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learning</title> <meta name="description" content="Search results for: extreme learning"> <meta name="keywords" content="extreme learning"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="extreme learning"> <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> 8019</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: extreme learning</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8019</span> Orthogonal Basis Extreme Learning Algorithm and Function Approximation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ying%20Li">Ying Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan%20Li"> Yan Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A new algorithm for single hidden layer feedforward neural networks (SLFN), Orthogonal Basis Extreme Learning (OBEL) algorithm, is proposed and the algorithm derivation is given in the paper. The algorithm can decide both the NNs parameters and the neuron number of hidden layer(s) during training while providing extreme fast learning speed. It will provide a practical way to develop NNs. The simulation results of function approximation showed that the algorithm is effective and feasible with good accuracy and adaptability. <p class="card-text"><strong>Keywords:</strong> <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=orthogonal%20basis%20extreme%20learning" title=" orthogonal basis extreme learning"> orthogonal basis extreme learning</a>, <a href="https://publications.waset.org/abstracts/search?q=function%20approximation" title=" function approximation"> function approximation</a> </p> <a href="https://publications.waset.org/abstracts/15129/orthogonal-basis-extreme-learning-algorithm-and-function-approximation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15129.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">534</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">8018</span> A Machine Learning-Based Approach to Capture Extreme Rainfall Events</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Willy%20Mbenza">Willy Mbenza</a>, <a href="https://publications.waset.org/abstracts/search?q=Sho%20Kenjiro"> Sho Kenjiro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Increasing efforts are directed towards a better understanding and foreknowledge of extreme precipitation likelihood, given the adverse effects associated with their occurrence. This knowledge plays a crucial role in long-term planning and the formulation of effective emergency response. However, predicting extreme events reliably presents a challenge to conventional empirical/statistics due to the involvement of numerous variables spanning different time and space scales. In the recent time, Machine Learning has emerged as a promising tool for predicting the dynamics of extreme precipitation. ML techniques enables the consideration of both local and regional physical variables that have a strong influence on the likelihood of extreme precipitation. These variables encompasses factors such as air temperature, soil moisture, specific humidity, aerosol concentration, among others. In this study, we develop an ML model that incorporates both local and regional variables while establishing a robust relationship between physical variables and precipitation during the downscaling process. Furthermore, the model provides valuable information on the frequency and duration of a given intensity of precipitation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20%28ML%29" title="machine learning (ML)">machine learning (ML)</a>, <a href="https://publications.waset.org/abstracts/search?q=predictions" title=" predictions"> predictions</a>, <a href="https://publications.waset.org/abstracts/search?q=rainfall%20events" title=" rainfall events"> rainfall events</a>, <a href="https://publications.waset.org/abstracts/search?q=regional%20variables" title=" regional variables"> regional variables</a> </p> <a href="https://publications.waset.org/abstracts/168878/a-machine-learning-based-approach-to-capture-extreme-rainfall-events" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168878.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">8017</span> 3D Printing Perceptual Models of Preference Using a Fuzzy Extreme Learning Machine Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xinyi%20Le">Xinyi Le</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, 3D printing orientations were determined through our perceptual model. Some FDM (Fused Deposition Modeling) 3D printers, which are widely used in universities and industries, often require support structures during the additive manufacturing. After removing the residual material, some surface artifacts remain at the contact points. These artifacts will damage the function and visual effect of the model. To prevent the impact of these artifacts, we present a fuzzy extreme learning machine approach to find printing directions that avoid placing supports in perceptually significant regions. The proposed approach is able to solve the evaluation problem by combing both the subjective knowledge and objective information. Our method combines the advantages of fuzzy theory, auto-encoders, and extreme learning machine. Fuzzy set theory is applied for dealing with subjective preference information, and auto-encoder step is used to extract good features without supervised labels before extreme learning machine. An extreme learning machine method is then developed successfully for training and learning perceptual models. The performance of this perceptual model will be demonstrated on both natural and man-made objects. It is a good human-computer interaction practice which draws from supporting knowledge on both the machine side and the human side. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3d%20printing" title="3d printing">3d printing</a>, <a href="https://publications.waset.org/abstracts/search?q=perceptual%20model" title=" perceptual model"> perceptual model</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20evaluation" title=" fuzzy evaluation"> fuzzy evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=data-driven%20approach" title=" data-driven approach"> data-driven approach</a> </p> <a href="https://publications.waset.org/abstracts/67233/3d-printing-perceptual-models-of-preference-using-a-fuzzy-extreme-learning-machine-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67233.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">438</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">8016</span> Applying the Extreme-Based Teaching Model in Post-Secondary Online Classroom Setting: A Field Experiment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Leon%20Pan">Leon Pan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The first programming course within post-secondary education has long been recognized as a challenging endeavor for both educators and students alike. Historically, these courses have exhibited high failure rates and a notable number of dropouts. Instructors often lament students' lack of effort in their coursework, and students often express frustration that the teaching methods employed are not effective. Drawing inspiration from the successful principles of Extreme Programming, this study introduces an approach—the Extremes-based teaching model — aimed at enhancing the teaching of introductory programming courses. To empirically determine the effectiveness of the model, a comparison was made between a section taught using the extreme-based model and another utilizing traditional teaching methods. Notably, the extreme-based teaching class required students to work collaboratively on projects while also demanding continuous assessment and performance enhancement within groups. This paper details the application of the extreme-based model within the post-secondary online classroom context and presents the compelling results that emphasize its effectiveness in advancing the teaching and learning experiences. The extreme-based model led to a significant increase of 13.46 points in the weighted total average and a commendable 10% reduction in the failure rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme-based%20teaching%20model" title="extreme-based teaching model">extreme-based teaching model</a>, <a href="https://publications.waset.org/abstracts/search?q=innovative%20pedagogical%20methods" title=" innovative pedagogical methods"> innovative pedagogical methods</a>, <a href="https://publications.waset.org/abstracts/search?q=project-based%20learning" title=" project-based learning"> project-based learning</a>, <a href="https://publications.waset.org/abstracts/search?q=team-based%20learning" title=" team-based learning"> team-based learning</a> </p> <a href="https://publications.waset.org/abstracts/171936/applying-the-extreme-based-teaching-model-in-post-secondary-online-classroom-setting-a-field-experiment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171936.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">59</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">8015</span> Structural Reliability Analysis Using Extreme Learning Machine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mehul%20Srivastava">Mehul Srivastava</a>, <a href="https://publications.waset.org/abstracts/search?q=Sharma%20Tushar%20Ravikant"> Sharma Tushar Ravikant</a>, <a href="https://publications.waset.org/abstracts/search?q=Mridul%20Krishn%20Mishra"> Mridul Krishn Mishra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In structural design, the evaluation of safety and probability failure of structure is of significant importance, mainly when the variables are random. On real structures, structural reliability can be evaluated obtaining an implicit limit state function. The structural reliability limit state function is obtained depending upon the statistically independent variables. In the analysis of reliability, we considered the statistically independent random variables to be the load intensity applied and the depth or height of the beam member considered. There are many approaches for structural reliability problems. In this paper Extreme Learning Machine technique and First Order Second Moment Method is used to determine the reliability indices for the same set of variables. The reliability index obtained using ELM is compared with the reliability index obtained using FOSM. Higher the reliability index, more feasible is the method to determine the reliability. <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=reliability%20index" title=" reliability index"> reliability index</a>, <a href="https://publications.waset.org/abstracts/search?q=statistically%20independent" title=" statistically independent"> statistically independent</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20learning%20machine" title=" extreme learning machine"> extreme learning machine</a> </p> <a href="https://publications.waset.org/abstracts/21683/structural-reliability-analysis-using-extreme-learning-machine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21683.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">682</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">8014</span> Breast Cancer Diagnosing Based on Online Sequential Extreme Learning Machine Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Musatafa%20Abbas%20Abbood%20Albadr">Musatafa Abbas Abbood Albadr</a>, <a href="https://publications.waset.org/abstracts/search?q=Masri%20Ayob"> Masri Ayob</a>, <a href="https://publications.waset.org/abstracts/search?q=Sabrina%20Tiun"> Sabrina Tiun</a>, <a href="https://publications.waset.org/abstracts/search?q=Fahad%20Taha%20Al-Dhief"> Fahad Taha Al-Dhief</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Kamrul%20Hasan"> Mohammad Kamrul Hasan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Breast Cancer (BC) is considered one of the most frequent reasons of cancer death in women between 40 to 55 ages. The BC is diagnosed by using digital images of the FNA (Fine Needle Aspirate) for both benign and malignant tumors of the breast mass. Therefore, this work proposes the Online Sequential Extreme Learning Machine (OSELM) algorithm for diagnosing BC by using the tumor features of the breast mass. The current work has used the Wisconsin Diagnosis Breast Cancer (WDBC) dataset, which contains 569 samples (i.e., 357 samples for benign class and 212 samples for malignant class). Further, numerous measurements of assessment were used in order to evaluate the proposed OSELM algorithm, such as specificity, precision, F-measure, accuracy, G-mean, MCC, and recall. According to the outcomes of the experiment, the highest performance of the proposed OSELM was accomplished with 97.66% accuracy, 98.39% recall, 95.31% precision, 97.25% specificity, 96.83% F-measure, 95.00% MCC, and 96.84% G-Mean. The proposed OSELM algorithm demonstrates promising results in diagnosing BC. Besides, the performance of the proposed OSELM algorithm was superior to all its comparatives with respect to the rate of classification. <p class="card-text"><strong>Keywords:</strong> <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=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20sequential%20extreme%20learning%20machine" title=" online sequential extreme learning machine"> online sequential extreme learning machine</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a> </p> <a href="https://publications.waset.org/abstracts/157482/breast-cancer-diagnosing-based-on-online-sequential-extreme-learning-machine-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157482.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">111</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">8013</span> An Extension of the Generalized Extreme Value Distribution</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Serge%20Provost">Serge Provost</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdous%20Saboor"> Abdous Saboor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A q-analogue of the generalized extreme value distribution which includes the Gumbel distribution is introduced. The additional parameter q allows for increased modeling flexibility. The resulting distribution can have a finite, semi-infinite or infinite support. It can also produce several types of hazard rate functions. The model parameters are determined by making use of the method of maximum likelihood. It will be shown that it compares favourably to three related distributions in connection with the modeling of a certain hydrological data set. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20theory" title="extreme value theory">extreme value theory</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20extreme%20value%20distribution" title=" generalized extreme value distribution"> generalized extreme value distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=goodness-of-fit%20statistics" title=" goodness-of-fit statistics"> goodness-of-fit statistics</a>, <a href="https://publications.waset.org/abstracts/search?q=Gumbel%20distribution" title=" Gumbel distribution"> Gumbel distribution</a> </p> <a href="https://publications.waset.org/abstracts/72656/an-extension-of-the-generalized-extreme-value-distribution" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72656.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">349</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">8012</span> Influence of Precipitation and Land Use on Extreme Flow in Prek Thnot River Basin of Mekong River in Cambodia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chhordaneath%20Hen">Chhordaneath Hen</a>, <a href="https://publications.waset.org/abstracts/search?q=Ty%20Sok"> Ty Sok</a>, <a href="https://publications.waset.org/abstracts/search?q=Ilan%20Ich"> Ilan Ich</a>, <a href="https://publications.waset.org/abstracts/search?q=Ratboren%20Chan"> Ratboren Chan</a>, <a href="https://publications.waset.org/abstracts/search?q=Chantha%20Oeurng"> Chantha Oeurng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The damages caused by hydrological extremes such as flooding have been severe globally, and several research studies indicated extreme precipitations play a crucial role. Cambodia is one of the most vulnerable countries exposed to floods and drought as consequences of climate impact. Prek Thnot River Basin in the southwest part of Cambodia, which is in the plate and plateau region and a part of the Mekong Delta, was selected to investigate the changes in extreme precipitation and hydrological extreme. Furthermore, to develop a statistical relationship between these phenomena in this basin from 1995 to 2020 using Multiple Linear Regression. The precipitation and hydrological extreme were assessed via the attributes and trends of rainfall patterns during the study periods. The extreme flow was defined as a dependent variable, while the independent variables are various extreme precipitation indices. The study showed that all extreme precipitations indices (R10, R20, R35, CWD, R95p, R99p, and PRCPTOT) had increasing decency. However, the number of rain days per year had a decreasing tendency, which can conclude that extreme rainfall was more intense in a shorter period of the year. The study showed a similar relationship between extreme precipitation and hydrological extreme and land use change association with hydrological extreme. The direct combination of land use and precipitation equals 37% of the flood causes in this river. This study provided information on these two causes of flood events and an understanding of expectations of climate change consequences for flood and water resources management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme%20precipitation" title="extreme precipitation">extreme precipitation</a>, <a href="https://publications.waset.org/abstracts/search?q=hydrological%20extreme" title=" hydrological extreme"> hydrological extreme</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20use" title=" land use"> land use</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20cover" title=" land cover"> land cover</a>, <a href="https://publications.waset.org/abstracts/search?q=Prek%20Thnot%20river%20basin" title=" Prek Thnot river basin"> Prek Thnot river basin</a> </p> <a href="https://publications.waset.org/abstracts/155816/influence-of-precipitation-and-land-use-on-extreme-flow-in-prek-thnot-river-basin-of-mekong-river-in-cambodia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155816.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">111</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">8011</span> Estimating The Population Mean by Using Stratified Double Extreme Ranked Set Sample</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20I.%20Syam">Mahmoud I. Syam</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamarulzaman%20Ibrahim"> Kamarulzaman Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Amer%20I.%20Al-Omari"> Amer I. Al-Omari </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Stratified double extreme ranked set sampling (SDERSS) method is introduced and considered for estimating the population mean. The SDERSS is compared with the simple random sampling (SRS), stratified ranked set sampling (SRSS) and stratified simple set sampling (SSRS). It is shown that the SDERSS estimator is an unbiased of the population mean and more efficient than the estimators using SRS, SRSS and SSRS when the underlying distribution of the variable of interest is symmetric or asymmetric. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=double%20extreme%20ranked%20set%20sampling" title="double extreme ranked set sampling">double extreme ranked set sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20ranked%20set%20sampling" title=" extreme ranked set sampling"> extreme ranked set sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=ranked%20set%20sampling" title=" ranked set sampling"> ranked set sampling</a>, <a href="https://publications.waset.org/abstracts/search?q=stratified%20double%20extreme%20ranked%20set%20sampling" title=" stratified double extreme ranked set sampling"> stratified double extreme ranked set sampling</a> </p> <a href="https://publications.waset.org/abstracts/25207/estimating-the-population-mean-by-using-stratified-double-extreme-ranked-set-sample" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/25207.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">456</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">8010</span> Artificial Intelligence-Based Detection of Individuals Suffering from Vestibular Disorder</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dua%20Hi%C5%9Fam">Dua Hişam</a>, <a href="https://publications.waset.org/abstracts/search?q=Serhat%20%C4%B0kizo%C4%9Flu"> Serhat İkizoğlu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Identifying the problem behind balance disorder is one of the most interesting topics in the medical literature. This study has considerably enhanced the development of artificial intelligence (AI) algorithms applying multiple machine learning (ML) models to sensory data on gait collected from humans to classify between normal people and those suffering from Vestibular System (VS) problems. Although AI is widely utilized as a diagnostic tool in medicine, AI models have not been used to perform feature extraction and identify VS disorders through training on raw data. In this study, three machine learning (ML) models, the Random Forest Classifier (RF), Extreme Gradient Boosting (XGB), and K-Nearest Neighbor (KNN), have been trained to detect VS disorder, and the performance comparison of the algorithms has been made using accuracy, recall, precision, and f1-score. With an accuracy of 95.28 %, Random Forest Classifier (RF) was the most accurate model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=vestibular%20disorder" title="vestibular disorder">vestibular disorder</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=random%20forest%20classifier" title=" random forest classifier"> random forest classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=k-nearest%20neighbor" title=" k-nearest neighbor"> k-nearest neighbor</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20gradient%20boosting" title=" extreme gradient boosting"> extreme gradient boosting</a> </p> <a href="https://publications.waset.org/abstracts/162312/artificial-intelligence-based-detection-of-individuals-suffering-from-vestibular-disorder" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162312.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">69</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">8009</span> Climate Change and Extreme Weather: Understanding Interconnections and Implications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Johnstone%20Walubengo%20Wangusi">Johnstone Walubengo Wangusi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Climate change is undeniably altering the frequency, intensity, and geographic distribution of extreme weather events worldwide. In this paper, we explore the complex interconnections between climate change and extreme weather phenomena, drawing upon research from atmospheric science, geology, and climatology. We examine the underlying mechanisms driving these changes, the impacts on natural ecosystems and human societies, and strategies for adaptation and mitigation. By synthesizing insights from interdisciplinary research, this paper aims to provide a comprehensive understanding of the multifaceted relationship between climate change and extreme weather, informing efforts to address the challenges posed by a changing climate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=climate%20change" title="climate change">climate change</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20weather" title=" extreme weather"> extreme weather</a>, <a href="https://publications.waset.org/abstracts/search?q=atmospheric%20science" title=" atmospheric science"> atmospheric science</a>, <a href="https://publications.waset.org/abstracts/search?q=geology" title=" geology"> geology</a>, <a href="https://publications.waset.org/abstracts/search?q=climatology" title=" climatology"> climatology</a>, <a href="https://publications.waset.org/abstracts/search?q=impacts" title=" impacts"> impacts</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptation" title=" adaptation"> adaptation</a>, <a href="https://publications.waset.org/abstracts/search?q=mitigation" title=" mitigation"> mitigation</a> </p> <a href="https://publications.waset.org/abstracts/184530/climate-change-and-extreme-weather-understanding-interconnections-and-implications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/184530.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">64</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">8008</span> Optimization Based Extreme Learning Machine for Watermarking of an Image in DWT Domain</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=RAM%20PAL%20SINGH">RAM PAL SINGH</a>, <a href="https://publications.waset.org/abstracts/search?q=VIKASH%20CHAUDHARY"> VIKASH CHAUDHARY</a>, <a href="https://publications.waset.org/abstracts/search?q=MONIKA%20VERMA"> MONIKA VERMA</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we proposed the implementation of optimization based Extreme Learning Machine (ELM) for watermarking of B-channel of color image in discrete wavelet transform (DWT) domain. ELM, a regularization algorithm, works based on generalized single-hidden-layer feed-forward neural networks (SLFNs). However, hidden layer parameters, generally called feature mapping in context of ELM need not to be tuned every time. This paper shows the embedding and extraction processes of watermark with the help of ELM and results are compared with already used machine learning models for watermarking.Here, a cover image is divide into suitable numbers of non-overlapping blocks of required size and DWT is applied to each block to be transformed in low frequency sub-band domain. Basically, ELM gives a unified leaning platform with a feature mapping, that is, mapping between hidden layer and output layer of SLFNs, is tried for watermark embedding and extraction purpose in a cover image. Although ELM has widespread application right from binary classification, multiclass classification to regression and function estimation etc. Unlike SVM based algorithm which achieve suboptimal solution with high computational complexity, ELM can provide better generalization performance results with very small complexity. Efficacy of optimization method based ELM algorithm is measured by using quantitative and qualitative parameters on a watermarked image even though image is subjected to different types of geometrical and conventional attacks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BER" title="BER">BER</a>, <a href="https://publications.waset.org/abstracts/search?q=DWT" title=" DWT"> DWT</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20leaning%20machine%20%28ELM%29" title=" extreme leaning machine (ELM)"> extreme leaning machine (ELM)</a>, <a href="https://publications.waset.org/abstracts/search?q=PSNR" title=" PSNR "> PSNR </a> </p> <a href="https://publications.waset.org/abstracts/4331/optimization-based-extreme-learning-machine-for-watermarking-of-an-image-in-dwt-domain" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4331.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">8007</span> Downscaling Seasonal Sea Surface Temperature Forecasts over the Mediterranean Sea Using Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Redouane%20Larbi%20Boufeniza">Redouane Larbi Boufeniza</a>, <a href="https://publications.waset.org/abstracts/search?q=Jing-Jia%20Luo"> Jing-Jia Luo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study assesses the suitability of deep learning (DL) for downscaling sea surface temperature (SST) over the Mediterranean Sea in the context of seasonal forecasting. We design a set of experiments that compare different DL configurations and deploy the best-performing architecture to downscale one-month lead forecasts of June–September (JJAS) SST from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0) for the period of 1982–2020. We have also introduced predictors over a larger area to include information about the main large-scale circulations that drive SST over the Mediterranean Sea region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results showed that the convolutional neural network (CNN)-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme SST spatial patterns. Besides, the CNN-based downscaling yields a much more accurate forecast of extreme SST and spell indicators and reduces the significant relevant biases exhibited by the raw model predictions. Moreover, our results show that the CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of the Mediterranean Sea. The results demonstrate the potential usefulness of CNN in downscaling seasonal SST predictions over the Mediterranean Sea, particularly in providing improved forecast products. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mediterranean%20Sea" title="Mediterranean Sea">Mediterranean Sea</a>, <a href="https://publications.waset.org/abstracts/search?q=sea%20surface%20temperature" title=" sea surface temperature"> sea surface temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=seasonal%20forecasting" title=" seasonal forecasting"> seasonal forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=downscaling" title=" downscaling"> downscaling</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/166824/downscaling-seasonal-sea-surface-temperature-forecasts-over-the-mediterranean-sea-using-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166824.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">76</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">8006</span> Application of Stochastic Models to Annual Extreme Streamflow Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karim%20Hamidi%20Machekposhti">Karim Hamidi Machekposhti</a>, <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Sedghi"> Hossein Sedghi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study was designed to find the best stochastic model (using of time series analysis) for annual extreme streamflow (peak and maximum streamflow) of Karkheh River at Iran. The Auto-regressive Integrated Moving Average (ARIMA) model used to simulate these series and forecast those in future. For the analysis, annual extreme streamflow data of Jelogir Majin station (above of Karkheh dam reservoir) for the years 1958–2005 were used. A visual inspection of the time plot gives a little increasing trend; therefore, series is not stationary. The stationarity observed in Auto-Correlation Function (ACF) and Partial Auto-Correlation Function (PACF) plots of annual extreme streamflow was removed using first order differencing (d=1) in order to the development of the ARIMA model. Interestingly, the ARIMA(4,1,1) model developed was found to be most suitable for simulating annual extreme streamflow for Karkheh River. The model was found to be appropriate to forecast ten years of annual extreme streamflow and assist decision makers to establish priorities for water demand. The Statistical Analysis System (SAS) and Statistical Package for the Social Sciences (SPSS) codes were used to determinate of the best model for this series. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stochastic%20models" title="stochastic models">stochastic models</a>, <a href="https://publications.waset.org/abstracts/search?q=ARIMA" title=" ARIMA"> ARIMA</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20streamflow" title=" extreme streamflow"> extreme streamflow</a>, <a href="https://publications.waset.org/abstracts/search?q=Karkheh%20river" title=" Karkheh river"> Karkheh river</a> </p> <a href="https://publications.waset.org/abstracts/97759/application-of-stochastic-models-to-annual-extreme-streamflow-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97759.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">148</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">8005</span> Extreme Value Theory Applied in Reliability Analysis: Case Study of Diesel Generator Fans</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jelena%20Vucicevic">Jelena Vucicevic</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Reliability analysis represents a very important task in different areas of work. In any industry, this is crucial for maintenance, efficiency, safety and monetary costs. There are ways to calculate reliability, unreliability, failure density and failure rate. In this paper, the results for the reliability of diesel generator fans were calculated through Extreme Value Theory. The Extreme Value Theory is not widely used in the engineering field. Its usage is well known in other areas such as hydrology, meteorology, finance. The significance of this theory is in the fact that unlike the other statistical methods it is focused on rare and extreme values, and not on average. It should be noted that this theory is not designed exclusively for extreme events, but for extreme values in any event. Therefore, this is a great opportunity to apply the theory and test if it could be applied in this situation. The significance of the work is the calculation of time to failure or reliability in a new way, using statistic. Another advantage of this calculation is that there is no need for technical details and it can be implemented in any part for which we need to know the time to fail in order to have appropriate maintenance, but also to maximize usage and minimize costs. In this case, calculations have been made on diesel generator fans but the same principle can be applied to any other part. The data for this paper came from a field engineering study of the time to failure of diesel generator fans. The ultimate goal was to decide whether or not to replace the working fans with a higher quality fan to prevent future failures. The results achieved in this method will show the approximation of time for which the fans will work as they should, and the percentage of probability of fans working more than certain estimated time. Extreme Value Theory can be applied not only for rare and extreme events, but for any event that has values which we can consider as extreme. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20theory" title="extreme value theory">extreme value theory</a>, <a href="https://publications.waset.org/abstracts/search?q=lifetime" title=" lifetime"> lifetime</a>, <a href="https://publications.waset.org/abstracts/search?q=reliability%20analysis" title=" reliability analysis"> reliability analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=statistic" title=" statistic"> statistic</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20to%20failure" title=" time to failure"> time to failure</a> </p> <a href="https://publications.waset.org/abstracts/78201/extreme-value-theory-applied-in-reliability-analysis-case-study-of-diesel-generator-fans" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78201.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">328</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">8004</span> Modeling of Maximum Rainfall Using Poisson-Generalized Pareto Distribution in Kigali, Rwanda</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Emmanuel%20Iyamuremye">Emmanuel Iyamuremye</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Extreme rainfall events have caused significant damage to agriculture, ecology, and infrastructure, disruption of human activities, injury, and loss of life. They also have significant social, economic, and environmental consequences because they considerably damage urban as well as rural areas. Early detection of extreme maximum rainfall helps to implement strategies and measures, before they occur, hence mitigating the consequences. Extreme value theory has been used widely in modeling extreme rainfall and in various disciplines, such as financial markets, the insurance industry, failure cases. Climatic extremes have been analyzed by using either generalized extreme value (GEV) or generalized Pareto (GP) distributions, which provides evidence of the importance of modeling extreme rainfall from different regions of the world. In this paper, we focused on Peak Over Thresholds approach, where the Poisson-generalized Pareto distribution is considered as the proper distribution for the study of the exceedances. This research also considers the use of the generalized Pareto (GP) distribution with a Poisson model for arrivals to describe peaks over a threshold. The research used statistical techniques to fit models that used to predict extreme rainfall in Kigali. The results indicate that the proposed Poisson-GP distribution provides a better fit to maximum monthly rainfall data. Further, the Poisson-GP models are able to estimate various return levels. The research also found a slow increase in return levels for maximum monthly rainfall for higher return periods, and further, the intervals are increasingly wider as the return period is increasing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exceedances" title="exceedances">exceedances</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20theory" title=" extreme value theory"> extreme value theory</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20Pareto%20distribution" title=" generalized Pareto distribution"> generalized Pareto distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=Poisson%20generalized%20Pareto%20distribution" title=" Poisson generalized Pareto distribution"> Poisson generalized Pareto distribution</a> </p> <a href="https://publications.waset.org/abstracts/127379/modeling-of-maximum-rainfall-using-poisson-generalized-pareto-distribution-in-kigali-rwanda" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127379.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">135</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">8003</span> Prediction of Extreme Precipitation in East Asia Using Complex Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Feng%20Guolin">Feng Guolin</a>, <a href="https://publications.waset.org/abstracts/search?q=Gong%20Zhiqiang"> Gong Zhiqiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to study the spatial structure and dynamical mechanism of extreme precipitation in East Asia, a corresponding climate network is constructed by employing the method of event synchronization. It is found that the area of East Asian summer extreme precipitation can be separated into two regions: one with high area weighted connectivity receiving heavy precipitation mostly during the active phase of the East Asian Summer Monsoon (EASM), and another one with low area weighted connectivity receiving heavy precipitation during both the active and the retreat phase of the EASM. Besides，a way for the prediction of extreme precipitation is also developed by constructing a directed climate networks. The simulation accuracy in East Asia is 58% with a 0-day lead, and the prediction accuracy is 21% and average 12% with a 1-day and an n-day (2≤n≤10) lead, respectively. Compare to the normal EASM year, the prediction accuracy is lower in a weak year and higher in a strong year, which is relevant to the differences in correlations and extreme precipitation rates in different EASM situations. Recognizing and identifying these effects is good for understanding and predicting extreme precipitation in East Asia. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=synchronization" title="synchronization">synchronization</a>, <a href="https://publications.waset.org/abstracts/search?q=climate%20network" title=" climate network"> climate network</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=rainfall" title=" rainfall"> rainfall</a> </p> <a href="https://publications.waset.org/abstracts/64827/prediction-of-extreme-precipitation-in-east-asia-using-complex-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64827.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">442</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">8002</span> Generalized Extreme Value Regression with Binary Dependent Variable: An Application for Predicting Meteorological Drought Probabilities</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Retius%20Chifurira">Retius Chifurira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Logistic regression model is the most used regression model to predict meteorological drought probabilities. When the dependent variable is extreme, the logistic model fails to adequately capture drought probabilities. In order to adequately predict drought probabilities, we use the generalized linear model (GLM) with the quantile function of the generalized extreme value distribution (GEVD) as the link function. The method maximum likelihood estimation is used to estimate the parameters of the generalized extreme value (GEV) regression model. We compare the performance of the logistic and the GEV regression models in predicting drought probabilities for Zimbabwe. The performance of the regression models are assessed using the goodness-of-fit tests, namely; relative root mean square error (RRMSE) and relative mean absolute error (RMAE). Results show that the GEV regression model performs better than the logistic model, thereby providing a good alternative candidate for predicting drought probabilities. This paper provides the first application of GLM derived from extreme value theory to predict drought probabilities for a drought-prone country such as Zimbabwe. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=generalized%20extreme%20value%20distribution" title="generalized extreme value distribution">generalized extreme value distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=general%20linear%20model" title=" general linear model"> general linear model</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20annual%20rainfall" title=" mean annual rainfall"> mean annual rainfall</a>, <a href="https://publications.waset.org/abstracts/search?q=meteorological%20drought%20probabilities" title=" meteorological drought probabilities"> meteorological drought probabilities</a> </p> <a href="https://publications.waset.org/abstracts/99321/generalized-extreme-value-regression-with-binary-dependent-variable-an-application-for-predicting-meteorological-drought-probabilities" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99321.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">200</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">8001</span> A Bayesian Model with Improved Prior in Extreme Value Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eva%20L.%20Sanju%C3%A1n">Eva L. Sanjuán</a>, <a href="https://publications.waset.org/abstracts/search?q=Jacinto%20Mart%C3%ADn"> Jacinto Martín</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Isabel%20Parra"> M. Isabel Parra</a>, <a href="https://publications.waset.org/abstracts/search?q=Mario%20M.%20Pizarro"> Mario M. Pizarro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In Extreme Value Theory, inference estimation for the parameters of the distribution is made employing a small part of the observation values. When block maxima values are taken, many data are discarded. We developed a new Bayesian inference model to seize all the information provided by the data, introducing informative priors and using the relations between baseline and limit parameters. Firstly, we studied the accuracy of the new model for three baseline distributions that lead to a Gumbel extreme distribution: Exponential, Normal and Gumbel. Secondly, we considered mixtures of Normal variables, to simulate practical situations when data do not adjust to pure distributions, because of perturbations (noise). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bayesian%20inference" title="bayesian inference">bayesian inference</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20theory" title=" extreme value theory"> extreme value theory</a>, <a href="https://publications.waset.org/abstracts/search?q=Gumbel%20distribution" title=" Gumbel distribution"> Gumbel distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=highly%20informative%20prior" title=" highly informative prior"> highly informative prior</a> </p> <a href="https://publications.waset.org/abstracts/141776/a-bayesian-model-with-improved-prior-in-extreme-value-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141776.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">198</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">8000</span> Gradient-Based Reliability Optimization of Integrated Energy Systems Under Extreme Weather Conditions: A Case Study in Ningbo, China</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Da%20LI">Da LI</a>, <a href="https://publications.waset.org/abstracts/search?q=Peng%20Xu"> Peng Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recent extreme weather events, such as the 2021 European floods and North American heatwaves, have exposed the vulnerability of energy systems to both extreme demand scenarios and potential physical damage. Current integrated energy system designs often overlook performance under these challenging conditions. This research, focusing on a regional integrated energy system in Ningbo, China, proposes a distinct design method to optimize system reliability during extreme events. A multi-scenario model was developed, encompassing various extreme load conditions and potential system damages caused by severe weather. Based on this model, a comprehensive reliability improvement scheme was designed, incorporating a gradient approach to address different levels of disaster severity through the integration of advanced technologies like distributed energy storage. The scheme's effectiveness was validated through Monte Carlo simulations. Results demonstrate significant enhancements in energy supply reliability and peak load reduction capability under extreme scenarios. The findings provide several insights for improving energy system adaptability in the face of climate-induced challenges, offering valuable references for building reliable energy infrastructure capable of withstanding both extreme demands and physical threats across a spectrum of disaster intensities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme%20weather%20events" title="extreme weather events">extreme weather events</a>, <a href="https://publications.waset.org/abstracts/search?q=integrated%20energy%20systems" title=" integrated energy systems"> integrated energy systems</a>, <a href="https://publications.waset.org/abstracts/search?q=reliability%20improvement" title=" reliability improvement"> reliability improvement</a>, <a href="https://publications.waset.org/abstracts/search?q=climate%20change%20adaptation" title=" climate change adaptation"> climate change adaptation</a> </p> <a href="https://publications.waset.org/abstracts/190789/gradient-based-reliability-optimization-of-integrated-energy-systems-under-extreme-weather-conditions-a-case-study-in-ningbo-china" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/190789.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">25</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">7999</span> Aspects of Diglossia in Arabic Language Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adil%20Ishag">Adil Ishag</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Diglossia emerges in a situation where two distinctive varieties of a language are used alongside within a certain community. In this case, one is considered as a high or standard variety and the second one as a low or colloquial variety. Arabic is an extreme example of a highly diglossic language. This diglossity is due to the fact that Arabic is one of the most spoken languages and spread over 22 Countries in two continents as a mother tongue, and it is also widely spoken in many other Islamic countries as a second language or simply the language of Quran. The geographical variation between the countries where the language is spoken and the duality of the classical Arabic and daily spoken dialects in the Arab world on the other hand; makes the Arabic language one of the most diglossic languages. This paper tries to investigate this phenomena and its relation to learning Arabic as a first and second language. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Arabic%20language" title="Arabic language">Arabic language</a>, <a href="https://publications.waset.org/abstracts/search?q=diglossia" title=" diglossia"> diglossia</a>, <a href="https://publications.waset.org/abstracts/search?q=first%20and%20second%20language" title=" first and second language"> first and second language</a>, <a href="https://publications.waset.org/abstracts/search?q=language%20learning" title=" language learning"> language learning</a> </p> <a href="https://publications.waset.org/abstracts/24533/aspects-of-diglossia-in-arabic-language-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24533.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">564</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">7998</span> Changes in Temperature and Precipitation Extremes in Northern Thailand</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chakrit%20Chotamonsak">Chakrit Chotamonsak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study was analyzed changes in temperature and precipitation extremes in northern Thailand for the period 1981-2011.The study includes an analysis of the average and trends of changes in temperature and precipitation using 22 climate indices, related to the intensity, frequency and duration of extreme climate events. The results showed that the averaged trend of maximum, minimum and mean temperature is likely to increase over the study area in rate of 0.5, 0.9 and 0.7 °C in last 30 years. Changes in temperature at nighttime, then rising at a rate higher daytime is resulting to decline of diurnal temperature range throughout the area. Trend of changes in average precipitation during the year 1981-2011 is expected to increase at an average rate of 21%. The intensity of extreme temperature events is increasing almost all station. In particular, the changes of the night were unusually hot has intensified throughout the region. In some provinces such as Chiang Mai and Lampang are likely be faced with the severity of hot days and hot nights in increasing rate. Frequency of extreme temperature events are likely to increase each station, especially hot days, and hot nights are increasing at a rate of 2.38 and 3.58 days per decade. Changes in the cold days and cold nights are declining at a rate of 0.82 and 3.03 days per decade. The duration of extreme temperature events is expected to increase the events hot in every station. An average of 17.8 days per decade for the number of consecutive cold winter nights likely shortens the rate of 2.90 days per decade. The analysis of the precipitation indices reveals the intensity of extreme precipitation is increasing almost across the region. The intensify expressed the heavy rain in one day (Rx1day) and very heavy rain accumulated in 5 days (RX5day) which is likely to increase, and very heavy rainfall is likely to increase in intensity. Frequency of extreme precipitation events is likely to increase over the station. The average frequency of heavy precipitation events increased xxx days per decade. The duration of extreme precipitation events, such as the consecutive dry days are likely to reduce the numbers almost all station while the consecutive wet days tends to increase and decrease at different numbers in different areas. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=climate%20extreme" title="climate extreme">climate extreme</a>, <a href="https://publications.waset.org/abstracts/search?q=temperature%20extreme" title=" temperature extreme"> temperature extreme</a>, <a href="https://publications.waset.org/abstracts/search?q=precipitation%20extreme" title=" precipitation extreme"> precipitation extreme</a>, <a href="https://publications.waset.org/abstracts/search?q=Northern%20Thailand" title=" Northern Thailand"> Northern Thailand</a> </p> <a href="https://publications.waset.org/abstracts/35651/changes-in-temperature-and-precipitation-extremes-in-northern-thailand" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35651.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">283</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">7997</span> Damage Cost for Private Property by Extreme Wind over the past 10 Years in Korea</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gou-Moon%20Choi">Gou-Moon Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Woo-Young%20Jung"> Woo-Young Jung</a>, <a href="https://publications.waset.org/abstracts/search?q=Chan-Young%20Yune"> Chan-Young Yune</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, the natural disaster has increased worldwide. In Korea, the damage to life and property caused by a typhoon, heavy rain, heavy snow, and an extreme wind also increases every year. Among natural disasters, the frequency and the strength of wind have increased because sea surface temperature has risen due to the increase of the average temperature of the Earth. In the case of extreme wind disaster, it is impossible to control or reduce the occurrence, and the recovery cost always exceeds the damage cost. Therefore, quantitative estimation of the damage cost for extreme wind needs to be established beforehand to install proactive countermeasures. In this study, the damage cost for private properties was analyzed based on the data for the past 10 years in Korea. The damage cost curve was also suggested for the metropolitan cities and provinces. The result shows the possibility for the regional application of the damage cost curve because the damage cost of the regional area is estimated based on the cost of cities and provinces. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=damage%20cost" title="damage cost">damage cost</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20wind" title=" extreme wind"> extreme wind</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20disaster" title=" natural disaster"> natural disaster</a>, <a href="https://publications.waset.org/abstracts/search?q=private%20property" title=" private property"> private property</a> </p> <a href="https://publications.waset.org/abstracts/44424/damage-cost-for-private-property-by-extreme-wind-over-the-past-10-years-in-korea" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44424.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">305</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7996</span> Combining Multiscale Patterns of Weather and Sea States into a Machine Learning Classifier for Mid-Term Prediction of Extreme Rainfall in North-Western Mediterranean Sea</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pinel%20Sebastien">Pinel Sebastien</a>, <a href="https://publications.waset.org/abstracts/search?q=Bourrin%20Fran%C3%A7ois"> Bourrin François</a>, <a href="https://publications.waset.org/abstracts/search?q=De%20Madron%20Du%20Rieu%20Xavier"> De Madron Du Rieu Xavier</a>, <a href="https://publications.waset.org/abstracts/search?q=Ludwig%20Wolfgang"> Ludwig Wolfgang</a>, <a href="https://publications.waset.org/abstracts/search?q=Arnau%20Pedro"> Arnau Pedro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Heavy precipitation constitutes a major meteorological threat in the western Mediterranean. Research has investigated the relationship between the states of the Mediterranean Sea and the atmosphere with the precipitation for short temporal windows. However, at a larger temporal scale, the precursor signals of heavy rainfall in the sea and atmosphere have drawn little attention. Moreover, despite ongoing improvements in numerical weather prediction, the medium-term forecasting of rainfall events remains a difficult task. Here, we aim to investigate the influence of early-spring environmental parameters on the following autumnal heavy precipitations. Hence, we develop a machine learning model to predict extreme autumnal rainfall with a 6-month lead time over the Spanish Catalan coastal area, based on i) the sea pattern (main current-LPC and Sea Surface Temperature-SST) at the mesoscale scale, ii) 4 European weather teleconnection patterns (NAO, WeMo, SCAND, MO) at synoptic scale, and iii) the hydrological regime of the main local river (Rhône River). The accuracy of the developed model classifier is evaluated via statistical analysis based on classification accuracy, logarithmic and confusion matrix by comparing with rainfall estimates from rain gauges and satellite observations (CHIRPS-2.0). Sensitivity tests are carried out by changing the model configuration, such as sea SST, sea LPC, river regime, and synoptic atmosphere configuration. The sensitivity analysis suggests a negligible influence from the hydrological regime, unlike SST, LPC, and specific teleconnection weather patterns. At last, this study illustrates how public datasets can be integrated into a machine learning model for heavy rainfall prediction and can interest local policies for management purposes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme%20hazards" title="extreme hazards">extreme hazards</a>, <a href="https://publications.waset.org/abstracts/search?q=sensitivity%20analysis" title=" sensitivity analysis"> sensitivity analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=heavy%20rainfall" title=" heavy rainfall"> heavy rainfall</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=sea-atmosphere%20modeling" title=" sea-atmosphere modeling"> sea-atmosphere modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=precipitation%20forecasting" title=" precipitation forecasting"> precipitation forecasting</a> </p> <a href="https://publications.waset.org/abstracts/149584/combining-multiscale-patterns-of-weather-and-sea-states-into-a-machine-learning-classifier-for-mid-term-prediction-of-extreme-rainfall-in-north-western-mediterranean-sea" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149584.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">7995</span> A Review of Machine Learning for Big Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Devatha%20Kalyan%20Kumar">Devatha Kalyan Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Aravindraj%20D."> Aravindraj D.</a>, <a href="https://publications.waset.org/abstracts/search?q=Sadathulla%20A."> Sadathulla A.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Big data are now rapidly expanding in all engineering and science and many other domains. The potential of large or massive data is undoubtedly significant, make sense to require new ways of thinking and learning techniques to address the various big data challenges. Machine learning is continuously unleashing its power in a wide range of applications. In this paper, the latest advances and advancements in the researches on machine learning for big data processing. First, the machine learning techniques methods in recent studies, such as deep learning, representation learning, transfer learning, active learning and distributed and parallel learning. Then focus on the challenges and possible solutions of machine learning for big data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=active%20learning" title="active learning">active learning</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</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=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/72161/a-review-of-machine-learning-for-big-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72161.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">446</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">7994</span> Extreme Value Modelling of Ghana Stock Exchange Indices</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kwabena%20Asare">Kwabena Asare</a>, <a href="https://publications.waset.org/abstracts/search?q=Ezekiel%20N.%20N.%20Nortey"> Ezekiel N. N. Nortey</a>, <a href="https://publications.waset.org/abstracts/search?q=Felix%20O.%20Mettle"> Felix O. Mettle</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Modelling of extreme events has always been of interest in fields such as hydrology and meteorology. However, after the recent global financial crises, appropriate models for modelling of such rare events leading to these crises have become quite essential in the finance and risk management fields. This paper models the extreme values of the Ghana Stock Exchange All-Shares indices (2000-2010) by applying the Extreme Value Theory to fit a model to the tails of the daily stock returns data. A conditional approach of the EVT was preferred and hence an ARMA-GARCH model was fitted to the data to correct for the effects of autocorrelation and conditional heteroscedastic terms present in the returns series, before EVT method was applied. The Peak Over Threshold (POT) approach of the EVT, which fits a Generalized Pareto Distribution (GPD) model to excesses above a certain selected threshold, was employed. Maximum likelihood estimates of the model parameters were obtained and the model’s goodness of fit was assessed graphically using Q-Q, P-P and density plots. The findings indicate that the GPD provides an adequate fit to the data of excesses. The size of the extreme daily Ghanaian stock market movements were then computed using the Value at Risk (VaR) and Expected Shortfall (ES) risk measures at some high quantiles, based on the fitted GPD model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme%20value%20theory" title="extreme value theory">extreme value theory</a>, <a href="https://publications.waset.org/abstracts/search?q=expected%20shortfall" title=" expected shortfall"> expected shortfall</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20pareto%20distribution" title=" generalized pareto distribution"> generalized pareto distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=peak%20over%20threshold" title=" peak over threshold"> peak over threshold</a>, <a href="https://publications.waset.org/abstracts/search?q=value%20at%20risk" title=" value at risk"> value at risk</a> </p> <a href="https://publications.waset.org/abstracts/35743/extreme-value-modelling-of-ghana-stock-exchange-indices" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35743.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">557</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">7993</span> Outstanding Lubricant Using Fluorographene as an Extreme Pressure Additive</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adriana%20Hernandez-Martinez">Adriana Hernandez-Martinez</a>, <a href="https://publications.waset.org/abstracts/search?q=Edgar%20D.%20Ramon-Raygoza"> Edgar D. Ramon-Raygoza</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Currently, there has been a great interest, during the last years, on graphene due to its lubricant properties on friction and antiwear processes. Likewise, fluorographene has also been gaining renown due to its excellent chemical and physical properties which have been mostly applied in the electronics industry. Nevertheless, its tribological properties haven’t been analyzed thoroughly. In this paper, fluorographene was examined as an extreme pressure additive and the nano lubricant made with a cutting fluid and fluorographene in the range of 0.01-0.5% wt, which proved to withstand 53.78% more pounds than the conventional product and 7.12% more than the nano lubricant with graphene in a range between 0.01-0.5% wt. Said extreme pressure test was carried out with a Pin and Vee Block Tribometer following an ASTM D3233A test. The fluorographene used has a low C/F ratio, which reflects a greater presence of atomic fluorine and its low oxygen percentage, supports the substitution of oxygen-containing groups by fluorine. XPS Spectra shows high atomic fluorine content of 56.12%, and SEM analysis details the formation of long and clear crystalline structures, in the fluorographene used. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=extreme%20pressure%20additive" title="extreme pressure additive">extreme pressure additive</a>, <a href="https://publications.waset.org/abstracts/search?q=fluorographene" title=" fluorographene"> fluorographene</a>, <a href="https://publications.waset.org/abstracts/search?q=nanofluids" title=" nanofluids"> nanofluids</a>, <a href="https://publications.waset.org/abstracts/search?q=nanolubricant" title=" nanolubricant"> nanolubricant</a> </p> <a href="https://publications.waset.org/abstracts/105687/outstanding-lubricant-using-fluorographene-as-an-extreme-pressure-additive" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105687.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">125</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">7992</span> Innovative Approaches to Formal Education: Effect of Online Cooperative Learning Embedded Blended Learning on Student's Academic Achievement and Attitude</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohsin%20Javed">Mohsin Javed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> School Education department is usually criticized for utilizing quite low or fewer academic days due to many reasons like extreme weather conditions, sudden holidays, summer vocations, pandemics and, terrorism etc. The purpose of the experimental study was to determine the efficacy of online cooperative learning (OCL) integrated in the rotation model of blended learning. The effects on academic achievement of students and students' attitude about OCL embedded learning were assessed. By using a posttest only control group design, sixty-two first-year students were randomly allocated to either the experimental (30) or control (32) group. The control group received face to face classes for six sessions per week, while the experimental group had three OCL and three formal sessions per week under rotation model. Students' perceptions of OCL were evaluated using a survey questionnaire. Data was analyzed by independent sample t test and one sample t test. According to findings, the intervention greatly improved the state of the dependent variables. The results demonstrate that OCL can be successfully implemented in formal education using a blended learning rotation approach. Higher secondary institutions are advised to use this model in situations like Covid 19, smog, unexpected holidays, instructor absence from class due to increased responsibilities, and summer vacations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blended%20learning" title="blended learning">blended learning</a>, <a href="https://publications.waset.org/abstracts/search?q=online%20cooperative%20learning" title=" online cooperative learning"> online cooperative learning</a>, <a href="https://publications.waset.org/abstracts/search?q=rotation%20model%20of%20blended%20learning" title=" rotation model of blended learning"> rotation model of blended learning</a>, <a href="https://publications.waset.org/abstracts/search?q=supplementing" title=" supplementing"> supplementing</a> </p> <a href="https://publications.waset.org/abstracts/173300/innovative-approaches-to-formal-education-effect-of-online-cooperative-learning-embedded-blended-learning-on-students-academic-achievement-and-attitude" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173300.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">59</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">7991</span> Killed by the ‘Subhuman’: Jane Longhurst’s Murder and the Construction of the ‘Extreme Pornography’ Problem in the British National Press </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dimitrios%20Akrivos">Dimitrios Akrivos</a>, <a href="https://publications.waset.org/abstracts/search?q=Alexandros%20K.%20Antoniou"> Alexandros K. Antoniou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper looks at the crucial role of the British news media in the construction of extreme pornography as a social problem, suggesting that this paved the way for the subsequent criminalization of such material through the introduction of the Criminal Justice and Immigration Act 2008. Focusing on the high-profile case of Graham Coutts, it examines the British national press’ reaction to Jane Longhurst’s murder through a qualitative content analysis of 251 relevant news articles. Specifically, the paper documents the key arguments expressed in the corresponding claims-making process. It considers the different ways in which the consequent ‘trial by media’ presented this exceptional case as the ‘tip of the iceberg’ and eventually translated into policy. The analysis sheds light on the attempts to ‘piggyback’ the issue of extreme pornography on child sexual abuse images as well as the textual and visual mechanisms used to establish an ‘us versus them’ dichotomy in the pertinent media discourse. Finally, the paper assesses the severity of the actual risk posed by extreme pornography, concluding that its criminalization should not merely be dismissed as the outcome of an institutionalized media panic. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=criminalization" title="criminalization">criminalization</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20pornography" title=" extreme pornography"> extreme pornography</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20problem" title=" social problem"> social problem</a>, <a href="https://publications.waset.org/abstracts/search?q=trial%20by%20media" title=" trial by media"> trial by media</a> </p> <a href="https://publications.waset.org/abstracts/60449/killed-by-the-subhuman-jane-longhursts-murder-and-the-construction-of-the-extreme-pornography-problem-in-the-british-national-press" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/60449.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">240</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">7990</span> Present and Future Climate Extreme Indices over Sinai Peninsula, Egypt</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20Roushdi">Mahmoud Roushdi</a>, <a href="https://publications.waset.org/abstracts/search?q=Hany%20Mostafa"> Hany Mostafa</a>, <a href="https://publications.waset.org/abstracts/search?q=Khaled%20Kheireldin"> Khaled Kheireldin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sinai Peninsula and Suez Canal Corridor are promising and important economic regions in Egypt due to the unique location and development opportunities. Thus, the climate change impacts should be assessed over the mentioned area. Accordingly, this paper aims to assess the climate extreme indices in through the last 35 year over Sinai Peninsula and Suez Canal Corridor in addition to predict the climate extreme indices up to 2100. Present and future climate indices were analyzed with using different RCP scenarios 4.5 and 8.5 from 2010 until 2100 for Sinai Peninsula and Suez Canal Corridor. Furthermore, both CanESM and HadGEM2 global circulation models were used. The results indicate that the number of summer days is predicted to increase, on the other hand the frost days is predicted to decrease. Moreover, it is noted a slight positive trend for the percentile of wet and extremely days R95p and R99p for RCP4.5 and negative trend for RCP8.5. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=climate%20change" title="climate change">climate change</a>, <a href="https://publications.waset.org/abstracts/search?q=extreme%20indices" title=" extreme indices"> extreme indices</a>, <a href="https://publications.waset.org/abstracts/search?q=RCP" title=" RCP"> RCP</a>, <a href="https://publications.waset.org/abstracts/search?q=Sinai%20Peninsula" title=" Sinai Peninsula"> Sinai Peninsula</a> </p> <a href="https://publications.waset.org/abstracts/36682/present-and-future-climate-extreme-indices-over-sinai-peninsula-egypt" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36682.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">436</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=extreme%20learning&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=extreme%20learning&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=extreme%20learning&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=extreme%20learning&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=extreme%20learning&page=6">6</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=extreme%20learning&page=7">7</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=extreme%20learning&page=8">8</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=extreme%20learning&page=9">9</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=extreme%20learning&page=10">10</a></li> <li class="page-item disabled"><span class="page-link">...</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=extreme%20learning&page=267">267</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=extreme%20learning&page=268">268</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=extreme%20learning&page=2" 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