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dataset</title> <meta name="description" content="Search results for: BoT-IoT dataset"> <meta name="keywords" content="BoT-IoT dataset"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="BoT-IoT dataset"> <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> 1166</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: BoT-IoT dataset</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1166</span> Distorted Document Images Dataset for Text Detection and Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ilia%20Zharikov">Ilia Zharikov</a>, <a href="https://publications.waset.org/abstracts/search?q=Philipp%20Nikitin"> Philipp Nikitin</a>, <a href="https://publications.waset.org/abstracts/search?q=Ilia%20Vasiliev"> Ilia Vasiliev</a>, <a href="https://publications.waset.org/abstracts/search?q=Vladimir%20Dokholyan"> Vladimir Dokholyan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increasing popularity of document analysis and recognition systems, text detection (TD) and optical character recognition (OCR) in document images become challenging tasks. However, according to our best knowledge, no publicly available datasets for these particular problems exist. In this paper, we introduce a Distorted Document Images dataset (DDI-100) and provide a detailed analysis of the DDI-100 in its current state. To create the dataset we collected 7000 unique document pages, and extend it by applying different types of distortions and geometric transformations. In total, DDI-100 contains more than 100,000 document images together with binary text masks, text and character locations in terms of bounding boxes. We also present an analysis of several state-of-the-art TD and OCR approaches on the presented dataset. Lastly, we demonstrate the usefulness of DDI-100 to improve accuracy and stability of the considered TD and OCR models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=document%20analysis" title="document analysis">document analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=open%20dataset" title=" open dataset"> open dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20character%20recognition" title=" optical character recognition"> optical character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=text%20detection" title=" text detection"> text detection</a> </p> <a href="https://publications.waset.org/abstracts/106148/distorted-document-images-dataset-for-text-detection-and-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/106148.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">173</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">1165</span> SAMRA: Dataset in Al-Soudani Arabic Maghrebi Script for Recognition of Arabic Ancient Words Handwritten</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sidi%20Ahmed%20Maouloud">Sidi Ahmed Maouloud</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheikh%20Ba"> Cheikh Ba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Much of West Africa’s cultural heritage is written in the Al-Soudani Arabic script, which was widely used in West Africa before the time of European colonization. This Al-Soudani Arabic script is an African version of the Maghrebi script, in particular, the Al-Mebssout script. However, the local African qualities were incorporated into the Al-Soudani script in a way that gave it a unique African diversity and character. Despite the existence of several Arabic datasets in Oriental script, allowing for the analysis, layout, and recognition of texts written in these calligraphies, many Arabic scripts and written traditions remain understudied. In this paper, we present a dataset of words from Al-Soudani calligraphy scripts. This dataset consists of 100 images selected from three different manuscripts written in Al-Soudani Arabic script by different copyists. The primary source for this database was the libraries of Boston University and Cambridge University. This dataset highlights the unique characteristics of the Al-Soudani Arabic script as well as the new challenges it presents in terms of automatic word recognition of Arabic manuscripts. An HTR system based on a hybrid ANN (CRNN-CTC) is also proposed to test this dataset. SAMRA is a dataset of annotated Arabic manuscript words in the Al-Soudani script that can help researchers automatically recognize and analyze manuscript words written in this script. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dataset" title="dataset">dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=CRNN-CTC" title=" CRNN-CTC"> CRNN-CTC</a>, <a href="https://publications.waset.org/abstracts/search?q=handwritten%20words%20recognition" title=" handwritten words recognition"> handwritten words recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Al-Soudani%20Arabic%20script" title=" Al-Soudani Arabic script"> Al-Soudani Arabic script</a>, <a href="https://publications.waset.org/abstracts/search?q=HTR" title=" HTR"> HTR</a>, <a href="https://publications.waset.org/abstracts/search?q=manuscripts" title=" manuscripts"> manuscripts</a> </p> <a href="https://publications.waset.org/abstracts/155632/samra-dataset-in-al-soudani-arabic-maghrebi-script-for-recognition-of-arabic-ancient-words-handwritten" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155632.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">130</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">1164</span> Fuzzy-Machine Learning Models for the Prediction of Fire Outbreak: A Comparative Analysis </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Uduak%20Umoh">Uduak Umoh</a>, <a href="https://publications.waset.org/abstracts/search?q=Imo%20Eyoh"> Imo Eyoh</a>, <a href="https://publications.waset.org/abstracts/search?q=Emmauel%20Nyoho"> Emmauel Nyoho</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper compares fuzzy-machine learning algorithms such as Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) for the predicting cases of fire outbreak. The paper uses the fire outbreak dataset with three features (Temperature, Smoke, and Flame). The data is pre-processed using Interval Type-2 Fuzzy Logic (IT2FL) algorithm. Min-Max Normalization and Principal Component Analysis (PCA) are used to predict feature labels in the dataset, normalize the dataset, and select relevant features respectively. The output of the pre-processing is a dataset with two principal components (PC1 and PC2). The pre-processed dataset is then used in the training of the aforementioned machine learning models. K-fold (with K=10) cross-validation method is used to evaluate the performance of the models using the matrices – ROC (Receiver Operating Curve), Specificity, and Sensitivity. The model is also tested with 20% of the dataset. The validation result shows KNN is the better model for fire outbreak detection with an ROC value of 0.99878, followed by SVM with an ROC value of 0.99753. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Machine%20Learning%20Algorithms" title="Machine Learning Algorithms ">Machine Learning Algorithms </a>, <a href="https://publications.waset.org/abstracts/search?q=Interval%20Type-2%20Fuzzy%20Logic" title=" Interval Type-2 Fuzzy Logic"> Interval Type-2 Fuzzy Logic</a>, <a href="https://publications.waset.org/abstracts/search?q=Fire%20Outbreak" title=" Fire Outbreak"> Fire Outbreak</a>, <a href="https://publications.waset.org/abstracts/search?q=Support%20Vector%20Machine" title=" Support Vector Machine"> Support Vector Machine</a>, <a href="https://publications.waset.org/abstracts/search?q=K-Nearest%20Neighbour" title=" K-Nearest Neighbour"> K-Nearest Neighbour</a>, <a href="https://publications.waset.org/abstracts/search?q=Principal%20Component%20Analysis" title=" Principal Component Analysis "> Principal Component Analysis </a> </p> <a href="https://publications.waset.org/abstracts/128079/fuzzy-machine-learning-models-for-the-prediction-of-fire-outbreak-a-comparative-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128079.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">182</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">1163</span> A Ratio-Weighted Decision Tree Algorithm for Imbalance Dataset Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Doyin%20Afolabi">Doyin Afolabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Phillip%20Adewole"> Phillip Adewole</a>, <a href="https://publications.waset.org/abstracts/search?q=Oladipupo%20Sennaike"> Oladipupo Sennaike</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most well-known classifiers, including the decision tree algorithm, can make predictions on balanced datasets efficiently. However, the decision tree algorithm tends to be biased towards imbalanced datasets because of the skewness of the distribution of such datasets. To overcome this problem, this study proposes a weighted decision tree algorithm that aims to remove the bias toward the majority class and prevents the reduction of majority observations in imbalance datasets classification. The proposed weighted decision tree algorithm was tested on three imbalanced datasets- cancer dataset, german credit dataset, and banknote dataset. The specificity, sensitivity, and accuracy metrics were used to evaluate the performance of the proposed decision tree algorithm on the datasets. The evaluation results show that for some of the weights of our proposed decision tree, the specificity, sensitivity, and accuracy metrics gave better results compared to that of the ID3 decision tree and decision tree induced with minority entropy for all three 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=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=imbalance%20dataset" title=" imbalance dataset"> imbalance dataset</a> </p> <a href="https://publications.waset.org/abstracts/157609/a-ratio-weighted-decision-tree-algorithm-for-imbalance-dataset-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157609.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">1162</span> Intelligent Recognition of Diabetes Disease via FCM Based Attribute Weighting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kemal%20Polat">Kemal Polat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, an attribute weighting method called fuzzy C-means clustering based attribute weighting (FCMAW) for classification of Diabetes disease dataset has been used. The aims of this study are to reduce the variance within attributes of diabetes dataset and to improve the classification accuracy of classifier algorithm transforming from non-linear separable datasets to linearly separable datasets. Pima Indians Diabetes dataset has two classes including normal subjects (500 instances) and diabetes subjects (268 instances). Fuzzy C-means clustering is an improved version of K-means clustering method and is one of most used clustering methods in data mining and machine learning applications. In this study, as the first stage, fuzzy C-means clustering process has been used for finding the centers of attributes in Pima Indians diabetes dataset and then weighted the dataset according to the ratios of the means of attributes to centers of theirs. Secondly, after weighting process, the classifier algorithms including support vector machine (SVM) and k-NN (k- nearest neighbor) classifiers have been used for classifying weighted Pima Indians diabetes dataset. Experimental results show that the proposed attribute weighting method (FCMAW) has obtained very promising results in the classification of Pima Indians diabetes dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20C-means%20clustering" title="fuzzy C-means clustering">fuzzy C-means clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20C-means%20clustering%20based%20attribute%20weighting" title=" fuzzy C-means clustering based attribute weighting"> fuzzy C-means clustering based attribute weighting</a>, <a href="https://publications.waset.org/abstracts/search?q=Pima%20Indians%20diabetes" title=" Pima Indians diabetes"> Pima Indians diabetes</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a> </p> <a href="https://publications.waset.org/abstracts/46171/intelligent-recognition-of-diabetes-disease-via-fcm-based-attribute-weighting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46171.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">413</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">1161</span> Optimizing the Capacity of a Convolutional Neural Network for Image Segmentation and Pattern Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yalong%20Jiang">Yalong Jiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zheru%20Chi"> Zheru Chi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we study the factors which determine the capacity of a Convolutional Neural Network (CNN) model and propose the ways to evaluate and adjust the capacity of a CNN model for best matching to a specific pattern recognition task. Firstly, a scheme is proposed to adjust the number of independent functional units within a CNN model to make it be better fitted to a task. Secondly, the number of independent functional units in the capsule network is adjusted to fit it to the training dataset. Thirdly, a method based on Bayesian GAN is proposed to enrich the variances in the current dataset to increase its complexity. Experimental results on the PASCAL VOC 2010 Person Part dataset and the MNIST dataset show that, in both conventional CNN models and capsule networks, the number of independent functional units is an important factor that determines the capacity of a network model. By adjusting the number of functional units, the capacity of a model can better match the complexity of a dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CNN" title="CNN">CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=capsule%20network" title=" capsule network"> capsule network</a>, <a href="https://publications.waset.org/abstracts/search?q=capacity%20optimization" title=" capacity optimization"> capacity optimization</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=data%20augmentation" title=" data augmentation"> data augmentation</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/95551/optimizing-the-capacity-of-a-convolutional-neural-network-for-image-segmentation-and-pattern-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95551.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">153</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">1160</span> Energy Complementary in Colombia: Imputation of Dataset</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Felipe%20Villegas-Velasquez">Felipe Villegas-Velasquez</a>, <a href="https://publications.waset.org/abstracts/search?q=Harold%20Pantoja-Villota"> Harold Pantoja-Villota</a>, <a href="https://publications.waset.org/abstracts/search?q=Sergio%20Holguin-Cardona"> Sergio Holguin-Cardona</a>, <a href="https://publications.waset.org/abstracts/search?q=Alejandro%20Osorio-Botero"> Alejandro Osorio-Botero</a>, <a href="https://publications.waset.org/abstracts/search?q=Brayan%20Candamil-Arango"> Brayan Candamil-Arango</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Colombian electricity comes mainly from hydric resources, affected by environmental variations such as the El Niño phenomenon. That is why incorporating other types of resources is necessary to provide electricity constantly. This research seeks to fill the wind speed and global solar irradiance dataset for two years with the highest amount of information. A further result is the characterization of the data by region that led to infer which errors occurred and offered the incomplete dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=energy" title="energy">energy</a>, <a href="https://publications.waset.org/abstracts/search?q=wind%20speed" title=" wind speed"> wind speed</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20solar%20irradiance" title=" global solar irradiance"> global solar irradiance</a>, <a href="https://publications.waset.org/abstracts/search?q=Colombia" title=" Colombia"> Colombia</a>, <a href="https://publications.waset.org/abstracts/search?q=imputation" title=" imputation"> imputation</a> </p> <a href="https://publications.waset.org/abstracts/148689/energy-complementary-in-colombia-imputation-of-dataset" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148689.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">146</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">1159</span> The Clustering of Multiple Sclerosis Subgroups through L2 Norm Multifractal Denoising Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yeliz%20Karaca">Yeliz Karaca</a>, <a href="https://publications.waset.org/abstracts/search?q=Rana%20Karabudak"> Rana Karabudak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multifractal Denoising techniques are used in the identification of significant attributes by removing the noise of the dataset. Magnetic resonance (MR) image technique is the most sensitive method so as to identify chronic disorders of the nervous system such as Multiple Sclerosis. MRI and Expanded Disability Status Scale (EDSS) data belonging to 120 individuals who have one of the subgroups of MS (Relapsing Remitting MS (RRMS), Secondary Progressive MS (SPMS), Primary Progressive MS (PPMS)) as well as 19 healthy individuals in the control group have been used in this study. The study is comprised of the following stages: (i) L2 Norm Multifractal Denoising technique, one of the multifractal technique, has been used with the application on the MS data (MRI and EDSS). In this way, the new dataset has been obtained. (ii) The new MS dataset obtained from the MS dataset and L2 Multifractal Denoising technique has been applied to the K-Means and Fuzzy C Means clustering algorithms which are among the unsupervised methods. Thus, the clustering performances have been compared. (iii) In the identification of significant attributes in the MS dataset through the Multifractal denoising (L2 Norm) technique using K-Means and FCM algorithms on the MS subgroups and control group of healthy individuals, excellent performance outcome has been yielded. According to the clustering results based on the MS subgroups obtained in the study, successful clustering results have been obtained in the K-Means and FCM algorithms by applying the L2 norm of multifractal denoising technique for the MS dataset. Clustering performance has been more successful with the MS Dataset (L2_Norm MS Data Set) K-Means and FCM in which significant attributes are obtained by applying L2 Norm Denoising technique. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=clinical%20decision%20support" title="clinical decision support">clinical decision support</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering%20algorithms" title=" clustering algorithms"> clustering algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20sclerosis" title=" multiple sclerosis"> multiple sclerosis</a>, <a href="https://publications.waset.org/abstracts/search?q=multifractal%20techniques" title=" multifractal techniques"> multifractal techniques</a> </p> <a href="https://publications.waset.org/abstracts/91074/the-clustering-of-multiple-sclerosis-subgroups-through-l2-norm-multifractal-denoising-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/91074.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">168</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">1158</span> Facial Expression Phoenix (FePh): An Annotated Sequenced Dataset for Facial and Emotion-Specified Expressions in Sign Language</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marie%20Alaghband">Marie Alaghband</a>, <a href="https://publications.waset.org/abstracts/search?q=Niloofar%20Yousefi"> Niloofar Yousefi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ivan%20Garibay"> Ivan Garibay</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Facial expressions are important parts of both gesture and sign language recognition systems. Despite the recent advances in both fields, annotated facial expression datasets in the context of sign language are still scarce resources. In this manuscript, we introduce an annotated sequenced facial expression dataset in the context of sign language, comprising over 3000 facial images extracted from the daily news and weather forecast of the public tv-station PHOENIX. Unlike the majority of currently existing facial expression datasets, FePh provides sequenced semi-blurry facial images with different head poses, orientations, and movements. In addition, in the majority of images, identities are mouthing the words, which makes the data more challenging. To annotate this dataset we consider primary, secondary, and tertiary dyads of seven basic emotions of "sad", "surprise", "fear", "angry", "neutral", "disgust", and "happy". We also considered the "None" class if the image’s facial expression could not be described by any of the aforementioned emotions. Although we provide FePh as a facial expression dataset of signers in sign language, it has a wider application in gesture recognition and Human Computer Interaction (HCI) systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=annotated%20facial%20expression%20dataset" title="annotated facial expression dataset">annotated facial expression dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=gesture%20recognition" title=" gesture recognition"> gesture recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=sequenced%20facial%20expression%20dataset" title=" sequenced facial expression dataset"> sequenced facial expression dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=sign%20language%20recognition" title=" sign language recognition"> sign language recognition</a> </p> <a href="https://publications.waset.org/abstracts/129717/facial-expression-phoenix-feph-an-annotated-sequenced-dataset-for-facial-and-emotion-specified-expressions-in-sign-language" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129717.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">159</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">1157</span> Data Augmentation for Automatic Graphical User Interface Generation Based on Generative Adversarial Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xulu%20Yao">Xulu Yao</a>, <a href="https://publications.waset.org/abstracts/search?q=Moi%20Hoon%20Yap"> Moi Hoon Yap</a>, <a href="https://publications.waset.org/abstracts/search?q=Yanlong%20Zhang"> Yanlong Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As a branch of artificial neural network, deep learning is widely used in the field of image recognition, but the lack of its dataset leads to imperfect model learning. By analysing the data scale requirements of deep learning and aiming at the application in GUI generation, it is found that the collection of GUI dataset is a time-consuming and labor-consuming project, which is difficult to meet the needs of current deep learning network. To solve this problem, this paper proposes a semi-supervised deep learning model that relies on the original small-scale datasets to produce a large number of reliable data sets. By combining the cyclic neural network with the generated countermeasure network, the cyclic neural network can learn the sequence relationship and characteristics of data, make the generated countermeasure network generate reasonable data, and then expand the Rico dataset. Relying on the network structure, the characteristics of collected data can be well analysed, and a large number of reasonable data can be generated according to these characteristics. After data processing, a reliable dataset for model training can be formed, which alleviates the problem of dataset shortage in deep learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GUI" title="GUI">GUI</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=GAN" title=" GAN"> GAN</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20augmentation" title=" data augmentation"> data augmentation</a> </p> <a href="https://publications.waset.org/abstracts/143650/data-augmentation-for-automatic-graphical-user-interface-generation-based-on-generative-adversarial-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143650.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">184</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1156</span> Pose Normalization Network for Object Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bingquan%20Shen">Bingquan Shen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Convolutional Neural Networks (CNN) have demonstrated their effectiveness in synthesizing 3D views of object instances at various viewpoints. Given the problem where one have limited viewpoints of a particular object for classification, we present a pose normalization architecture to transform the object to existing viewpoints in the training dataset before classification to yield better classification performance. We have demonstrated that this Pose Normalization Network (PNN) can capture the style of the target object and is able to re-render it to a desired viewpoint. Moreover, we have shown that the PNN improves the classification result for the 3D chairs dataset and ShapeNet airplanes dataset when given only images at limited viewpoint, as compared to a CNN baseline. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title="convolutional neural networks">convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20classification" title=" object classification"> object classification</a>, <a href="https://publications.waset.org/abstracts/search?q=pose%20normalization" title=" pose normalization"> pose normalization</a>, <a href="https://publications.waset.org/abstracts/search?q=viewpoint%20invariant" title=" viewpoint invariant"> viewpoint invariant</a> </p> <a href="https://publications.waset.org/abstracts/56852/pose-normalization-network-for-object-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56852.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">352</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1155</span> Data Gathering and Analysis for Arabic Historical Documents</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Dulla">Ali Dulla</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces a new dataset (and the methodology used to generate it) based on a wide range of historical Arabic documents containing clean data simple and homogeneous-page layouts. The experiments are implemented on printed and handwritten documents obtained respectively from some important libraries such as Qatar Digital Library, the British Library and the Library of Congress. We have gathered and commented on 150 archival document images from different locations and time periods. It is based on different documents from the 17th-19th century. The dataset comprises differing page layouts and degradations that challenge text line segmentation methods. Ground truth is produced using the Aletheia tool by PRImA and stored in an XML representation, in the PAGE (Page Analysis and Ground truth Elements) format. The dataset presented will be easily available to researchers world-wide for research into the obstacles facing various historical Arabic documents such as geometric correction of historical Arabic documents. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dataset%20production" title="dataset production">dataset production</a>, <a href="https://publications.waset.org/abstracts/search?q=ground%20truth%20production" title=" ground truth production"> ground truth production</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=arbitrary%20warping" title=" arbitrary warping"> arbitrary warping</a>, <a href="https://publications.waset.org/abstracts/search?q=geometric%20correction" title=" geometric correction"> geometric correction</a> </p> <a href="https://publications.waset.org/abstracts/90467/data-gathering-and-analysis-for-arabic-historical-documents" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90467.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">168</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">1154</span> Enhancing Fault Detection in Rotating Machinery Using Wiener-CNN Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamad%20R.%20Moshtagh">Mohamad R. Moshtagh</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmad%20Bagheri"> Ahmad Bagheri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accurate fault detection in rotating machinery is of utmost importance to ensure optimal performance and prevent costly downtime in industrial applications. This study presents a robust fault detection system based on vibration data collected from rotating gears under various operating conditions. The considered scenarios include: (1) both gears being healthy, (2) one healthy gear and one faulty gear, and (3) introducing an imbalanced condition to a healthy gear. Vibration data was acquired using a Hentek 1008 device and stored in a CSV file. Python code implemented in the Spider environment was used for data preprocessing and analysis. Winner features were extracted using the Wiener feature selection method. These features were then employed in multiple machine learning algorithms, including Convolutional Neural Networks (CNN), Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), and Random Forest, to evaluate their performance in detecting and classifying faults in both the training and validation datasets. The comparative analysis of the methods revealed the superior performance of the Wiener-CNN approach. The Wiener-CNN method achieved a remarkable accuracy of 100% for both the two-class (healthy gear and faulty gear) and three-class (healthy gear, faulty gear, and imbalanced) scenarios in the training and validation datasets. In contrast, the other methods exhibited varying levels of accuracy. The Wiener-MLP method attained 100% accuracy for the two-class training dataset and 100% for the validation dataset. For the three-class scenario, the Wiener-MLP method demonstrated 100% accuracy in the training dataset and 95.3% accuracy in the validation dataset. The Wiener-KNN method yielded 96.3% accuracy for the two-class training dataset and 94.5% for the validation dataset. In the three-class scenario, it achieved 85.3% accuracy in the training dataset and 77.2% in the validation dataset. The Wiener-Random Forest method achieved 100% accuracy for the two-class training dataset and 85% for the validation dataset, while in the three-class training dataset, it attained 100% accuracy and 90.8% accuracy for the validation dataset. The exceptional accuracy demonstrated by the Wiener-CNN method underscores its effectiveness in accurately identifying and classifying fault conditions in rotating machinery. The proposed fault detection system utilizes vibration data analysis and advanced machine learning techniques to improve operational reliability and productivity. By adopting the Wiener-CNN method, industrial systems can benefit from enhanced fault detection capabilities, facilitating proactive maintenance and reducing equipment downtime. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fault%20detection" title="fault detection">fault detection</a>, <a href="https://publications.waset.org/abstracts/search?q=gearbox" title=" gearbox"> gearbox</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=wiener%20method" title=" wiener method"> wiener method</a> </p> <a href="https://publications.waset.org/abstracts/169701/enhancing-fault-detection-in-rotating-machinery-using-wiener-cnn-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169701.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">80</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">1153</span> Evaluating Models Through Feature Selection Methods Using Data Driven Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shital%20Patil">Shital Patil</a>, <a href="https://publications.waset.org/abstracts/search?q=Surendra%20Bhosale"> Surendra Bhosale</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cardiac diseases are the leading causes of mortality and morbidity in the world, from recent few decades accounting for a large number of deaths have emerged as the most life-threatening disorder globally. Machine learning and Artificial intelligence have been playing key role in predicting the heart diseases. A relevant set of feature can be very helpful in predicting the disease accurately. In this study, we proposed a comparative analysis of 4 different features selection methods and evaluated their performance with both raw (Unbalanced dataset) and sampled (Balanced) dataset. The publicly available Z-Alizadeh Sani dataset have been used for this study. Four feature selection methods: Data Analysis, minimum Redundancy maximum Relevance (mRMR), Recursive Feature Elimination (RFE), Chi-squared are used in this study. These methods are tested with 8 different classification models to get the best accuracy possible. Using balanced and unbalanced dataset, the study shows promising results in terms of various performance metrics in accurately predicting heart disease. Experimental results obtained by the proposed method with the raw data obtains maximum AUC of 100%, maximum F1 score of 94%, maximum Recall of 98%, maximum Precision of 93%. While with the balanced dataset obtained results are, maximum AUC of 100%, F1-score 95%, maximum Recall of 95%, maximum Precision of 97%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cardio%20vascular%20diseases" title="cardio vascular diseases">cardio vascular diseases</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=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=SMOTE" title=" SMOTE"> SMOTE</a> </p> <a href="https://publications.waset.org/abstracts/151612/evaluating-models-through-feature-selection-methods-using-data-driven-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151612.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">118</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">1152</span> Static and Dynamic Hand Gesture Recognition Using Convolutional Neural Network Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Keyi%20Wang">Keyi Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Similar to the touchscreen, hand gesture based human-computer interaction (HCI) is a technology that could allow people to perform a variety of tasks faster and more conveniently. This paper proposes a training method of an image-based hand gesture image and video clip recognition system using a CNN (Convolutional Neural Network) with a dataset. A dataset containing 6 hand gesture images is used to train a 2D CNN model. ~98% accuracy is achieved. Furthermore, a 3D CNN model is trained on a dataset containing 4 hand gesture video clips resulting in ~83% accuracy. It is demonstrated that a Cozmo robot loaded with pre-trained models is able to recognize static and dynamic hand gestures. <p class="card-text"><strong>Keywords:</strong> <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=hand%20gesture%20recognition" title=" hand gesture recognition"> hand gesture recognition</a>, <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=image%20processing" title=" image processing"> image processing</a> </p> <a href="https://publications.waset.org/abstracts/132854/static-and-dynamic-hand-gesture-recognition-using-convolutional-neural-network-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132854.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">139</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">1151</span> Data Mining Approach: Classification Model Evaluation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lubabatu%20Sada%20Sodangi">Lubabatu Sada Sodangi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The rapid growth in exchange and accessibility of information via the internet makes many organisations acquire data on their own operation. The aim of data mining is to analyse the different behaviour of a dataset using observation. Although, the subset of the dataset being analysed may not display all the behaviours and relationships of the entire data and, therefore, may not represent other parts that exist in the dataset. There is a range of techniques used in data mining to determine the hidden or unknown information in datasets. In this paper, the performance of two algorithms Chi-Square Automatic Interaction Detection (CHAID) and multilayer perceptron (MLP) would be matched using an Adult dataset to find out the percentage of an/the adults that earn > 50k and those that earn <= 50k per year. The two algorithms were studied and compared using IBM SPSS statistics software. The result for CHAID shows that the most important predictors are relationship and education. The algorithm shows that those are married (husband) and have qualification: Bachelor, Masters, Doctorate or Prof-school whose their age is > 41<57 earn > 50k. Also, multilayer perceptron displays marital status and capital gain as the most important predictors of the income. It also shows that individuals that their capital gain is less than 6,849 and are single, separated or widow, earn <= 50K, whereas individuals with their capital gain is > 6,849, work > 35 hrs/wk, and > 27yrs their income will be > 50k. By comparing the two algorithms, it is observed that both algorithms are reliable but there is strong reliability in CHAID which clearly shows that relation and education contribute to the prediction as displayed in the data visualisation. <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=CHAID" title=" CHAID"> CHAID</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-layer%20perceptron" title=" multi-layer perceptron"> multi-layer perceptron</a>, <a href="https://publications.waset.org/abstracts/search?q=SPSS" title=" SPSS"> SPSS</a>, <a href="https://publications.waset.org/abstracts/search?q=Adult%20dataset" title=" Adult dataset"> Adult dataset</a> </p> <a href="https://publications.waset.org/abstracts/49909/data-mining-approach-classification-model-evaluation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49909.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">1150</span> Video Object Segmentation for Automatic Image Annotation of Ethernet Connectors with Environment Mapping and 3D Projection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marrone%20Silverio%20Melo%20Dantas%20Pedro%20Henrique%20Dreyer">Marrone Silverio Melo Dantas Pedro Henrique Dreyer</a>, <a href="https://publications.waset.org/abstracts/search?q=Gabriel%20Fonseca%20Reis%20de%20Souza"> Gabriel Fonseca Reis de Souza</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Bezerra"> Daniel Bezerra</a>, <a href="https://publications.waset.org/abstracts/search?q=Ricardo%20Souza"> Ricardo Souza</a>, <a href="https://publications.waset.org/abstracts/search?q=Silvia%20Lins"> Silvia Lins</a>, <a href="https://publications.waset.org/abstracts/search?q=Judith%20Kelner"> Judith Kelner</a>, <a href="https://publications.waset.org/abstracts/search?q=Djamel%20Fawzi%20Hadj%20Sadok"> Djamel Fawzi Hadj Sadok</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The creation of a dataset is time-consuming and often discourages researchers from pursuing their goals. To overcome this problem, we present and discuss two solutions adopted for the automation of this process. Both optimize valuable user time and resources and support video object segmentation with object tracking and 3D projection. In our scenario, we acquire images from a moving robotic arm and, for each approach, generate distinct annotated datasets. We evaluated the precision of the annotations by comparing these with a manually annotated dataset, as well as the efficiency in the context of detection and classification problems. For detection support, we used YOLO and obtained for the projection dataset an F1-Score, accuracy, and mAP values of 0.846, 0.924, and 0.875, respectively. Concerning the tracking dataset, we achieved an F1-Score of 0.861, an accuracy of 0.932, whereas mAP reached 0.894. In order to evaluate the quality of the annotated images used for classification problems, we employed deep learning architectures. We adopted metrics accuracy and F1-Score, for VGG, DenseNet, MobileNet, Inception, and ResNet. The VGG architecture outperformed the others for both projection and tracking datasets. It reached an accuracy and F1-score of 0.997 and 0.993, respectively. Similarly, for the tracking dataset, it achieved an accuracy of 0.991 and an F1-Score of 0.981. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=RJ45" title="RJ45">RJ45</a>, <a href="https://publications.waset.org/abstracts/search?q=automatic%20annotation" title=" automatic annotation"> automatic annotation</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20tracking" title=" object tracking"> object tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20projection" title=" 3D projection"> 3D projection</a> </p> <a href="https://publications.waset.org/abstracts/130540/video-object-segmentation-for-automatic-image-annotation-of-ethernet-connectors-with-environment-mapping-and-3d-projection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130540.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">167</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1149</span> Engagement Analysis Using DAiSEE Dataset</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Naman%20Solanki">Naman Solanki</a>, <a href="https://publications.waset.org/abstracts/search?q=Souraj%20Mondal"> Souraj Mondal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the world moving towards online communication, the video datastore has exploded in the past few years. Consequently, it has become crucial to analyse participant’s engagement levels in online communication videos. Engagement prediction of people in videos can be useful in many domains, like education, client meetings, dating, etc. Video-level or frame-level prediction of engagement for a user involves the development of robust models that can capture facial micro-emotions efficiently. For the development of an engagement prediction model, it is necessary to have a widely-accepted standard dataset for engagement analysis. DAiSEE is one of the datasets which consist of in-the-wild data and has a gold standard annotation for engagement prediction. Earlier research done using the DAiSEE dataset involved training and testing standard models like CNN-based models, but the results were not satisfactory according to industry standards. In this paper, a multi-level classification approach has been introduced to create a more robust model for engagement analysis using the DAiSEE dataset. This approach has recorded testing accuracies of 0.638, 0.7728, 0.8195, and 0.866 for predicting boredom level, engagement level, confusion level, and frustration level, respectively. <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=engagement%20prediction" title=" engagement prediction"> engagement prediction</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=multi-level%20classification" title=" multi-level classification"> multi-level classification</a> </p> <a href="https://publications.waset.org/abstracts/152133/engagement-analysis-using-daisee-dataset" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152133.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">114</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1148</span> Monocular Depth Estimation Benchmarking with Thermal Dataset</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Akyar">Ali Akyar</a>, <a href="https://publications.waset.org/abstracts/search?q=Osman%20Serdar%20Gedik"> Osman Serdar Gedik</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Depth estimation is a challenging computer vision task that involves estimating the distance between objects in a scene and the camera. It predicts how far each pixel in the 2D image is from the capturing point. There are some important Monocular Depth Estimation (MDE) studies that are based on Vision Transformers (ViT). We benchmark three major studies. The first work aims to build a simple and powerful foundation model that deals with any images under any condition. The second work proposes a method by mixing multiple datasets during training and a robust training objective. The third work combines generalization performance and state-of-the-art results on specific datasets. Although there are studies with thermal images too, we wanted to benchmark these three non-thermal, state-of-the-art studies with a hybrid image dataset which is taken by Multi-Spectral Dynamic Imaging (MSX) technology. MSX technology produces detailed thermal images by bringing together the thermal and visual spectrums. Using this technology, our dataset images are not blur and poorly detailed as the normal thermal images. On the other hand, they are not taken at the perfect light conditions as RGB images. We compared three methods under test with our thermal dataset which was not done before. Additionally, we propose an image enhancement deep learning model for thermal data. This model helps extract the features required for monocular depth estimation. The experimental results demonstrate that, after using our proposed model, the performance of these three methods under test increased significantly for thermal image depth prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=monocular%20depth%20estimation" title="monocular depth estimation">monocular depth estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=thermal%20dataset" title=" thermal dataset"> thermal dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=benchmarking" title=" benchmarking"> benchmarking</a>, <a href="https://publications.waset.org/abstracts/search?q=vision%20transformers" title=" vision transformers"> vision transformers</a> </p> <a href="https://publications.waset.org/abstracts/186398/monocular-depth-estimation-benchmarking-with-thermal-dataset" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186398.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">32</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">1147</span> Face Recognition Using Body-Worn Camera: Dataset and Baseline Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Almadan">Ali Almadan</a>, <a href="https://publications.waset.org/abstracts/search?q=Anoop%20Krishnan"> Anoop Krishnan</a>, <a href="https://publications.waset.org/abstracts/search?q=Ajita%20Rattani"> Ajita Rattani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Facial recognition is a widely adopted technology in surveillance, border control, healthcare, banking services, and lately, in mobile user authentication with Apple introducing “Face ID” moniker with iPhone X. A lot of research has been conducted in the area of face recognition on datasets captured by surveillance cameras, DSLR, and mobile devices. Recently, face recognition technology has also been deployed on body-worn cameras to keep officers safe, enabling situational awareness and providing evidence for trial. However, limited academic research has been conducted on this topic so far, without the availability of any publicly available datasets with a sufficient sample size. This paper aims to advance research in the area of face recognition using body-worn cameras. To this aim, the contribution of this work is two-fold: (1) collection of a dataset consisting of a total of 136,939 facial images of 102 subjects captured using body-worn cameras in in-door and daylight conditions and (2) evaluation of various deep-learning architectures for face identification on the collected dataset. Experimental results suggest a maximum True Positive Rate(TPR) of 99.86% at False Positive Rate(FPR) of 0.000 obtained by SphereFace based deep learning architecture in daylight condition. The collected dataset and the baseline algorithms will promote further research and development. A downloadable link of the dataset and the algorithms is available by contacting the authors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=face%20recognition" title="face recognition">face recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=body-worn%20cameras" title=" body-worn cameras"> body-worn cameras</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=person%20identification" title=" person identification"> person identification</a> </p> <a href="https://publications.waset.org/abstracts/127551/face-recognition-using-body-worn-camera-dataset-and-baseline-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127551.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">163</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">1146</span> Design and Implementation a Platform for Adaptive Online Learning Based on Fuzzy Logic</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Budoor%20Al%20Abid">Budoor Al Abid</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Educational systems are increasingly provided as open online services, providing guidance and support for individual learners. To adapt the learning systems, a proper evaluation must be made. This paper builds the evaluation model Fuzzy C Means Adaptive System (FCMAS) based on data mining techniques to assess the difficulty of the questions. The following steps are implemented; first using a dataset from an online international learning system called (slepemapy.cz) the dataset contains over 1300000 records with 9 features for students, questions and answers information with feedback evaluation. Next, a normalization process as preprocessing step was applied. Then FCM clustering algorithms are used to adaptive the difficulty of the questions. The result is three cluster labeled data depending on the higher Wight (easy, Intermediate, difficult). The FCM algorithm gives a label to all the questions one by one. Then Random Forest (RF) Classifier model is constructed on the clustered dataset uses 70% of the dataset for training and 30% for testing; the result of the model is a 99.9% accuracy rate. This approach improves the Adaptive E-learning system because it depends on the student behavior and gives accurate results in the evaluation process more than the evaluation system that depends on feedback only. <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=adaptive" title=" adaptive"> adaptive</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=data%20mining" title=" data mining"> data mining</a> </p> <a href="https://publications.waset.org/abstracts/139852/design-and-implementation-a-platform-for-adaptive-online-learning-based-on-fuzzy-logic" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139852.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">196</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">1145</span> Using Satellite Images Datasets for Road Intersection Detection in Route Planning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatma%20El-Zahraa%20El-Taher">Fatma El-Zahraa El-Taher</a>, <a href="https://publications.waset.org/abstracts/search?q=Ayman%20Taha"> Ayman Taha</a>, <a href="https://publications.waset.org/abstracts/search?q=Jane%20Courtney"> Jane Courtney</a>, <a href="https://publications.waset.org/abstracts/search?q=Susan%20Mckeever"> Susan Mckeever</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Understanding road networks plays an important role in navigation applications such as self-driving vehicles and route planning for individual journeys. Intersections of roads are essential components of road networks. Understanding the features of an intersection, from a simple T-junction to larger multi-road junctions, is critical to decisions such as crossing roads or selecting the safest routes. The identification and profiling of intersections from satellite images is a challenging task. While deep learning approaches offer the state-of-the-art in image classification and detection, the availability of training datasets is a bottleneck in this approach. In this paper, a labelled satellite image dataset for the intersection recognition problem is presented. It consists of 14,692 satellite images of Washington DC, USA. To support other users of the dataset, an automated download and labelling script is provided for dataset replication. The challenges of construction and fine-grained feature labelling of a satellite image dataset is examined, including the issue of how to address features that are spread across multiple images. Finally, the accuracy of the detection of intersections in satellite images is evaluated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=satellite%20images" title="satellite images">satellite images</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing%20images" title=" remote sensing images"> remote sensing images</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20acquisition" title=" data acquisition"> data acquisition</a>, <a href="https://publications.waset.org/abstracts/search?q=autonomous%20vehicles" title=" autonomous vehicles"> autonomous vehicles</a> </p> <a href="https://publications.waset.org/abstracts/145141/using-satellite-images-datasets-for-road-intersection-detection-in-route-planning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/145141.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">144</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">1144</span> Adaptive Swarm Balancing Algorithms for Rare-Event Prediction in Imbalanced Healthcare Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jinyan%20Li">Jinyan Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Simon%20Fong"> Simon Fong</a>, <a href="https://publications.waset.org/abstracts/search?q=Raymond%20Wong"> Raymond Wong</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Sabah"> Mohammed Sabah</a>, <a href="https://publications.waset.org/abstracts/search?q=Fiaidhi%20Jinan"> Fiaidhi Jinan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Clinical data analysis and forecasting have make great contributions to disease control, prevention and detection. However, such data usually suffer from highly unbalanced samples in class distributions. In this paper, we target at the binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuristic algorithms, particle swarm optimization and bat-inspired algorithm, and combine both of them with the synthetic minority over-sampling technique (SMOTE) for processing the datasets. One approach is to process the full dataset as a whole. The other is to split up the dataset and adaptively process it one segment at a time. The experimental results reveal that while the performance improvements obtained by the former methods are not scalable to larger data scales, the later one, which we call Adaptive Swarm Balancing Algorithms, leads to significant efficiency and effectiveness improvements on large datasets. We also find it more consistent with the practice of the typical large imbalanced medical datasets. We further use the meta-heuristic algorithms to optimize two key parameters of SMOTE. Leading to more credible performances of the classifier, and shortening the running time compared with the brute-force method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Imbalanced%20dataset" title="Imbalanced dataset">Imbalanced dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-heuristic%20algorithm" title=" meta-heuristic algorithm"> meta-heuristic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=SMOTE" title=" SMOTE"> SMOTE</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data "> big data </a> </p> <a href="https://publications.waset.org/abstracts/41481/adaptive-swarm-balancing-algorithms-for-rare-event-prediction-in-imbalanced-healthcare-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/41481.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">441</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">1143</span> Performance Analysis of Traffic Classification with Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Htay%20Htay%20Yi">Htay Htay Yi</a>, <a href="https://publications.waset.org/abstracts/search?q=Zin%20May%20Aye"> Zin May Aye</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Network security is role of the ICT environment because malicious users are continually growing that realm of education, business, and then related with ICT. The network security contravention is typically described and examined centrally based on a security event management system. The firewalls, Intrusion Detection System (IDS), and Intrusion Prevention System are becoming essential to monitor or prevent of potential violations, incidents attack, and imminent threats. In this system, the firewall rules are set only for where the system policies are needed. Dataset deployed in this system are derived from the testbed environment. The traffic as in DoS and PortScan traffics are applied in the testbed with firewall and IDS implementation. The network traffics are classified as normal or attacks in the existing testbed environment based on six machine learning classification methods applied in the system. It is required to be tested to get datasets and applied for DoS and PortScan. The dataset is based on CICIDS2017 and some features have been added. This system tested 26 features from the applied dataset. The system is to reduce false positive rates and to improve accuracy in the implemented testbed design. The system also proves good performance by selecting important features and comparing existing a dataset by machine learning classifiers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=false%20negative%20rate" title="false negative rate">false negative rate</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=machine%20learning%20methods" title=" machine learning methods"> machine learning methods</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a> </p> <a href="https://publications.waset.org/abstracts/133091/performance-analysis-of-traffic-classification-with-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133091.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">118</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">1142</span> Drone Classification Using Classification Methods Using Conventional Model With Embedded Audio-Visual Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hrishi%20Rakshit">Hrishi Rakshit</a>, <a href="https://publications.waset.org/abstracts/search?q=Pooneh%20Bagheri%20Zadeh"> Pooneh Bagheri Zadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper investigates the performance of drone classification methods using conventional DCNN with different hyperparameters, when additional drone audio data is embedded in the dataset for training and further classification. In this paper, first a custom dataset is created using different images of drones from University of South California (USC) datasets and Leeds Beckett university datasets with embedded drone audio signal. The three well-known DCNN architectures namely, Resnet50, Darknet53 and Shufflenet are employed over the created dataset tuning their hyperparameters such as, learning rates, maximum epochs, Mini Batch size with different optimizers. Precision-Recall curves and F1 Scores-Threshold curves are used to evaluate the performance of the named classification algorithms. Experimental results show that Resnet50 has the highest efficiency compared to other DCNN methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=drone%20classifications" title="drone classifications">drone classifications</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20convolutional%20neural%20network" title=" deep convolutional neural network"> deep convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperparameters" title=" hyperparameters"> hyperparameters</a>, <a href="https://publications.waset.org/abstracts/search?q=drone%20audio%20signal" title=" drone audio signal"> drone audio signal</a> </p> <a href="https://publications.waset.org/abstracts/172929/drone-classification-using-classification-methods-using-conventional-model-with-embedded-audio-visual-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/172929.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">104</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">1141</span> 2D Fingerprint Performance for PubChem Chemical Database</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatimah%20Zawani%20Abdullah">Fatimah Zawani Abdullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Shereena%20Mohd%20Arif"> Shereena Mohd Arif</a>, <a href="https://publications.waset.org/abstracts/search?q=Nurul%20Malim"> Nurul Malim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study of molecular similarity search in chemical database is increasingly widespread, especially in the area of drug discovery. Similarity search is an application in the field of Chemoinformatics to measure the similarity between the molecular structure which is known as the query and the structure of chemical compounds in the database. Similarity search is also one of the approaches in virtual screening which involves computational techniques and scoring the probabilities of activity. The main objective of this work is to determine the best fingerprint when compared to the other five fingerprints selected in this study using PubChem chemical dataset. This paper will discuss the similarity searching process conducted using 6 types of descriptors, which are ECFP4, ECFC4, FCFP4, FCFC4, SRECFC4 and SRFCFC4 on 15 activity classes of PubChem dataset using Tanimoto coefficient to calculate the similarity between the query structures and each of the database structure. The results suggest that ECFP4 performs the best to be used with Tanimoto coefficient in the PubChem dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=2D%20fingerprints" title="2D fingerprints">2D fingerprints</a>, <a href="https://publications.waset.org/abstracts/search?q=Tanimoto" title=" Tanimoto"> Tanimoto</a>, <a href="https://publications.waset.org/abstracts/search?q=PubChem" title=" PubChem"> PubChem</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity%20searching" title=" similarity searching"> similarity searching</a>, <a href="https://publications.waset.org/abstracts/search?q=chemoinformatics" title=" chemoinformatics"> chemoinformatics</a> </p> <a href="https://publications.waset.org/abstracts/15097/2d-fingerprint-performance-for-pubchem-chemical-database" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15097.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">293</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">1140</span> A Large Dataset Imputation Approach Applied to Country Conflict Prediction Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Benjamin%20Leiby">Benjamin Leiby</a>, <a href="https://publications.waset.org/abstracts/search?q=Darryl%20Ahner"> Darryl Ahner</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study demonstrates an alternative stochastic imputation approach for large datasets when preferred commercial packages struggle to iterate due to numerical problems. A large country conflict dataset motivates the search to impute missing values well over a common threshold of 20% missingness. The methodology capitalizes on correlation while using model residuals to provide the uncertainty in estimating unknown values. Examination of the methodology provides insight toward choosing linear or nonlinear modeling terms. Static tolerances common in most packages are replaced with tailorable tolerances that exploit residuals to fit each data element. The methodology evaluation includes observing computation time, model fit, and the comparison of known values to replaced values created through imputation. Overall, the country conflict dataset illustrates promise with modeling first-order interactions while presenting a need for further refinement that mimics predictive mean matching. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=correlation" title="correlation">correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=country%20conflict" title=" country conflict"> country conflict</a>, <a href="https://publications.waset.org/abstracts/search?q=imputation" title=" imputation"> imputation</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20regression" title=" stochastic regression"> stochastic regression</a> </p> <a href="https://publications.waset.org/abstracts/147858/a-large-dataset-imputation-approach-applied-to-country-conflict-prediction-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147858.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">120</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">1139</span> Fine Grained Action Recognition of Skateboarding Tricks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Frederik%20Calsius">Frederik Calsius</a>, <a href="https://publications.waset.org/abstracts/search?q=Mirela%20Popa"> Mirela Popa</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexia%20Briassouli"> Alexia Briassouli</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the field of machine learning, it is common practice to use benchmark datasets to prove the working of a method. The domain of action recognition in videos often uses datasets like Kinet-ics, Something-Something, UCF-101 and HMDB-51 to report results. Considering the properties of the datasets, there are no datasets that focus solely on very short clips (2 to 3 seconds), and on highly-similar fine-grained actions within one specific domain. This paper researches how current state-of-the-art action recognition methods perform on a dataset that consists of highly similar, fine-grained actions. To do so, a dataset of skateboarding tricks was created. The performed analysis highlights both benefits and limitations of state-of-the-art methods, while proposing future research directions in the activity recognition domain. The conducted research shows that the best results are obtained by fusing RGB data with OpenPose data for the Temporal Shift Module. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=activity%20recognition" title="activity recognition">activity recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=fused%20deep%20representations" title=" fused deep representations"> fused deep representations</a>, <a href="https://publications.waset.org/abstracts/search?q=fine-grained%20dataset" title=" fine-grained dataset"> fine-grained dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=temporal%20modeling" title=" temporal modeling"> temporal modeling</a> </p> <a href="https://publications.waset.org/abstracts/138954/fine-grained-action-recognition-of-skateboarding-tricks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138954.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">231</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">1138</span> Reducing the Imbalance Penalty Through Artificial Intelligence Methods Geothermal Production Forecasting: A Case Study for Turkey</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hayriye%20An%C4%B1l">Hayriye Anıl</a>, <a href="https://publications.waset.org/abstracts/search?q=G%C3%B6rkem%20Kar"> Görkem Kar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In addition to being rich in renewable energy resources, Turkey is one of the countries that promise potential in geothermal energy production with its high installed power, cheapness, and sustainability. Increasing imbalance penalties become an economic burden for organizations since geothermal generation plants cannot maintain the balance of supply and demand due to the inadequacy of the production forecasts given in the day-ahead market. A better production forecast reduces the imbalance penalties of market participants and provides a better imbalance in the day ahead market. In this study, using machine learning, deep learning, and, time series methods, the total generation of the power plants belonging to Zorlu Natural Electricity Generation, which has a high installed capacity in terms of geothermal, was estimated for the first one and two weeks of March, then the imbalance penalties were calculated with these estimates and compared with the real values. These modeling operations were carried out on two datasets, the basic dataset and the dataset created by extracting new features from this dataset with the feature engineering method. According to the results, Support Vector Regression from traditional machine learning models outperformed other models and exhibited the best performance. In addition, the estimation results in the feature engineering dataset showed lower error rates than the basic dataset. It has been concluded that the estimated imbalance penalty calculated for the selected organization is lower than the actual imbalance penalty, optimum and profitable accounts. <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=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20models" title=" time series models"> time series models</a>, <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=geothermal%20energy%20production%20forecasting" title=" geothermal energy production forecasting"> geothermal energy production forecasting</a> </p> <a href="https://publications.waset.org/abstracts/157107/reducing-the-imbalance-penalty-through-artificial-intelligence-methods-geothermal-production-forecasting-a-case-study-for-turkey" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157107.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">110</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">1137</span> Audit of TPS photon beam dataset for small field output factors using OSLDs against RPC standard dataset</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asad%20Yousuf">Asad Yousuf</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Purpose: The aim of the present study was to audit treatment planning system beam dataset for small field output factors against standard dataset produced by radiological physics center (RPC) from a multicenter study. Such data are crucial for validity of special techniques, i.e., IMRT or stereotactic radiosurgery. Materials/Method: In this study, multiple small field size output factor datasets were measured and calculated for 6 to 18 MV x-ray beams using the RPC recommend methods. These beam datasets were measured at 10 cm depth for 10 × 10 cm2 to 2 × 2 cm2 field sizes, defined by collimator jaws at 100 cm. The measurements were made with a Landauer’s nanoDot OSLDs whose volume is small enough to gather a full ionization reading even for the 1×1 cm2 field size. At our institute the beam data including output factors have been commissioned at 5 cm depth with an SAD setup. For comparison with the RPC data, the output factors were converted to an SSD setup using tissue phantom ratios. SSD setup also enables coverage of the ion chamber in 2×2 cm2 field size. The measured output factors were also compared with those calculated by Eclipse™ treatment planning software. Result: The measured and calculated output factors are in agreement with RPC dataset within 1% and 4% respectively. The large discrepancies in TPS reflect the increased challenge in converting measured data into a commissioned beam model for very small fields. Conclusion: OSLDs are simple, durable, and accurate tool to verify doses that delivered using small photon beam fields down to a 1x1 cm2 field sizes. The study emphasizes that the treatment planning system should always be evaluated for small field out factors for the accurate dose delivery in clinical setting. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=small%20field%20dosimetry" title="small field dosimetry">small field dosimetry</a>, <a href="https://publications.waset.org/abstracts/search?q=optically%20stimulated%20luminescence" title=" optically stimulated luminescence"> optically stimulated luminescence</a>, <a href="https://publications.waset.org/abstracts/search?q=audit%20treatment" title=" audit treatment"> audit treatment</a>, <a href="https://publications.waset.org/abstracts/search?q=radiological%20physics%20center" title=" radiological physics center"> radiological physics center</a> </p> <a href="https://publications.waset.org/abstracts/7998/audit-of-tps-photon-beam-dataset-for-small-field-output-factors-using-oslds-against-rpc-standard-dataset" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7998.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">327</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=BoT-IoT%20dataset&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=BoT-IoT%20dataset&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=BoT-IoT%20dataset&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=BoT-IoT%20dataset&page=5">5</a></li> 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