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features</title> <meta name="description" content="Search results for: Haar features"> <meta name="keywords" content="Haar features"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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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="Haar features"> <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> 3856</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Haar features</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3856</span> Comparative Analysis of Feature Extraction and Classification Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20L.%20Ujjwal">R. L. Ujjwal</a>, <a href="https://publications.waset.org/abstracts/search?q=Abhishek%20Jain"> Abhishek Jain</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the field of computer vision, most facial variations such as identity, expression, emotions and gender have been extensively studied. Automatic age estimation has been rarely explored. With age progression of a human, the features of the face changes. This paper is providing a new comparable study of different type of algorithm to feature extraction [Hybrid features using HAAR cascade & HOG features] & classification [KNN & SVM] training dataset. By using these algorithms we are trying to find out one of the best classification algorithms. Same thing we have done on the feature selection part, we extract the feature by using HAAR cascade and HOG. This work will be done in context of age group classification model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=age%20group" title=" age group"> age group</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20detection" title=" face detection"> face detection</a> </p> <a href="https://publications.waset.org/abstracts/58670/comparative-analysis-of-feature-extraction-and-classification-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58670.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">368</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">3855</span> Application of the Discrete Rationalized Haar Transform to Distributed Parameter System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Joon-Hoon%20Park">Joon-Hoon Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper the rationalized Haar transform is applied for distributed parameter system identification and estimation. A distributed parameter system is a dynamical and mathematical model described by a partial differential equation. And system identification concerns the problem of determining mathematical models from observed data. The Haar function has some disadvantages of calculation because it contains irrational numbers, for these reasons the rationalized Haar function that has only rational numbers. The algorithm adopted in this paper is based on the transform and operational matrix of the rationalized Haar function. This approach provides more convenient and efficient computational results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distributed%20parameter%20system" title="distributed parameter system">distributed parameter system</a>, <a href="https://publications.waset.org/abstracts/search?q=rationalized%20Haar%20transform" title=" rationalized Haar transform"> rationalized Haar transform</a>, <a href="https://publications.waset.org/abstracts/search?q=operational%20matrix" title=" operational matrix"> operational matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=system%20identification" title=" system identification "> system identification </a> </p> <a href="https://publications.waset.org/abstracts/24246/application-of-the-discrete-rationalized-haar-transform-to-distributed-parameter-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24246.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">509</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">3854</span> Design and Implementation of an Image Based System to Enhance the Security of ATM</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Nima%20Tayarani%20Bathaie">Seyed Nima Tayarani Bathaie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, an image-receiving system was designed and implemented through optimization of object detection algorithms using Haar features. This optimized algorithm served as face and eye detection separately. Then, cascading them led to a clear image of the user. Utilization of this feature brought about higher security by preventing fraud. This attribute results from the fact that services will be given to the user on condition that a clear image of his face has already been captured which would exclude the inappropriate person. In order to expedite processing and eliminating unnecessary ones, the input image was compressed, a motion detection function was included in the program, and detection window size was confined. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=face%20detection%20algorithm" title="face detection algorithm">face detection algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Haar%20features" title=" Haar features"> Haar features</a>, <a href="https://publications.waset.org/abstracts/search?q=security%20of%20ATM" title=" security of ATM"> security of ATM</a> </p> <a href="https://publications.waset.org/abstracts/3011/design-and-implementation-of-an-image-based-system-to-enhance-the-security-of-atm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3011.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">419</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">3853</span> The Use of Haar Wavelet Mother Signal Tool for Performance Analysis Response of Distillation Column (Application to Moroccan Case Study) </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahacine%20Amrani">Mahacine Amrani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims at reviewing some Moroccan industrial applications of wavelet especially in the dynamic identification of a process model using Haar wavelet mother response. Two recent Moroccan study cases are described using dynamic data originated by a distillation column and an industrial polyethylene process plant. The purpose of the wavelet scheme is to build on-line dynamic models. In both case studies, a comparison is carried out between the Haar wavelet mother response model and a linear difference equation model. Finally it concludes, on the base of the comparison of the process performances and the best responses, which may be useful to create an estimated on-line internal model control and its application towards model-predictive controllers (MPC). All calculations were implemented using AutoSignal Software. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=process%20performance" title="process performance">process performance</a>, <a href="https://publications.waset.org/abstracts/search?q=model" title=" model"> model</a>, <a href="https://publications.waset.org/abstracts/search?q=wavelets" title=" wavelets"> wavelets</a>, <a href="https://publications.waset.org/abstracts/search?q=Haar" title=" Haar"> Haar</a>, <a href="https://publications.waset.org/abstracts/search?q=Moroccan" title=" Moroccan"> Moroccan</a> </p> <a href="https://publications.waset.org/abstracts/36970/the-use-of-haar-wavelet-mother-signal-tool-for-performance-analysis-response-of-distillation-column-application-to-moroccan-case-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36970.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">317</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">3852</span> Training of Future Computer Science Teachers Based on Machine Learning Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Meruert%20Serik">Meruert Serik</a>, <a href="https://publications.waset.org/abstracts/search?q=Nassipzhan%20Duisegaliyeva"> Nassipzhan Duisegaliyeva</a>, <a href="https://publications.waset.org/abstracts/search?q=Danara%20Tleumagambetova"> Danara Tleumagambetova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The article highlights and describes the characteristic features of real-time face detection in images and videos using machine learning algorithms. Students of educational programs reviewed the research work "6B01511-Computer Science", "7M01511-Computer Science", "7M01525- STEM Education," and "8D01511-Computer Science" of Eurasian National University named after L.N. Gumilyov. As a result, the advantages and disadvantages of Haar Cascade (Haar Cascade OpenCV), HoG SVM (Histogram of Oriented Gradients, Support Vector Machine), and MMOD CNN Dlib (Max-Margin Object Detection, convolutional neural network) detectors used for face detection were determined. Dlib is a general-purpose cross-platform software library written in the programming language C++. It includes detectors used for determining face detection. The Cascade OpenCV algorithm is efficient for fast face detection. The considered work forms the basis for the development of machine learning methods by future computer science teachers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithm" title="algorithm">algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=education" title=" education"> education</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/170539/training-of-future-computer-science-teachers-based-on-machine-learning-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170539.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">73</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">3851</span> Rationalized Haar Transforms Approach to Design of Observer for Control Systems with Unknown Inputs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Joon-Hoon%20Park">Joon-Hoon Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The fundamental concept of observability is important in both theoretical and practical points of modern control systems. In modern control theory, a control system has criteria for determining the design solution exists for the system parameters and design objectives. The idea of observability relates to the condition of observing or estimating the state variables from the output variables that is generally measurable. To design closed-loop control system, the practical problems of implementing the feedback of the state variables must be considered and implementing state feedback control problem has been existed in this case. All the state variables are not available, so it is requisite to design and implement an observer that will estimate the state variables form the output parameters. However sometimes unknown inputs are presented in control systems as practical cases. This paper presents a design method and algorithm for observer of control system with unknown input parameters based on Rationalized Haar transform. The proposed method is more advantageous than the other numerical method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=orthogonal%20functions" title="orthogonal functions">orthogonal functions</a>, <a href="https://publications.waset.org/abstracts/search?q=rationalized%20Haar%20transforms" title=" rationalized Haar transforms"> rationalized Haar transforms</a>, <a href="https://publications.waset.org/abstracts/search?q=control%20system%20observer" title=" control system observer"> control system observer</a>, <a href="https://publications.waset.org/abstracts/search?q=algebraic%20method" title=" algebraic method"> algebraic method</a> </p> <a href="https://publications.waset.org/abstracts/69296/rationalized-haar-transforms-approach-to-design-of-observer-for-control-systems-with-unknown-inputs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69296.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">370</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">3850</span> Comparison of the Effectiveness of Tree Algorithms in Classification of Spongy Tissue Texture</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Roza%20Dzierzak">Roza Dzierzak</a>, <a href="https://publications.waset.org/abstracts/search?q=Waldemar%20Wojcik"> Waldemar Wojcik</a>, <a href="https://publications.waset.org/abstracts/search?q=Piotr%20Kacejko"> Piotr Kacejko</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Analysis of the texture of medical images consists of determining the parameters and characteristics of the examined tissue. The main goal is to assign the analyzed area to one of two basic groups: as a healthy tissue or a tissue with pathological changes. The CT images of the thoracic lumbar spine from 15 healthy patients and 15 with confirmed osteoporosis were used for the analysis. As a result, 120 samples with dimensions of 50x50 pixels were obtained. The set of features has been obtained based on the histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model, and Haar wavelet. As a result of the image analysis, 290 descriptors of textural features were obtained. The dimension of the space of features was reduced by the use of three selection methods: Fisher coefficient (FC), mutual information (MI), minimization of the classification error probability and average correlation coefficients between the chosen features minimization of classification error probability (POE) and average correlation coefficients (ACC). Each of them returned ten features occupying the initial place in the ranking devised according to its own coefficient. As a result of the Fisher coefficient and mutual information selections, the same features arranged in a different order were obtained. In both rankings, the 50% percentile (Perc.50%) was found in the first place. The next selected features come from the co-occurrence matrix. The sets of features selected in the selection process were evaluated using six classification tree methods. These were: decision stump (DS), Hoeffding tree (HT), logistic model trees (LMT), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). In order to assess the accuracy of classifiers, the following parameters were used: overall classification accuracy (ACC), true positive rate (TPR, classification sensitivity), true negative rate (TNR, classification specificity), positive predictive value (PPV) and negative predictive value (NPV). Taking into account the classification results, it should be stated that the best results were obtained for the Hoeffding tree and logistic model trees classifiers, using the set of features selected by the POE + ACC method. In the case of the Hoeffding tree classifier, the highest values of three parameters were obtained: ACC = 90%, TPR = 93.3% and PPV = 93.3%. Additionally, the values of the other two parameters, i.e., TNR = 86.7% and NPV = 86.6% were close to the maximum values obtained for the LMT classifier. In the case of logistic model trees classifier, the same ACC value was obtained ACC=90% and the highest values for TNR=88.3% and NPV= 88.3%. The values of the other two parameters remained at a level close to the highest TPR = 91.7% and PPV = 91.6%. The results obtained in the experiment show that the use of classification trees is an effective method of classification of texture features. This allows identifying the conditions of the spongy tissue for healthy cases and those with the porosis. <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=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20analysis" title=" texture analysis"> texture analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=tree%20algorithms" title=" tree algorithms"> tree algorithms</a> </p> <a href="https://publications.waset.org/abstracts/107923/comparison-of-the-effectiveness-of-tree-algorithms-in-classification-of-spongy-tissue-texture" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107923.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">177</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3849</span> An Automatic Large Classroom Attendance Conceptual Model Using Face Counting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sirajdin%20Olagoke%20Adeshina">Sirajdin Olagoke Adeshina</a>, <a href="https://publications.waset.org/abstracts/search?q=Haidi%20Ibrahim"> Haidi Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Akeem%20Salawu"> Akeem Salawu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> large lecture theatres cannot be covered by a single camera but rather by a multicamera setup because of their size, shape, and seating arrangements. Although, classroom capture is achievable through a single camera. Therefore, a design and implementation of a multicamera setup for a large lecture hall were considered. Researchers have shown emphasis on the impact of class attendance taken on the academic performance of students. However, the traditional method of carrying out this exercise is below standard, especially for large lecture theatres, because of the student population, the time required, sophistication, exhaustiveness, and manipulative influence. An automated large classroom attendance system is, therefore, imperative. The common approach in this system is face detection and recognition, where known student faces are captured and stored for recognition purposes. This approach will require constant face database updates due to constant changes in the facial features. Alternatively, face counting can be performed by cropping the localized faces on the video or image into a folder and then count them. This research aims to develop a face localization-based approach to detect student faces in classroom images captured using a multicamera setup. A selected Haar-like feature cascade face detector trained with an asymmetric goal to minimize the False Rejection Rate (FRR) relative to the False Acceptance Rate (FAR) was applied on Raspberry Pi 4B. A relationship between the two factors (FRR and FAR) was established using a constant (λ) as a trade-off between the two factors for automatic adjustment during training. An evaluation of the proposed approach and the conventional AdaBoost on classroom datasets shows an improvement of 8% TPR (output result of low FRR) and 7% minimization of the FRR. The average learning speed of the proposed approach was improved with 1.19s execution time per image compared to 2.38s of the improved AdaBoost. Consequently, the proposed approach achieved 97% TPR with an overhead constraint time of 22.9s compared to 46.7s of the improved Adaboost when evaluated on images obtained from a large lecture hall (DK5) USM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20attendance" title="automatic attendance">automatic attendance</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20detection" title=" face detection"> face detection</a>, <a href="https://publications.waset.org/abstracts/search?q=haar-like%20cascade" title=" haar-like cascade"> haar-like cascade</a>, <a href="https://publications.waset.org/abstracts/search?q=manual%20attendance" title=" manual attendance"> manual attendance</a> </p> <a href="https://publications.waset.org/abstracts/165576/an-automatic-large-classroom-attendance-conceptual-model-using-face-counting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165576.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">71</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">3848</span> Relevant LMA Features for Human Motion Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Insaf%20Ajili">Insaf Ajili</a>, <a href="https://publications.waset.org/abstracts/search?q=Malik%20Mallem"> Malik Mallem</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean-Yves%20Didier"> Jean-Yves Didier</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Motion recognition from videos is actually a very complex task due to the high variability of motions. This paper describes the challenges of human motion recognition, especially motion representation step with relevant features. Our descriptor vector is inspired from Laban Movement Analysis method. We propose discriminative features using the Random Forest algorithm in order to remove redundant features and make learning algorithms operate faster and more effectively. We validate our method on MSRC-12 and UTKinect datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discriminative%20LMA%20features" title="discriminative LMA features">discriminative LMA features</a>, <a href="https://publications.waset.org/abstracts/search?q=features%20reduction" title=" features reduction"> features reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20motion%20recognition" title=" human motion recognition"> human motion recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a> </p> <a href="https://publications.waset.org/abstracts/96299/relevant-lma-features-for-human-motion-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/96299.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">195</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">3847</span> Best-Performing Color Space for Land-Sea Segmentation Using Wavelet Transform Color-Texture Features and Fusion of over Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seynabou%20Toure">Seynabou Toure</a>, <a href="https://publications.waset.org/abstracts/search?q=Oumar%20Diop"> Oumar Diop</a>, <a href="https://publications.waset.org/abstracts/search?q=Kidiyo%20Kpalma"> Kidiyo Kpalma</a>, <a href="https://publications.waset.org/abstracts/search?q=Amadou%20S.%20Maiga"> Amadou S. Maiga</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Color and texture are the two most determinant elements for perception and recognition of the objects in an image. For this reason, color and texture analysis find a large field of application, for example in image classification and segmentation. But, the pioneering work in texture analysis was conducted on grayscale images, thus discarding color information. Many grey-level texture descriptors have been proposed and successfully used in numerous domains for image classification: face recognition, industrial inspections, food science medical imaging among others. Taking into account color in the definition of these descriptors makes it possible to better characterize images. Color texture is thus the subject of recent work, and the analysis of color texture images is increasingly attracting interest in the scientific community. In optical remote sensing systems, sensors measure separately different parts of the electromagnetic spectrum; the visible ones and even those that are invisible to the human eye. The amounts of light reflected by the earth in spectral bands are then transformed into grayscale images. The primary natural colors Red (R) Green (G) and Blue (B) are then used in mixtures of different spectral bands in order to produce RGB images. Thus, good color texture discrimination can be achieved using RGB under controlled illumination conditions. Some previous works investigate the effect of using different color space for color texture classification. However, the selection of the best performing color space in land-sea segmentation is an open question. Its resolution may bring considerable improvements in certain applications like coastline detection, where the detection result is strongly dependent on the performance of the land-sea segmentation. The aim of this paper is to present the results of a study conducted on different color spaces in order to show the best-performing color space for land-sea segmentation. In this sense, an experimental analysis is carried out using five different color spaces (RGB, XYZ, Lab, HSV, YCbCr). For each color space, the Haar wavelet decomposition is used to extract different color texture features. These color texture features are then used for Fusion of Over Segmentation (FOOS) based classification; this allows segmentation of the land part from the sea one. By analyzing the different results of this study, the HSV color space is found as the best classification performance while using color and texture features; which is perfectly coherent with the results presented in the literature. <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=coastline" title=" coastline"> coastline</a>, <a href="https://publications.waset.org/abstracts/search?q=color" title=" color"> color</a>, <a href="https://publications.waset.org/abstracts/search?q=sea-land%20segmentation" title=" sea-land segmentation"> sea-land segmentation</a> </p> <a href="https://publications.waset.org/abstracts/84598/best-performing-color-space-for-land-sea-segmentation-using-wavelet-transform-color-texture-features-and-fusion-of-over-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84598.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">247</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3846</span> Impact of Variability in Delineation on PET Radiomics Features in Lung Tumors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahsa%20Falahatpour">Mahsa Falahatpour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: This study aims to explore how inter-observer variability in manual tumor segmentation impacts the reliability of radiomic features in non–small cell lung cancer (NSCLC). Methods: The study included twenty-three NSCLC tumors. Each patient had three tumor segmentations (VOL1, VOL2, VOL3) contoured on PET/CT scans by three radiation oncologists. Dice coefficients (DCS) were used to measure the segmentation variability. Radiomic features were extracted with 3D-slicer software, consisting of 66 features: first-order (n=15), second-order (GLCM, GLDM, GLRLM, and GLSZM) (n=33). The inter-observer variability of radiomic features was assessed using the intraclass correlation coefficient (ICC). An ICC > 0.8 indicates good stability. Results: The mean DSC of VOL1, VOL2, and VOL3 was 0.80 ± 0.04, 0.85 ± 0.03, and 0.76 ± 0.06, respectively. 92% of all extracted radiomic features were found to be stable (ICC > 0.8). The GLCM texture features had the highest stability (96%), followed by GLRLM features (90%) and GLSZM features (87%). The DSC was found to be highly correlated with the stability of radiomic features. Conclusion: The variability in inter-observer segmentation significantly impacts radiomics analysis, leading to a reduction in the number of appropriate radiomic features. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=PET%2FCT" title="PET/CT">PET/CT</a>, <a href="https://publications.waset.org/abstracts/search?q=radiomics" title=" radiomics"> radiomics</a>, <a href="https://publications.waset.org/abstracts/search?q=radiotherapy" title=" radiotherapy"> radiotherapy</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=NSCLC" title=" NSCLC"> NSCLC</a> </p> <a href="https://publications.waset.org/abstracts/186981/impact-of-variability-in-delineation-on-pet-radiomics-features-in-lung-tumors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186981.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">44</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">3845</span> Tree Species Classification Using Effective Features of Polarimetric SAR and Hyperspectral Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Milad%20Vahidi">Milad Vahidi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmod%20R.%20Sahebi"> Mahmod R. Sahebi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mehrnoosh%20Omati"> Mehrnoosh Omati</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20Mohammadi"> Reza Mohammadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forest management organizations need information to perform their work effectively. Remote sensing is an effective method to acquire information from the Earth. Two datasets of remote sensing images were used to classify forested regions. Firstly, all of extractable features from hyperspectral and PolSAR images were extracted. The optical features were spectral indexes related to the chemical, water contents, structural indexes, effective bands and absorption features. Also, PolSAR features were the original data, target decomposition components, and SAR discriminators features. Secondly, the particle swarm optimization (PSO) and the genetic algorithms (GA) were applied to select optimization features. Furthermore, the support vector machine (SVM) classifier was used to classify the image. The results showed that the combination of PSO and SVM had higher overall accuracy than the other cases. This combination provided overall accuracy about 90.56%. The effective features were the spectral index, the bands in shortwave infrared (SWIR) and the visible ranges and certain PolSAR features. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hyperspectral" title="hyperspectral">hyperspectral</a>, <a href="https://publications.waset.org/abstracts/search?q=PolSAR" title=" PolSAR"> PolSAR</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=SVM" title=" SVM"> SVM</a> </p> <a href="https://publications.waset.org/abstracts/95461/tree-species-classification-using-effective-features-of-polarimetric-sar-and-hyperspectral-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95461.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">416</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">3844</span> Active Features Determination: A Unified Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Meenal%20Badki">Meenal Badki</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We address the issue of active feature determination, where the objective is to determine the set of examples on which additional data (such as lab tests) needs to be gathered, given a large number of examples with some features (such as demographics) and some examples with all the features (such as the complete Electronic Health Record). We note that certain features may be more costly, unique, or laborious to gather. Our proposal is a general active learning approach that is independent of classifiers and similarity metrics. It allows us to identify examples that differ from the full data set and obtain all the features for the examples that match. Our comprehensive evaluation shows the efficacy of this approach, which is driven by four authentic clinical tasks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20determination" title="feature determination">feature determination</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=active%20learning" title=" active learning"> active learning</a>, <a href="https://publications.waset.org/abstracts/search?q=sample-efficiency" title=" sample-efficiency"> sample-efficiency</a> </p> <a href="https://publications.waset.org/abstracts/180994/active-features-determination-a-unified-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/180994.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">75</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">3843</span> 2D Point Clouds Features from Radar for Helicopter Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Danilo%20Habermann">Danilo Habermann</a>, <a href="https://publications.waset.org/abstracts/search?q=Aleksander%20Medella"> Aleksander Medella</a>, <a href="https://publications.waset.org/abstracts/search?q=Carla%20Cremon"> Carla Cremon</a>, <a href="https://publications.waset.org/abstracts/search?q=Yusef%20Caceres"> Yusef Caceres</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims to analyze the ability of 2d point clouds features to classify different models of helicopters using radars. This method does not need to estimate the blade length, the number of blades of helicopters, and the period of their micro-Doppler signatures. It is also not necessary to generate spectrograms (or any other image based on time and frequency domain). This work transforms a radar return signal into a 2D point cloud and extracts features of it. Three classifiers are used to distinguish 9 different helicopter models in order to analyze the performance of the features used in this work. The high accuracy obtained with each of the classifiers demonstrates that the 2D point clouds features are very useful for classifying helicopters from radar signal. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=helicopter%20classification" title="helicopter classification">helicopter classification</a>, <a href="https://publications.waset.org/abstracts/search?q=point%20clouds%20features" title=" point clouds features"> point clouds features</a>, <a href="https://publications.waset.org/abstracts/search?q=radar" title=" radar"> radar</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20classifiers" title=" supervised classifiers"> supervised classifiers</a> </p> <a href="https://publications.waset.org/abstracts/85676/2d-point-clouds-features-from-radar-for-helicopter-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85676.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">227</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">3842</span> Dynamic Gabor Filter Facial Features-Based Recognition of Emotion in Video Sequences</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20Hari%20Prasath">T. Hari Prasath</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Ithaya%20Rani"> P. Ithaya Rani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the world of visual technology, recognizing emotions from the face images is a challenging task. Several related methods have not utilized the dynamic facial features effectively for high performance. This paper proposes a method for emotions recognition using dynamic facial features with high performance. Initially, local features are captured by Gabor filter with different scale and orientations in each frame for finding the position and scale of face part from different backgrounds. The Gabor features are sent to the ensemble classifier for detecting Gabor facial features. The region of dynamic features is captured from the Gabor facial features in the consecutive frames which represent the dynamic variations of facial appearances. In each region of dynamic features is normalized using Z-score normalization method which is further encoded into binary pattern features with the help of threshold values. The binary features are passed to Multi-class AdaBoost classifier algorithm with the well-trained database contain happiness, sadness, surprise, fear, anger, disgust, and neutral expressions to classify the discriminative dynamic features for emotions recognition. The developed method is deployed on the Ryerson Multimedia Research Lab and Cohn-Kanade databases and they show significant performance improvement owing to their dynamic features when compared with the existing methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=detecting%20face" title="detecting face">detecting face</a>, <a href="https://publications.waset.org/abstracts/search?q=Gabor%20filter" title=" Gabor filter"> Gabor filter</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-class%20AdaBoost%20classifier" title=" multi-class AdaBoost classifier"> multi-class AdaBoost classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=Z-score%20normalization" title=" Z-score normalization"> Z-score normalization</a> </p> <a href="https://publications.waset.org/abstracts/85005/dynamic-gabor-filter-facial-features-based-recognition-of-emotion-in-video-sequences" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85005.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">3841</span> New Features for Copy-Move Image Forgery Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Michael%20Zimba">Michael Zimba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A novel set of features for copy-move image forgery, CMIF, detection method is proposed. The proposed set presents a new approach which relies on electrostatic field theory, EFT. Solely for the purpose of reducing the dimension of a suspicious image, firstly performs discrete wavelet transform, DWT, of the suspicious image and extracts only the approximation subband. The extracted subband is then bijectively mapped onto a virtual electrostatic field where concepts of EFT are utilised to extract robust features. The extracted features are shown to be invariant to additive noise, JPEG compression, and affine transformation. The proposed features can also be used in general object matching. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=virtual%20electrostatic%20field" title="virtual electrostatic field">virtual electrostatic field</a>, <a href="https://publications.waset.org/abstracts/search?q=features" title=" features"> features</a>, <a href="https://publications.waset.org/abstracts/search?q=affine%20transformation" title=" affine transformation"> affine transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=copy-move%20image%20forgery" title=" copy-move image forgery"> copy-move image forgery</a> </p> <a href="https://publications.waset.org/abstracts/29604/new-features-for-copy-move-image-forgery-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29604.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">543</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">3840</span> Using Reservoir Models for Monitoring Geothermal Surface Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=John%20P.%20O%E2%80%99Sullivan">John P. O’Sullivan</a>, <a href="https://publications.waset.org/abstracts/search?q=Thomas%20M.%20P.%20Ratouis"> Thomas M. P. Ratouis</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20J.%20O%E2%80%99Sullivan"> Michael J. O’Sullivan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As the use of geothermal energy grows internationally more effort is required to monitor and protect areas with rare and important geothermal surface features. A number of approaches are presented for developing and calibrating numerical geothermal reservoir models that are capable of accurately representing geothermal surface features. The approaches are discussed in the context of cases studies of the Rotorua geothermal system and the Orakei-korako geothermal system, both of which contain important surface features. The results show that models are able to match the available field data accurately and hence can be used as valuable tools for predicting the future response of the systems to changes in use. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=geothermal%20reservoir%20models" title="geothermal reservoir models">geothermal reservoir models</a>, <a href="https://publications.waset.org/abstracts/search?q=surface%20features" title=" surface features"> surface features</a>, <a href="https://publications.waset.org/abstracts/search?q=monitoring" title=" monitoring"> monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=TOUGH2" title=" TOUGH2"> TOUGH2</a> </p> <a href="https://publications.waset.org/abstracts/25882/using-reservoir-models-for-monitoring-geothermal-surface-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25882.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">414</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">3839</span> Myanmar Character Recognition Using Eight Direction Chain Code Frequency Features </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kyi%20Pyar%20Zaw">Kyi Pyar Zaw</a>, <a href="https://publications.waset.org/abstracts/search?q=Zin%20Mar%20Kyu"> Zin Mar Kyu </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Character recognition is the process of converting a text image file into editable and searchable text file. Feature Extraction is the heart of any character recognition system. The character recognition rate may be low or high depending on the extracted features. In the proposed paper, 25 features for one character are used in character recognition. Basically, there are three steps of character recognition such as character segmentation, feature extraction and classification. In segmentation step, horizontal cropping method is used for line segmentation and vertical cropping method is used for character segmentation. In the Feature extraction step, features are extracted in two ways. The first way is that the 8 features are extracted from the entire input character using eight direction chain code frequency extraction. The second way is that the input character is divided into 16 blocks. For each block, although 8 feature values are obtained through eight-direction chain code frequency extraction method, we define the sum of these 8 feature values as a feature for one block. Therefore, 16 features are extracted from that 16 blocks in the second way. We use the number of holes feature to cluster the similar characters. We can recognize the almost Myanmar common characters with various font sizes by using these features. All these 25 features are used in both training part and testing part. In the classification step, the characters are classified by matching the all features of input character with already trained features of characters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chain%20code%20frequency" title="chain code frequency">chain code frequency</a>, <a href="https://publications.waset.org/abstracts/search?q=character%20recognition" title=" character recognition"> character recognition</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=features%20matching" title=" features matching"> features matching</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a> </p> <a href="https://publications.waset.org/abstracts/77278/myanmar-character-recognition-using-eight-direction-chain-code-frequency-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77278.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">320</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">3838</span> An Experimental Study for Assessing Email Classification Attributes Using Feature Selection Methods </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Issa%20Qabaja">Issa Qabaja</a>, <a href="https://publications.waset.org/abstracts/search?q=Fadi%20Thabtah"> Fadi Thabtah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Email phishing classification is one of the vital problems in the online security research domain that have attracted several scholars due to its impact on the users payments performed daily online. One aspect to reach a good performance by the detection algorithms in the email phishing problem is to identify the minimal set of features that significantly have an impact on raising the phishing detection rate. This paper investigate three known feature selection methods named Information Gain (IG), Chi-square and Correlation Features Set (CFS) on the email phishing problem to separate high influential features from low influential ones in phishing detection. We measure the degree of influentially by applying four data mining algorithms on a large set of features. We compare the accuracy of these algorithms on the complete features set before feature selection has been applied and after feature selection has been applied. After conducting experiments, the results show 12 common significant features have been chosen among the considered features by the feature selection methods. Further, the average detection accuracy derived by the data mining algorithms on the reduced 12-features set was very slight affected when compared with the one derived from the 47-features set. <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=email%20classification" title=" email classification"> email classification</a>, <a href="https://publications.waset.org/abstracts/search?q=phishing" title=" phishing"> phishing</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20security" title=" online security"> online security</a> </p> <a href="https://publications.waset.org/abstracts/19757/an-experimental-study-for-assessing-email-classification-attributes-using-feature-selection-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19757.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">432</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">3837</span> Exploring Syntactic and Semantic Features for Text-Based Authorship Attribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Haiyan%20Wu">Haiyan Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Ying%20Liu"> Ying Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Shaoyun%20Shi"> Shaoyun Shi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. Besides, predictions by neural networks are difficult to explain, which is vital in authorship attribution tasks. In this paper, we not only utilize the statistical style and content features but also take advantage of both syntactic and semantic features. Different from an end-to-end neural model, feature selection and prediction are two steps in our method. An attentive n-gram network is utilized to select useful features, and logistic regression is applied to give prediction and understandable representation of writing style. Experiments show that our extracted features can improve the state-of-the-art methods on three benchmark datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=authorship%20attribution" title="authorship attribution">authorship attribution</a>, <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=syntactic%20feature" title=" syntactic feature"> syntactic feature</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a> </p> <a href="https://publications.waset.org/abstracts/129270/exploring-syntactic-and-semantic-features-for-text-based-authorship-attribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129270.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">136</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">3836</span> Using New Machine Algorithms to Classify Iranian Musical Instruments According to Temporal, Spectral and Coefficient Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ronak%20Khosravi">Ronak Khosravi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmood%20Abbasi%20Layegh"> Mahmood Abbasi Layegh</a>, <a href="https://publications.waset.org/abstracts/search?q=Siamak%20Haghipour"> Siamak Haghipour</a>, <a href="https://publications.waset.org/abstracts/search?q=Avin%20Esmaili"> Avin Esmaili</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a study on classification of musical woodwind instruments using a small set of features selected from a broad range of extracted ones by the sequential forward selection method was carried out. Firstly, we extract 42 features for each record in the music database of 402 sound files belonging to five different groups of Flutes (end blown and internal duct), Single –reed, Double –reed (exposed and capped), Triple reed and Quadruple reed. Then, the sequential forward selection method is adopted to choose the best feature set in order to achieve very high classification accuracy. Two different classification techniques of support vector machines and relevance vector machines have been tested out and an accuracy of up to 96% can be achieved by using 21 time, frequency and coefficient features and relevance vector machine with the Gaussian kernel function. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coefficient%20features" title="coefficient features">coefficient features</a>, <a href="https://publications.waset.org/abstracts/search?q=relevance%20vector%20machines" title=" relevance vector machines"> relevance vector machines</a>, <a href="https://publications.waset.org/abstracts/search?q=spectral%20features" title=" spectral features"> spectral features</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machines" title=" support vector machines"> support vector machines</a>, <a href="https://publications.waset.org/abstracts/search?q=temporal%20features" title=" temporal features"> temporal features</a> </p> <a href="https://publications.waset.org/abstracts/54321/using-new-machine-algorithms-to-classify-iranian-musical-instruments-according-to-temporal-spectral-and-coefficient-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54321.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">320</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">3835</span> Exploring Chess Game AI Features Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bashayer%20Almalki">Bashayer Almalki</a>, <a href="https://publications.waset.org/abstracts/search?q=Mayar%20Bajrai"> Mayar Bajrai</a>, <a href="https://publications.waset.org/abstracts/search?q=Dana%20Mirah"> Dana Mirah</a>, <a href="https://publications.waset.org/abstracts/search?q=Kholood%20Alghamdi"> Kholood Alghamdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hala%20Sanyour"> Hala Sanyour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research aims to investigate the features of an AI chess app that are most preferred by users. A questionnaire was used as the methodology to gather responses from a varied group of participants. The questionnaire consisted of several questions related to the features of the AI chess app. The responses were analyzed using descriptive statistics and factor analysis. The findings indicate that the most preferred features of an AI chess app are the ability to play against the computer, the option to adjust the difficulty level, and the availability of tutorials and puzzles. The results of this research could be useful for developers of AI chess apps to enhance the user experience and satisfaction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chess" title="chess">chess</a>, <a href="https://publications.waset.org/abstracts/search?q=game" title=" game"> game</a>, <a href="https://publications.waset.org/abstracts/search?q=application" title=" application"> application</a>, <a href="https://publications.waset.org/abstracts/search?q=computics" title=" computics"> computics</a> </p> <a href="https://publications.waset.org/abstracts/167493/exploring-chess-game-ai-features-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167493.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">68</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">3834</span> Research on Perceptual Features of Couchsurfers on New Hospitality Tourism Platform Couchsurfing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuanxiang%20Miao">Yuanxiang Miao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims to examine the perceptual features of couchsurfers on a new hospitality tourism platform, the free homestay website couchsurfing. As a local host, the author has accepted 61 couchsurfers in Kyoto, Japan, and attempted to figure out couchsurfers' characteristics on perception by hosting them. Moreover, the methodology of this research is mainly based on in-depth interviews, by talking with couchsurfers, observing their behaviors, doing questionnaires, etc. Five dominant perceptual features of couchsurfers were identified: (1) Trusting; (2) Meeting; (3) Sharing; (4) Reciprocity; (5) Worries. The value of this research lies in figuring out a deeper understanding of the perceptual features of couchsurfers, and the author indeed hosted and stayed with 61 couchsurfers from 30 countries and areas over one year. Lastly, the author offers practical suggestions for future research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=couchsurfing" title="couchsurfing">couchsurfing</a>, <a href="https://publications.waset.org/abstracts/search?q=depth%20interview" title=" depth interview"> depth interview</a>, <a href="https://publications.waset.org/abstracts/search?q=hospitality%20tourism" title=" hospitality tourism"> hospitality tourism</a>, <a href="https://publications.waset.org/abstracts/search?q=perceptual%20features" title=" perceptual features"> perceptual features</a> </p> <a href="https://publications.waset.org/abstracts/125558/research-on-perceptual-features-of-couchsurfers-on-new-hospitality-tourism-platform-couchsurfing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/125558.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">3833</span> The Latent Model of Linguistic Features in Korean College Students’ L2 Argumentative Writings: Syntactic Complexity, Lexical Complexity, and Fluency</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiyoung%20Bae">Jiyoung Bae</a>, <a href="https://publications.waset.org/abstracts/search?q=Gyoomi%20Kim"> Gyoomi Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study explores a range of linguistic features used in Korean college students’ argumentative writings for the purpose of developing a model that identifies variables which predict writing proficiencies. This study investigated the latent variable structure of L2 linguistic features, including syntactic complexity, the lexical complexity, and fluency. One hundred forty-six university students in Korea participated in this study. The results of the study’s confirmatory factor analysis (CFA) showed that indicators of linguistic features from this study-provided a foundation for re-categorizing indicators found in extant research on L2 Korean writers depending on each latent variable of linguistic features. The CFA models indicated one measurement model of L2 syntactic complexity and L2 learners’ writing proficiency; these two latent factors were correlated with each other. Based on the overall findings of the study, integrated linguistic features of L2 writings suggested some pedagogical implications in L2 writing instructions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=linguistic%20features" title="linguistic features">linguistic features</a>, <a href="https://publications.waset.org/abstracts/search?q=syntactic%20complexity" title=" syntactic complexity"> syntactic complexity</a>, <a href="https://publications.waset.org/abstracts/search?q=lexical%20complexity" title=" lexical complexity"> lexical complexity</a>, <a href="https://publications.waset.org/abstracts/search?q=fluency" title=" fluency"> fluency</a> </p> <a href="https://publications.waset.org/abstracts/100664/the-latent-model-of-linguistic-features-in-korean-college-students-l2-argumentative-writings-syntactic-complexity-lexical-complexity-and-fluency" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/100664.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">170</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3832</span> Comparison between XGBoost, LightGBM and CatBoost Using a Home Credit Dataset</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Essam%20Al%20Daoud">Essam Al Daoud</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gradient boosting methods have been proven to be a very important strategy. Many successful machine learning solutions were developed using the XGBoost and its derivatives. The aim of this study is to investigate and compare the efficiency of three gradient methods. Home credit dataset is used in this work which contains 219 features and 356251 records. However, new features are generated and several techniques are used to rank and select the best features. The implementation indicates that the LightGBM is faster and more accurate than CatBoost and XGBoost using variant number of features and records. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gradient%20boosting" title="gradient boosting">gradient boosting</a>, <a href="https://publications.waset.org/abstracts/search?q=XGBoost" title=" XGBoost"> XGBoost</a>, <a href="https://publications.waset.org/abstracts/search?q=LightGBM" title=" LightGBM"> LightGBM</a>, <a href="https://publications.waset.org/abstracts/search?q=CatBoost" title=" CatBoost"> CatBoost</a>, <a href="https://publications.waset.org/abstracts/search?q=home%20credit" title=" home credit"> home credit</a> </p> <a href="https://publications.waset.org/abstracts/104573/comparison-between-xgboost-lightgbm-and-catboost-using-a-home-credit-dataset" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104573.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">171</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3831</span> Native Language Identification with Cross-Corpus Evaluation Using Social Media Data: ’Reddit’</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yasmeen%20Bassas">Yasmeen Bassas</a>, <a href="https://publications.waset.org/abstracts/search?q=Sandra%20Kuebler"> Sandra Kuebler</a>, <a href="https://publications.waset.org/abstracts/search?q=Allen%20Riddell"> Allen Riddell</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Native language identification is one of the growing subfields in natural language processing (NLP). The task of native language identification (NLI) is mainly concerned with predicting the native language of an author’s writing in a second language. In this paper, we investigate the performance of two types of features; content-based features vs. content independent features, when they are evaluated on a different corpus (using social media data “Reddit”). In this NLI task, the predefined models are trained on one corpus (TOEFL), and then the trained models are evaluated on different data using an external corpus (Reddit). Three classifiers are used in this task; the baseline, linear SVM, and logistic regression. Results show that content-based features are more accurate and robust than content independent ones when tested within the corpus and across corpus. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=NLI" title="NLI">NLI</a>, <a href="https://publications.waset.org/abstracts/search?q=NLP" title=" NLP"> NLP</a>, <a href="https://publications.waset.org/abstracts/search?q=content-based%20features" title=" content-based features"> content-based features</a>, <a href="https://publications.waset.org/abstracts/search?q=content%20independent%20features" title=" content independent features"> content independent features</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media%20corpus" title=" social media corpus"> social media corpus</a>, <a href="https://publications.waset.org/abstracts/search?q=ML" title=" ML"> ML</a> </p> <a href="https://publications.waset.org/abstracts/142396/native-language-identification-with-cross-corpus-evaluation-using-social-media-data-reddit" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/142396.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">137</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">3830</span> Hybrid Anomaly Detection Using Decision Tree and Support Vector Machine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elham%20Serkani">Elham Serkani</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Gharaee%20Garakani"> Hossein Gharaee Garakani</a>, <a href="https://publications.waset.org/abstracts/search?q=Naser%20Mohammadzadeh"> Naser Mohammadzadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Elaheh%20Vaezpour"> Elaheh Vaezpour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Intrusion detection systems (IDS) are the main components of network security. These systems analyze the network events for intrusion detection. The design of an IDS is through the training of normal traffic data or attack. The methods of machine learning are the best ways to design IDSs. In the method presented in this article, the pruning algorithm of C5.0 decision tree is being used to reduce the features of traffic data used and training IDS by the least square vector algorithm (LS-SVM). Then, the remaining features are arranged according to the predictor importance criterion. The least important features are eliminated in the order. The remaining features of this stage, which have created the highest level of accuracy in LS-SVM, are selected as the final features. The features obtained, compared to other similar articles which have examined the selected features in the least squared support vector machine model, are better in the accuracy, true positive rate, and false positive. The results are tested by the UNSW-NB15 dataset. <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%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=intrusion%20detection%20system" title=" intrusion detection system"> intrusion detection system</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/90456/hybrid-anomaly-detection-using-decision-tree-and-support-vector-machine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90456.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">265</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">3829</span> Task Distraction vs. Visual Enhancement: Which Is More Effective?</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Huangmei%20Liu">Huangmei Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Si%20Liu"> Si Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jia%E2%80%99nan%20Liu"> Jia’nan Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The present experiment investigated and compared the effectiveness of two kinds of methods of attention control: Task distraction and visual enhancement. In the study, the effectiveness of task distractions to explicit features and of visual enhancement to implicit features of the same group of Chinese characters were compared based on their effect on the participants’ reaction time, subjective confidence rating, and verbal report. We found support that the visual enhancement on implicit features did overcome the contrary effect of training distraction and led to awareness of those implicit features, at least to some extent. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=task%20distraction" title="task distraction">task distraction</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20enhancement" title=" visual enhancement"> visual enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=attention" title=" attention"> attention</a>, <a href="https://publications.waset.org/abstracts/search?q=awareness" title=" awareness"> awareness</a>, <a href="https://publications.waset.org/abstracts/search?q=learning" title=" learning"> learning</a> </p> <a href="https://publications.waset.org/abstracts/3302/task-distraction-vs-visual-enhancement-which-is-more-effective" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3302.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">430</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">3828</span> Security Features for Remote Healthcare System: A Feasibility Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tamil%20Chelvi%20Vadivelu">Tamil Chelvi Vadivelu</a>, <a href="https://publications.waset.org/abstracts/search?q=Nurazean%20Maarop"> Nurazean Maarop</a>, <a href="https://publications.waset.org/abstracts/search?q=Rasimah%20Che%20Yusoff"> Rasimah Che Yusoff</a>, <a href="https://publications.waset.org/abstracts/search?q=Farhana%20Aini%20Saludin"> Farhana Aini Saludin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Implementing a remote healthcare system needs to consider many security features. Therefore, before any deployment of the remote healthcare system, a feasibility study from the security perspective is crucial. Remote healthcare system using WBAN technology has been used in other countries for medical purposes but in Malaysia, such projects are still not yet implemented. This study was conducted qualitatively. The interview results involving five healthcare practitioners are further elaborated. The study has addressed four important security features in order to incorporate remote healthcare system using WBAN in Malaysian government hospitals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=remote%20healthcare" title="remote healthcare">remote healthcare</a>, <a href="https://publications.waset.org/abstracts/search?q=IT%20security" title=" IT security"> IT security</a>, <a href="https://publications.waset.org/abstracts/search?q=security%20features" title=" security features"> security features</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20sensor%20application" title=" wireless sensor application"> wireless sensor application</a> </p> <a href="https://publications.waset.org/abstracts/20183/security-features-for-remote-healthcare-system-a-feasibility-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20183.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">3827</span> Mood Recognition Using Indian Music</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vishwa%20Joshi">Vishwa Joshi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study of mood recognition in the field of music has gained a lot of momentum in the recent years with machine learning and data mining techniques and many audio features contributing considerably to analyze and identify the relation of mood plus music. In this paper we consider the same idea forward and come up with making an effort to build a system for automatic recognition of mood underlying the audio song’s clips by mining their audio features and have evaluated several data classification algorithms in order to learn, train and test the model describing the moods of these audio songs and developed an open source framework. Before classification, Preprocessing and Feature Extraction phase is necessary for removing noise and gathering features respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=music" title="music">music</a>, <a href="https://publications.waset.org/abstracts/search?q=mood" title=" mood"> mood</a>, <a href="https://publications.waset.org/abstracts/search?q=features" title=" features"> features</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/24275/mood-recognition-using-indian-music" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24275.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> <ul 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