometric set comprising of sides, angles and perimeter of a triangle which is derived from the center of gravity of a signature image. For classification purpose, Euclidean classifier along with Voting-based classifier is used to verify the tendency of forgery signature. This classification process is experimented using triangular geometric feature and selected global features. Based on an experiment that was validated using Grupo de Senales 960 (GPDS-960) signature database, the proposed triangular geometric feature achieves a lower Average Error Rates (AER) value with a percentage of 34% as compared to 43% of the selected global feature. As a conclusion, the proposed triangular geometric feature proves to be a more reliable feature for accurate signature verification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=biometrics" title="biometrics">biometrics</a>, <a href="https://publications.waset.org/abstracts/search?q=euclidean%20classifier" title=" euclidean classifier"> euclidean classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=features%20extraction" title=" features extraction"> features extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=offline%20signature%20verification" title=" offline signature verification"> offline signature verification</a>, <a href="https://publications.waset.org/abstracts/search?q=voting-based%20classifier" title=" voting-based classifier"> voting-based classifier</a> </p> <a href="https://publications.waset.org/abstracts/45300/triangular-geometric-feature-for-offline-signature-verification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45300.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">378</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">17595</span> Registration of Multi-Temporal Unmanned Aerial Vehicle Images for Facility Monitoring</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dongyeob%20Han">Dongyeob Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Jungwon%20Huh"> Jungwon Huh</a>, <a href="https://publications.waset.org/abstracts/search?q=Quang%20Huy%20Tran"> Quang Huy Tran</a>, <a href="https://publications.waset.org/abstracts/search?q=Choonghyun%20Kang"> Choonghyun Kang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Unmanned Aerial Vehicles (UAVs) have been used for surveillance, monitoring, inspection, and mapping. In this paper, we present a systematic approach for automatic registration of UAV images for monitoring facilities such as building, green house, and civil structures. The two-step process is applied; 1) an image matching technique based on SURF (Speeded up Robust Feature) and RANSAC (Random Sample Consensus), 2) bundle adjustment of multi-temporal images. Image matching to find corresponding points is one of the most important steps for the precise registration of multi-temporal images. We used the SURF algorithm to find a quick and effective matching points. RANSAC algorithm was used in the process of finding matching points between images and in the bundle adjustment process. Experimental results from UAV images showed that our approach has a good accuracy to be applied to the change detection of facility. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=building" title="building">building</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20matching" title=" image matching"> image matching</a>, <a href="https://publications.waset.org/abstracts/search?q=temperature" title=" temperature"> temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=unmanned%20aerial%20vehicle" title=" unmanned aerial vehicle"> unmanned aerial vehicle</a> </p> <a href="https://publications.waset.org/abstracts/85064/registration-of-multi-temporal-unmanned-aerial-vehicle-images-for-facility-monitoring" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85064.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">292</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17594</span> A Similarity Measure for Classification and Clustering in Image Based Medical and Text Based Banking Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20P.%20Sandesh">K. P. Sandesh</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20H.%20Suman"> M. H. Suman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Text processing plays an important role in information retrieval, data-mining, and web search. Measuring the similarity between the documents is an important operation in the text processing field. In this project, a new similarity measure is proposed. To compute the similarity between two documents with respect to a feature the proposed measure takes the following three cases into account: (1) The feature appears in both documents; (2) The feature appears in only one document and; (3) The feature appears in none of the documents. The proposed measure is extended to gauge the similarity between two sets of documents. The effectiveness of our measure is evaluated on several real-world data sets for text classification and clustering problems, especially in banking and health sectors. The results show that the performance obtained by the proposed measure is better than that achieved by the other measures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=document%20classification" title="document classification">document classification</a>, <a href="https://publications.waset.org/abstracts/search?q=document%20clustering" title=" document clustering"> document clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=entropy" title=" entropy"> entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=classifiers" title=" classifiers"> classifiers</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20algorithms" title=" clustering algorithms"> clustering algorithms</a> </p> <a href="https://publications.waset.org/abstracts/22708/a-similarity-measure-for-classification-and-clustering-in-image-based-medical-and-text-based-banking-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22708.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">518</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">17593</span> The Effect of Feature Selection on Pattern Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chih-Fong%20Tsai">Chih-Fong Tsai</a>, <a href="https://publications.waset.org/abstracts/search?q=Ya-Han%20Hu"> Ya-Han Hu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of feature selection (or dimensionality reduction) is to filter out unrepresentative features (or variables) making the classifier perform better than the one without feature selection. Since there are many well-known feature selection algorithms, and different classifiers based on different selection results may perform differently, very few studies consider examining the effect of performing different feature selection algorithms on the classification performances by different classifiers over different types of datasets. In this paper, two widely used algorithms, which are the genetic algorithm (GA) and information gain (IG), are used to perform feature selection. On the other hand, three well-known classifiers are constructed, which are the CART decision tree (DT), multi-layer perceptron (MLP) neural network, and support vector machine (SVM). Based on 14 different types of datasets, the experimental results show that in most cases IG is a better feature selection algorithm than GA. In addition, the combinations of IG with DT and IG with SVM perform best and second best for small and large scale datasets. <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=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20classification" title=" pattern classification"> pattern classification</a>, <a href="https://publications.waset.org/abstracts/search?q=dimensionality%20reduction" title=" dimensionality reduction"> dimensionality reduction</a> </p> <a href="https://publications.waset.org/abstracts/5047/the-effect-of-feature-selection-on-pattern-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5047.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">669</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">17592</span> An Optimized RDP Algorithm for Curve Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jean-Pierre%20Lomaliza">Jean-Pierre Lomaliza</a>, <a href="https://publications.waset.org/abstracts/search?q=Kwang-Seok%20Moon"> Kwang-Seok Moon</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanhoon%20Park"> Hanhoon Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is well-known that Ramer Douglas Peucker (RDP) algorithm greatly depends on the method of choosing starting points. Therefore, this paper focuses on finding such starting points that will optimize the results of RDP algorithm. Specifically, this paper proposes a curve approximation algorithm that finds flat points, called essential points, of an input curve, divides the curve into corner-like sub-curves using the essential points, and applies the RDP algorithm to the sub-curves. The number of essential points play a role on optimizing the approximation results by balancing the degree of shape information loss and the amount of data reduction. Through experiments with curves of various types and complexities of shape, we compared the performance of the proposed algorithm with three other methods, i.e., the RDP algorithm itself and its variants. As a result, the proposed algorithm outperformed the others in term of maintaining the original shapes of the input curve, which is important in various applications like pattern recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=curve%20approximation" title="curve approximation">curve approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=essential%20point" title=" essential point"> essential point</a>, <a href="https://publications.waset.org/abstracts/search?q=RDP%20algorithm" title=" RDP algorithm"> RDP algorithm</a> </p> <a href="https://publications.waset.org/abstracts/29359/an-optimized-rdp-algorithm-for-curve-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29359.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">535</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">17591</span> A Generalized Framework for Adaptive Machine Learning Deployments in Algorithmic Trading</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Robert%20Caulk">Robert Caulk</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A generalized framework for adaptive machine learning deployments in algorithmic trading is introduced, tested, and released as open-source code. The presented software aims to test the hypothesis that recent data contains enough information to form a probabilistically favorable short-term price prediction. Further, the framework contains various adaptive machine learning techniques that are geared toward generating profit during strong trends and minimizing losses during trend changes. Results demonstrate that this adaptive machine learning approach is capable of capturing trends and generating profit. The presentation also discusses the importance of defining the parameter space associated with the dynamic training data-set and using the parameter space to identify and remove outliers from prediction data points. Meanwhile, the generalized architecture enables common users to exploit the powerful machinery while focusing on high-level feature engineering and model testing. The presentation also highlights common strengths and weaknesses associated with the presented technique and presents a broad range of well-tested starting points for feature set construction, target setting, and statistical methods for enforcing risk management and maintaining probabilistically favorable entry and exit points. The presentation also describes the end-to-end data processing tools associated with FreqAI, including automatic data fetching, data aggregation, feature engineering, safe and robust data pre-processing, outlier detection, custom machine learning and statistical tools, data post-processing, and adaptive training backtest emulation, and deployment of adaptive training in live environments. Finally, the generalized user interface is also discussed in the presentation. Feature engineering is simplified so that users can seed their feature sets with common indicator libraries (e.g. TA-lib, pandas-ta). The user also feeds data expansion parameters to fill out a large feature set for the model, which can contain as many as 10,000+ features. The presentation describes the various object-oriented programming techniques employed to make FreqAI agnostic to third-party libraries and external data sources. In other words, the back-end is constructed in such a way that users can leverage a broad range of common regression libraries (Catboost, LightGBM, Sklearn, etc) as well as common Neural Network libraries (TensorFlow, PyTorch) without worrying about the logistical complexities associated with data handling and API interactions. The presentation finishes by drawing conclusions about the most important parameters associated with a live deployment of the adaptive learning framework and provides the road map for future development in FreqAI. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=market%20trend%20detection" title=" market trend detection"> market trend detection</a>, <a href="https://publications.waset.org/abstracts/search?q=open-source" title=" open-source"> open-source</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20learning" title=" adaptive learning"> adaptive learning</a>, <a href="https://publications.waset.org/abstracts/search?q=parameter%20space%20exploration" title=" parameter space exploration"> parameter space exploration</a> </p> <a href="https://publications.waset.org/abstracts/153650/a-generalized-framework-for-adaptive-machine-learning-deployments-in-algorithmic-trading" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153650.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">88</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">17590</span> Multi-Granularity Feature Extraction and Optimization for Pathological Speech Intelligibility Evaluation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chunying%20Fang">Chunying Fang</a>, <a href="https://publications.waset.org/abstracts/search?q=Haifeng%20Li"> Haifeng Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Lin%20Ma"> Lin Ma</a>, <a href="https://publications.waset.org/abstracts/search?q=Mancai%20Zhang"> Mancai Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Speech intelligibility assessment is an important measure to evaluate the functional outcomes of surgical and non-surgical treatment, speech therapy and rehabilitation. The assessment of pathological speech plays an important role in assisting the experts. Pathological speech usually is non-stationary and mutational, in this paper, we describe a multi-granularity combined feature schemes, and which is optimized by hierarchical visual method. First of all, the difference granularity level pathological features are extracted which are BAFS (Basic acoustics feature set), local spectral characteristics MSCC (Mel s-transform cepstrum coefficients) and nonlinear dynamic characteristics based on chaotic analysis. Latterly, radar chart and F-score are proposed to optimize the features by the hierarchical visual fusion. The feature set could be optimized from 526 to 96-dimensions.The experimental results denote that new features by support vector machine (SVM) has the best performance, with a recognition rate of 84.4% on NKI-CCRT corpus. The proposed method is thus approved to be effective and reliable for pathological speech intelligibility evaluation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pathological%20speech" title="pathological speech">pathological speech</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-granularity%20feature" title=" multi-granularity feature"> multi-granularity feature</a>, <a href="https://publications.waset.org/abstracts/search?q=MSCC%20%28Mel%20s-transform%20cepstrum%20coefficients%29" title=" MSCC (Mel s-transform cepstrum coefficients)"> MSCC (Mel s-transform cepstrum coefficients)</a>, <a href="https://publications.waset.org/abstracts/search?q=F-score" title=" F-score"> F-score</a>, <a href="https://publications.waset.org/abstracts/search?q=radar%20chart" title=" radar chart"> radar chart</a> </p> <a href="https://publications.waset.org/abstracts/52914/multi-granularity-feature-extraction-and-optimization-for-pathological-speech-intelligibility-evaluation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52914.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">283</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">17589</span> Sentiment Classification of Documents</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Swarnadip%20Ghosh">Swarnadip Ghosh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sentiment Analysis is the process of detecting the contextual polarity of text. In other words, it determines whether a piece of writing is positive, negative or neutral.Sentiment analysis of documents holds great importance in today's world, when numerous information is stored in databases and in the world wide web. An efficient algorithm to illicit such information, would be beneficial for social, economic as well as medical purposes. In this project, we have developed an algorithm to classify a document into positive or negative. Using our algorithm, we obtained a feature set from the data, and classified the documents based on this feature set. It is important to note that, in the classification, we have not used the independence assumption, which is considered by many procedures like the Naive Bayes. This makes the algorithm more general in scope. Moreover, because of the sparsity and high dimensionality of such data, we did not use empirical distribution for estimation, but developed a method by finding degree of close clustering of the data points. We have applied our algorithm on a movie review data set obtained from IMDb and obtained satisfactory results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sentiment" title="sentiment">sentiment</a>, <a href="https://publications.waset.org/abstracts/search?q=Run%27s%20Test" title=" Run's Test"> Run's Test</a>, <a href="https://publications.waset.org/abstracts/search?q=cross%20validation" title=" cross validation"> cross validation</a>, <a href="https://publications.waset.org/abstracts/search?q=higher%20dimensional%20pmf%20estimation" title=" higher dimensional pmf estimation"> higher dimensional pmf estimation</a> </p> <a href="https://publications.waset.org/abstracts/19401/sentiment-classification-of-documents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19401.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right 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