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model</title> <meta name="description" content="Search results for: xception model"> <meta name="keywords" content="xception model"> <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="xception model"> <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> 16796</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: xception model</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16796</span> SEMCPRA-Sar-Esembled Model for Climate Prediction in Remote Area</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kamalpreet%20Kaur">Kamalpreet Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Renu%20Dhir"> Renu Dhir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Climate prediction is an essential component of climate research, which helps evaluate possible effects on economies, communities, and ecosystems. Climate prediction involves short-term weather prediction, seasonal prediction, and long-term climate change prediction. Climate prediction can use the information gathered from satellites, ground-based stations, and ocean buoys, among other sources. The paper's four architectures, such as ResNet50, VGG19, Inception-v3, and Xception, have been combined using an ensemble approach for overall performance and robustness. An ensemble of different models makes a prediction, and the majority vote determines the final prediction. The various architectures such as ResNet50, VGG19, Inception-v3, and Xception efficiently classify the dataset RSI-CB256, which contains satellite images into cloudy and non-cloudy. The generated ensembled S-E model (Sar-ensembled model) provides an accuracy of 99.25%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=climate" title="climate">climate</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20images" title=" satellite images"> satellite images</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/178864/semcpra-sar-esembled-model-for-climate-prediction-in-remote-area" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/178864.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">74</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">16795</span> Brain Tumor Detection and Classification Using Pre-Trained Deep Learning Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aditya%20Karade">Aditya Karade</a>, <a href="https://publications.waset.org/abstracts/search?q=Sharada%20Falane"> Sharada Falane</a>, <a href="https://publications.waset.org/abstracts/search?q=Dhananjay%20Deshmukh"> Dhananjay Deshmukh</a>, <a href="https://publications.waset.org/abstracts/search?q=Vijaykumar%20Mantri"> Vijaykumar Mantri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Brain tumors pose a significant challenge in healthcare due to their complex nature and impact on patient outcomes. The application of deep learning (DL) algorithms in medical imaging have shown promise in accurate and efficient brain tumour detection. This paper explores the performance of various pre-trained DL models ResNet50, Xception, InceptionV3, EfficientNetB0, DenseNet121, NASNetMobile, VGG19, VGG16, and MobileNet on a brain tumour dataset sourced from Figshare. The dataset consists of MRI scans categorizing different types of brain tumours, including meningioma, pituitary, glioma, and no tumour. The study involves a comprehensive evaluation of these models’ accuracy and effectiveness in classifying brain tumour images. Data preprocessing, augmentation, and finetuning techniques are employed to optimize model performance. Among the evaluated deep learning models for brain tumour detection, ResNet50 emerges as the top performer with an accuracy of 98.86%. Following closely is Xception, exhibiting a strong accuracy of 97.33%. These models showcase robust capabilities in accurately classifying brain tumour images. On the other end of the spectrum, VGG16 trails with the lowest accuracy at 89.02%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=brain%20tumour" title="brain tumour">brain tumour</a>, <a href="https://publications.waset.org/abstracts/search?q=MRI%20image" title=" MRI image"> MRI image</a>, <a href="https://publications.waset.org/abstracts/search?q=detecting%20and%20classifying%20tumour" title=" detecting and classifying tumour"> detecting and classifying tumour</a>, <a href="https://publications.waset.org/abstracts/search?q=pre-trained%20models" title=" pre-trained models"> pre-trained models</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title=" image segmentation"> image segmentation</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/178879/brain-tumor-detection-and-classification-using-pre-trained-deep-learning-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/178879.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">74</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">16794</span> Deepfake Detection for Compressed Media</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sushil%20Kumar%20Gupta">Sushil Kumar Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Atharva%20Joshi"> Atharva Joshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ayush%20Sonawale"> Ayush Sonawale</a>, <a href="https://publications.waset.org/abstracts/search?q=Sachin%20Naik"> Sachin Naik</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajshree%20Khande"> Rajshree Khande</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The usage of artificially created videos and audio by deep learning is a major problem of the current media landscape, as it pursues the goal of misinformation and distrust. In conclusion, the objective of this work targets generating a reliable deepfake detection model using deep learning that will help detect forged videos accurately. In this work, CelebDF v1, one of the largest deepfake benchmark datasets in the literature, is adopted to train and test the proposed models. The data includes authentic and synthetic videos of high quality, therefore allowing an assessment of the model’s performance against realistic distortions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deepfake%20detection" title="deepfake detection">deepfake detection</a>, <a href="https://publications.waset.org/abstracts/search?q=CelebDF%20v1" title=" CelebDF v1"> CelebDF v1</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network%20%28CNN%29" title=" convolutional neural network (CNN)"> convolutional neural network (CNN)</a>, <a href="https://publications.waset.org/abstracts/search?q=xception%20model" title=" xception model"> xception model</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=media%20manipulation" title=" media manipulation"> media manipulation</a> </p> <a href="https://publications.waset.org/abstracts/194487/deepfake-detection-for-compressed-media" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194487.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">9</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">16793</span> Optimized Deep Learning-Based Facial Emotion Recognition System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Erick%20C.%20Valverde">Erick C. Valverde</a>, <a href="https://publications.waset.org/abstracts/search?q=Wansu%20Lim"> Wansu Lim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Facial emotion recognition (FER) system has been recently developed for more advanced computer vision applications. The ability to identify human emotions would enable smart healthcare facility to diagnose mental health illnesses (e.g., depression and stress) as well as better human social interactions with smart technologies. The FER system involves two steps: 1) face detection task and 2) facial emotion recognition task. It classifies the human expression in various categories such as angry, disgust, fear, happy, sad, surprise, and neutral. This system requires intensive research to address issues with human diversity, various unique human expressions, and variety of human facial features due to age differences. These issues generally affect the ability of the FER system to detect human emotions with high accuracy. Early stage of FER systems used simple supervised classification task algorithms like K-nearest neighbors (KNN) and artificial neural networks (ANN). These conventional FER systems have issues with low accuracy due to its inefficiency to extract significant features of several human emotions. To increase the accuracy of FER systems, deep learning (DL)-based methods, like convolutional neural networks (CNN), are proposed. These methods can find more complex features in the human face by means of the deeper connections within its architectures. However, the inference speed and computational costs of a DL-based FER system is often disregarded in exchange for higher accuracy results. To cope with this drawback, an optimized DL-based FER system is proposed in this study.An extreme version of Inception V3, known as Xception model, is leveraged by applying different network optimization methods. Specifically, network pruning and quantization are used to enable lower computational costs and reduce memory usage, respectively. To support low resource requirements, a 68-landmark face detector from Dlib is used in the early step of the FER system.Furthermore, a DL compiler is utilized to incorporate advanced optimization techniques to the Xception model to improve the inference speed of the FER system. In comparison to VGG-Net and ResNet50, the proposed optimized DL-based FER system experimentally demonstrates the objectives of the network optimization methods used. As a result, the proposed approach can be used to create an efficient and real-time FER 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=face%20detection" title=" face detection"> face detection</a>, <a href="https://publications.waset.org/abstracts/search?q=facial%20emotion%20recognition" title=" facial emotion recognition"> facial emotion recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=network%20optimization%20methods" title=" network optimization methods"> network optimization methods</a> </p> <a href="https://publications.waset.org/abstracts/147341/optimized-deep-learning-based-facial-emotion-recognition-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/147341.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">16792</span> Efficient Deep Neural Networks for Real-Time Strawberry Freshness Monitoring: A Transfer Learning Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mst.%20Tuhin%20Akter">Mst. Tuhin Akter</a>, <a href="https://publications.waset.org/abstracts/search?q=Sharun%20Akter%20Khushbu"> Sharun Akter Khushbu</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20M.%20Shaqib"> S. M. Shaqib</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A real-time system architecture is highly effective for monitoring and detecting various damaged products or fruits that may deteriorate over time or become infected with diseases. Deep learning models have proven to be effective in building such architectures. However, building a deep learning model from scratch is a time-consuming and costly process. A more efficient solution is to utilize deep neural network (DNN) based transfer learning models in the real-time monitoring architecture. This study focuses on using a novel strawberry dataset to develop effective transfer learning models for the proposed real-time monitoring system architecture, specifically for evaluating and detecting strawberry freshness. Several state-of-the-art transfer learning models were employed, and the best performing model was found to be Xception, demonstrating higher performance across evaluation metrics such as accuracy, recall, precision, and F1-score. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=strawberry%20freshness%20evaluation" title="strawberry freshness evaluation">strawberry freshness evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20network" title=" deep neural network"> deep neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20augmentation" title=" image augmentation"> image augmentation</a> </p> <a href="https://publications.waset.org/abstracts/177872/efficient-deep-neural-networks-for-real-time-strawberry-freshness-monitoring-a-transfer-learning-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/177872.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">90</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">16791</span> Computer Aided Analysis of Breast Based Diagnostic Problems from Mammograms Using Image Processing and Deep Learning Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Berkan%20Ural">Ali Berkan Ural</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the analysis, evaluation, and pre-diagnosis of early stage breast based diagnostic problems (breast cancer, nodulesorlumps) by Computer Aided Diagnosing (CAD) system from mammogram radiological images. According to the statistics, the time factor is crucial to discover the disease in the patient (especially in women) as possible as early and fast. In the study, a new algorithm is developed using advanced image processing and deep learning method to detect and classify the problem at earlystagewithmoreaccuracy. This system first works with image processing methods (Image acquisition, Noiseremoval, Region Growing Segmentation, Morphological Operations, Breast BorderExtraction, Advanced Segmentation, ObtainingRegion Of Interests (ROIs), etc.) and segments the area of interest of the breast and then analyzes these partly obtained area for cancer detection/lumps in order to diagnosis the disease. After segmentation, with using the Spectrogramimages, 5 different deep learning based methods (specified Convolutional Neural Network (CNN) basedAlexNet, ResNet50, VGG16, DenseNet, Xception) are applied to classify the breast based problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20aided%20diagnosis" title="computer aided diagnosis">computer aided diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title=" breast cancer"> breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=region%20growing" title=" region growing"> region growing</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/155700/computer-aided-analysis-of-breast-based-diagnostic-problems-from-mammograms-using-image-processing-and-deep-learning-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155700.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">95</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">16790</span> A New Nonlinear State-Space Model and Its Application</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdullah%20Eqal%20Al%20Mazrooei">Abdullah Eqal Al Mazrooei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, a new nonlinear model will be introduced. The model is in the state-space form. The nonlinearity of this model is in the state equation where the state vector is multiplied by its self. This technique makes our model generalizes many famous models as Lotka-Volterra model and Lorenz model which have many applications in the real life. We will apply our new model to estimate the wind speed by using a new nonlinear estimator which suitable to work with our model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20systems" title="nonlinear systems">nonlinear systems</a>, <a href="https://publications.waset.org/abstracts/search?q=state-space%20model" title=" state-space model"> state-space model</a>, <a href="https://publications.waset.org/abstracts/search?q=Kronecker%20product" title=" Kronecker product"> Kronecker product</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20estimator" title=" nonlinear estimator"> nonlinear estimator</a> </p> <a href="https://publications.waset.org/abstracts/34407/a-new-nonlinear-state-space-model-and-its-application" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34407.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">691</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">16789</span> Deep Learning Approach to Trademark Design Code Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Girish%20J.%20Showkatramani">Girish J. Showkatramani</a>, <a href="https://publications.waset.org/abstracts/search?q=Arthi%20M.%20Krishna"> Arthi M. Krishna</a>, <a href="https://publications.waset.org/abstracts/search?q=Sashi%20Nareddi"> Sashi Nareddi</a>, <a href="https://publications.waset.org/abstracts/search?q=Naresh%20Nula"> Naresh Nula</a>, <a href="https://publications.waset.org/abstracts/search?q=Aaron%20Pepe"> Aaron Pepe</a>, <a href="https://publications.waset.org/abstracts/search?q=Glen%20Brown"> Glen Brown</a>, <a href="https://publications.waset.org/abstracts/search?q=Greg%20Gabel"> Greg Gabel</a>, <a href="https://publications.waset.org/abstracts/search?q=Chris%20Doninger"> Chris Doninger</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Trademark examination and approval is a complex process that involves analysis and review of the design components of the marks such as the visual representation as well as the textual data associated with marks such as marks' description. Currently, the process of identifying marks with similar visual representation is done manually in United States Patent and Trademark Office (USPTO) and takes a considerable amount of time. Moreover, the accuracy of these searches depends heavily on the experts determining the trademark design codes used to catalog the visual design codes in the mark. In this study, we explore several methods to automate trademark design code classification. Based on recent successes of convolutional neural networks in image classification, we have used several different convolutional neural networks such as Google’s Inception v3, Inception-ResNet-v2, and Xception net. The study also looks into other techniques to augment the results from CNNs such as using Open Source Computer Vision Library (OpenCV) to pre-process the images. This paper reports the results of the various models trained on year of annotated trademark images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=trademark%20design%20code" title="trademark design code">trademark design code</a>, <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=trademark%20image%20classification" title=" trademark image classification"> trademark image classification</a>, <a href="https://publications.waset.org/abstracts/search?q=trademark%20image%20search" title=" trademark image search"> trademark image search</a>, <a href="https://publications.waset.org/abstracts/search?q=Inception-ResNet-v2" title=" Inception-ResNet-v2"> Inception-ResNet-v2</a> </p> <a href="https://publications.waset.org/abstracts/85337/deep-learning-approach-to-trademark-design-code-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85337.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">232</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">16788</span> Autism Disease Detection Using Transfer Learning Techniques: Performance Comparison between Central Processing Unit vs. Graphics Processing Unit Functions for Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mst%20Shapna%20Akter">Mst Shapna Akter</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossain%20Shahriar"> Hossain Shahriar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Neural network approaches are machine learning methods used in many domains, such as healthcare and cyber security. Neural networks are mostly known for dealing with image datasets. While training with the images, several fundamental mathematical operations are carried out in the Neural Network. The operation includes a number of algebraic and mathematical functions, including derivative, convolution, and matrix inversion and transposition. Such operations require higher processing power than is typically needed for computer usage. Central Processing Unit (CPU) is not appropriate for a large image size of the dataset as it is built with serial processing. While Graphics Processing Unit (GPU) has parallel processing capabilities and, therefore, has higher speed. This paper uses advanced Neural Network techniques such as VGG16, Resnet50, Densenet, Inceptionv3, Xception, Mobilenet, XGBOOST-VGG16, and our proposed models to compare CPU and GPU resources. A system for classifying autism disease using face images of an autistic and non-autistic child was used to compare performance during testing. We used evaluation matrices such as Accuracy, F1 score, Precision, Recall, and Execution time. It has been observed that GPU runs faster than the CPU in all tests performed. Moreover, the performance of the Neural Network models in terms of accuracy increases on GPU compared to CPU. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autism%20disease" title="autism disease">autism disease</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=CPU" title=" CPU"> CPU</a>, <a href="https://publications.waset.org/abstracts/search?q=GPU" title=" GPU"> GPU</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a> </p> <a href="https://publications.waset.org/abstracts/160218/autism-disease-detection-using-transfer-learning-techniques-performance-comparison-between-central-processing-unit-vs-graphics-processing-unit-functions-for-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160218.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">16787</span> Logistic Regression Model versus Additive Model for Recurrent Event Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Entisar%20A.%20Elgmati">Entisar A. Elgmati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recurrent infant diarrhea is studied using daily data collected in Salvador, Brazil over one year and three months. A logistic regression model is fitted instead of Aalen's additive model using the same covariates that were used in the analysis with the additive model. The model gives reasonably similar results to that using additive regression model. In addition, the problem with the estimated conditional probabilities not being constrained between zero and one in additive model is solved here. Also martingale residuals that have been used to judge the goodness of fit for the additive model are shown to be useful for judging the goodness of fit of the logistic model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=additive%20model" title="additive model">additive model</a>, <a href="https://publications.waset.org/abstracts/search?q=cumulative%20probabilities" title=" cumulative probabilities"> cumulative probabilities</a>, <a href="https://publications.waset.org/abstracts/search?q=infant%20diarrhoea" title=" infant diarrhoea"> infant diarrhoea</a>, <a href="https://publications.waset.org/abstracts/search?q=recurrent%20event" title=" recurrent event"> recurrent event</a> </p> <a href="https://publications.waset.org/abstracts/27829/logistic-regression-model-versus-additive-model-for-recurrent-event-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27829.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">635</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">16786</span> Mathematical Model to Quantify the Phenomenon of Democracy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mechlouch%20Ridha%20Fethi">Mechlouch Ridha Fethi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a recent mathematical model in political sciences concerning democracy. The model is represented by a logarithmic equation linking the Relative Index of Democracy (RID) to Participation Ratio (PR). Firstly the meanings of the different parameters of the model were presented; and the variation curve of the RID according to PR with different critical areas was discussed. Secondly, the model was applied to a virtual group where we show that the model can be applied depending on the gender. Thirdly, it was observed that the model can be extended to different language models of democracy and that little use to assess the state of democracy for some International organizations like UNO. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=democracy" title="democracy">democracy</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematic" title=" mathematic"> mathematic</a>, <a href="https://publications.waset.org/abstracts/search?q=modelization" title=" modelization"> modelization</a>, <a href="https://publications.waset.org/abstracts/search?q=quantification" title=" quantification"> quantification</a> </p> <a href="https://publications.waset.org/abstracts/61269/mathematical-model-to-quantify-the-phenomenon-of-democracy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61269.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">368</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16785</span> The Achievement Model of University Social Responsibility</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Le%20Kang">Le Kang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> On the research question of 'how to achieve USR', this contribution reflects the concept of university social responsibility, identify three achievement models of USR as the society - diversified model, the university-cooperation model, the government - compound model, also conduct a case study to explore characteristics of Chinese achievement model of USR. The contribution concludes with discussion of how the university, government and society balance demands and roles, make necessarily strategic adjustment and innovative approach to repair the shortcomings of each achievement model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=modern%20university" title="modern university">modern university</a>, <a href="https://publications.waset.org/abstracts/search?q=USR" title=" USR"> USR</a>, <a href="https://publications.waset.org/abstracts/search?q=achievement%20model" title=" achievement model"> achievement model</a>, <a href="https://publications.waset.org/abstracts/search?q=compound%20model" title=" compound model"> compound model</a> </p> <a href="https://publications.waset.org/abstracts/1884/the-achievement-model-of-university-social-responsibility" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1884.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">758</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">16784</span> Model Averaging for Poisson Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhou%20Jianhong">Zhou Jianhong </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Model averaging is a desirable approach to deal with model uncertainty, which, however, has rarely been explored for Poisson regression. In this paper, we propose a model averaging procedure based on an unbiased estimator of the expected Kullback-Leibler distance for the Poisson regression. Simulation study shows that the proposed model average estimator outperforms some other commonly used model selection and model average estimators in some situations. Our proposed methods are further applied to a real data example and the advantage of this method is demonstrated again. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20averaging" title="model averaging">model averaging</a>, <a href="https://publications.waset.org/abstracts/search?q=poission%20regression" title=" poission regression"> poission regression</a>, <a href="https://publications.waset.org/abstracts/search?q=Kullback-Leibler%20distance" title=" Kullback-Leibler distance"> Kullback-Leibler distance</a>, <a href="https://publications.waset.org/abstracts/search?q=statistics" title=" statistics"> statistics</a> </p> <a href="https://publications.waset.org/abstracts/5501/model-averaging-for-poisson-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5501.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">520</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">16783</span> Implementation and Validation of a Damage-Friction Constitutive Model for Concrete</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=L.%20Madouni">L. Madouni</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Ould%20Ouali"> M. Ould Ouali</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20E.%20Hannachi"> N. E. Hannachi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Two constitutive models for concrete are available in ABAQUS/Explicit, the Brittle Cracking Model and the Concrete Damaged Plasticity Model, and their suitability and limitations are well known. The aim of the present paper is to implement a damage-friction concrete constitutive model and to evaluate the performance of this model by comparing the predicted response with experimental data. The constitutive formulation of this material model is reviewed. In order to have consistent results, the parameter identification and calibration for the model have been performed. Several numerical simulations are presented in this paper, whose results allow for validating the capability of the proposed model for reproducing the typical nonlinear performances of concrete structures under different monotonic and cyclic load conditions. The results of the evaluation will be used for recommendations concerning the application and further improvements of the investigated model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abaqus" title="Abaqus">Abaqus</a>, <a href="https://publications.waset.org/abstracts/search?q=concrete" title=" concrete"> concrete</a>, <a href="https://publications.waset.org/abstracts/search?q=constitutive%20model" title=" constitutive model"> constitutive model</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20simulation" title=" numerical simulation"> numerical simulation</a> </p> <a href="https://publications.waset.org/abstracts/77318/implementation-and-validation-of-a-damage-friction-constitutive-model-for-concrete" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77318.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">364</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">16782</span> Model Driven Architecture Methodologies: A Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arslan%20Murtaza">Arslan Murtaza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Model Driven Architecture (MDA) is technique presented by OMG (Object Management Group) for software development in which different models are proposed and converted them into code. The main plan is to identify task by using PIM (Platform Independent Model) and transform it into PSM (Platform Specific Model) and then converted into code. In this review paper describes some challenges and issues that are faced in MDA, type and transformation of models (e.g. CIM, PIM and PSM), and evaluation of MDA-based methodologies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=OMG" title="OMG">OMG</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20driven%20rrchitecture%20%28MDA%29" title=" model driven rrchitecture (MDA)"> model driven rrchitecture (MDA)</a>, <a href="https://publications.waset.org/abstracts/search?q=computation%20independent%20model%20%28CIM%29" title=" computation independent model (CIM)"> computation independent model (CIM)</a>, <a href="https://publications.waset.org/abstracts/search?q=platform%20independent%20model%20%28PIM%29" title=" platform independent model (PIM)"> platform independent model (PIM)</a>, <a href="https://publications.waset.org/abstracts/search?q=platform%20specific%20model%28PSM%29" title=" platform specific model(PSM)"> platform specific model(PSM)</a>, <a href="https://publications.waset.org/abstracts/search?q=MDA-based%20methodologies" title=" MDA-based methodologies"> MDA-based methodologies</a> </p> <a href="https://publications.waset.org/abstracts/34919/model-driven-architecture-methodologies-a-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34919.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">458</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">16781</span> The Influence of the Concentration and Temperature on the Rheological Behavior of Carbonyl-Methylcellulose </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Rabhi">Mohamed Rabhi</a>, <a href="https://publications.waset.org/abstracts/search?q=Kouider%20Halim%20Benrahou"> Kouider Halim Benrahou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The rheological properties of the carbonyl-methylcellulose (CMC), of different concentrations (25000, 50000, 60000, 80000 and 100000 ppm) and different temperatures were studied. We found that the rheological behavior of all CMC solutions presents a pseudo-plastic behavior, it follows the model of Ostwald-de Waele. The objective of this work is the modeling of flow by the CMC Cross model. The Cross model gives us the variation of the viscosity according to the shear rate. This model allowed us to adjust more clearly the rheological characteristics of CMC solutions. A comparison between the Cross model and the model of Ostwald was made. Cross the model fitting parameters were determined by a numerical simulation to make an approach between the experimental curve and those given by the two models. Our study has shown that the model of Cross, describes well the flow of "CMC" for low concentrations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CMC" title="CMC">CMC</a>, <a href="https://publications.waset.org/abstracts/search?q=rheological%20modeling" title=" rheological modeling"> rheological modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=Ostwald%20model" title=" Ostwald model"> Ostwald model</a>, <a href="https://publications.waset.org/abstracts/search?q=cross%20model" title=" cross model"> cross model</a>, <a href="https://publications.waset.org/abstracts/search?q=viscosity" title=" viscosity"> viscosity</a> </p> <a href="https://publications.waset.org/abstracts/52311/the-influence-of-the-concentration-and-temperature-on-the-rheological-behavior-of-carbonyl-methylcellulose" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52311.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">405</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">16780</span> 3D Model of Rain-Wind Induced Vibration of Inclined Cable</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Viet-Hung%20Truong">Viet-Hung Truong</a>, <a href="https://publications.waset.org/abstracts/search?q=Seung-Eock%20Kim"> Seung-Eock Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rain–wind induced vibration of inclined cable is a special aerodynamic phenomenon because it is easily influenced by many factors, especially the distribution of rivulet and wind velocity. This paper proposes a new 3D model of inclined cable, based on single degree-of-freedom model. Aerodynamic forces are firstly established and verified with the existing results from a 2D model. The 3D model of inclined cable is developed. The 3D model is then applied to assess the effects of wind velocity distribution and the continuity of rivulets on the cable. Finally, an inclined cable model with small sag is investigated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D%20model" title="3D model">3D model</a>, <a href="https://publications.waset.org/abstracts/search?q=rain%20-%20wind%20induced%20vibration" title=" rain - wind induced vibration"> rain - wind induced vibration</a>, <a href="https://publications.waset.org/abstracts/search?q=rivulet" title=" rivulet"> rivulet</a>, <a href="https://publications.waset.org/abstracts/search?q=analytical%20model" title=" analytical model"> analytical model</a> </p> <a href="https://publications.waset.org/abstracts/24366/3d-model-of-rain-wind-induced-vibration-of-inclined-cable" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24366.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">489</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">16779</span> Identifying Model to Predict Deterioration of Water Mains Using Robust Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Go%20Bong%20Choi">Go Bong Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Shin%20Je%20Lee"> Shin Je Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Sung%20Jin%20Yoo"> Sung Jin Yoo</a>, <a href="https://publications.waset.org/abstracts/search?q=Gibaek%20Lee"> Gibaek Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Jong%20Min%20Lee"> Jong Min Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In South Korea, it is difficult to obtain data for statistical pipe assessment. In this paper, to address these issues, we find that various statistical model presented before is how data mixed with noise and are whether apply in South Korea. Three major type of model is studied and if data is presented in the paper, we add noise to data, which affects how model response changes. Moreover, we generate data from model in paper and analyse effect of noise. From this we can find robustness and applicability in Korea of each model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=proportional%20hazard%20model" title="proportional hazard model">proportional hazard model</a>, <a href="https://publications.waset.org/abstracts/search?q=survival%20model" title=" survival model"> survival model</a>, <a href="https://publications.waset.org/abstracts/search?q=water%20main%20deterioration" title=" water main deterioration"> water main deterioration</a>, <a href="https://publications.waset.org/abstracts/search?q=ecological%20sciences" title=" ecological sciences"> ecological sciences</a> </p> <a href="https://publications.waset.org/abstracts/3084/identifying-model-to-predict-deterioration-of-water-mains-using-robust-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3084.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">743</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">16778</span> Equivalent Circuit Model for the Eddy Current Damping with Frequency-Dependence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhiguo%20Shi">Zhiguo Shi</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheng%20Ning%20Loong"> Cheng Ning Loong</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiazeng%20Shan"> Jiazeng Shan</a>, <a href="https://publications.waset.org/abstracts/search?q=Weichao%20Wu">Weichao Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study proposes an equivalent circuit model to simulate the eddy current damping force with shaking table tests and finite element modeling. The model is firstly proposed and applied to a simple eddy current damper, which is modelled in ANSYS, indicating that the proposed model can simulate the eddy current damping force under different types of excitations. Then, a non-contact and friction-free eddy current damper is designed and tested, and the proposed model can reproduce the experimental observations. The excellent agreement between the simulated results and the experimental data validates the accuracy and reliability of the equivalent circuit model. Furthermore, a more complicated model is performed in ANSYS to verify the feasibility of the equivalent circuit model in complex eddy current damper, and the higher-order fractional model and viscous model are adopted for comparison. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=equivalent%20circuit%20model" title="equivalent circuit model">equivalent circuit model</a>, <a href="https://publications.waset.org/abstracts/search?q=eddy%20current%20damping" title=" eddy current damping"> eddy current damping</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20model" title=" finite element model"> finite element model</a>, <a href="https://publications.waset.org/abstracts/search?q=shake%20table%20test" title=" shake table test"> shake table test</a> </p> <a href="https://publications.waset.org/abstracts/119732/equivalent-circuit-model-for-the-eddy-current-damping-with-frequency-dependence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/119732.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">191</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">16777</span> The Extended Skew Gaussian Process for Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20T.%20Alodat">M. T. Alodat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a generalization to the Gaussian process regression(GPR) model called the extended skew Gaussian process for regression(ESGPr) model. The ESGPR model works better than the GPR model when the errors are skewed. We derive the predictive distribution for the ESGPR model at a new input. Also we apply the ESGPR model to FOREX data and we find that it fits the Forex data better than the GPR model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extended%20skew%20normal%20distribution" title="extended skew normal distribution">extended skew normal distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20for%20regression" title=" Gaussian process for regression"> Gaussian process for regression</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20distribution" title=" predictive distribution"> predictive distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=ESGPr%20model" title=" ESGPr model"> ESGPr model</a> </p> <a href="https://publications.waset.org/abstracts/2233/the-extended-skew-gaussian-process-for-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2233.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">553</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">16776</span> Camera Model Identification for Mi Pad 4, Oppo A37f, Samsung M20, and Oppo f9</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ulrich%20Wake">Ulrich Wake</a>, <a href="https://publications.waset.org/abstracts/search?q=Eniman%20Syamsuddin"> Eniman Syamsuddin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The model for camera model identificaiton is trained using pretrained model ResNet43 and ResNet50. The dataset consists of 500 photos of each phone. Dataset is divided into 1280 photos for training, 320 photos for validation and 400 photos for testing. The model is trained using One Cycle Policy Method and tested using Test-Time Augmentation. Furthermore, the model is trained for 50 epoch using regularization such as drop out and early stopping. The result is 90% accuracy for validation set and above 85% for Test-Time Augmentation using ResNet50. Every model is also trained by slightly updating the pretrained model’s weights <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=%E2%80%8B%20One%20Cycle%20Policy" title=" One Cycle Policy"> One Cycle Policy</a>, <a href="https://publications.waset.org/abstracts/search?q=ResNet34" title=" ResNet34"> ResNet34</a>, <a href="https://publications.waset.org/abstracts/search?q=ResNet50" title=" ResNet50"> ResNet50</a>, <a href="https://publications.waset.org/abstracts/search?q=Test-Time%20Agumentation" title=" Test-Time Agumentation"> Test-Time Agumentation</a> </p> <a href="https://publications.waset.org/abstracts/124445/camera-model-identification-for-mi-pad-4-oppo-a37f-samsung-m20-and-oppo-f9" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124445.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">208</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">16775</span> A Theoretical Hypothesis on Ferris Wheel Model of University Social Responsibility</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Le%20Kang">Le Kang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> According to the nature of the university, as a free and responsible academic community, USR is based on a different foundation —academic responsibility, so the Pyramid and the IC Model of CSR could not fully explain the most distinguished feature of USR. This paper sought to put forward a new model— Ferris Wheel Model, to illustrate the nature of USR and the process of achievement. The Ferris Wheel Model of USR shows the university creates a balanced, fairness and neutrality systemic structure to afford social responsibilities; that makes the organization could obtain a synergistic effect to achieve more extensive interests of stakeholders and wider social responsibilities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=USR" title="USR">USR</a>, <a href="https://publications.waset.org/abstracts/search?q=achievement%20model" title=" achievement model"> achievement model</a>, <a href="https://publications.waset.org/abstracts/search?q=ferris%20wheel%20model" title=" ferris wheel model"> ferris wheel model</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20responsibilities" title=" social responsibilities"> social responsibilities</a> </p> <a href="https://publications.waset.org/abstracts/29125/a-theoretical-hypothesis-on-ferris-wheel-model-of-university-social-responsibility" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29125.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">725</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">16774</span> Model Predictive Control of Three Phase Inverter for PV Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Irtaza%20M.%20Syed">Irtaza M. Syed</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaamran%20Raahemifar"> Kaamran Raahemifar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a model predictive control (MPC) of a utility interactive three phase inverter (TPI) for a photovoltaic (PV) system at commercial level. The proposed model uses phase locked loop (PLL) to synchronize TPI with the power electric grid (PEG) and performs MPC control in a dq reference frame. TPI model consists of boost converter (BC), maximum power point tracking (MPPT) control, and a three leg voltage source inverter (VSI). Operational model of VSI is used to synthesize sinusoidal current and track the reference. Model is validated using a 35.7 kW PV system in Matlab/Simulink. Implementation and results show simplicity and accuracy, as well as reliability of the model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20predictive%20control" title="model predictive control">model predictive control</a>, <a href="https://publications.waset.org/abstracts/search?q=three%20phase%20voltage%20source%20inverter" title=" three phase voltage source inverter"> three phase voltage source inverter</a>, <a href="https://publications.waset.org/abstracts/search?q=PV%20system" title=" PV system"> PV system</a>, <a href="https://publications.waset.org/abstracts/search?q=Matlab%2Fsimulink" title=" Matlab/simulink"> Matlab/simulink</a> </p> <a href="https://publications.waset.org/abstracts/40124/model-predictive-control-of-three-phase-inverter-for-pv-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40124.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">596</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">16773</span> Model Observability – A Monitoring Solution for Machine Learning Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amreth%20Chandrasehar">Amreth Chandrasehar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine Learning (ML) Models are developed and run in production to solve various use cases that help organizations to be more efficient and help drive the business. But this comes at a massive development cost and lost business opportunities. According to the Gartner report, 85% of data science projects fail, and one of the factors impacting this is not paying attention to Model Observability. Model Observability helps the developers and operators to pinpoint the model performance issues data drift and help identify root cause of issues. This paper focuses on providing insights into incorporating model observability in model development and operationalizing it in production. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model%20observability" title="model observability">model observability</a>, <a href="https://publications.waset.org/abstracts/search?q=monitoring" title=" monitoring"> monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=drift%20detection" title=" drift detection"> drift detection</a>, <a href="https://publications.waset.org/abstracts/search?q=ML%20observability%20platform" title=" ML observability platform"> ML observability platform</a> </p> <a href="https://publications.waset.org/abstracts/174152/model-observability-a-monitoring-solution-for-machine-learning-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174152.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">112</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">16772</span> All-or-None Principle and Weakness of Hodgkin-Huxley Mathematical Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20A.%20Sadegh%20Zadeh">S. A. Sadegh Zadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Kambhampati"> C. Kambhampati</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mathematical and computational modellings are the necessary tools for reviewing, analysing, and predicting processes and events in the wide spectrum range of scientific fields. Therefore, in a field as rapidly developing as neuroscience, the combination of these two modellings can have a significant role in helping to guide the direction the field takes. The paper combined mathematical and computational modelling to prove a weakness in a very precious model in neuroscience. This paper is intended to analyse all-or-none principle in Hodgkin-Huxley mathematical model. By implementation the computational model of Hodgkin-Huxley model and applying the concept of all-or-none principle, an investigation on this mathematical model has been performed. The results clearly showed that the mathematical model of Hodgkin-Huxley does not observe this fundamental law in neurophysiology to generating action potentials. This study shows that further mathematical studies on the Hodgkin-Huxley model are needed in order to create a model without this weakness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=all-or-none" title="all-or-none">all-or-none</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20modelling" title=" computational modelling"> computational modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20model" title=" mathematical model"> mathematical model</a>, <a href="https://publications.waset.org/abstracts/search?q=transmembrane%20voltage" title=" transmembrane voltage"> transmembrane voltage</a>, <a href="https://publications.waset.org/abstracts/search?q=action%20potential" title=" action potential"> action potential</a> </p> <a href="https://publications.waset.org/abstracts/80739/all-or-none-principle-and-weakness-of-hodgkin-huxley-mathematical-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80739.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">617</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">16771</span> Multiscale Modelling of Citrus Black Spot Transmission Dynamics along the Pre-Harvest Supply Chain</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muleya%20Nqobile">Muleya Nqobile</a>, <a href="https://publications.waset.org/abstracts/search?q=Winston%20Garira"> Winston Garira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We presented a compartmental deterministic multi-scale model which encompass internal plant defensive mechanism and pathogen interaction, then we consider nesting the model into the epidemiological model. The objective was to improve our understanding of the transmission dynamics of within host and between host of Guignardia citricapa Kiely. The inflow of infected class was scaled down to individual level while the outflow was scaled up to average population level. Conceptual model and mathematical model were constructed to display a theoretical framework which can be used for predicting or identify disease pattern. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=epidemiological%20model" title="epidemiological model">epidemiological model</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20modelling" title=" mathematical modelling"> mathematical modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-scale%20modelling" title=" multi-scale modelling"> multi-scale modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=immunological%20model" title=" immunological model"> immunological model</a> </p> <a href="https://publications.waset.org/abstracts/52713/multiscale-modelling-of-citrus-black-spot-transmission-dynamics-along-the-pre-harvest-supply-chain" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52713.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">459</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">16770</span> Proposal for a Generic Context Meta-Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jaouadi%20Imen">Jaouadi Imen</a>, <a href="https://publications.waset.org/abstracts/search?q=Ben%20Djemaa%20Raoudha"> Ben Djemaa Raoudha</a>, <a href="https://publications.waset.org/abstracts/search?q=Ben%20Abdallah%20Hanene"> Ben Abdallah Hanene</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The access to relevant information that is adapted to users’ needs, preferences and environment is a challenge in many applications running. That causes an appearance of context-aware systems. To facilitate the development of this class of applications, it is necessary that these applications share a common context meta-model. In this article, we will present our context meta-model that is defined using the OMG Meta Object facility (MOF). This meta-model is based on the analysis and synthesis of context concepts proposed in literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=context" title="context">context</a>, <a href="https://publications.waset.org/abstracts/search?q=meta-model" title=" meta-model"> meta-model</a>, <a href="https://publications.waset.org/abstracts/search?q=MOF" title=" MOF"> MOF</a>, <a href="https://publications.waset.org/abstracts/search?q=awareness%20system" title=" awareness system"> awareness system</a> </p> <a href="https://publications.waset.org/abstracts/1509/proposal-for-a-generic-context-meta-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1509.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">561</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">16769</span> Model of MSD Risk Assessment at Workplace</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20Sekulov%C3%A1">K. Sekulová</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20%C5%A0imon"> M. Šimon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article focuses on upper-extremity musculoskeletal disorders risk assessment model at workplace. In this model are used risk factors that are responsible for musculoskeletal system damage. Based on statistic calculations the model is able to define what risk of MSD threatens workers who are under risk factors. The model is also able to say how MSD risk would decrease if these risk factors are eliminated. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ergonomics" title="ergonomics">ergonomics</a>, <a href="https://publications.waset.org/abstracts/search?q=musculoskeletal%20disorders" title=" musculoskeletal disorders"> musculoskeletal disorders</a>, <a href="https://publications.waset.org/abstracts/search?q=occupational%20diseases" title=" occupational diseases"> occupational diseases</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20factors" title=" risk factors"> risk factors</a> </p> <a href="https://publications.waset.org/abstracts/13024/model-of-msd-risk-assessment-at-workplace" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13024.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">551</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">16768</span> Identification of Classes of Bilinear Time Series Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anthony%20Usoro">Anthony Usoro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, two classes of bilinear time series model are obtained under certain conditions from the general bilinear autoregressive moving average model. Bilinear Autoregressive (BAR) and Bilinear Moving Average (BMA) Models have been identified. From the general bilinear model, BAR and BMA models have been proved to exist for q = Q = 0, => j = 0, and p = P = 0, => i = 0 respectively. These models are found useful in modelling most of the economic and financial data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autoregressive%20model" title="autoregressive model">autoregressive model</a>, <a href="https://publications.waset.org/abstracts/search?q=bilinear%20autoregressive%20model" title=" bilinear autoregressive model"> bilinear autoregressive model</a>, <a href="https://publications.waset.org/abstracts/search?q=bilinear%20moving%20average%20model" title=" bilinear moving average model"> bilinear moving average model</a>, <a href="https://publications.waset.org/abstracts/search?q=moving%20average%20model" title=" moving average model"> moving average model</a> </p> <a href="https://publications.waset.org/abstracts/56430/identification-of-classes-of-bilinear-time-series-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56430.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">407</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">16767</span> A Nonlinear Visco-Hyper Elastic Constitutive Model for Modelling Behavior of Polyurea at Large Deformations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shank%20Kulkarni">Shank Kulkarni</a>, <a href="https://publications.waset.org/abstracts/search?q=Alireza%20Tabarraei"> Alireza Tabarraei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The fantastic properties of polyurea such as flexibility, durability, and chemical resistance have brought it a wide range of application in various industries. Effective prediction of the response of polyurea under different loading and environmental conditions necessitates the development of an accurate constitutive model. Similar to most polymers, the behavior of polyurea depends on both strain and strain rate. Therefore, the constitutive model should be able to capture both these effects on the response of polyurea. To achieve this objective, in this paper, a nonlinear hyper-viscoelastic constitutive model is developed by the superposition of a hyperelastic and a viscoelastic model. The proposed constitutive model can capture the behavior of polyurea under compressive loading conditions at various strain rates. Four parameter Ogden model and Mooney Rivlin model are used to modeling the hyperelastic behavior of polyurea. The viscoelastic behavior is modeled using both a three-parameter standard linear solid (SLS) model and a K-BKZ model. Comparison of the modeling results with experiments shows that Odgen and SLS model can more accurately predict the behavior of polyurea. The material parameters of the model are found by curve fitting of the proposed model to the uniaxial compression test data. The proposed model can closely reproduce the stress-strain behavior of polyurea for strain rates up to 6500 /s. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=constitutive%20modelling" title="constitutive modelling">constitutive modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=ogden%20model" title=" ogden model"> ogden model</a>, <a href="https://publications.waset.org/abstracts/search?q=polyurea" title=" polyurea"> polyurea</a>, <a href="https://publications.waset.org/abstracts/search?q=SLS%20model" title=" SLS model"> SLS model</a>, <a href="https://publications.waset.org/abstracts/search?q=uniaxial%20compression%20test" title=" uniaxial compression test"> uniaxial compression test</a> </p> <a href="https://publications.waset.org/abstracts/90268/a-nonlinear-visco-hyper-elastic-constitutive-model-for-modelling-behavior-of-polyurea-at-large-deformations" class="btn btn-primary btn-sm">Procedia</a> <a 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