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quality</title> <meta name="description" content="Search results for: dataset quality"> <meta name="keywords" content="dataset quality"> <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="dataset quality"> <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> 10755</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: dataset quality</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10755</span> Dataset Quality Index:Development of Composite Indicator Based on Standard Data Quality Indicators </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sakda%20Loetpiparwanich">Sakda Loetpiparwanich</a>, <a href="https://publications.waset.org/abstracts/search?q=Preecha%20Vichitthamaros"> Preecha Vichitthamaros</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, poor data quality is considered one of the majority costs for a data project. The data project with data quality awareness almost as much time to data quality processes while data project without data quality awareness negatively impacts financial resources, efficiency, productivity, and credibility. One of the processes that take a long time is defining the expectations and measurements of data quality because the expectation is different up to the purpose of each data project. Especially, big data project that maybe involves with many datasets and stakeholders, that take a long time to discuss and define quality expectations and measurements. Therefore, this study aimed at developing meaningful indicators to describe overall data quality for each dataset to quick comparison and priority. The objectives of this study were to: (1) Develop a practical data quality indicators and measurements, (2) Develop data quality dimensions based on statistical characteristics and (3) Develop Composite Indicator that can describe overall data quality for each dataset. The sample consisted of more than 500 datasets from public sources obtained by random sampling. After datasets were collected, there are five steps to develop the Dataset Quality Index (SDQI). First, we define standard data quality expectations. Second, we find any indicators that can measure directly to data within datasets. Thirdly, each indicator aggregates to dimension using factor analysis. Next, the indicators and dimensions were weighted by an effort for data preparing process and usability. Finally, the dimensions aggregate to Composite Indicator. The results of these analyses showed that: (1) The developed useful indicators and measurements contained ten indicators. (2) the developed data quality dimension based on statistical characteristics, we found that ten indicators can be reduced to 4 dimensions. (3) The developed Composite Indicator, we found that the SDQI can describe overall datasets quality of each dataset and can separate into 3 Level as Good Quality, Acceptable Quality, and Poor Quality. The conclusion, the SDQI provide an overall description of data quality within datasets and meaningful composition. We can use SQDI to assess for all data in the data project, effort estimation, and priority. The SDQI also work well with Agile Method by using SDQI to assessment in the first sprint. After passing the initial evaluation, we can add more specific data quality indicators into the next sprint. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20quality" title="data quality">data quality</a>, <a href="https://publications.waset.org/abstracts/search?q=dataset%20quality" title=" dataset quality"> dataset quality</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20quality%20management" title=" data quality management"> data quality management</a>, <a href="https://publications.waset.org/abstracts/search?q=composite%20indicator" title=" composite indicator"> composite indicator</a>, <a href="https://publications.waset.org/abstracts/search?q=factor%20analysis" title=" factor analysis"> factor analysis</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/111833/dataset-quality-indexdevelopment-of-composite-indicator-based-on-standard-data-quality-indicators" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/111833.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">10754</span> Generation of High-Quality Synthetic CT Images from Cone Beam CT Images Using A.I. Based Generative Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Heeba%20A.%20Gurku">Heeba A. Gurku</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Cone Beam CT(CBCT) images play an integral part in proper patient positioning in cancer patients undergoing radiation therapy treatment. But these images are low in quality. The purpose of this study is to generate high-quality synthetic CT images from CBCT using generative models. Material and Methods: This study utilized two datasets from The Cancer Imaging Archive (TCIA) 1) Lung cancer dataset of 20 patients (with full view CBCT images) and 2) Pancreatic cancer dataset of 40 patients (only 27 patients having limited view images were included in the study). Cycle Generative Adversarial Networks (GAN) and its variant Attention Guided Generative Adversarial Networks (AGGAN) models were used to generate the synthetic CTs. Models were evaluated by visual evaluation and on four metrics, Structural Similarity Index Measure (SSIM), Peak Signal Noise Ratio (PSNR) Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), to compare the synthetic CT and original CT images. Results: For pancreatic dataset with limited view CBCT images, our study showed that in Cycle GAN model, MAE, RMSE, PSNR improved from 12.57to 8.49, 20.94 to 15.29 and 21.85 to 24.63, respectively but structural similarity only marginally increased from 0.78 to 0.79. Similar, results were achieved with AGGAN with no improvement over Cycle GAN. However, for lung dataset with full view CBCT images Cycle GAN was able to reduce MAE significantly from 89.44 to 15.11 and AGGAN was able to reduce it to 19.77. Similarly, RMSE was also decreased from 92.68 to 23.50 in Cycle GAN and to 29.02 in AGGAN. SSIM and PSNR also improved significantly from 0.17 to 0.59 and from 8.81 to 21.06 in Cycle GAN respectively while in AGGAN SSIM increased to 0.52 and PSNR increased to 19.31. In both datasets, GAN models were able to reduce artifacts, reduce noise, have better resolution, and better contrast enhancement. Conclusion and Recommendation: Both Cycle GAN and AGGAN were significantly able to reduce MAE, RMSE and PSNR in both datasets. However, full view lung dataset showed more improvement in SSIM and image quality than limited view pancreatic dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CT%20images" title="CT images">CT images</a>, <a href="https://publications.waset.org/abstracts/search?q=CBCT%20images" title=" CBCT images"> CBCT images</a>, <a href="https://publications.waset.org/abstracts/search?q=cycle%20GAN" title=" cycle GAN"> cycle GAN</a>, <a href="https://publications.waset.org/abstracts/search?q=AGGAN" title=" AGGAN"> AGGAN</a> </p> <a href="https://publications.waset.org/abstracts/167226/generation-of-high-quality-synthetic-ct-images-from-cone-beam-ct-images-using-ai-based-generative-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167226.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">83</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">10753</span> Quality Analysis of Vegetables Through Image Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdul%20Khalique%20Baloch">Abdul Khalique Baloch</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Okatan"> Ali Okatan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The quality analysis of food and vegetable from image is hot topic now a day, where researchers make them better then pervious findings through different technique and methods. In this research we have review the literature, and find gape from them, and suggest better proposed approach, design the algorithm, developed a software to measure the quality from images, where accuracy of image show better results, and compare the results with Perouse work done so for. The Application we uses an open-source dataset and python language with tensor flow lite framework. In this research we focus to sort food and vegetable from image, in the images, the application can sorts and make them grading after process the images, it could create less errors them human base sorting errors by manual grading. Digital pictures datasets were created. The collected images arranged by classes. The classification accuracy of the system was about 94%. As fruits and vegetables play main role in day-to-day life, the quality of fruits and vegetables is necessary in evaluating agricultural produce, the customer always buy good quality fruits and vegetables. This document is about quality detection of fruit and vegetables using images. Most of customers suffering due to unhealthy foods and vegetables by suppliers, so there is no proper quality measurement level followed by hotel managements. it have developed software to measure the quality of the fruits and vegetables by using images, it will tell you how is your fruits and vegetables are fresh or rotten. Some algorithms reviewed in this thesis including digital images, ResNet, VGG16, CNN and Transfer Learning grading feature extraction. This application used an open source dataset of images and language used python, and designs a framework of system. <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=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>, <a href="https://publications.waset.org/abstracts/search?q=rotten%20fruit%20detection" title=" rotten fruit detection"> rotten fruit detection</a>, <a href="https://publications.waset.org/abstracts/search?q=fruits%20quality%20criteria" title=" fruits quality criteria"> fruits quality criteria</a>, <a href="https://publications.waset.org/abstracts/search?q=vegetables%20quality%20criteria" title=" vegetables quality criteria"> vegetables quality criteria</a> </p> <a href="https://publications.waset.org/abstracts/168045/quality-analysis-of-vegetables-through-image-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168045.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">70</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">10752</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">10751</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">10750</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">10749</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">10748</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">10747</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">10746</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">10745</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">10744</span> Deep Learning for Qualitative and Quantitative Grain Quality Analysis Using Hyperspectral Imaging</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ole-Christian%20Galbo%20Engstr%C3%B8m">Ole-Christian Galbo Engstrøm</a>, <a href="https://publications.waset.org/abstracts/search?q=Erik%20Schou%20Dreier"> Erik Schou Dreier</a>, <a href="https://publications.waset.org/abstracts/search?q=Birthe%20M%C3%B8ller%20Jespersen"> Birthe Møller Jespersen</a>, <a href="https://publications.waset.org/abstracts/search?q=Kim%20Steenstrup%20Pedersen"> Kim Steenstrup Pedersen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type. <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=grain%20analysis" title=" grain analysis"> grain analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=hyperspectral%20imaging" title=" hyperspectral imaging"> hyperspectral imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=preprocessing%20techniques" title=" preprocessing techniques"> preprocessing techniques</a> </p> <a href="https://publications.waset.org/abstracts/157339/deep-learning-for-qualitative-and-quantitative-grain-quality-analysis-using-hyperspectral-imaging" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157339.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">99</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">10743</span> Drinking Water Quality Assessment Using Fuzzy Inference System Method: A Case Study of Rome, Italy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yas%20Barzegar">Yas Barzegar</a>, <a href="https://publications.waset.org/abstracts/search?q=Atrin%20Barzegar"> Atrin Barzegar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Drinking water quality assessment is a major issue today; technology and practices are continuously improving; Artificial Intelligence (AI) methods prove their efficiency in this domain. The current research seeks a hierarchical fuzzy model for predicting drinking water quality in Rome (Italy). The Mamdani fuzzy inference system (FIS) is applied with different defuzzification methods. The Proposed Model includes three fuzzy intermediate models and one fuzzy final model. Each fuzzy model consists of three input parameters and 27 fuzzy rules. The model is developed for water quality assessment with a dataset considering nine parameters (Alkalinity, Hardness, pH, Ca, Mg, Fluoride, Sulphate, Nitrates, and Iron). Fuzzy-logic-based methods have been demonstrated to be appropriate to address uncertainty and subjectivity in drinking water quality assessment; it is an effective method for managing complicated, uncertain water systems and predicting drinking water quality. The FIS method can provide an effective solution to complex systems; this method can be modified easily to improve performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=water%20quality" title="water quality">water quality</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=smart%20cities" title=" smart cities"> smart cities</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20attribute" title=" water attribute"> water attribute</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20inference%20system" title=" fuzzy inference system"> fuzzy inference system</a>, <a href="https://publications.waset.org/abstracts/search?q=membership%20function" title=" membership function"> membership function</a> </p> <a href="https://publications.waset.org/abstracts/170172/drinking-water-quality-assessment-using-fuzzy-inference-system-method-a-case-study-of-rome-italy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170172.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">10742</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">10741</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">10740</span> Quality and Quality Assurance in Education: Examining the Possible Relationship</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rodoula%20Stavroula%20Gkarnara">Rodoula Stavroula Gkarnara</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikolaos%20Andreadakis"> Nikolaos Andreadakis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this paper is to examine the relationship between quality and quality assurance in education. It constitutes a critical review of the bibliography regarding quality and its delimitation in the field of education, as well as the quality assurance in education and the approaches identified for its extensive study. The two prevailing and opposite views on the correlation of the two concepts are that on the one hand there is an inherent distance between these concepts as they are two separate terms and on the other hand they are interrelated and interdependent concepts that contribute to the improvement of quality in education. Finally, the last part of the paper, adopting the second view, refers to the contribution of quality assurance to quality, where it is pointed out that the first concept leads to the improvement of the latter by quality assurance being the means of feedback for the quality achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=education" title="education">education</a>, <a href="https://publications.waset.org/abstracts/search?q=quality" title=" quality"> quality</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20assurance" title=" quality assurance"> quality assurance</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20improvement" title=" quality improvement"> quality improvement</a> </p> <a href="https://publications.waset.org/abstracts/107623/quality-and-quality-assurance-in-education-examining-the-possible-relationship" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/107623.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">216</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">10739</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">10738</span> Assessing Bus Service Quality in Dhaka City from the Perspective of Female Passengers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20K.%20Subah">S. K. Subah</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Tasnim"> R. Tasnim</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20I.%20Jahan"> M. I. Jahan</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20R.%20Islam"> M. R. Islam </a> </p> <p class="card-text"><strong>Abstract:</strong></p> While talking about how comfortable and convenient Dhaka's bus service is, the minimum emphasis is placed on the female commuters of the Dhaka city. Recognizing the contemporary situation, the supreme focus is to develop experimental model based on statistical methods. SEM has been adopted to quantify passenger satisfaction, which is affected by the perceived service quality. The study deals with 16 observed variables and three latent variables, which were correlated to identify their significance on the regulation of perceived SQ (Service Quality). To calibrate the model, a dataset of 250 responses from female users of local buses has been utilized through survey. A questionnaire structured with SQ variables was prepared in consultation with prevailing literature, practitioners, academicians, and users. The result concludes that the attributes of safe and secured environment have the most significant impact on the overall bus service quality according to the insight of female respondents. The study outcome might be a great help for the policymakers, women's organizations, and NGOs to formulate transport policy that will ensure a women-friendly public bus service. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bus%20service%20quality" title="bus service quality">bus service quality</a>, <a href="https://publications.waset.org/abstracts/search?q=female%20perception" title=" female perception"> female perception</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20equation%20modelling" title=" structural equation modelling"> structural equation modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=safety-security" title=" safety-security"> safety-security</a>, <a href="https://publications.waset.org/abstracts/search?q=women%20friendly%20bus" title=" women friendly bus"> women friendly bus</a> </p> <a href="https://publications.waset.org/abstracts/133406/assessing-bus-service-quality-in-dhaka-city-from-the-perspective-of-female-passengers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/133406.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">157</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">10737</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">10736</span> Evaluating Surface Water Quality Using WQI, Trend Analysis, and Cluster Classification in Kebir Rhumel Basin, Algeria</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lazhar%20Belkhiri">Lazhar Belkhiri</a>, <a href="https://publications.waset.org/abstracts/search?q=Ammar%20Tiri"> Ammar Tiri</a>, <a href="https://publications.waset.org/abstracts/search?q=Lotfi%20Mouni"> Lotfi Mouni</a>, <a href="https://publications.waset.org/abstracts/search?q=Fatma%20Elhadj%20Lakouas"> Fatma Elhadj Lakouas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study evaluates the surface water quality in the Kebir Rhumel Basin by analyzing hydrochemical parameters. To assess spatial and temporal variations in water quality, we applied the Water Quality Index (WQI), Mann-Kendall (MK) trend analysis, and hierarchical cluster analysis (HCA). Monthly measurements of eleven hydrochemical parameters were collected across eight stations from January 2016 to December 2020. Calcium and sulfate emerged as the dominant cation and anion, respectively. WQI analysis indicated a high incidence of poor water quality at stations Ain Smara (AS), Beni Haroune (BH), Grarem (GR), and Sidi Khalifa (SK), where 89.5%, 90.6%, 78.2%, and 62.7% of samples, respectively, fell into this category. The MK trend analysis revealed a significant upward trend in WQI at Oued Boumerzoug (ON) and SK stations, signaling temporal deterioration in these areas. HCA grouped the dataset into three clusters, covering approximately 22%, 30%, and 48% of the months, respectively. Within these clusters, specific stations exhibited elevated WQI values: GR and ON in the first cluster, OB and SK in the second, and AS, BH, El Milia (EM), and Hammam Grouz (HG) in the third. Furthermore, approximately 38%, 41%, and 38% of samples in clusters one, two, and three, respectively, were classified as having poor water quality. These findings provide essential insights for policymakers in formulating strategies to restore and manage surface water quality in the region. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=surface%20water%20quality" title="surface water quality">surface water quality</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20quality%20index%20%28WQI%29" title=" water quality index (WQI)"> water quality index (WQI)</a>, <a href="https://publications.waset.org/abstracts/search?q=Mann-Kendall%20Trend%20Analysis" title=" Mann-Kendall Trend Analysis"> Mann-Kendall Trend Analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=hierarchical%20cluster%20analysis%20%28HCA%29" title=" hierarchical cluster analysis (HCA)"> hierarchical cluster analysis (HCA)</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial-temporal%20distribution" title=" spatial-temporal distribution"> spatial-temporal distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=Kebir%20Rhumel%20Basin" title=" Kebir Rhumel Basin"> Kebir Rhumel Basin</a> </p> <a href="https://publications.waset.org/abstracts/193200/evaluating-surface-water-quality-using-wqi-trend-analysis-and-cluster-classification-in-kebir-rhumel-basin-algeria" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193200.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">16</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">10735</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">10734</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">10733</span> Evaluation of E-Government Service Quality</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nguyen%20Manh%20Hien">Nguyen Manh Hien</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Service quality is the highest requirement from users, especially for the service in electronic government. During the past decades, it has become a major area of academic investigation. Considering this issue, there are many researches that evaluated the dimensions and e-service contexts. This study also identified the dimensions of service quality but focused on a new conceptual and provides a new methodological in developing measurement scales of e-service quality such as information quality, service quality and organization quality. Finally, the study will suggest a key factor to evaluate e-government service quality better. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dimensionality" title="dimensionality">dimensionality</a>, <a href="https://publications.waset.org/abstracts/search?q=e-government" title=" e-government"> e-government</a>, <a href="https://publications.waset.org/abstracts/search?q=e-service" title=" e-service"> e-service</a>, <a href="https://publications.waset.org/abstracts/search?q=e-service%20quality" title=" e-service quality"> e-service quality</a> </p> <a href="https://publications.waset.org/abstracts/2685/evaluation-of-e-government-service-quality" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2685.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">541</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">10732</span> Management as a Proxy for Firm Quality</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Petar%20Dobrev">Petar Dobrev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There is no agreed-upon definition of firm quality. While profitability and stock performance often qualify as popular proxies of quality, in this project, we aim to identify quality without relying on a firm’s financial statements or stock returns as selection criteria. Instead, we use firm-level data on management practices across small to medium-sized U.S. manufacturing firms from the World Management Survey (WMS) to measure firm quality. Each firm in the WMS dataset is assigned a mean management score from 0 to 5, with higher scores identifying better-managed firms. This management score serves as our proxy for firm quality and is the sole criteria we use to separate firms into portfolios comprised of high-quality and low-quality firms. We define high-quality (low-quality) firms as those firms with a management score of one standard deviation above (below) the mean. To study whether this proxy for firm quality can identify better-performing firms, we link this data to Compustat and The Center for Research in Security Prices (CRSP) to obtain firm-level data on financial performance and monthly stock returns, respectively. We find that from 1999 to 2019 (our sample data period), firms in the high-quality portfolio are consistently more profitable — higher operating profitability and return on equity compared to low-quality firms. In addition, high-quality firms also exhibit a lower risk of bankruptcy — a higher Altman Z-score. Next, we test whether the stocks of the firms in the high-quality portfolio earn superior risk-adjusted excess returns. We regress the monthly excess returns on each portfolio on the Fama-French 3-factor, 4-factor, and 5-factor models, the betting-against-beta factor, and the quality-minus-junk factor. We find no statistically significant differences in excess returns between both portfolios, suggesting that stocks of high-quality (well managed) firms do not earn superior risk-adjusted returns compared to low-quality (poorly managed) firms. In short, our proxy for firm quality, the WMS management score, can identify firms with superior financial performance (higher profitability and reduced risk of bankruptcy). However, our management proxy cannot identify stocks that earn superior risk-adjusted returns, suggesting no statistically significant relationship between managerial quality and stock performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=excess%20stock%20returns" title="excess stock returns">excess stock returns</a>, <a href="https://publications.waset.org/abstracts/search?q=management" title=" management"> management</a>, <a href="https://publications.waset.org/abstracts/search?q=profitability" title=" profitability"> profitability</a>, <a href="https://publications.waset.org/abstracts/search?q=quality" title=" quality"> quality</a> </p> <a href="https://publications.waset.org/abstracts/150268/management-as-a-proxy-for-firm-quality" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150268.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">93</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10731</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">10730</span> Time Series Forecasting (TSF) Using Various Deep Learning Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jimeng%20Shi">Jimeng Shi</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahek%20Jain"> Mahek Jain</a>, <a href="https://publications.waset.org/abstracts/search?q=Giri%20Narasimhan"> Giri Narasimhan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed-length window in the past as an explicit input. In this paper, we study how the performance of predictive models changes as a function of different look-back window sizes and different amounts of time to predict the future. We also consider the performance of the recent attention-based Transformer models, which have had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (RNN, LSTM, GRU, and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the UCI website, which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean Average Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=air%20quality%20prediction" title="air quality prediction">air quality prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning%20algorithms" title=" deep learning algorithms"> deep learning algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series%20forecasting" title=" time series forecasting"> time series forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=look-back%20window" title=" look-back window"> look-back window</a> </p> <a href="https://publications.waset.org/abstracts/146879/time-series-forecasting-tsf-using-various-deep-learning-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146879.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">154</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10729</span> Multivariate Analysis on Water Quality Attributes Using Master-Slave Neural Network Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Clementking">A. Clementking</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Jothi%20Venkateswaran"> C. Jothi Venkateswaran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mathematical and computational functionalities such as descriptive mining, optimization, and predictions are espoused to resolve natural resource planning. The water quality prediction and its attributes influence determinations are adopted optimization techniques. The water properties are tainted while merging water resource one with another. This work aimed to predict influencing water resource distribution connectivity in accordance to water quality and sediment using an innovative proposed master-slave neural network back-propagation model. The experiment results are arrived through collecting water quality attributes, computation of water quality index, design and development of neural network model to determine water quality and sediment, master–slave back propagation neural network back-propagation model to determine variations on water quality and sediment attributes between the water resources and the recommendation for connectivity. The homogeneous and parallel biochemical reactions are influences water quality and sediment while distributing water from one location to another. Therefore, an innovative master-slave neural network model [M (9:9:2)::S(9:9:2)] designed and developed to predict the attribute variations. The result of training dataset given as an input to master model and its maximum weights are assigned as an input to the slave model to predict the water quality. The developed master-slave model is predicted physicochemical attributes weight variations for 85 % to 90% of water quality as a target values.The sediment level variations also predicated from 0.01 to 0.05% of each water quality percentage. The model produced the significant variations on physiochemical attribute weights. According to the predicated experimental weight variation on training data set, effective recommendations are made to connect different resources. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=master-slave%20back%20propagation%20neural%20network%20model%28MSBPNNM%29" title="master-slave back propagation neural network model(MSBPNNM)">master-slave back propagation neural network model(MSBPNNM)</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20quality%20analysis" title=" water quality analysis"> water quality analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=multivariate%20analysis" title=" multivariate analysis"> multivariate analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=environmental%20mining" title=" environmental mining"> environmental mining</a> </p> <a href="https://publications.waset.org/abstracts/31405/multivariate-analysis-on-water-quality-attributes-using-master-slave-neural-network-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31405.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">477</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">10728</span> Software Quality Measurement System for Telecommunication Industry in Malaysia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nor%20Fazlina%20Iryani%20Abdul%20Hamid">Nor Fazlina Iryani Abdul Hamid</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamad%20Khatim%20Hasan"> Mohamad Khatim Hasan </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Evolution of software quality measurement has been started since McCall introduced his quality model in year 1977. Starting from there, several software quality models and software quality measurement methods had emerged but none of them focused on telecommunication industry. In this paper, the implementation of software quality measurement system for telecommunication industry was compulsory to accommodate the rapid growth of telecommunication industry. The quality value of the telecommunication related software could be calculated using this system by entering the required parameters. The system would calculate the quality value of the measured system based on predefined quality metrics and aggregated by referring to the quality model. It would classify the quality level of the software based on Net Satisfaction Index (NSI). Thus, software quality measurement system was important to both developers and users in order to produce high quality software product for telecommunication industry. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=software%20quality" title="software quality">software quality</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20measurement" title=" quality measurement"> quality measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20model" title=" quality model"> quality model</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20metric" title=" quality metric"> quality metric</a>, <a href="https://publications.waset.org/abstracts/search?q=net%20satisfaction%20index" title=" net satisfaction index"> net satisfaction index</a> </p> <a href="https://publications.waset.org/abstracts/15875/software-quality-measurement-system-for-telecommunication-industry-in-malaysia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15875.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">592</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">10727</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">10726</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> <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=dataset%20quality&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=dataset%20quality&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=dataset%20quality&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=dataset%20quality&page=5">5</a></li> <li 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