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dataset</title> <meta name="description" content="Search results for: mixed dataset"> <meta name="keywords" content="mixed dataset"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="mixed dataset"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 3932</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: mixed dataset</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3932</span> PatchMix: Learning Transferable Semi-Supervised Representation by Predicting Patches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arpit%20Rai">Arpit Rai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we propose PatchMix, a semi-supervised method for pre-training visual representations. PatchMix mixes patches of two images and then solves an auxiliary task of predicting the label of each patch in the mixed image. Our experiments on the CIFAR-10, 100 and the SVHN dataset show that the representations learned by this method encodes useful information for transfer to new tasks and outperform the baseline Residual Network encoders by on CIFAR 10 by 12% on ResNet 101 and 2% on ResNet-56, by 4% on CIFAR-100 on ResNet101 and by 6% on SVHN dataset on the ResNet-101 baseline model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=self-supervised%20learning" title="self-supervised learning">self-supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=representation%20learning" title=" representation learning"> representation 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=generalization" title=" generalization"> generalization</a> </p> <a href="https://publications.waset.org/abstracts/150013/patchmix-learning-transferable-semi-supervised-representation-by-predicting-patches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150013.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">89</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">3931</span> Mixed Number Algebra and Its Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Md.%20Shah%20Alam">Md. Shah Alam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mushfiq Ahmad has defined a Mixed Number, which is the sum of a scalar and a Cartesian vector. He has also defined the elementary group operations of Mixed numbers i.e. the norm of Mixed numbers, the product of two Mixed numbers, the identity element and the inverse. It has been observed that Mixed Number is consistent with Pauli matrix algebra and a handy tool to work with Dirac electron theory. Its use as a mathematical method in Physics has been studied. (1) We have applied Mixed number in Quantum Mechanics: Mixed Number version of Displacement operator, Vector differential operator, and Angular momentum operator has been developed. Mixed Number method has also been applied to Klein-Gordon equation. (2) We have applied Mixed number in Electrodynamics: Mixed Number version of Maxwell’s equation, the Electric and Magnetic field quantities and Lorentz Force has been found. (3) An associative transformation of Mixed Number numbers fulfilling Lorentz invariance requirement is developed. (4) We have applied Mixed number algebra as an extension of Complex number. Mixed numbers and the Quaternions have isomorphic correspondence, but they are different in algebraic details. The multiplication of unit Mixed number and the multiplication of unit Quaternions are different. Since Mixed Number has properties similar to those of Pauli matrix algebra, Mixed Number algebra is a more convenient tool to deal with Dirac equation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mixed%20number" title="mixed number">mixed number</a>, <a href="https://publications.waset.org/abstracts/search?q=special%20relativity" title=" special relativity"> special relativity</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20mechanics" title=" quantum mechanics"> quantum mechanics</a>, <a href="https://publications.waset.org/abstracts/search?q=electrodynamics" title=" electrodynamics"> electrodynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=pauli%20matrix" title=" pauli matrix"> pauli matrix</a> </p> <a href="https://publications.waset.org/abstracts/39999/mixed-number-algebra-and-its-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39999.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">363</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">3930</span> Reliability Prediction of Tires Using Linear Mixed-Effects Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Myung%20Hwan%20Na">Myung Hwan Na</a>, <a href="https://publications.waset.org/abstracts/search?q=Ho-%20Chun%20Song"> Ho- Chun Song</a>, <a href="https://publications.waset.org/abstracts/search?q=EunHee%20Hong"> EunHee Hong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We widely use normal linear mixed-effects model to analysis data in repeated measurement. In case of detecting heteroscedasticity and the non-normality of the population distribution at the same time, normal linear mixed-effects model can give improper result of analysis. To achieve more robust estimation, we use heavy tailed linear mixed-effects model which gives more exact and reliable analysis conclusion than standard normal linear mixed-effects model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=reliability" title="reliability">reliability</a>, <a href="https://publications.waset.org/abstracts/search?q=tires" title=" tires"> tires</a>, <a href="https://publications.waset.org/abstracts/search?q=field%20data" title=" field data"> field data</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20mixed-effects%20model" title=" linear mixed-effects model"> linear mixed-effects model</a> </p> <a href="https://publications.waset.org/abstracts/37815/reliability-prediction-of-tires-using-linear-mixed-effects-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37815.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">563</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">3929</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">172</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">3928</span> Corrosion of Fe-(9~37) Wt%Cr Alloys at 700-800 °C in N₂-H₂O-H₂S Mixed Gas</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Min%20Jung%20Kim">Min Jung Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fe-(9, 19, 28, 37) wt%Cr alloys were corroded at 700 and 800 °C for 70 h under 1 atm of N₂, 1 atm of N₂/3.2%H₂O-mixed gas, and 1 atm of N₂/3.1%H₂O/2.42%H₂S-mixed gas. The corrosion rate of Fe-9Cr alloy increased with the addition of H₂O and increased further with the addition of H₂S in N₂/H₂O gas. Fe-9Cr alloy was non-protective in all gas types. In contrast, Fe-(19, 28, 37) wt%Cr alloys were protective in N₂ and N₂/H₂O-mixed gas because of the formation of the Cr₂O₃ layer. They were, however, non-protective in N₂/H₂O/H₂S-mixed gas because sulfidation dominated, forming the outer FeS layer and the inner Cr₂S₃ layer containing some FeCr₂S₄. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fe-%289" title="Fe-(9">Fe-(9</a>, <a href="https://publications.waset.org/abstracts/search?q=19" title=" 19"> 19</a>, <a href="https://publications.waset.org/abstracts/search?q=28" title=" 28"> 28</a>, <a href="https://publications.waset.org/abstracts/search?q=37%29%20wt%25Cr%20alloys" title=" 37) wt%Cr alloys"> 37) wt%Cr alloys</a>, <a href="https://publications.waset.org/abstracts/search?q=corrosion" title=" corrosion"> corrosion</a>, <a href="https://publications.waset.org/abstracts/search?q=sulfidation" title=" sulfidation"> sulfidation</a>, <a href="https://publications.waset.org/abstracts/search?q=FeS" title=" FeS"> FeS</a> </p> <a href="https://publications.waset.org/abstracts/50849/corrosion-of-fe-937-wtcr-alloys-at-700-800-c-in-n2-h2o-h2s-mixed-gas" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50849.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">432</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3927</span> Mixed-ownership Reform and Quality of Internal Control of State-owned Enterprises: Logic and Evidence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mao%20Ju">Mao Ju</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As a capital organizing form, the mixed-ownership reform of state-owned enterprises (SOEs) is an important way to stimulate enterprises’ vitality through reshaping the shareholding structure, enhancing mutual complementation of shareholders’ resources, and improving corporate governance and the quality of internal control. Based on the process of mixed-ownership reform and according to IPO and the change in the key shareholding structure of the listed companies, this paper divides the reform into two stages: primary mixed-ownership reform and secondary mixed-ownership reform (deeper mixed-ownership reform), and uses this as the basis to construct the proxy variable of the mixed-ownership reform of SOEs, research on the relationship between the mixed-ownership reform of SOEs and the quality of internal control. The research reveals that: (1) SOEs completing a secondary mixed-ownership reform can enhance the quality of internal control; (2) In the secondary mixed-ownership reform, the introduction of heterogeneous major shareholders will generate more obvious enhancement in the quality of internal control than the introduction of homogeneous major shareholders. Further research shows that the internal environment and marketization process play a moderating role in the process of the secondary mixed-ownership reform affecting the quality of internal control, that is, a better internal environment or a higher degree of marketization can promote the improvement of the quality of internal control in secondary mixed-ownership reform. The conclusion of the research provides experimental evidence for the expected results of the mixed-ownership reform policy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mixed-ownership%20reform%20of%20state-owned%20enterprises" title="mixed-ownership reform of state-owned enterprises">mixed-ownership reform of state-owned enterprises</a>, <a href="https://publications.waset.org/abstracts/search?q=secondary%20mixed-ownership%20reform" title=" secondary mixed-ownership reform"> secondary mixed-ownership reform</a>, <a href="https://publications.waset.org/abstracts/search?q=quality%20of%20internal%20control" title=" quality of internal control"> quality of internal control</a>, <a href="https://publications.waset.org/abstracts/search?q=primary%20mixed-ownership%20reform" title=" primary mixed-ownership reform"> primary mixed-ownership reform</a> </p> <a href="https://publications.waset.org/abstracts/192530/mixed-ownership-reform-and-quality-of-internal-control-of-state-owned-enterprises-logic-and-evidence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192530.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">20</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">3926</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">129</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">3925</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">181</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">3924</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">136</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3923</span> Different Formula of Mixed Bacteria as a Bio-Treatment for Sewage Wastewater</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=E.%20Marei">E. Marei</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Hammad"> A. Hammad</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Ismail"> S. Ismail</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20El-Gindy"> A. El-Gindy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aims to investigate the ability of different formula of mixed bacteria as a biological treatments of wastewater after primary treatment as a bio-treatment and bio-removal and bio-adsorbent of different heavy metals in natural circumstances. The wastewater was collected from Sarpium forest site-Ismailia Governorate, Egypt. These treatments were mixture of free cells and mixture of immobilized cells of different bacteria. These different formulas of mixed bacteria were prepared under Lab. condition. The obtained data indicated that, as a result of wastewater bio-treatment, the removal rate was found to be 76.92 and 76.70% for biological oxygen demand, 79.78 and 71.07% for chemical oxygen demand, 32.45 and 36.84 % for ammonia nitrogen as well as 91.67 and 50.0% for phosphate after 24 and 28 hrs with mixed free cells and mixed immobilized cells, respectively. Moreover, the bio-removals of different heavy metals were found to reach 90.0 and 50. 0% for Cu ion, 98.0 and 98.5% for Fe ion, 97.0 and 99.3% for Mn ion, 90.0 and 90.0% Pb, 80.0% and 75.0% for Zn ion after 24 and 28 hrs with mixed free cells and mixed immobilized cells, respectively. The results indicated that 13.86 and 17.43% of removal efficiency and reduction of total dissolved solids were achieved after 24 and 28 hrs with mixed free cells and mixed immobilized cells, respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wastewater%20%20bio-treatment" title="wastewater bio-treatment ">wastewater bio-treatment </a>, <a href="https://publications.waset.org/abstracts/search?q=bio-sorption%20heavy%20metals" title=" bio-sorption heavy metals"> bio-sorption heavy metals</a>, <a href="https://publications.waset.org/abstracts/search?q=biological%20desalination" title=" biological desalination"> biological desalination</a>, <a href="https://publications.waset.org/abstracts/search?q=immobilized%20bacteria" title=" immobilized bacteria"> immobilized bacteria</a>, <a href="https://publications.waset.org/abstracts/search?q=free%20cell%20bacteria" title=" free cell bacteria"> free cell bacteria</a> </p> <a href="https://publications.waset.org/abstracts/88568/different-formula-of-mixed-bacteria-as-a-bio-treatment-for-sewage-wastewater" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/88568.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">201</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">3922</span> Numerical Analysis of Laminar Mixed Convection within a Complex Geometry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Y.%20Lasbet">Y. Lasbet</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20L.%20Boukhalkhal"> A. L. Boukhalkhal</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Loubar"> K. Loubar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The study of mixed convection is, usually, focused on the straight channels in which the onset of the mixed convection is well defined as function of the ratio between Grashof number and Reynolds number, Gr/Re. This is not the case for a complex channel wherein the mixed convection is not sufficiently examined in the literature. Our paper focuses on the study of the mixed convection in a complex geometry in which our main contribution reveals that the critical value of the ratio Gr/Re for the onset of the mixed convection increases highly in the type of geometry contrary to the straight channel. Furthermore, the accentuated secondary flow in this geometry prevents the thermal stratification in the flow and consequently the buoyancy driven becomes negligible. To perform these objectives, a numerical study in complex geometry for several values of the ratio Gr/Re with prescribed wall heat flux (H2), was realized by using the CFD code. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=complex%20geometry" title="complex geometry">complex geometry</a>, <a href="https://publications.waset.org/abstracts/search?q=heat%20transfer" title=" heat transfer"> heat transfer</a>, <a href="https://publications.waset.org/abstracts/search?q=laminar%20flow" title=" laminar flow"> laminar flow</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20convection" title=" mixed convection"> mixed convection</a>, <a href="https://publications.waset.org/abstracts/search?q=Nusselt%20number" title=" Nusselt number"> Nusselt number</a> </p> <a href="https://publications.waset.org/abstracts/35925/numerical-analysis-of-laminar-mixed-convection-within-a-complex-geometry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35925.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">493</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">3921</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">3920</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">3919</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">3918</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">3917</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">3916</span> Hydrothermal Synthesis of ZIF-7 Crystals and Their Composite ZIF-7/CS Membranes for Water/Ethanol Separation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kai-Sheng%20Ji">Kai-Sheng Ji</a>, <a href="https://publications.waset.org/abstracts/search?q=Yi-Feng%20Lin"> Yi-Feng Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The pervaporation process for solvent and water separation has attracted research attention due to its lower energy consumption compared with conventional distillation processes. The membranes used for the pervaporation approach should exhibit high flux and separation factors. In this study, the ZIF-7 crystal particles were successfully incorporated into chitosan (CS) membranes to form ZIF-7/CS mixed-matrix membranes. The as-prepared ZIF-7/CS mixed-matrix membranes were used to separate mixtures of water/ethanol at 25℃ in the pervaporation process. The mixed-matrix membranes with different ZIF-7 wt% incorporation showed better separation efficiency than the pristine CS membranes because of the smaller pore size of the mixed-matrix membranes. The separation factor and the flux of the ZIF-7/CS membranes clearly exceed the upper limit of the previously reported CS-based and mixed-matrix membranes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pervaporation" title="pervaporation">pervaporation</a>, <a href="https://publications.waset.org/abstracts/search?q=chitosan" title=" chitosan"> chitosan</a>, <a href="https://publications.waset.org/abstracts/search?q=ZIF-7" title=" ZIF-7"> ZIF-7</a>, <a href="https://publications.waset.org/abstracts/search?q=memberane%20separation" title=" memberane separation"> memberane separation</a> </p> <a href="https://publications.waset.org/abstracts/21520/hydrothermal-synthesis-of-zif-7-crystals-and-their-composite-zif-7cs-membranes-for-waterethanol-separation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21520.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">430</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3915</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">3914</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">3913</span> Land Equivalent Ration of Chickpea - Barley as Affected by Mixed Cropping System and Vermicompost in Water Stress Condition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Masoud%20Rafiee">Masoud Rafiee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Study of the effect of vermin compost on yield, and Land equivalent ration (LER) of chickpea-barley mixed cropping under normal dry land condition can be useful in order to increase qualitative and quantitative performance. In this case, two factors include fertilizer (vermicompost biological fertilizer, ammonium phosphate chemical fertilizer, vermicompost + %75 chemical fertilizer) and chickpea + barley mixed cropping (sole chickpea, %75 chickpea: %25 barley, %50 chickpea: %50 barley, %25 chickpea: %75 barley, and sole barley) in RCBD in three replications in two experiments include normal and dry land conditions were studied. Result showed that total LER base on dry matter was affected by environment and mixed cropping interaction and was more than 1 in all mixed cropping treatments. In different mixed cropping rates, wet forage yield decreased by decreasing chickpea ratio as well as increasing barley ratio. Total LER mean in base on forage dry matter in mixed-, chemical-, and vermicompost fertilizer treatments were 1.12, 1.05 and 1.10 in normal condition and 1.15, 1.08 and 1.14 in dry land condition, respectively, represented the important of biological fertilizer in mixed cropping systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=land%20equivalent%20ration" title="land equivalent ration">land equivalent ration</a>, <a href="https://publications.waset.org/abstracts/search?q=biological%20fertilizer" title=" biological fertilizer"> biological fertilizer</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20cropping%20systems" title=" mixed cropping systems"> mixed cropping systems</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20stress" title=" water stress"> water stress</a> </p> <a href="https://publications.waset.org/abstracts/37487/land-equivalent-ration-of-chickpea-barley-as-affected-by-mixed-cropping-system-and-vermicompost-in-water-stress-condition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37487.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">311</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">3912</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">3911</span> One Period Loops of Memristive Circuits with Mixed-Mode Oscillations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wieslaw%20Marszalek">Wieslaw Marszalek</a>, <a href="https://publications.waset.org/abstracts/search?q=Zdzislaw%20Trzaska"> Zdzislaw Trzaska</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Interesting properties of various one-period loops of singularly perturbed memristive circuits with mixed-mode oscillations (MMOs) are analyzed in this paper. The analysis is mixed, both analytical and numerical and focused on the properties of pinched hysteresis of the memristive element and other one-period loops formed by pairs of time-series solutions for various circuits' variables. The memristive element is the only nonlinear element in the two circuits. A theorem on periods of mixed-mode oscillations of the circuits is formulated and proved. Replacements of memristors by parallel G-C or series R-L circuits for a MMO response with equivalent RMS values is also discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mixed-mode%20oscillations" title="mixed-mode oscillations">mixed-mode oscillations</a>, <a href="https://publications.waset.org/abstracts/search?q=memristive%20circuits" title=" memristive circuits"> memristive circuits</a>, <a href="https://publications.waset.org/abstracts/search?q=pinched%20hysteresis" title=" pinched hysteresis"> pinched hysteresis</a>, <a href="https://publications.waset.org/abstracts/search?q=one-period%20loops" title=" one-period loops"> one-period loops</a>, <a href="https://publications.waset.org/abstracts/search?q=singularly%20perturbed%20circuits" title=" singularly perturbed circuits"> singularly perturbed circuits</a> </p> <a href="https://publications.waset.org/abstracts/20949/one-period-loops-of-memristive-circuits-with-mixed-mode-oscillations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20949.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">470</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">3910</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">3909</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">3908</span> Mixed Sub-Fractional Brownian Motion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mounir%20Zili">Mounir Zili</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We will introduce a new extension of the Brownian motion, that could serve to get a good model of many natural phenomena. It is a linear combination of a finite number of sub-fractional Brownian motions; that is why we will call it the mixed sub-fractional Brownian motion. We will present some basic properties of this process. Among others, we will check that our process is non-Markovian and that it has non-stationary increments. We will also give the conditions under which it is a semimartingale. Finally, the main features of its sample paths will be specified. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mixed%20Gaussian%20processes" title="mixed Gaussian processes">mixed Gaussian processes</a>, <a href="https://publications.waset.org/abstracts/search?q=Sub-fractional%20Brownian%20motion" title=" Sub-fractional Brownian motion"> Sub-fractional Brownian motion</a>, <a href="https://publications.waset.org/abstracts/search?q=sample%20paths" title=" sample paths"> sample paths</a> </p> <a href="https://publications.waset.org/abstracts/32479/mixed-sub-fractional-brownian-motion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32479.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">488</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">3907</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">138</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">3906</span> Data Mining Approach: Classification Model Evaluation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lubabatu%20Sada%20Sodangi">Lubabatu Sada Sodangi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The rapid growth in exchange and accessibility of information via the internet makes many organisations acquire data on their own operation. The aim of data mining is to analyse the different behaviour of a dataset using observation. Although, the subset of the dataset being analysed may not display all the behaviours and relationships of the entire data and, therefore, may not represent other parts that exist in the dataset. There is a range of techniques used in data mining to determine the hidden or unknown information in datasets. In this paper, the performance of two algorithms Chi-Square Automatic Interaction Detection (CHAID) and multilayer perceptron (MLP) would be matched using an Adult dataset to find out the percentage of an/the adults that earn > 50k and those that earn <= 50k per year. The two algorithms were studied and compared using IBM SPSS statistics software. The result for CHAID shows that the most important predictors are relationship and education. The algorithm shows that those are married (husband) and have qualification: Bachelor, Masters, Doctorate or Prof-school whose their age is > 41<57 earn > 50k. Also, multilayer perceptron displays marital status and capital gain as the most important predictors of the income. It also shows that individuals that their capital gain is less than 6,849 and are single, separated or widow, earn <= 50K, whereas individuals with their capital gain is > 6,849, work > 35 hrs/wk, and > 27yrs their income will be > 50k. By comparing the two algorithms, it is observed that both algorithms are reliable but there is strong reliability in CHAID which clearly shows that relation and education contribute to the prediction as displayed in the data visualisation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=CHAID" title=" CHAID"> CHAID</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-layer%20perceptron" title=" multi-layer perceptron"> multi-layer perceptron</a>, <a href="https://publications.waset.org/abstracts/search?q=SPSS" title=" SPSS"> SPSS</a>, <a href="https://publications.waset.org/abstracts/search?q=Adult%20dataset" title=" Adult dataset"> Adult dataset</a> </p> <a href="https://publications.waset.org/abstracts/49909/data-mining-approach-classification-model-evaluation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49909.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">378</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3905</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">3904</span> Insecticidal Activity of Piper aduncum Fruit and Tephrosia vogelii Leaf Mixed Formulations against Cabbage Pest Plutella xylostella (L.) (Lepidoptera: Plutellidae) </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eka%20Candra%20Lina">Eka Candra Lina</a>, <a href="https://publications.waset.org/abstracts/search?q=Indah%20Widhianingrum"> Indah Widhianingrum</a>, <a href="https://publications.waset.org/abstracts/search?q=Mita%20Eka%20Putri"> Mita Eka Putri</a>, <a href="https://publications.waset.org/abstracts/search?q=Nur%20Afni%20Evalia"> Nur Afni Evalia</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Makky"> Muhammad Makky</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The emulsifiable concentrate (EC) and wettable powder (WP) of Piper aduncum and Tephrosia vogelii mixed formulations were tested for their activities in the laboratory and their effectiveness in the field against cabbage pest Plutella xyostella. Cabbage leaves soaked in six different mixed formulation concentrations were tested to 2ⁿᵈ instar larvae of P. xylostella with six replications. The observation was conducted everyday until larvae reached 4ᵗʰ instar stage. Correlation between concentration and larvae mortality was analyzed using probit (POLO-PC). The survived larvae was observed by looking at the growth and development, as well as the antifeedant effects. Field efficacy test was based on LC₉₅ value from laboratory test result. The experiment used a randomized block design with 5 treatments and 3 replications to test the populations of P. xylostella larvae and insecticide effectivity. The results showed that the EC and WP mixed formulations showed insecticidal activity against P. xylostella larvae, with LC₉₅ value of 0.35% and 0.37%, respectively. The highest antifeedant effect on EC mixed formulation was 85.01% and WP mixed formulation was 86.23%. Both mixed formulations also slowed the development of larvae when compared with control. Field effication result showed that applications of EC mixed formulation were able to restrain the population of P. xylostella, with effectivity value of 71.06%. Insecticide effectivity value of EC mixed formulation was higher than WP mixed formulation and Bacillus thuringiensis formulation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=botanical%20insecticide" title="botanical insecticide">botanical insecticide</a>, <a href="https://publications.waset.org/abstracts/search?q=efficacy" title=" efficacy"> efficacy</a>, <a href="https://publications.waset.org/abstracts/search?q=emulsifiable%20concentrate%20%28EC%29" title=" emulsifiable concentrate (EC)"> emulsifiable concentrate (EC)</a>, <a href="https://publications.waset.org/abstracts/search?q=Plutella%20xylostella" title=" Plutella xylostella"> Plutella xylostella</a>, <a href="https://publications.waset.org/abstracts/search?q=wettable%20powder%20%28WP%29" title=" wettable powder (WP)"> wettable powder (WP)</a> </p> <a href="https://publications.waset.org/abstracts/86856/insecticidal-activity-of-piper-aduncum-fruit-and-tephrosia-vogelii-leaf-mixed-formulations-against-cabbage-pest-plutella-xylostella-l-lepidoptera-plutellidae" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86856.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">242</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">3903</span> Mixed-Sub Fractional Brownian Motion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mounir%20Zili">Mounir Zili</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We will introduce a new extension of the Brownian motion, that could serve to get a good model of many natural phenomena. It is a linear combination of a finite number of sub-fractional Brownian motions; that is why we will call it the mixed sub-fractional Brownian motion. We will present some basic properties of this process. Among others, we will check that our process is non-markovian and that it has non-stationary increments. We will also give the conditions under which it is a semi-martingale. Finally, the main features of its sample paths will be specified. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fractal%20dimensions" title="fractal dimensions">fractal dimensions</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed%20gaussian%20processes" title=" mixed gaussian processes"> mixed gaussian processes</a>, <a href="https://publications.waset.org/abstracts/search?q=sample%20paths" title=" sample paths"> sample paths</a>, <a href="https://publications.waset.org/abstracts/search?q=sub-fractional%20brownian%20motion" title=" sub-fractional brownian motion "> sub-fractional brownian motion </a> </p> <a href="https://publications.waset.org/abstracts/36677/mixed-sub-fractional-brownian-motion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36677.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">420</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=mixed%20dataset&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=mixed%20dataset&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=mixed%20dataset&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=mixed%20dataset&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=mixed%20dataset&page=6">6</a></li> <li class="page-item"><a class="page-link" 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