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non-response</title> <meta name="description" content="Search results for: random non-response"> <meta name="keywords" content="random non-response"> <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="random non-response"> <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> 2086</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: random non-response</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1816</span> Comparison between Separable and Irreducible Goppa Code in McEliece Cryptosystem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Newroz%20Nooralddin%20Abdulrazaq">Newroz Nooralddin Abdulrazaq</a>, <a href="https://publications.waset.org/abstracts/search?q=Thuraya%20Mahmood%20Qaradaghi"> Thuraya Mahmood Qaradaghi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The McEliece cryptosystem is an asymmetric type of cryptography based on error correction code. The classical McEliece used irreducible binary Goppa code which considered unbreakable until now especially with parameter [1024, 524, and 101], but it is suffering from large public key matrix which leads to be difficult to be used practically. In this work Irreducible and Separable Goppa codes have been introduced. The Irreducible and Separable Goppa codes used are with flexible parameters and dynamic error vectors. A Comparison between Separable and Irreducible Goppa code in McEliece Cryptosystem has been done. For encryption stage, to get better result for comparison, two types of testing have been chosen; in the first one the random message is constant while the parameters of Goppa code have been changed. But for the second test, the parameters of Goppa code are constant (m=8 and t=10) while the random message have been changed. The results show that the time needed to calculate parity check matrix in separable are higher than the one for irreducible McEliece cryptosystem, which is considered expected results due to calculate extra parity check matrix in decryption process for g2(z) in separable type, and the time needed to execute error locator in decryption stage in separable type is better than the time needed to calculate it in irreducible type. The proposed implementation has been done by Visual studio C#. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=McEliece%20cryptosystem" title="McEliece cryptosystem">McEliece cryptosystem</a>, <a href="https://publications.waset.org/abstracts/search?q=Goppa%20code" title=" Goppa code"> Goppa code</a>, <a href="https://publications.waset.org/abstracts/search?q=separable" title=" separable"> separable</a>, <a href="https://publications.waset.org/abstracts/search?q=irreducible" title=" irreducible"> irreducible</a> </p> <a href="https://publications.waset.org/abstracts/37017/comparison-between-separable-and-irreducible-goppa-code-in-mceliece-cryptosystem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37017.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">266</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">1815</span> Machine Learning Techniques in Seismic Risk Assessment of Structures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Farid%20Khosravikia">Farid Khosravikia</a>, <a href="https://publications.waset.org/abstracts/search?q=Patricia%20Clayton"> Patricia Clayton</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title="artificial neural network">artificial neural network</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" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=seismic%20risk%20analysis" title=" seismic risk analysis"> seismic risk analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=seismic%20hazard%20analysis" title=" seismic hazard analysis"> seismic hazard analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/109757/machine-learning-techniques-in-seismic-risk-assessment-of-structures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/109757.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">106</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">1814</span> Optimal Design of Step-Stress Partially Life Test Using Multiply Censored Exponential Data with Random Removals </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Showkat%20Ahmad%20Lone">Showkat Ahmad Lone</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahmadur%20Rahman"> Ahmadur Rahman</a>, <a href="https://publications.waset.org/abstracts/search?q=Ariful%20Islam"> Ariful Islam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The major assumption in accelerated life tests (ALT) is that the mathematical model relating the lifetime of a test unit and the stress are known or can be assumed. In some cases, such life–stress relationships are not known and cannot be assumed, i.e. ALT data cannot be extrapolated to use condition. So, in such cases, partially accelerated life test (PALT) is a more suitable test to be performed for which tested units are subjected to both normal and accelerated conditions. This study deals with estimating information about failure times of items under step-stress partially accelerated life tests using progressive failure-censored hybrid data with random removals. The life data of the units under test is considered to follow exponential life distribution. The removals from the test are assumed to have binomial distributions. The point and interval maximum likelihood estimations are obtained for unknown distribution parameters and tampering coefficient. An optimum test plan is developed using the D-optimality criterion. The performances of the resulting estimators of the developed model parameters are evaluated and investigated by using a simulation algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binomial%20distribution" title="binomial distribution">binomial distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=d-optimality" title=" d-optimality"> d-optimality</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20censoring" title=" multiple censoring"> multiple censoring</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20design" title=" optimal design"> optimal design</a>, <a href="https://publications.waset.org/abstracts/search?q=partially%20accelerated%20life%20testing" title=" partially accelerated life testing"> partially accelerated life testing</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation%20study" title=" simulation study"> simulation study</a> </p> <a href="https://publications.waset.org/abstracts/69460/optimal-design-of-step-stress-partially-life-test-using-multiply-censored-exponential-data-with-random-removals" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69460.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">320</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">1813</span> Feeling Ambivalence Towards Values</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aysheh%20Maslemani">Aysheh Maslemani</a>, <a href="https://publications.waset.org/abstracts/search?q=Ruth%20Mayo"> Ruth Mayo</a>, <a href="https://publications.waset.org/abstracts/search?q=Greg%20Maio"> Greg Maio</a>, <a href="https://publications.waset.org/abstracts/search?q=Ariel%20Knafo-Noam"> Ariel Knafo-Noam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Values are abstract ideals that serve as guiding principles in one's life. As inherently positive and desirable concepts, values are seen as motivators for actions and behaviors. However, research has largely ignored the possibility that values may elicit negative feelings despite being explicitly important to us. In the current study, we aim to examine this possibility. Four hundred participants over 18 years(M=41.6, SD=13.7, Female=178) from the UK completed a questionnaire in which they were asked to indicate their level of positive/negative feelings towards a comprehensive list of values and then report the importance of these values to them. The results support our argument by showing that people can have negative feelings towards their values and that people can feel both positive and negative emotions towards their values simultaneously, which means feeling ambivalence. We ran a mixed-effect model with ambivalence, value type, and their interaction as fixed effects, with by subject random intercept and by subject random slope for ambivalence. The results reveal that values that elicit less ambivalence predicted higher ratings for value importance. This research contributes to the field of values on multiple levels. Theoretically, it will uncover new insights about values, such as the existence of negative emotions towards them and the presence of ambivalence towards values. These findings may inspire future studies to explore the effects of ambivalence on people's well-being, behaviors, cognition, and their affect. We discuss the findings and consider their implications for understanding the social psychological mechanisms underpinning value ambivalence. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=emotion" title="emotion">emotion</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20cognition" title=" social cognition"> social cognition</a>, <a href="https://publications.waset.org/abstracts/search?q=values." title=" values."> values.</a>, <a href="https://publications.waset.org/abstracts/search?q=ambivalence" title=" ambivalence"> ambivalence</a> </p> <a href="https://publications.waset.org/abstracts/173900/feeling-ambivalence-towards-values" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173900.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">67</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">1812</span> Feeling Ambivalence Towards Yours Values</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aysheh%20Maslemani">Aysheh Maslemani</a>, <a href="https://publications.waset.org/abstracts/search?q=Ruth%20Mayo"> Ruth Mayo</a>, <a href="https://publications.waset.org/abstracts/search?q=Greg%20Maio"> Greg Maio</a>, <a href="https://publications.waset.org/abstracts/search?q=Ariel%20Knafo-Noam"> Ariel Knafo-Noam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Values are abstract ideals that serve as guiding principles in one's life. As inherently positive and desirable concepts, values are seen as motivators for actions and behaviors. However, research has largely ignored the possibility that values may elicit negative feelings despite being explicitly important to us. In the current study we aim to examine this possibility. Four hundred participants over 18 years(M=41.6,SD=13.7,Female=178) from the UK completed a questionnaire in which they were asked to indicate their level of positive/negative feelings towards a comprehensive list of values and then report the importance of these values to them. The results support our argument by showing that people can have negative feelings towards their values and that people can feel both positive and negative emotions towards their values simultaneously, which means feeling ambivalence. We ran a mixed-effect model with ambivalence, value type, and their interaction as fixed effects, with by subject random intercept, and by subject random slope for ambivalence. The results reveal that values that elicit less ambivalence predicted higher ratings for value importance. This research contributes to the field of values on multiple levels. Theoretically, it will uncover new insights about values, such as the existence of negative emotions towards them, the presence of ambivalence towards values. These findings may inspire future studies to explore the effects of ambivalence on people's well-being, behaviors, cognition, and their affect. We discuss the findings and consider their implications for understanding the social psychological mechanisms underpinning value ambivalence. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ambivalence" title="ambivalence">ambivalence</a>, <a href="https://publications.waset.org/abstracts/search?q=emotion" title=" emotion"> emotion</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20cognition" title=" social cognition"> social cognition</a>, <a href="https://publications.waset.org/abstracts/search?q=values" title=" values"> values</a> </p> <a href="https://publications.waset.org/abstracts/174248/feeling-ambivalence-towards-yours-values" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174248.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">67</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">1811</span> On the Design of a Secure Two-Party Authentication Scheme for Internet of Things Using Cancelable Biometrics and Physically Unclonable Functions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Behnam%20Zahednejad">Behnam Zahednejad</a>, <a href="https://publications.waset.org/abstracts/search?q=Saeed%20Kosari"> Saeed Kosari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Widespread deployment of Internet of Things (IoT) has raised security and privacy issues in this environment. Designing a secure two-factor authentication scheme between the user and server is still a challenging task. In this paper, we focus on Cancelable Biometric (CB) as an authentication factor in IoT. We show that previous CB-based scheme fail to provide real two-factor security, Perfect Forward Secrecy (PFS) and suffer database attacks and traceability of the user. Then we propose our improved scheme based on CB and Physically Unclonable Functions (PUF), which can provide real two-factor security, PFS, user’s unlinkability, and resistance to database attack. In addition, Key Compromise Impersonation (KCI) resilience is achieved in our scheme. We also prove the security of our proposed scheme formally using both Real-Or-Random (RoR) model and the ProVerif analysis tool. For the usability of our scheme, we conducted a performance analysis and showed that our scheme has the least communication cost compared to the previous CB-based scheme. The computational cost of our scheme is also acceptable for the IoT environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=IoT" title="IoT">IoT</a>, <a href="https://publications.waset.org/abstracts/search?q=two-factor%20security" title=" two-factor security"> two-factor security</a>, <a href="https://publications.waset.org/abstracts/search?q=cancelable%20biometric" title=" cancelable biometric"> cancelable biometric</a>, <a href="https://publications.waset.org/abstracts/search?q=key%20compromise%20impersonation%20resilience" title=" key compromise impersonation resilience"> key compromise impersonation resilience</a>, <a href="https://publications.waset.org/abstracts/search?q=perfect%20forward%20secrecy" title=" perfect forward secrecy"> perfect forward secrecy</a>, <a href="https://publications.waset.org/abstracts/search?q=database%20attack" title=" database attack"> database attack</a>, <a href="https://publications.waset.org/abstracts/search?q=real-or-random%20model" title=" real-or-random model"> real-or-random model</a>, <a href="https://publications.waset.org/abstracts/search?q=ProVerif" title=" ProVerif"> ProVerif</a> </p> <a href="https://publications.waset.org/abstracts/160983/on-the-design-of-a-secure-two-party-authentication-scheme-for-internet-of-things-using-cancelable-biometrics-and-physically-unclonable-functions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160983.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">102</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">1810</span> A Data-Mining Model for Protection of FACTS-Based Transmission Line</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashok%20Kalagura">Ashok Kalagura</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a data-mining model for fault-zone identification of ﬂexible AC transmission systems (FACTS)-based transmission line including a thyristor-controlled series compensator (TCSC) and unified power-ﬂow controller (UPFC), using ensemble decision trees. Given the randomness in the ensemble of decision trees stacked inside the random forests model, it provides an effective decision on the fault-zone identification. Half-cycle post-fault current and voltage samples from the fault inception are used as an input vector against target output ‘1’ for the fault after TCSC/UPFC and ‘1’ for the fault before TCSC/UPFC for fault-zone identification. The algorithm is tested on simulated fault data with wide variations in operating parameters of the power system network, including noisy environment providing a reliability measure of 99% with faster response time (3/4th cycle from fault inception). The results of the presented approach using the RF model indicate the reliable identification of the fault zone in FACTS-based transmission lines. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distance%20relaying" title="distance relaying">distance relaying</a>, <a href="https://publications.waset.org/abstracts/search?q=fault-zone%20identification" title=" fault-zone identification"> fault-zone identification</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forests" title=" random forests"> random forests</a>, <a href="https://publications.waset.org/abstracts/search?q=RFs" title=" RFs"> RFs</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a>, <a href="https://publications.waset.org/abstracts/search?q=thyristor-controlled%20series%20compensator" title=" thyristor-controlled series compensator"> thyristor-controlled series compensator</a>, <a href="https://publications.waset.org/abstracts/search?q=TCSC" title=" TCSC"> TCSC</a>, <a href="https://publications.waset.org/abstracts/search?q=unified%20power-%EF%AC%82ow%20controller" title=" unified power-ﬂow controller"> unified power-ﬂow controller</a>, <a href="https://publications.waset.org/abstracts/search?q=UPFC" title=" UPFC "> UPFC </a> </p> <a href="https://publications.waset.org/abstracts/32579/a-data-mining-model-for-protection-of-facts-based-transmission-line" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32579.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">423</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">1809</span> Multilevel Modelling of Modern Contraceptive Use in Nigeria: Analysis of the 2013 NDHS</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akiode%20Ayobami">Akiode Ayobami</a>, <a href="https://publications.waset.org/abstracts/search?q=Akiode%20Akinsewa"> Akiode Akinsewa</a>, <a href="https://publications.waset.org/abstracts/search?q=Odeku%20Mojisola"> Odeku Mojisola</a>, <a href="https://publications.waset.org/abstracts/search?q=Salako%20Busola"> Salako Busola</a>, <a href="https://publications.waset.org/abstracts/search?q=Odutolu%20Omobola"> Odutolu Omobola</a>, <a href="https://publications.waset.org/abstracts/search?q=Nuhu%20Khadija"> Nuhu Khadija</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Purpose: Evidence exists that family planning use can contribute to reduction in infant and maternal mortality in any country. Despite these benefits, contraceptive use in Nigeria still remains very low, only 10% among married women. Understanding factors that predict contraceptive use is very important in order to improve the situation. In this paper, we analysed data from the 2013 Nigerian Demographic and Health Survey (NDHS) to better understand predictors of contraceptive use in Nigeria. The use of logistics regression and other traditional models in this type of situation is not appropriate as they do not account for social structure influence brought about by the hierarchical nature of the data on response variable. We therefore used multilevel modelling to explore the determinants of contraceptive use in order to account for the significant variation in modern contraceptive use by socio-demographic, and other proximate variables across the different Nigerian states. Method: This data has a two-level hierarchical structure. We considered the data of 26, 403 married women of reproductive age at level 1 and nested them within the 36 states and the Federal Capital Territory, Abuja at level 2. We modelled use of modern contraceptive against demographic variables, being told about FP at health facility, heard of FP on TV, Magazine or radio, husband desire for more children nested within the state. Results: Our results showed that the independent variables in the model were significant predictors of modern contraceptive use. The estimated variance component for the null model, random intercept, and random slope models were significant (p=0.00), indicating that the variation in contraceptive use across the Nigerian states is significant, and needs to be accounted for in order to accurately determine the predictors of contraceptive use, hence the data is best fitted by the multilevel model. Only being told about family planning at the health facility and religion have a significant random effect, implying that their predictability of contraceptive use varies across the states. Conclusion and Recommendation: Results showed that providing FP information at the health facility and religion needs to be considered when programming to improve contraceptive use at the state levels. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multilevel%20modelling" title="multilevel modelling">multilevel modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=family%20planning" title=" family planning"> family planning</a>, <a href="https://publications.waset.org/abstracts/search?q=predictors" title=" predictors"> predictors</a>, <a href="https://publications.waset.org/abstracts/search?q=Nigeria" title=" Nigeria"> Nigeria</a> </p> <a href="https://publications.waset.org/abstracts/21600/multilevel-modelling-of-modern-contraceptive-use-in-nigeria-analysis-of-the-2013-ndhs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21600.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">419</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">1808</span> Ensemble Sampler For Infinite-Dimensional Inverse Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeremie%20Coullon">Jeremie Coullon</a>, <a href="https://publications.waset.org/abstracts/search?q=Robert%20J.%20Webber"> Robert J. Webber</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We introduce a Markov chain Monte Carlo (MCMC) sam-pler for infinite-dimensional inverse problems. Our sam-pler is based on the affine invariant ensemble sampler, which uses interacting walkers to adapt to the covariance structure of the target distribution. We extend this ensem-ble sampler for the first time to infinite-dimensional func-tion spaces, yielding a highly efficient gradient-free MCMC algorithm. Because our ensemble sampler does not require gradients or posterior covariance estimates, it is simple to implement and broadly applicable. In many Bayes-ian inverse problems, Markov chain Monte Carlo (MCMC) meth-ods are needed to approximate distributions on infinite-dimensional function spaces, for example, in groundwater flow, medical imaging, and traffic flow. Yet designing efficient MCMC methods for function spaces has proved challenging. Recent gradi-ent-based MCMC methods preconditioned MCMC methods, and SMC methods have improved the computational efficiency of functional random walk. However, these samplers require gradi-ents or posterior covariance estimates that may be challenging to obtain. Calculating gradients is difficult or impossible in many high-dimensional inverse problems involving a numerical integra-tor with a black-box code base. Additionally, accurately estimating posterior covariances can require a lengthy pilot run or adaptation period. These concerns raise the question: is there a functional sampler that outperforms functional random walk without requir-ing gradients or posterior covariance estimates? To address this question, we consider a gradient-free sampler that avoids explicit covariance estimation yet adapts naturally to the covariance struc-ture of the sampled distribution. This sampler works by consider-ing an ensemble of walkers and interpolating and extrapolating between walkers to make a proposal. This is called the affine in-variant ensemble sampler (AIES), which is easy to tune, easy to parallelize, and efficient at sampling spaces of moderate dimen-sionality (less than 20). The main contribution of this work is to propose a functional ensemble sampler (FES) that combines func-tional random walk and AIES. To apply this sampler, we first cal-culate the Karhunen–Loeve (KL) expansion for the Bayesian prior distribution, assumed to be Gaussian and trace-class. Then, we use AIES to sample the posterior distribution on the low-wavenumber KL components and use the functional random walk to sample the posterior distribution on the high-wavenumber KL components. Alternating between AIES and functional random walk updates, we obtain our functional ensemble sampler that is efficient and easy to use without requiring detailed knowledge of the target dis-tribution. In past work, several authors have proposed splitting the Bayesian posterior into low-wavenumber and high-wavenumber components and then applying enhanced sampling to the low-wavenumber components. Yet compared to these other samplers, FES is unique in its simplicity and broad applicability. FES does not require any derivatives, and the need for derivative-free sam-plers has previously been emphasized. FES also eliminates the requirement for posterior covariance estimates. Lastly, FES is more efficient than other gradient-free samplers in our tests. In two nu-merical examples, we apply FES to challenging inverse problems that involve estimating a functional parameter and one or more scalar parameters. We compare the performance of functional random walk, FES, and an alternative derivative-free sampler that explicitly estimates the posterior covariance matrix. We conclude that FES is the fastest available gradient-free sampler for these challenging and multimodal test problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20inverse%20problems" title="Bayesian inverse problems">Bayesian inverse problems</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20chain%20Monte%20Carlo" title=" Markov chain Monte Carlo"> Markov chain Monte Carlo</a>, <a href="https://publications.waset.org/abstracts/search?q=infinite-dimensional%20inverse%20problems" title=" infinite-dimensional inverse problems"> infinite-dimensional inverse problems</a>, <a href="https://publications.waset.org/abstracts/search?q=dimensionality%20reduction" title=" dimensionality reduction"> dimensionality reduction</a> </p> <a href="https://publications.waset.org/abstracts/136397/ensemble-sampler-for-infinite-dimensional-inverse-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136397.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">154</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1807</span> Fraud Detection in Credit Cards with Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anjali%20Chouksey">Anjali Chouksey</a>, <a href="https://publications.waset.org/abstracts/search?q=Riya%20Nimje"> Riya Nimje</a>, <a href="https://publications.waset.org/abstracts/search?q=Jahanvi%20Saraf"> Jahanvi Saraf</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Online transactions have increased dramatically in this new ‘social-distancing’ era. With online transactions, Fraud in online payments has also increased significantly. Frauds are a significant problem in various industries like insurance companies, baking, etc. These frauds include leaking sensitive information related to the credit card, which can be easily misused. Due to the government also pushing online transactions, E-commerce is on a boom. But due to increasing frauds in online payments, these E-commerce industries are suffering a great loss of trust from their customers. These companies are finding credit card fraud to be a big problem. People have started using online payment options and thus are becoming easy targets of credit card fraud. In this research paper, we will be discussing machine learning algorithms. We have used a decision tree, XGBOOST, k-nearest neighbour, logistic-regression, random forest, and SVM on a dataset in which there are transactions done online mode using credit cards. We will test all these algorithms for detecting fraud cases using the confusion matrix, F1 score, and calculating the accuracy score for each model to identify which algorithm can be used in detecting frauds. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title="machine learning">machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=fraud%20detection" title=" fraud detection"> fraud detection</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=k%20nearest%20neighbour" title=" k nearest neighbour"> k nearest neighbour</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=XGBOOST" title=" XGBOOST"> XGBOOST</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/136504/fraud-detection-in-credit-cards-with-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136504.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">1806</span> Using Machine Learning as an Alternative for Predicting Exchange Rates</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pedro%20Paulo%20Galindo%20Francisco">Pedro Paulo Galindo Francisco</a>, <a href="https://publications.waset.org/abstracts/search?q=Eli%20Dhadad%20Junior"> Eli Dhadad Junior</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study addresses the Meese-Rogoff Puzzle by introducing the latest machine learning techniques as alternatives for predicting the exchange rates. Using RMSE as a comparison metric, Meese and Rogoff discovered that economic models are unable to outperform the random walk model as short-term exchange rate predictors. Decades after this study, no statistical prediction technique has proven effective in overcoming this obstacle; although there were positive results, they did not apply to all currencies and defined periods. Recent advancements in artificial intelligence technologies have paved the way for a new approach to exchange rate prediction. Leveraging this technology, we applied five machine learning techniques to attempt to overcome the Meese-Rogoff puzzle. We considered daily data for the real, yen, British pound, euro, and Chinese yuan against the US dollar over a time horizon from 2010 to 2023. Our results showed that none of the presented techniques were able to produce an RMSE lower than the Random Walk model. However, the performance of some models, particularly LSTM and N-BEATS were able to outperform the ARIMA model. The results also suggest that machine learning models have untapped potential and could represent an effective long-term possibility for overcoming the Meese-Rogoff puzzle. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=exchage%20rate" title="exchage rate">exchage rate</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</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=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/190034/using-machine-learning-as-an-alternative-for-predicting-exchange-rates" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/190034.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">32</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1805</span> Rejection Sensitivity and Romantic Relationships: A Systematic Review and Meta-Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mandira%20Mishra">Mandira Mishra</a>, <a href="https://publications.waset.org/abstracts/search?q=Mark%20Allen"> Mark Allen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This meta-analysis explored whether rejection sensitivity relates to facets of romantic relationships. A comprehensive literature search identified 60 studies (147 effect sizes; 16,955 participants) that met inclusion criteria. Data were analysed using inverse-variance weighted random effects meta-analysis. Mean effect sizes from 21 meta-analyses provided evidence that more rejection sensitive individuals report lower levels of relationship satisfaction and relationship closeness, lower levels of perceived partner satisfaction, a greater likelihood of intimate partner violence (perpetration and victimization), higher levels of relationship concerns and relationship conflict, and higher levels of jealousy and self-silencing behaviours. There was also some evidence that rejection sensitive individuals are more likely to engage in risky sexual behaviour and are more prone to sexual compulsivity. There was no evidence of publication bias and various levels of heterogeneity in computed averages. Random effects meta-regression identified participant age and sex as important moderators of pooled mean effects. These findings provide a foundation for the theoretical development of rejection sensitivity in romantic relationships and should be of interest to relationship and marriage counsellors and other relationship professionals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intimate%20partner%20violence" title="intimate partner violence">intimate partner violence</a>, <a href="https://publications.waset.org/abstracts/search?q=relationship%20satisfaction" title=" relationship satisfaction"> relationship satisfaction</a>, <a href="https://publications.waset.org/abstracts/search?q=commitment" title=" commitment"> commitment</a>, <a href="https://publications.waset.org/abstracts/search?q=sexual%20orientation" title=" sexual orientation"> sexual orientation</a>, <a href="https://publications.waset.org/abstracts/search?q=risky%20sexual%20behaviour" title=" risky sexual behaviour"> risky sexual behaviour</a> </p> <a href="https://publications.waset.org/abstracts/157330/rejection-sensitivity-and-romantic-relationships-a-systematic-review-and-meta-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157330.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">81</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">1804</span> Optimization of Hate Speech and Abusive Language Detection on Indonesian-language Twitter using Genetic Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rikson%20Gultom">Rikson Gultom</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Hate Speech and Abusive language on social media is difficult to detect, usually, it is detected after it becomes viral in cyberspace, of course, it is too late for prevention. An early detection system that has a fairly good accuracy is needed so that it can reduce conflicts that occur in society caused by postings on social media that attack individuals, groups, and governments in Indonesia. The purpose of this study is to find an early detection model on Twitter social media using machine learning that has high accuracy from several machine learning methods studied. In this study, the support vector machine (SVM), Naïve Bayes (NB), and Random Forest Decision Tree (RFDT) methods were compared with the Support Vector machine with genetic algorithm (SVM-GA), Nave Bayes with genetic algorithm (NB-GA), and Random Forest Decision Tree with Genetic Algorithm (RFDT-GA). The study produced a comparison table for the accuracy of the hate speech and abusive language detection model, and presented it in the form of a graph of the accuracy of the six algorithms developed based on the Indonesian-language Twitter dataset, and concluded the best model with the highest accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=abusive%20language" title="abusive language">abusive language</a>, <a href="https://publications.waset.org/abstracts/search?q=hate%20speech" title=" hate speech"> hate speech</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=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media" title=" social media"> social media</a> </p> <a href="https://publications.waset.org/abstracts/146661/optimization-of-hate-speech-and-abusive-language-detection-on-indonesian-language-twitter-using-genetic-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/146661.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">128</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">1803</span> Application of Machine Learning on Google Earth Engine for Forest Fire Severity, Burned Area Mapping and Land Surface Temperature Analysis: Rajasthan, India</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alisha%20Sinha">Alisha Sinha</a>, <a href="https://publications.waset.org/abstracts/search?q=Laxmi%20Kant%20Sharma"> Laxmi Kant Sharma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forest fires are a recurring issue in many parts of the world, including India. These fires can have various causes, including human activities (such as agricultural burning, campfires, or discarded cigarettes) and natural factors (such as lightning). This study presents a comprehensive and advanced methodology for assessing wildfire susceptibility by integrating diverse environmental variables and leveraging cutting-edge machine learning techniques across Rajasthan, India. The primary goal of the study is to utilize Google Earth Engine to compare locations in Sariska National Park, Rajasthan (India), before and after forest fires. High-resolution satellite data were used to assess the amount and types of changes caused by forest fires. The present study meticulously analyzes various environmental variables, i.e., slope orientation, elevation, normalized difference vegetation index (NDVI), drainage density, precipitation, and temperature, to understand landscape characteristics and assess wildfire susceptibility. In addition, a sophisticated random forest regression model is used to predict land surface temperature based on a set of environmental parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wildfire%20susceptibility%20mapping" title="wildfire susceptibility mapping">wildfire susceptibility mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=LST" title=" LST"> LST</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=GEE" title=" GEE"> GEE</a>, <a href="https://publications.waset.org/abstracts/search?q=MODIS" title=" MODIS"> MODIS</a>, <a href="https://publications.waset.org/abstracts/search?q=climatic%20parameters" title=" climatic parameters"> climatic parameters</a> </p> <a href="https://publications.waset.org/abstracts/191316/application-of-machine-learning-on-google-earth-engine-for-forest-fire-severity-burned-area-mapping-and-land-surface-temperature-analysis-rajasthan-india" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/191316.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">22</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">1802</span> Deconstructing Local Area Networks Using MaatPeace</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gerald%20Todd">Gerald Todd</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recent advances in random epistemologies and ubiquitous theory have paved the way for web services. Given the current status of linear-time communication, cyberinformaticians compellingly desire the exploration of link-level acknowledgements. In order to realize this purpose, we concentrate our efforts on disconfirming that DHTs and model checking are mostly incompatible. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=LAN" title="LAN">LAN</a>, <a href="https://publications.waset.org/abstracts/search?q=cyberinformatics" title=" cyberinformatics"> cyberinformatics</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20checking" title=" model checking"> model checking</a>, <a href="https://publications.waset.org/abstracts/search?q=communication" title=" communication"> communication</a> </p> <a href="https://publications.waset.org/abstracts/21498/deconstructing-local-area-networks-using-maatpeace" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21498.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">401</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">1801</span> Effectiveness of Cranberry Ingesting for Prevention of Urinary Tract Infection: A Systematic Review and Meta-Analysis of Randomized Controlled Trials</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yu-Chieh%20Huang">Yu-Chieh Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Pei-Shih%20Chen"> Pei-Shih Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Tao-Hsin%20Tung"> Tao-Hsin Tung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Urinary tract infection is the most common bacterial infection to our best knowledge. Objective: This study is to investigate whether cranberry ingesting could improve the urinary tract infection. Methods: We searched the PubMed and Cochrane Library for relevant randomized controlled trials without language limitations between 9 March 1994 and June 30, 2017, with a priori defined inclusion and exclusion criteria. The search terms included (cranberry OR Vaccinium macrocarpon OR Vaccinium oxy-coccus OR Vaccinium microcarpum OR Vaccinium erythrocarpum OR Vaccinium) AND (urinary tract infection OR bacteriuria OR pyuria) AND (effect OR effective-ness OR efficacy) AND (random OR randomized). Results: There were 26 studies met the selection criteria included among 4709 eligible participants. We analyzed all trials in meta-analysis. The random-effects pooled risk ratio (RR) for the group using cranberry versus using placebo was 0.75; 95%CI[0.63, 0.880]; p-value=0.0002) and heterogeneity was 56%. Furthermore, we divided the subjects into different subgroup to analysis. Ingesting cranberry seemed to be more effective in some subgroups, including the patients with recurrent UTI (RR, 0.71; 95%CI[0.54,0.93]; p-value=0.002) (I²= 65%) and female population (RR, 0.73, 95%CI[0.58,0.92]; p-value=0.002) (I²= 59%). The prevention effect was not different between cranberry and trimethoprim (RR, 1.25, 95%CI[0.67, 2.33]; p-value=0.49) (I²= 68%). No matter the forms of cranberry were capsules or juice, the efficacy was useful. Conclusions: It is showed that cranberry ingesting is usefully associated with prevention UTI. There are more effective in prevention of UTI in some groups. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cranberry" title="cranberry">cranberry</a>, <a href="https://publications.waset.org/abstracts/search?q=effectiveness" title=" effectiveness"> effectiveness</a>, <a href="https://publications.waset.org/abstracts/search?q=prevention" title=" prevention"> prevention</a>, <a href="https://publications.waset.org/abstracts/search?q=urinary%20tract%20infect" title=" urinary tract infect"> urinary tract infect</a> </p> <a href="https://publications.waset.org/abstracts/79687/effectiveness-of-cranberry-ingesting-for-prevention-of-urinary-tract-infection-a-systematic-review-and-meta-analysis-of-randomized-controlled-trials" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79687.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">400</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">1800</span> Gaussian Probability Density for Forest Fire Detection Using Satellite Imagery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Benkraouda">S. Benkraouda</a>, <a href="https://publications.waset.org/abstracts/search?q=Z.%20Djelloul-Khedda"> Z. Djelloul-Khedda</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Yagoubi"> B. Yagoubi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> we present a method for early detection of forest fires from a thermal infrared satellite image, using the image matrix of the probability of belonging. The principle of the method is to compare a theoretical mathematical model to an experimental model. We considered that each line of the image matrix, as an embodiment of a non-stationary random process. Since the distribution of pixels in the satellite image is statistically dependent, we divided these lines into small stationary and ergodic intervals to characterize the image by an adequate mathematical model. A standard deviation was chosen to generate random variables, so each interval behaves naturally like white Gaussian noise. The latter has been selected as the mathematical model that represents a set of very majority pixels, which we can be considered as the image background. Before modeling the image, we made a few pretreatments, then the parameters of the theoretical Gaussian model were extracted from the modeled image, these settings will be used to calculate the probability of each interval of the modeled image to belong to the theoretical Gaussian model. The high intensities pixels are regarded as foreign elements to it, so they will have a low probability, and the pixels that belong to the background image will have a high probability. Finally, we did present the reverse of the matrix of probabilities of these intervals for a better fire detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=forest%20fire" title="forest fire">forest fire</a>, <a href="https://publications.waset.org/abstracts/search?q=forest%20fire%20detection" title=" forest fire detection"> forest fire detection</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20image" title=" satellite image"> satellite image</a>, <a href="https://publications.waset.org/abstracts/search?q=normal%20distribution" title=" normal distribution"> normal distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=theoretical%20gaussian%20model" title=" theoretical gaussian model"> theoretical gaussian model</a>, <a href="https://publications.waset.org/abstracts/search?q=thermal%20infrared%20matrix%20image" title=" thermal infrared matrix image"> thermal infrared matrix image</a> </p> <a href="https://publications.waset.org/abstracts/118320/gaussian-probability-density-for-forest-fire-detection-using-satellite-imagery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118320.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">142</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">1799</span> Solution-Processed Threshold Switching Selectors Based on Highly Flexible, Transparent and Scratchable Silver Nanowires Conductive Films</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peiyuan%20Guan">Peiyuan Guan</a>, <a href="https://publications.waset.org/abstracts/search?q=Tao%20Wan"> Tao Wan</a>, <a href="https://publications.waset.org/abstracts/search?q=Dewei%20Chu"> Dewei Chu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the flash memory approaching its physical limit, the emerging resistive random-access memory (RRAM) has been considered as one of the most promising candidates for the next-generation non-volatile memory. One selector-one resistor configuration has shown the most promising way to resolve the crosstalk issue without affecting the scalability and high-density integration of the RRAM array. By comparison with other candidates of selectors (such as diodes and nonlinear devices), threshold switching selectors dominated by formation/spontaneous rupture of fragile conductive filaments have been proved to possess low voltages, high selectivity, and ultra-low current leakage. However, the flexibility and transparency of selectors are barely mentioned. Therefore, it is a matter of urgency to develop a selector with highly flexible and transparent properties to assist the application of RRAM for a diversity of memory devices. In this work, threshold switching selectors were designed using a facilely solution-processed fabrication on AgNWs@PDMS composite films, which show high flexibility, transparency and scratch resistance. As-fabricated threshold switching selectors also have revealed relatively high selectivity (~107), low operating voltages (Vth < 1 V) and good switching performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flexible%20and%20transparent" title="flexible and transparent">flexible and transparent</a>, <a href="https://publications.waset.org/abstracts/search?q=resistive%20random-access%20memory" title=" resistive random-access memory"> resistive random-access memory</a>, <a href="https://publications.waset.org/abstracts/search?q=silver%20nanowires" title=" silver nanowires"> silver nanowires</a>, <a href="https://publications.waset.org/abstracts/search?q=threshold%20switching%20selector" title=" threshold switching selector"> threshold switching selector</a> </p> <a href="https://publications.waset.org/abstracts/119260/solution-processed-threshold-switching-selectors-based-on-highly-flexible-transparent-and-scratchable-silver-nanowires-conductive-films" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/119260.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">128</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">1798</span> Mechanical-Reliability Coupling for a Bearing Capacity Assessment of Shallow Foundations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amal%20Hentati">Amal Hentati</a>, <a href="https://publications.waset.org/abstracts/search?q=Mbarka%20Selmi"> Mbarka Selmi</a>, <a href="https://publications.waset.org/abstracts/search?q=Tarek%20Kormi"> Tarek Kormi</a>, <a href="https://publications.waset.org/abstracts/search?q=Julien%20Baroth"> Julien Baroth</a>, <a href="https://publications.waset.org/abstracts/search?q=Barthelemy%20Harthong"> Barthelemy Harthong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The impact of uncertainties on the performance assessment of shallow foundations is often significant. The need of the geotechnical engineers to a more objective and rigorous description of soil variations permitting to quantify these uncertainties and to incorporate them into calculation methods led to the development of reliability approaches. In this context, a mechanical-reliability coupling was developed in this paper, using a program coded in Matlab and the finite element software Abaqus, for the bearing capacity assessment of shallow foundations. The reliability analysis, based on the finite element method, assumed both soil cohesion and friction angle as uncertain parameters characterized by normal or lognormal probability distributions. The inherent spatial variability of both soil properties was, then, taken into account using 1D stationary random fields. The application of the proposed methodology to a shallow foundation subjected to a centered vertical loading permitted to highlight the proposed process interest. Findings proved the insufficiency of the conventional approach to predict the foundation failure and a high sensitivity of the ultimate loads to the soil properties uncertainties, mainly those related to the friction angle, was noted. Moreover, an asymmetry of both displacement and velocity fields was obtained. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mechanical-reliability%20coupling" title="mechanical-reliability coupling">mechanical-reliability coupling</a>, <a href="https://publications.waset.org/abstracts/search?q=finite%20element%20method" title=" finite element method"> finite element method</a>, <a href="https://publications.waset.org/abstracts/search?q=shallow%20foundation" title=" shallow foundation"> shallow foundation</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20fields" title=" random fields"> random fields</a>, <a href="https://publications.waset.org/abstracts/search?q=spatial%20variability" title=" spatial variability"> spatial variability</a> </p> <a href="https://publications.waset.org/abstracts/18588/mechanical-reliability-coupling-for-a-bearing-capacity-assessment-of-shallow-foundations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18588.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">661</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">1797</span> The Effect of Intimate Partner Violence on Child Abuse in South Korea: Focused on the Moderating Effects of Patriarchal Attitude and Informal Social Control</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hye%20Lin%20Yang">Hye Lin Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Clifton%20R.%20Emery"> Clifton R. Emery</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Purpose: The purpose of this study is to examine the effects of intimate partner violence on child abuse, whether patriarchal attitude and informal social control moderate the relationship between intimate partner violence and child abuse. This study was conducted with data from The Seoul Families and Neighborhoods Study (SFNS). The SFNS is a representative random probability 3-stage cluster sample of 541 cohabiting couples in Seoul, South Korea collected in 2012. To verify research models, Random effect analysis were used. All analyses were performed using the Stata program. Results: Crucial findings are the following. First, intimate partner violence showed a significantly positive relationship with Child abuse. Second, there are significant moderating effects of informal social control on intimate partner violence - child abuse. Third, there are significant moderating effects of patriarchal attitude on intimate partner violence - child abuse. In other words, Patriarchal attitude is a significant risk factor of child abuse and informal social control is a significant Protection factor of child abuse. Based on results, the policy and practical implications for preventing child abuse, promoting informal social control were discussed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Intimate%20partner%20violence" title="Intimate partner violence">Intimate partner violence</a>, <a href="https://publications.waset.org/abstracts/search?q=child%20abuse" title=" child abuse"> child abuse</a>, <a href="https://publications.waset.org/abstracts/search?q=informal%20social%20control" title=" informal social control"> informal social control</a>, <a href="https://publications.waset.org/abstracts/search?q=patriarchal%20attitude" title=" patriarchal attitude"> patriarchal attitude</a> </p> <a href="https://publications.waset.org/abstracts/44787/the-effect-of-intimate-partner-violence-on-child-abuse-in-south-korea-focused-on-the-moderating-effects-of-patriarchal-attitude-and-informal-social-control" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44787.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">302</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">1796</span> Nonlinear Analysis in Investigating the Complexity of Neurophysiological Data during Reflex Behavior</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Juliana%20A.%20Knocikova">Juliana A. Knocikova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Methods of nonlinear signal analysis are based on finding that random behavior can arise in deterministic nonlinear systems with a few degrees of freedom. Considering the dynamical systems, entropy is usually understood as a rate of information production. Changes in temporal dynamics of physiological data are indicating evolving of system in time, thus a level of new signal pattern generation. During last decades, many algorithms were introduced to assess some patterns of physiological responses to external stimulus. However, the reflex responses are usually characterized by short periods of time. This characteristic represents a great limitation for usual methods of nonlinear analysis. To solve the problems of short recordings, parameter of approximate entropy has been introduced as a measure of system complexity. Low value of this parameter is reflecting regularity and predictability in analyzed time series. On the other side, increasing of this parameter means unpredictability and a random behavior, hence a higher system complexity. Reduced neurophysiological data complexity has been observed repeatedly when analyzing electroneurogram and electromyogram activities during defence reflex responses. Quantitative phrenic neurogram changes are also obvious during severe hypoxia, as well as during airway reflex episodes. Concluding, the approximate entropy parameter serves as a convenient tool for analysis of reflex behavior characterized by short lasting time series. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=approximate%20entropy" title="approximate entropy">approximate entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=neurophysiological%20data" title=" neurophysiological data"> neurophysiological data</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20dynamics" title=" nonlinear dynamics"> nonlinear dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=reflex" title=" reflex"> reflex</a> </p> <a href="https://publications.waset.org/abstracts/13247/nonlinear-analysis-in-investigating-the-complexity-of-neurophysiological-data-during-reflex-behavior" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13247.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">300</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">1795</span> Parkinson’s Disease Detection Analysis through Machine Learning Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhtasim%20Shafi%20Kader">Muhtasim Shafi Kader</a>, <a href="https://publications.waset.org/abstracts/search?q=Fizar%20Ahmed"> Fizar Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Annesha%20Acharjee"> Annesha Acharjee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning and data mining are crucial in health care, as well as medical information and detection. Machine learning approaches are now being utilized to improve awareness of a variety of critical health issues, including diabetes detection, neuron cell tumor diagnosis, COVID 19 identification, and so on. Parkinson’s disease is basically a disease for our senior citizens in Bangladesh. Parkinson's Disease indications often seem progressive and get worst with time. People got affected trouble walking and communicating with the condition advances. Patients can also have psychological and social vagaries, nap problems, hopelessness, reminiscence loss, and weariness. Parkinson's disease can happen in both men and women. Though men are affected by the illness at a proportion that is around partial of them are women. In this research, we have to get out the accurate ML algorithm to find out the disease with a predictable dataset and the model of the following machine learning classifiers. Therefore, nine ML classifiers are secondhand to portion study to use machine learning approaches like as follows, Naive Bayes, Adaptive Boosting, Bagging Classifier, Decision Tree Classifier, Random Forest classifier, XBG Classifier, K Nearest Neighbor Classifier, Support Vector Machine Classifier, and Gradient Boosting Classifier are used. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=naive%20bayes" title="naive bayes">naive bayes</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20boosting" title=" adaptive boosting"> adaptive boosting</a>, <a href="https://publications.waset.org/abstracts/search?q=bagging%20classifier" title=" bagging classifier"> bagging classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree%20classifier" title=" decision tree classifier"> decision tree classifier</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=XBG%20classifier" title=" XBG classifier"> XBG classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=k%20nearest%20neighbor%20classifier" title=" k nearest neighbor classifier"> k nearest neighbor classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20classifier" title=" support vector classifier"> support vector classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=gradient%20boosting%20classifier" title=" gradient boosting classifier"> gradient boosting classifier</a> </p> <a href="https://publications.waset.org/abstracts/148163/parkinsons-disease-detection-analysis-through-machine-learning-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148163.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">129</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1794</span> The Relationship between the Use of Social Networks with Executive Functions and Academic Performance in High School Students in Tehran</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Esmail%20Sadipour">Esmail Sadipour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of social networks is increasing day by day in all societies. The purpose of this research was to know the relationship between the use of social networks (Instagram, WhatsApp, and Telegram) with executive functions and academic performance in first-year female high school students. This research was applied in terms of purpose, quantitative in terms of data type, and correlational in terms of technique. The population of this research consisted of all female high school students in the first year of district 2 of Tehran. Using Green's formula, the sample size of 150 people was determined and selected by cluster random method. In this way, from all 17 high schools in district 2 of Tehran, 5 high schools were selected by a simple random method and then one class was selected from each high school, and a total of 155 students were selected. To measure the use of social networks, a researcher-made questionnaire was used, the Barclay test (2012) was used for executive functions, and last semester's GPA was used for academic performance. Pearson's correlation coefficient and multivariate regression were used to analyze the data. The results showed that there is a negative relationship between the amount of use of social networks and self-control, self-motivation and time self-management. In other words, the more the use of social networks, the fewer executive functions of students, self-control, self-motivation, and self-management of their time. Also, with the increase in the use of social networks, the academic performance of students has decreased. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=social%20networks" title="social networks">social networks</a>, <a href="https://publications.waset.org/abstracts/search?q=executive%20function" title=" executive function"> executive function</a>, <a href="https://publications.waset.org/abstracts/search?q=academic%20performance" title=" academic performance"> academic performance</a>, <a href="https://publications.waset.org/abstracts/search?q=working%20memory" title=" working memory"> working memory</a> </p> <a href="https://publications.waset.org/abstracts/159924/the-relationship-between-the-use-of-social-networks-with-executive-functions-and-academic-performance-in-high-school-students-in-tehran" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159924.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">96</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">1793</span> Probabilistic Slope Stability Analysis of Excavation Induced Landslides Using Hermite Polynomial Chaos</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Schadrack%20Mwizerwa">Schadrack Mwizerwa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The characterization and prediction of landslides are crucial for assessing geological hazards and mitigating risks to infrastructure and communities. This research aims to develop a probabilistic framework for analyzing excavation-induced landslides, which is fundamental for assessing geological hazards and mitigating risks to infrastructure and communities. The study uses Hermite polynomial chaos, a non-stationary random process, to analyze the stability of a slope and characterize the failure probability of a real landslide induced by highway construction excavation. The correlation within the data is captured using the Karhunen-Loève (KL) expansion theory, and the finite element method is used to analyze the slope's stability. The research contributes to the field of landslide characterization by employing advanced random field approaches, providing valuable insights into the complex nature of landslide behavior and the effectiveness of advanced probabilistic models for risk assessment and management. The data collected from the Baiyuzui landslide, induced by highway construction, is used as an illustrative example. The findings highlight the importance of considering the probabilistic nature of landslides and provide valuable insights into the complex behavior of such hazards. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hermite%20polynomial%20chaos" title="Hermite polynomial chaos">Hermite polynomial chaos</a>, <a href="https://publications.waset.org/abstracts/search?q=Karhunen-Loeve" title=" Karhunen-Loeve"> Karhunen-Loeve</a>, <a href="https://publications.waset.org/abstracts/search?q=slope%20stability" title=" slope stability"> slope stability</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20analysis" title=" probabilistic analysis"> probabilistic analysis</a> </p> <a href="https://publications.waset.org/abstracts/176089/probabilistic-slope-stability-analysis-of-excavation-induced-landslides-using-hermite-polynomial-chaos" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176089.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">1792</span> Land Use/Land Cover Mapping Using Landsat 8 and Sentinel-2 in a Mediterranean Landscape</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Moschos%20Vogiatzis">Moschos Vogiatzis</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Perakis"> K. Perakis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Spatial-explicit and up-to-date land use/land cover information is fundamental for spatial planning, land management, sustainable development, and sound decision-making. In the last decade, many satellite-derived land cover products at different spatial, spectral, and temporal resolutions have been developed, such as the European Copernicus Land Cover product. However, more efficient and detailed information for land use/land cover is required at the regional or local scale. A typical Mediterranean basin with a complex landscape comprised of various forest types, crops, artificial surfaces, and wetlands was selected to test and develop our approach. In this study, we investigate the improvement of Copernicus Land Cover product (CLC2018) using Landsat 8 and Sentinel-2 pixel-based classification based on all available existing geospatial data (Forest Maps, LPIS, Natura2000 habitats, cadastral parcels, etc.). We examined and compared the performance of the Random Forest classifier for land use/land cover mapping. In total, 10 land use/land cover categories were recognized in Landsat 8 and 11 in Sentinel-2A. A comparison of the overall classification accuracies for 2018 shows that Landsat 8 classification accuracy was slightly higher than Sentinel-2A (82,99% vs. 80,30%). We concluded that the main land use/land cover types of CLC2018, even within a heterogeneous area, can be successfully mapped and updated according to CLC nomenclature. Future research should be oriented toward integrating spatiotemporal information from seasonal bands and spectral indexes in the classification process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20use%2Fland%20cover" title=" land use/land cover"> land use/land cover</a>, <a href="https://publications.waset.org/abstracts/search?q=mapping" title=" mapping"> mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a> </p> <a href="https://publications.waset.org/abstracts/152892/land-useland-cover-mapping-using-landsat-8-and-sentinel-2-in-a-mediterranean-landscape" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152892.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">126</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">1791</span> Predicting Options Prices Using Machine Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Krishang%20Surapaneni">Krishang Surapaneni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42% <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=finance" title="finance">finance</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20regression%20model" title=" linear regression model"> linear regression model</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20model" title=" machine learning model"> machine learning model</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=stock%20price" title=" stock price"> stock price</a> </p> <a href="https://publications.waset.org/abstracts/160197/predicting-options-prices-using-machine-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160197.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">75</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1790</span> A Machine Learning Approach for Earthquake Prediction in Various Zones Based on Solar Activity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Viacheslav%20Shkuratskyy">Viacheslav Shkuratskyy</a>, <a href="https://publications.waset.org/abstracts/search?q=Aminu%20Bello%20Usman"> Aminu Bello Usman</a>, <a href="https://publications.waset.org/abstracts/search?q=Michael%20O%E2%80%99Dea"> Michael O’Dea</a>, <a href="https://publications.waset.org/abstracts/search?q=Saifur%20Rahman%20Sabuj"> Saifur Rahman Sabuj</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper examines relationships between solar activity and earthquakes; it applied machine learning techniques: K-nearest neighbour, support vector regression, random forest regression, and long short-term memory network. Data from the SILSO World Data Center, the NOAA National Center, the GOES satellite, NASA OMNIWeb, and the United States Geological Survey were used for the experiment. The 23rd and 24th solar cycles, daily sunspot number, solar wind velocity, proton density, and proton temperature were all included in the dataset. The study also examined sunspots, solar wind, and solar flares, which all reflect solar activity and earthquake frequency distribution by magnitude and depth. The findings showed that the long short-term memory network model predicts earthquakes more correctly than the other models applied in the study, and solar activity is more likely to affect earthquakes of lower magnitude and shallow depth than earthquakes of magnitude 5.5 or larger with intermediate depth and deep depth. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=k-nearest%20neighbour" title="k-nearest neighbour">k-nearest neighbour</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20regression" title=" support vector regression"> support vector regression</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest%20regression" title=" random forest regression"> random forest regression</a>, <a href="https://publications.waset.org/abstracts/search?q=long%20short-term%20memory%20network" title=" long short-term memory network"> long short-term memory network</a>, <a href="https://publications.waset.org/abstracts/search?q=earthquakes" title=" earthquakes"> earthquakes</a>, <a href="https://publications.waset.org/abstracts/search?q=solar%20activity" title=" solar activity"> solar activity</a>, <a href="https://publications.waset.org/abstracts/search?q=sunspot%20number" title=" sunspot number"> sunspot number</a>, <a href="https://publications.waset.org/abstracts/search?q=solar%20wind" title=" solar wind"> solar wind</a>, <a href="https://publications.waset.org/abstracts/search?q=solar%20flares" title=" solar flares"> solar flares</a> </p> <a href="https://publications.waset.org/abstracts/170933/a-machine-learning-approach-for-earthquake-prediction-in-various-zones-based-on-solar-activity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170933.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">73</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">1789</span> A Comparative Study of Sampling-Based Uncertainty Propagation with First Order Error Analysis and Percentile-Based Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Gulam%20Kibria">M. Gulam Kibria</a>, <a href="https://publications.waset.org/abstracts/search?q=Shourav%20Ahmed"> Shourav Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Kais%20Zaman"> Kais Zaman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In system analysis, the information on the uncertain input variables cause uncertainty in the system responses. Different probabilistic approaches for uncertainty representation and propagation in such cases exist in the literature. Different uncertainty representation approaches result in different outputs. Some of the approaches might result in a better estimation of system response than the other approaches. The NASA Langley Multidisciplinary Uncertainty Quantification Challenge (MUQC) has posed challenges about uncertainty quantification. Subproblem A, the uncertainty characterization subproblem, of the challenge posed is addressed in this study. In this subproblem, the challenge is to gather knowledge about unknown model inputs which have inherent aleatory and epistemic uncertainties in them with responses (output) of the given computational model. We use two different methodologies to approach the problem. In the first methodology we use sampling-based uncertainty propagation with first order error analysis. In the other approach we place emphasis on the use of Percentile-Based Optimization (PBO). The NASA Langley MUQC’s subproblem A is developed in such a way that both aleatory and epistemic uncertainties need to be managed. The challenge problem classifies each uncertain parameter as belonging to one the following three types: (i) An aleatory uncertainty modeled as a random variable. It has a fixed functional form and known coefficients. This uncertainty cannot be reduced. (ii) An epistemic uncertainty modeled as a fixed but poorly known physical quantity that lies within a given interval. This uncertainty is reducible. (iii) A parameter might be aleatory but sufficient data might not be available to adequately model it as a single random variable. For example, the parameters of a normal variable, e.g., the mean and standard deviation, might not be precisely known but could be assumed to lie within some intervals. It results in a distributional p-box having the physical parameter with an aleatory uncertainty, but the parameters prescribing its mathematical model are subjected to epistemic uncertainties. Each of the parameters of the random variable is an unknown element of a known interval. This uncertainty is reducible. From the study, it is observed that due to practical limitations or computational expense, the sampling is not exhaustive in sampling-based methodology. That is why the sampling-based methodology has high probability of underestimating the output bounds. Therefore, an optimization-based strategy to convert uncertainty described by interval data into a probabilistic framework is necessary. This is achieved in this study by using PBO. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aleatory%20uncertainty" title="aleatory uncertainty">aleatory uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=epistemic%20uncertainty" title=" epistemic uncertainty"> epistemic uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=first%20order%20error%20analysis" title=" first order error analysis"> first order error analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20quantification" title=" uncertainty quantification"> uncertainty quantification</a>, <a href="https://publications.waset.org/abstracts/search?q=percentile-based%20optimization" title=" percentile-based optimization"> percentile-based optimization</a> </p> <a href="https://publications.waset.org/abstracts/90749/a-comparative-study-of-sampling-based-uncertainty-propagation-with-first-order-error-analysis-and-percentile-based-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90749.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">1788</span> A Methodology for the Synthesis of Multi-Processors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Yasinian">Hamid Yasinian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Random epistemologies and hash tables have garnered minimal interest from both security experts and experts in the last several years. In fact, few information theorists would disagree with the evaluation of expert systems. In our research, we discover how flip-flop gates can be applied to the study of superpages. Though such a hypothesis at first glance seems perverse, it is derived from known results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=synthesis" title="synthesis">synthesis</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-processors" title=" multi-processors"> multi-processors</a>, <a href="https://publications.waset.org/abstracts/search?q=interactive%20model" title=" interactive model"> interactive model</a>, <a href="https://publications.waset.org/abstracts/search?q=moor%E2%80%99s%20law" title=" moor’s law"> moor’s law</a> </p> <a href="https://publications.waset.org/abstracts/19087/a-methodology-for-the-synthesis-of-multi-processors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19087.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> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1787</span> Peak Frequencies in the Collective Membrane Potential of a Hindmarsh-Rose Small-World Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sun%20Zhe">Sun Zhe</a>, <a href="https://publications.waset.org/abstracts/search?q=Ruggero%20Micheletto"> Ruggero Micheletto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As discussed extensively in many studies, noise in neural networks have an important role in the functioning and time evolution of the system. The mechanism by which noise induce stochastic resonance enhancing and influencing certain operations is not clarified nor is the mechanism of information storage and coding. With the present research we want to study the role of noise, especially focusing on the frequency peaks in a three variable Hindmarsh−Rose Small−World network. We investigated the behaviour of the network to external noises. We demonstrate that a variation of signal to noise ratio of about 10 dB induces an increase in membrane potential signal of about 15%, averaged over the whole network. We also considered the integral of the whole membrane potential as a paradigm of internal noise, the one generated by the brain network. We showed that this internal noise is attenuated with the size of the network or with the number of random connections. By means of Fourier analysis we found that it has distinct peaks of frequencies, moreover, we showed that increasing the size of the network introducing more neurons, reduced the maximum frequencies generated by the network, whereas the increase in the number of random connections (determined by the small-world probability p) led to a trend toward higher frequencies. This study may give clues on how networks utilize noise to alter the collective behaviour of the system in their operations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title="neural networks">neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20processes" title=" stochastic processes"> stochastic processes</a>, <a href="https://publications.waset.org/abstracts/search?q=small-world%20networks" title=" small-world networks"> small-world networks</a>, <a href="https://publications.waset.org/abstracts/search?q=discrete%20Fourier%20analysis" title=" discrete Fourier analysis"> discrete Fourier analysis</a> </p> <a href="https://publications.waset.org/abstracts/27187/peak-frequencies-in-the-collective-membrane-potential-of-a-hindmarsh-rose-small-world-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27187.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">291</span> </span> </div> </div> <ul class="pagination"> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=random%20non-response&page=9" rel="prev">&lsaquo;</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=random%20non-response&page=1">1</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=random%20non-response&page=2">2</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=random%20non-response&page=7">7</a></li> <li class="page-item"><a class="page-link" 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