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interval</title> <meta name="description" content="Search results for: prediction interval"> <meta name="keywords" content="prediction interval"> <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="prediction interval"> <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> 3038</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: prediction interval</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3038</span> Classifying and Predicting Efficiencies Using Interval DEA Grid Setting</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yiannis%20G.%20Smirlis">Yiannis G. Smirlis </a> </p> <p class="card-text"><strong>Abstract:</strong></p> The classification and the prediction of efficiencies in Data Envelopment Analysis (DEA) is an important issue, especially in large scale problems or when new units frequently enter the under-assessment set. In this paper, we contribute to the subject by proposing a grid structure based on interval segmentations of the range of values for the inputs and outputs. Such intervals combined, define hyper-rectangles that partition the space of the problem. This structure, exploited by Interval DEA models and a dominance relation, acts as a DEA pre-processor, enabling the classification and prediction of efficiency scores, without applying any DEA models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20envelopment%20analysis" title="data envelopment analysis">data envelopment analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=interval%20DEA" title=" interval DEA"> interval DEA</a>, <a href="https://publications.waset.org/abstracts/search?q=efficiency%20classification" title=" efficiency classification"> efficiency classification</a>, <a href="https://publications.waset.org/abstracts/search?q=efficiency%20prediction" title=" efficiency prediction"> efficiency prediction</a> </p> <a href="https://publications.waset.org/abstracts/86988/classifying-and-predicting-efficiencies-using-interval-dea-grid-setting" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/86988.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">164</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">3037</span> Behind Fuzzy Regression Approach: An Exploration Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lavinia%20B.%20Dulla">Lavinia B. Dulla</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The exploration study of the fuzzy regression approach attempts to present that fuzzy regression can be used as a possible alternative to classical regression. It likewise seeks to assess the differences and characteristics of simple linear regression and fuzzy regression using the width of prediction interval, mean absolute deviation, and variance of residuals. Based on the simple linear regression model, the fuzzy regression approach is worth considering as an alternative to simple linear regression when the sample size is between 10 and 20. As the sample size increases, the fuzzy regression approach is not applicable to use since the assumption regarding large sample size is already operating within the framework of simple linear regression. Nonetheless, it can be suggested for a practical alternative when decisions often have to be made on the basis of small data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20regression%20approach" title="fuzzy regression approach">fuzzy regression approach</a>, <a href="https://publications.waset.org/abstracts/search?q=minimum%20fuzziness%20criterion" title=" minimum fuzziness criterion"> minimum fuzziness criterion</a>, <a href="https://publications.waset.org/abstracts/search?q=interval%20regression" title=" interval regression"> interval regression</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20interval" title=" prediction interval"> prediction interval</a> </p> <a href="https://publications.waset.org/abstracts/139364/behind-fuzzy-regression-approach-an-exploration-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/139364.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">298</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">3036</span> A New Approach to Interval Matrices and Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Obaid%20Algahtani">Obaid Algahtani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An interval may be defined as a convex combination as follows: I=[a,b]={x_α=(1-α)a+αb: α∈[0,1]}. Consequently, we may adopt interval operations by applying the scalar operation point-wise to the corresponding interval points: I ∙J={x_α∙y_α ∶ αϵ[0,1],x_α ϵI ,y_α ϵJ}, With the usual restriction 0∉J if ∙ = ÷. These operations are associative: I+( J+K)=(I+J)+ K, I*( J*K)=( I*J )* K. These two properties, which are missing in the usual interval operations, will enable the extension of the usual linear system concepts to the interval setting in a seamless manner. The arithmetic introduced here avoids such vague terms as ”interval extension”, ”inclusion function”, determinants which we encounter in the engineering literature that deal with interval linear systems. On the other hand, these definitions were motivated by our attempt to arrive at a definition of interval random variables and investigate the corresponding statistical properties. We feel that they are the natural ones to handle interval systems. We will enable the extension of many results from usual state space models to interval state space models. The interval state space model we will consider here is one of the form X_((t+1) )=AX_t+ W_t, Y_t=HX_t+ V_t, t≥0, where A∈ 〖IR〗^(k×k), H ∈ 〖IR〗^(p×k) are interval matrices and 〖W 〗_t ∈ 〖IR〗^k,V_t ∈〖IR〗^p are zero – mean Gaussian white-noise interval processes. This feeling is reassured by the numerical results we obtained in a simulation examples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=interval%20analysis" title="interval analysis">interval analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=interval%20matrices" title=" interval matrices"> interval matrices</a>, <a href="https://publications.waset.org/abstracts/search?q=state%20space%20model" title=" state space model"> state space model</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20Filter" title=" Kalman Filter"> Kalman Filter</a> </p> <a href="https://publications.waset.org/abstracts/20692/a-new-approach-to-interval-matrices-and-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20692.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">425</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">3035</span> Computing Maximum Uniquely Restricted Matchings in Restricted Interval Graphs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Swapnil%20Gupta">Swapnil Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Pandu%20Rangan"> C. Pandu Rangan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A uniquely restricted matching is defined to be a matching M whose matched vertices induces a sub-graph which has only one perfect matching. In this paper, we make progress on the open question of the status of this problem on interval graphs (graphs obtained as the intersection graph of intervals on a line). We give an algorithm to compute maximum cardinality uniquely restricted matchings on certain sub-classes of interval graphs. We consider two sub-classes of interval graphs, the former contained in the latter, and give O(|E|^2) time algorithms for both of them. It is to be noted that both sub-classes are incomparable to proper interval graphs (graphs obtained as the intersection graph of intervals in which no interval completely contains another interval), on which the problem can be solved in polynomial time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=uniquely%20restricted%20matching" title="uniquely restricted matching">uniquely restricted matching</a>, <a href="https://publications.waset.org/abstracts/search?q=interval%20graph" title=" interval graph"> interval graph</a>, <a href="https://publications.waset.org/abstracts/search?q=matching" title=" matching"> matching</a>, <a href="https://publications.waset.org/abstracts/search?q=induced%20matching" title=" induced matching"> induced matching</a>, <a href="https://publications.waset.org/abstracts/search?q=witness%20counting" title=" witness counting"> witness counting</a> </p> <a href="https://publications.waset.org/abstracts/45203/computing-maximum-uniquely-restricted-matchings-in-restricted-interval-graphs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45203.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">389</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">3034</span> Iraqi Short Term Electrical Load Forecasting Based on Interval Type-2 Fuzzy Logic</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Firas%20M.%20Tuaimah">Firas M. Tuaimah</a>, <a href="https://publications.waset.org/abstracts/search?q=Huda%20M.%20Abdul%20Abbas"> Huda M. Abdul Abbas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accurate Short Term Load Forecasting (STLF) is essential for a variety of decision making processes. However, forecasting accuracy can drop due to the presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. Interval Type 2 Fuzzy Logic System (IT2 FLS), with additional degrees of freedom, gives an excellent tool for handling uncertainties and it improved the prediction accuracy. The training data used in this study covers the period from January 1, 2012 to February 1, 2012 for winter season and the period from July 1, 2012 to August 1, 2012 for summer season. The actual load forecasting period starts from January 22, till 28, 2012 for winter model and from July 22 till 28, 2012 for summer model. The real data for Iraqi power system which belongs to the Ministry of Electricity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=short%20term%20load%20forecasting" title="short term load forecasting">short term load forecasting</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20interval" title=" prediction interval"> prediction interval</a>, <a href="https://publications.waset.org/abstracts/search?q=type%202%20fuzzy%20logic%20systems" title=" type 2 fuzzy logic systems"> type 2 fuzzy logic systems</a>, <a href="https://publications.waset.org/abstracts/search?q=electric" title=" electric"> electric</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20systems%20engineering" title=" computer systems engineering"> computer systems engineering</a> </p> <a href="https://publications.waset.org/abstracts/14301/iraqi-short-term-electrical-load-forecasting-based-on-interval-type-2-fuzzy-logic" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14301.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">397</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">3033</span> Effect of Genuine Missing Data Imputation on Prediction of Urinary Incontinence</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Suzan%20Arslanturk">Suzan Arslanturk</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad-Reza%20Siadat"> Mohammad-Reza Siadat</a>, <a href="https://publications.waset.org/abstracts/search?q=Theophilus%20Ogunyemi"> Theophilus Ogunyemi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ananias%20Diokno"> Ananias Diokno</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Missing data is a common challenge in statistical analyses of most clinical survey datasets. A variety of methods have been developed to enable analysis of survey data to deal with missing values. Imputation is the most commonly used among the above methods. However, in order to minimize the bias introduced due to imputation, one must choose the right imputation technique and apply it to the correct type of missing data. In this paper, we have identified different types of missing values: missing data due to skip pattern (SPMD), undetermined missing data (UMD), and genuine missing data (GMD) and applied rough set imputation on only the GMD portion of the missing data. We have used rough set imputation to evaluate the effect of such imputation on prediction by generating several simulation datasets based on an existing epidemiological dataset (MESA). To measure how well each dataset lends itself to the prediction model (logistic regression), we have used p-values from the Wald test. To evaluate the accuracy of the prediction, we have considered the width of 95% confidence interval for the probability of incontinence. Both imputed and non-imputed simulation datasets were fit to the prediction model, and they both turned out to be significant (p-value < 0.05). However, the Wald score shows a better fit for the imputed compared to non-imputed datasets (28.7 vs. 23.4). The average confidence interval width was decreased by 10.4% when the imputed dataset was used, meaning higher precision. The results show that using the rough set method for missing data imputation on GMD data improve the predictive capability of the logistic regression. Further studies are required to generalize this conclusion to other clinical survey datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=rough%20set" title="rough set">rough set</a>, <a href="https://publications.waset.org/abstracts/search?q=imputation" title=" imputation"> imputation</a>, <a href="https://publications.waset.org/abstracts/search?q=clinical%20survey%20data%20simulation" title=" clinical survey data simulation"> clinical survey data simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=genuine%20missing%20data" title=" genuine missing data"> genuine missing data</a>, <a href="https://publications.waset.org/abstracts/search?q=predictive%20index" title=" predictive index"> predictive index</a> </p> <a href="https://publications.waset.org/abstracts/92457/effect-of-genuine-missing-data-imputation-on-prediction-of-urinary-incontinence" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92457.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">168</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3032</span> A Fuzzy Nonlinear Regression Model for Interval Type-2 Fuzzy Sets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=O.%20Poleshchuk">O. Poleshchuk</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Komarov"> E. Komarov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a regression model for interval type-2 fuzzy sets based on the least squares estimation technique. Unknown coefficients are assumed to be triangular fuzzy numbers. The basic idea is to determine aggregation intervals for type-1 fuzzy sets, membership functions of whose are low membership function and upper membership function of interval type-2 fuzzy set. These aggregation intervals were called weighted intervals. Low and upper membership functions of input and output interval type-2 fuzzy sets for developed regression models are considered as piecewise linear functions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=interval%20type-2%20fuzzy%20sets" title="interval type-2 fuzzy sets">interval type-2 fuzzy sets</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20regression" title=" fuzzy regression"> fuzzy regression</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20interval" title=" weighted interval"> weighted interval</a> </p> <a href="https://publications.waset.org/abstracts/6138/a-fuzzy-nonlinear-regression-model-for-interval-type-2-fuzzy-sets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6138.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">373</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">3031</span> Asymptotic Confidence Intervals for the Difference of Coefficients of Variation in Gamma Distributions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Patarawan%20Sangnawakij">Patarawan Sangnawakij</a>, <a href="https://publications.waset.org/abstracts/search?q=Sa-Aat%20Niwitpong"> Sa-Aat Niwitpong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we proposed two new confidence intervals for the difference of coefficients of variation, CIw and CIs, in two independent gamma distributions. These proposed confidence intervals using the close form method of variance estimation which was presented by Donner and Zou (2010) based on concept of Wald and Score confidence interval, respectively. Monte Carlo simulation study is used to evaluate the performance, coverage probability and expected length, of these confidence intervals. The results indicate that values of coverage probabilities of the new confidence interval based on Wald and Score are satisfied the nominal coverage and close to nominal level 0.95 in various situations, particularly, the former proposed confidence interval is better when sample sizes are small. Moreover, the expected lengths of the proposed confidence intervals are nearly difference when sample sizes are moderate to large. Therefore, in this study, the confidence interval for the difference of coefficients of variation which based on Wald is preferable than the other one confidence interval. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=confidence%20interval" title="confidence interval">confidence interval</a>, <a href="https://publications.waset.org/abstracts/search?q=score%E2%80%99s%20interval" title=" score’s interval"> score’s interval</a>, <a href="https://publications.waset.org/abstracts/search?q=wald%E2%80%99s%20interval" title=" wald’s interval"> wald’s interval</a>, <a href="https://publications.waset.org/abstracts/search?q=coefficient%20of%20variation" title=" coefficient of variation"> coefficient of variation</a>, <a href="https://publications.waset.org/abstracts/search?q=gamma%20distribution" title=" gamma distribution"> gamma distribution</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/29254/asymptotic-confidence-intervals-for-the-difference-of-coefficients-of-variation-in-gamma-distributions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29254.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">427</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">3030</span> Effect of Irrigation Interval on Jojoba Plants under Circumstance of Sinai</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=E.%20Khattab">E. Khattab</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Halla"> S. Halla</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Jojoba plants are characterized by a tolerance of water stress, but due to the conditions of the Sinai in which the water is less, an irrigation interval study was carried out the jojoba plant from water stress without affecting the yield of oil. The field experiment was carried out at Maghara Research Station at North Sinai, Desert Research Center, Ministry of Agriculture, Egypt, to study the effect of irrigation interval on five clones of jojoba plants S-L, S-610, S- 700, S-B and S-G on growth and yield characters. Results showed that the clone S-700 has increase of all growth and yield characters under all interval irrigation compare with other clones. All variable of studied confirmed that clones of jojoba had significant effect with irrigation interval at one week but decrease value with three weeks. Jojoba plants tolerance to water stress but irrigation interval every week increased seed yield. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=interval%20irrigation" title="interval irrigation">interval irrigation</a>, <a href="https://publications.waset.org/abstracts/search?q=growth%20and%20yield%20characters" title=" growth and yield characters"> growth and yield characters</a>, <a href="https://publications.waset.org/abstracts/search?q=oil" title=" oil"> oil</a>, <a href="https://publications.waset.org/abstracts/search?q=jojoba" title=" jojoba"> jojoba</a>, <a href="https://publications.waset.org/abstracts/search?q=Sinai" title=" Sinai"> Sinai</a> </p> <a href="https://publications.waset.org/abstracts/80001/effect-of-irrigation-interval-on-jojoba-plants-under-circumstance-of-sinai" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80001.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">194</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">3029</span> Fracture and Fatigue Crack Growth Analysis and Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Volkmar%20Nolting">Volkmar Nolting</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fatigue crack growth prediction has become an important topic in both engineering and non-destructive evaluation. Crack propagation is influenced by the mechanical properties of the material and is conveniently modelled by the Paris-Erdogan equation. The critical crack size and the total number of load cycles are calculated. From a Larson-Miller plot the maximum operational temperature can for a given stress level be determined so that failure does not occur within a given time interval t. The study is used to determine a reasonable inspection cycle and thus enhances operational safety and reduces costs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fracturemechanics" title="fracturemechanics">fracturemechanics</a>, <a href="https://publications.waset.org/abstracts/search?q=crack%20growth%20prediction" title=" crack growth prediction"> crack growth prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=lifetime%20of%20a%20component" title=" lifetime of a component"> lifetime of a component</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20health%20monitoring" title=" structural health monitoring"> structural health monitoring</a> </p> <a href="https://publications.waset.org/abstracts/186532/fracture-and-fatigue-crack-growth-analysis-and-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186532.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">49</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">3028</span> SEMCPRA-Sar-Esembled Model for Climate Prediction in Remote Area</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kamalpreet%20Kaur">Kamalpreet Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Renu%20Dhir"> Renu Dhir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Climate prediction is an essential component of climate research, which helps evaluate possible effects on economies, communities, and ecosystems. Climate prediction involves short-term weather prediction, seasonal prediction, and long-term climate change prediction. Climate prediction can use the information gathered from satellites, ground-based stations, and ocean buoys, among other sources. The paper's four architectures, such as ResNet50, VGG19, Inception-v3, and Xception, have been combined using an ensemble approach for overall performance and robustness. An ensemble of different models makes a prediction, and the majority vote determines the final prediction. The various architectures such as ResNet50, VGG19, Inception-v3, and Xception efficiently classify the dataset RSI-CB256, which contains satellite images into cloudy and non-cloudy. The generated ensembled S-E model (Sar-ensembled model) provides an accuracy of 99.25%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=climate" title="climate">climate</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20images" title=" satellite images"> satellite images</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/178864/semcpra-sar-esembled-model-for-climate-prediction-in-remote-area" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/178864.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">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">3027</span> Stock Market Prediction Using Convolutional Neural Network That Learns from a Graph</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mo-Se%20Lee">Mo-Se Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheol-Hwi%20Ahn"> Cheol-Hwi Ahn</a>, <a href="https://publications.waset.org/abstracts/search?q=Kee-Young%20Kwahk"> Kee-Young Kwahk</a>, <a href="https://publications.waset.org/abstracts/search?q=Hyunchul%20Ahn"> Hyunchul Ahn</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title="convolutional neural network">convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=Korean%20stock%20market" title=" Korean stock market"> Korean stock market</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20market%20prediction" title=" stock market prediction"> stock market prediction</a> </p> <a href="https://publications.waset.org/abstracts/80318/stock-market-prediction-using-convolutional-neural-network-that-learns-from-a-graph" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80318.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">425</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">3026</span> Automatic Flood Prediction Using Rainfall Runoff Model in Moravian-Silesian Region</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Sir">B. Sir</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20Podhoranyi"> M. Podhoranyi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Kuchar"> S. Kuchar</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Kocyan"> T. Kocyan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rainfall-runoff models play important role in hydrological predictions. However, the model is only one part of the process for creation of flood prediction. The aim of this paper is to show the process of successful prediction for flood event (May 15–May 18 2014). The prediction was performed by rainfall runoff model HEC–HMS, one of the models computed within Floreon+ system. The paper briefly evaluates the results of automatic hydrologic prediction on the river Olše catchment and its gages Český Těšín and Věřňovice. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flood" title="flood">flood</a>, <a href="https://publications.waset.org/abstracts/search?q=HEC-HMS" title=" HEC-HMS"> HEC-HMS</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=rainfall" title=" rainfall"> rainfall</a>, <a href="https://publications.waset.org/abstracts/search?q=runoff" title=" runoff "> runoff </a> </p> <a href="https://publications.waset.org/abstracts/20151/automatic-flood-prediction-using-rainfall-runoff-model-in-moravian-silesian-region" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20151.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">394</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">3025</span> A Comparative Soft Computing Approach to Supplier Performance Prediction Using GEP and ANN Models: An Automotive Case Study </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seyed%20Esmail%20Seyedi%20Bariran">Seyed Esmail Seyedi Bariran</a>, <a href="https://publications.waset.org/abstracts/search?q=Khairul%20Salleh%20Mohamed%20Sahari"> Khairul Salleh Mohamed Sahari</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In multi-echelon supply chain networks, optimal supplier selection significantly depends on the accuracy of suppliers’ performance prediction. Different methods of multi criteria decision making such as ANN, GA, Fuzzy, AHP, etc have been previously used to predict the supplier performance but the “black-box” characteristic of these methods is yet a major concern to be resolved. Therefore, the primary objective in this paper is to implement an artificial intelligence-based gene expression programming (GEP) model to compare the prediction accuracy with that of ANN. A full factorial design with %95 confidence interval is initially applied to determine the appropriate set of criteria for supplier performance evaluation. A test-train approach is then utilized for the ANN and GEP exclusively. The training results are used to find the optimal network architecture and the testing data will determine the prediction accuracy of each method based on measures of root mean square error (RMSE) and correlation coefficient (R2). The results of a case study conducted in Supplying Automotive Parts Co. (SAPCO) with more than 100 local and foreign supply chain members revealed that, in comparison with ANN, gene expression programming has a significant preference in predicting supplier performance by referring to the respective RMSE and R-squared values. Moreover, using GEP, a mathematical function was also derived to solve the issue of ANN black-box structure in modeling the performance prediction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Supplier%20Performance%20Prediction" title="Supplier Performance Prediction">Supplier Performance Prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=ANN" title=" ANN"> ANN</a>, <a href="https://publications.waset.org/abstracts/search?q=GEP" title=" GEP"> GEP</a>, <a href="https://publications.waset.org/abstracts/search?q=Automotive" title=" Automotive"> Automotive</a>, <a href="https://publications.waset.org/abstracts/search?q=SAPCO" title=" SAPCO"> SAPCO</a> </p> <a href="https://publications.waset.org/abstracts/13162/a-comparative-soft-computing-approach-to-supplier-performance-prediction-using-gep-and-ann-models-an-automotive-case-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13162.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">3024</span> Physico-Chemical Properties of Silurian Hot Shale in Ahnet Basin, Algeria: Case Study Well ASS-1</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Mehdi%20Kadri">Mohamed Mehdi Kadri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The prediction of hot shale interval in Silurian formation in a well drilled vertically in Ahnet basin Is by logging Data (Resistivity, Gamma Ray, Sonic) with the calculation of total organic carbon (TOC) using ∆ log R Method. The aim of this paper is to present Physico-chemical Properties of Hot Shale using IR spectroscopy and gas chromatography-mass spectrometry analysis; this mixture of measurements, evaluation and characterization show that the hot shale interval located in the lower of Silurian, the molecules adsorbed at the surface of shale sheet are significantly different from petroleum hydrocarbons this result are also supported with gas-liquid chromatography showed that the study extract is a hydroxypropyl. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=physic-chemical%20analysis" title="physic-chemical analysis">physic-chemical analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=reservoirs%20characterization" title=" reservoirs characterization"> reservoirs characterization</a>, <a href="https://publications.waset.org/abstracts/search?q=sweet%20window%20evaluation" title=" sweet window evaluation"> sweet window evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=Silurian%20shale" title=" Silurian shale"> Silurian shale</a>, <a href="https://publications.waset.org/abstracts/search?q=Ahnet%20basin" title=" Ahnet basin"> Ahnet basin</a> </p> <a href="https://publications.waset.org/abstracts/151737/physico-chemical-properties-of-silurian-hot-shale-in-ahnet-basin-algeria-case-study-well-ass-1" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151737.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">99</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3023</span> Genetic and Environmental Variation in Reproductive and Lactational Performance of Holstein Cattle</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashraf%20Ward">Ashraf Ward</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Effect of calving interval on 305 day milk yield for first three lactations was studied in order to increase efficiency of selection schemes and to more efficiently manage Holstein cows that have been raised on small farms in Libya. Results obtained by processing data of 1476 cows, managed in 935 small scale farms, pointed out that current calving interval significantly affects on milk production for first three lactations (p<0.05). Preceding calving interval affected 305 day milk yield (p<0.05) in second lactation only. Linear regression model accounted for 20-25 % of the total variance of 305 day milk yield. Extension of calving interval over 420, 430, 450 days for first, second and third lactations respectively, did not increase milk production when converted to 305 day lactation. Stochastic relations between calving interval and calving age and month are moderated. Values of Pierson’s correlation coefficients ranged 0.38 to 0.69. Adjustment of milk production in order to reduce effect of calving interval on total phenotypic variance of milk yield is valid for first lactation only. Adjustment of 305 day milk yield for second and third lactations in order to reduce effects of factors “calving age and month” brings about, at the same time, elimination of calving interval effect. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=milk%20yield" title="milk yield">milk yield</a>, <a href="https://publications.waset.org/abstracts/search?q=Holstien" title=" Holstien"> Holstien</a>, <a href="https://publications.waset.org/abstracts/search?q=non%20genetic" title=" non genetic"> non genetic</a>, <a href="https://publications.waset.org/abstracts/search?q=calving" title=" calving"> calving</a> </p> <a href="https://publications.waset.org/abstracts/18095/genetic-and-environmental-variation-in-reproductive-and-lactational-performance-of-holstein-cattle" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18095.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">417</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">3022</span> Group Decision Making through Interval-Valued Intuitionistic Fuzzy Soft Set TOPSIS Method Using New Hybrid Score Function</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Syed%20Talib%20Abbas%20Raza">Syed Talib Abbas Raza</a>, <a href="https://publications.waset.org/abstracts/search?q=Tahseen%20Ahmed%20Jilani"> Tahseen Ahmed Jilani</a>, <a href="https://publications.waset.org/abstracts/search?q=Saleem%20Abdullah"> Saleem Abdullah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents interval-valued intuitionistic fuzzy soft sets based TOPSIS method for group decision making. The interval-valued intuitionistic fuzzy soft set is a mutation of an interval-valued intuitionistic fuzzy set and soft set. In group decision making problems IVIFSS makes the process much more algebraically elegant. We have used weighted arithmetic averaging operator for aggregating the information and define a new Hybrid Score Function as metric tool for comparison between interval-valued intuitionistic fuzzy values. In an illustrative example we have applied the developed method to a criminological problem. We have developed a group decision making model for integrating the imprecise and hesitant evaluations of multiple law enforcement agencies working on target killing cases in the country. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=group%20decision%20making" title="group decision making">group decision making</a>, <a href="https://publications.waset.org/abstracts/search?q=interval-valued%20intuitionistic%20fuzzy%20soft%20set" title=" interval-valued intuitionistic fuzzy soft set"> interval-valued intuitionistic fuzzy soft set</a>, <a href="https://publications.waset.org/abstracts/search?q=TOPSIS" title=" TOPSIS"> TOPSIS</a>, <a href="https://publications.waset.org/abstracts/search?q=score%20function" title=" score function"> score function</a>, <a href="https://publications.waset.org/abstracts/search?q=criminology" title=" criminology"> criminology</a> </p> <a href="https://publications.waset.org/abstracts/21620/group-decision-making-through-interval-valued-intuitionistic-fuzzy-soft-set-topsis-method-using-new-hybrid-score-function" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21620.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">604</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">3021</span> An Integrated Approach of Isolated and Combined Aerobic and Anaerobic Interval Training for Improvement of Stride Length and Stride Frequency of Soccer Players</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=K.%20A.%20Ramesh">K. A. Ramesh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Purpose: The study is to find out the effect of isolated and combined aerobic and anaerobic interval training on stride length and stride frequency of Soccer players. Method(s): To achieve this purpose, 45 women Soccer players who participated in the Anna University, Tamilnadu, India. Intercollegiate Tournament was selected as subjects and were randomly divided into three equal groups of fifteen each, such as an anaerobic interval training group (group-I), anaerobic interval training group (group-II) and combined aerobic-anaerobic interval training group (group-III). The training program was conducted three days per weeks for a period of six weeks. Stride length and Stride frequency was selected as dependent variables. All the subjects of the three groups were tested on selected criterion variables at prior to and immediately after the training program. The concepts of dependent test were employed to find out the significant improvement due to the influence of training programs on all the selected criterion variables. The analysis of covariance (ANCOVA) was also used to analyze the significant difference, if, any among the experimental groups. Result(s): The result of the study revealed that combined group was higher than aerobic interval training and anaerobic interval training groups. Conclusion(s): It was concluded that when experimental groups were compared with each other, the combined aerobic – anaerobic interval training program was found to be greater than the aerobic and the anaerobic interval training programs on the development of stride length and stride frequency. High intensity, combined aerobic – anaerobic interval training program can be carried out in a more soccer specific way than plain running. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stride%20length" title="stride length">stride length</a>, <a href="https://publications.waset.org/abstracts/search?q=stride%20frequency" title=" stride frequency"> stride frequency</a>, <a href="https://publications.waset.org/abstracts/search?q=interval%20training" title=" interval training"> interval training</a>, <a href="https://publications.waset.org/abstracts/search?q=soccer" title=" soccer"> soccer</a> </p> <a href="https://publications.waset.org/abstracts/40302/an-integrated-approach-of-isolated-and-combined-aerobic-and-anaerobic-interval-training-for-improvement-of-stride-length-and-stride-frequency-of-soccer-players" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40302.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">373</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">3020</span> Evaluation of the Effect of Milk Recording Intervals on the Accuracy of an Empirical Model Fitted to Dairy Sheep Lactations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=L.%20Guevara">L. Guevara</a>, <a href="https://publications.waset.org/abstracts/search?q=Gl%C3%B3ria%20L.%20S."> Glória L. S.</a>, <a href="https://publications.waset.org/abstracts/search?q=Corea%20E.%20E"> Corea E. E</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Ram%C3%ADrez-Zamora%20M."> A. Ramírez-Zamora M.</a>, <a href="https://publications.waset.org/abstracts/search?q=Salinas-Martinez%20J.%20A."> Salinas-Martinez J. A.</a>, <a href="https://publications.waset.org/abstracts/search?q=Angeles-Hernandez%20J.%20C."> Angeles-Hernandez J. C.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mathematical models are useful for identifying the characteristics of sheep lactation curves to develop and implement improved strategies. However, the accuracy of these models is influenced by factors such as the recording regime, mainly the intervals between test day records (TDR). The current study aimed to evaluate the effect of different TDR intervals on the goodness of fit of the Wood model (WM) applied to dairy sheep lactations. A total of 4,494 weekly TDRs from 156 lactations of dairy crossbred sheep were analyzed. Three new databases were generated from the original weekly TDR data (7D), comprising intervals of 14(14D), 21(21D), and 28(28D) days. The parameters of WM were estimated using the “minpack.lm” package in the R software. The shape of the lactation curve (typical and atypical) was defined based on the WM parameters. The goodness of fit was evaluated using the mean square of prediction error (MSPE), Root of MSPE (RMSPE), Akaike´s Information Criterion (AIC), Bayesian´s Information Criterion (BIC), and the coefficient of correlation (r) between the actual and estimated total milk yield (TMY). WM showed an adequate estimate of TMY regardless of the TDR interval (P=0.21) and shape of the lactation curve (P=0.42). However, we found higher values of r for typical curves compared to atypical curves (0.9vs.0.74), with the highest values for the 28D interval (r=0.95). In the same way, we observed an overestimated peak yield (0.92vs.6.6 l) and underestimated time of peak yield (21.5vs.1.46) in atypical curves. The best values of RMSPE were observed for the 28D interval in both lactation curve shapes. The significant lowest values of AIC (P=0.001) and BIC (P=0.001) were shown by the 7D interval for typical and atypical curves. These results represent the first approach to define the adequate interval to record the regime of dairy sheep in Latin America and showed a better fitting for the Wood model using a 7D interval. However, it is possible to obtain good estimates of TMY using a 28D interval, which reduces the sampling frequency and would save additional costs to dairy sheep producers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gamma%20incomplete" title="gamma incomplete">gamma incomplete</a>, <a href="https://publications.waset.org/abstracts/search?q=ewes" title=" ewes"> ewes</a>, <a href="https://publications.waset.org/abstracts/search?q=shape%20curves" title=" shape curves"> shape curves</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling" title=" modeling"> modeling</a> </p> <a href="https://publications.waset.org/abstracts/163600/evaluation-of-the-effect-of-milk-recording-intervals-on-the-accuracy-of-an-empirical-model-fitted-to-dairy-sheep-lactations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163600.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">78</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">3019</span> Monthly River Flow Prediction Using a Nonlinear Prediction Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=N.%20H.%20Adenan">N. H. Adenan</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20S.%20M.%20Noorani"> M. S. M. Noorani</a> </p> <p class="card-text"><strong>Abstract:</strong></p> River flow prediction is an essential to ensure proper management of water resources can be optimally distribute water to consumers. This study presents an analysis and prediction by using nonlinear prediction method involving monthly river flow data in Tanjung Tualang from 1976 to 2006. Nonlinear prediction method involves the reconstruction of phase space and local linear approximation approach. The phase space reconstruction involves the reconstruction of one-dimensional (the observed 287 months of data) in a multidimensional phase space to reveal the dynamics of the system. Revenue of phase space reconstruction is used to predict the next 72 months. A comparison of prediction performance based on correlation coefficient (CC) and root mean square error (RMSE) have been employed to compare prediction performance for nonlinear prediction method, ARIMA and SVM. Prediction performance comparisons show the prediction results using nonlinear prediction method is better than ARIMA and SVM. Therefore, the result of this study could be used to developed an efficient water management system to optimize the allocation water resources. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=river%20flow" title="river flow">river flow</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20prediction%20method" title=" nonlinear prediction method"> nonlinear prediction method</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20space" title=" phase space"> phase space</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20linear%20approximation" title=" local linear approximation"> local linear approximation</a> </p> <a href="https://publications.waset.org/abstracts/2867/monthly-river-flow-prediction-using-a-nonlinear-prediction-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2867.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">412</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">3018</span> Applied Complement of Probability and Information Entropy for Prediction in Student Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kennedy%20Efosa%20Ehimwenma">Kennedy Efosa Ehimwenma</a>, <a href="https://publications.waset.org/abstracts/search?q=Sujatha%20Krishnamoorthy"> Sujatha Krishnamoorthy</a>, <a href="https://publications.waset.org/abstracts/search?q=Safiya%20Al%E2%80%91Sharji"> Safiya Al‑Sharji</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The probability computation of events is in the interval of [0, 1], which are values that are determined by the number of outcomes of events in a sample space S. The probability Pr(A) that an event A will never occur is 0. The probability Pr(B) that event B will certainly occur is 1. This makes both events A and B a certainty. Furthermore, the sum of probabilities Pr(E₁) + Pr(E₂) + … + Pr(Eₙ) of a finite set of events in a given sample space S equals 1. Conversely, the difference of the sum of two probabilities that will certainly occur is 0. This paper first discusses Bayes, the complement of probability, and the difference of probability for occurrences of learning-events before applying them in the prediction of learning objects in student learning. Given the sum of 1; to make a recommendation for student learning, this paper proposes that the difference of argMaxPr(S) and the probability of student-performance quantifies the weight of learning objects for students. Using a dataset of skill-set, the computational procedure demonstrates i) the probability of skill-set events that have occurred that would lead to higher-level learning; ii) the probability of the events that have not occurred that requires subject-matter relearning; iii) accuracy of the decision tree in the prediction of student performance into class labels and iv) information entropy about skill-set data and its implication on student cognitive performance and recommendation of learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=complement%20of%20probability" title="complement of probability">complement of probability</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayes%E2%80%99%20rule" title=" Bayes’ rule"> Bayes’ rule</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=pre-assessments" title=" pre-assessments"> pre-assessments</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20education" title=" computational education"> computational education</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20theory" title=" information theory"> information theory</a> </p> <a href="https://publications.waset.org/abstracts/135595/applied-complement-of-probability-and-information-entropy-for-prediction-in-student-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/135595.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">161</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">3017</span> Approximate Confidence Interval for Effect Size Base on Bootstrap Resampling Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Phanyaem">S. Phanyaem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the confidence intervals for the effect size base on bootstrap resampling method. The meta-analytic confidence interval for effect size is proposed that are easy to compute. A Monte Carlo simulation study was conducted to compare the performance of the proposed confidence intervals with the existing confidence intervals. The best confidence interval method will have a coverage probability close to 0.95. Simulation results have shown that our proposed confidence intervals perform well in terms of coverage probability and expected length. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=effect%20size" title="effect size">effect size</a>, <a href="https://publications.waset.org/abstracts/search?q=confidence%20interval" title=" confidence interval"> confidence interval</a>, <a href="https://publications.waset.org/abstracts/search?q=bootstrap%20method" title=" bootstrap method"> bootstrap method</a>, <a href="https://publications.waset.org/abstracts/search?q=resampling" title=" resampling"> resampling</a> </p> <a href="https://publications.waset.org/abstracts/10667/approximate-confidence-interval-for-effect-size-base-on-bootstrap-resampling-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10667.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">596</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3016</span> Solution of Nonlinear Fractional Programming Problem with Bounded Parameters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mrinal%20Jana">Mrinal Jana</a>, <a href="https://publications.waset.org/abstracts/search?q=Geetanjali%20Panda"> Geetanjali Panda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper a methodology is developed to solve a nonlinear fractional programming problem in which the coefficients of the objective function and constraints are interval parameters. This model is transformed into a general optimization problem and relation between the original problem and the transformed problem is established. Finally the proposed methodology is illustrated through a numerical example. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fractional%20programming" title="fractional programming">fractional programming</a>, <a href="https://publications.waset.org/abstracts/search?q=interval%20valued%20function" title=" interval valued function"> interval valued function</a>, <a href="https://publications.waset.org/abstracts/search?q=interval%20inequalities" title=" interval inequalities"> interval inequalities</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20order%20relation" title=" partial order relation"> partial order relation</a> </p> <a href="https://publications.waset.org/abstracts/22261/solution-of-nonlinear-fractional-programming-problem-with-bounded-parameters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22261.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">519</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">3015</span> Using Combination of Sets of Features of Molecules for Aqueous Solubility Prediction: A Random Forest Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammet%20Baldan">Muhammet Baldan</a>, <a href="https://publications.waset.org/abstracts/search?q=Emel%20Timu%C3%A7in"> Emel Timuçin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Generally, absorption and bioavailability increase if solubility increases; therefore, it is crucial to predict them in drug discovery applications. Molecular descriptors and Molecular properties are traditionally used for the prediction of water solubility. There are various key descriptors that are used for this purpose, namely Drogan Descriptors, Morgan Descriptors, Maccs keys, etc., and each has different prediction capabilities with differentiating successes between different data sets. Another source for the prediction of solubility is structural features; they are commonly used for the prediction of solubility. However, there are little to no studies that combine three or more properties or descriptors for prediction to produce a more powerful prediction model. Unlike available models, we used a combination of those features in a random forest machine learning model for improved solubility prediction to better predict and, therefore, contribute to drug discovery systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=solubility" title="solubility">solubility</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=molecular%20descriptors" title=" molecular descriptors"> molecular descriptors</a>, <a href="https://publications.waset.org/abstracts/search?q=maccs%20keys" title=" maccs keys"> maccs keys</a> </p> <a href="https://publications.waset.org/abstracts/186736/using-combination-of-sets-of-features-of-molecules-for-aqueous-solubility-prediction-a-random-forest-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186736.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">46</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">3014</span> On Improving Breast Cancer Prediction Using GRNN-CP</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kefaya%20Qaddoum">Kefaya Qaddoum</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of this study is to predict breast cancer and to construct a supportive model that will stimulate a more reliable prediction as a factor that is fundamental for public health. In this study, we utilize general regression neural networks (GRNN) to replace the normal predictions with prediction periods to achieve a reasonable percentage of confidence. The mechanism employed here utilises a machine learning system called conformal prediction (CP), in order to assign consistent confidence measures to predictions, which are combined with GRNN. We apply the resulting algorithm to the problem of breast cancer diagnosis. The results show that the prediction constructed by this method is reasonable and could be useful in practice. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title="neural network">neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=conformal%20prediction" title=" conformal prediction"> conformal prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=cancer%20classification" title=" cancer classification"> cancer classification</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/74483/on-improving-breast-cancer-prediction-using-grnn-cp" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74483.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> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3013</span> The Effects of Eight Weeks of Interval Endurance Training on hs-CRP Levels and Anthropometric Parameters in Overweight Men</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Khoshemehry">S. Khoshemehry</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20J.%20Pourvaghar"> M. J. Pourvaghar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Inflammatory markers are known as the main predictors of cardiovascular diseases. This study aimed at determining the effect of 8 weeks of interval endurance training on hs-CRP level and some anthropometric parameters in overweight men. Following the call for participation in research project in Kashan, 73 volunteers participated in it and constituted the statistical population of the study. Then, 28 overweight young men from the age of 22 to 25 years old were randomly assigned into two groups of experimental and control group (n=14). Anthropometric and the blood sample was collected before and after the termination of the program for measuring hs-CRP. The interval endurance program was performed at 60 to 75% of maximum heart rate in 2 sessions per week for 8 weeks. Kolmogorov-Smirnov test was used to test whether two samples come from the same distribution and T-test was used to assess the difference of two groups which were statistically significant at the level of 0.05. The result indicated that there was a significant difference between the hs-RP, weight, BMI and W/H ratio of overweight men in posttest in the exercise group (P≤0.05) but not in the control group. Interval endurance training program causes decrease in hs-CRP level and anthropometric parameters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=interval%20endurance%20training%20program" title="interval endurance training program">interval endurance training program</a>, <a href="https://publications.waset.org/abstracts/search?q=HS-CRP" title=" HS-CRP"> HS-CRP</a>, <a href="https://publications.waset.org/abstracts/search?q=overweight" title=" overweight"> overweight</a>, <a href="https://publications.waset.org/abstracts/search?q=anthropometric" title=" anthropometric"> anthropometric</a> </p> <a href="https://publications.waset.org/abstracts/48475/the-effects-of-eight-weeks-of-interval-endurance-training-on-hs-crp-levels-and-anthropometric-parameters-in-overweight-men" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48475.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">306</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">3012</span> Analysis on Prediction Models of TBM Performance and Selection of Optimal Input Parameters</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hang%20Lo%20Lee">Hang Lo Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Ki%20Il%20Song"> Ki Il Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Hee%20Hwan%20Ryu"> Hee Hwan Ryu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An accurate prediction of TBM(Tunnel Boring Machine) performance is very difficult for reliable estimation of the construction period and cost in preconstruction stage. For this purpose, the aim of this study is to analyze the evaluation process of various prediction models published since 2000 for TBM performance, and to select the optimal input parameters for the prediction model. A classification system of TBM performance prediction model and applied methodology are proposed in this research. Input and output parameters applied for prediction models are also represented. Based on these results, a statistical analysis is performed using the collected data from shield TBM tunnel in South Korea. By performing a simple regression and residual analysis utilizinFg statistical program, R, the optimal input parameters are selected. These results are expected to be used for development of prediction model of TBM performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=TBM%20performance%20prediction%20model" title="TBM performance prediction model">TBM performance prediction model</a>, <a href="https://publications.waset.org/abstracts/search?q=classification%20system" title=" classification system"> classification system</a>, <a href="https://publications.waset.org/abstracts/search?q=simple%20regression%20analysis" title=" simple regression analysis"> simple regression analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=residual%20analysis" title=" residual analysis"> residual analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=optimal%20input%20parameters" title=" optimal input parameters"> optimal input parameters</a> </p> <a href="https://publications.waset.org/abstracts/52738/analysis-on-prediction-models-of-tbm-performance-and-selection-of-optimal-input-parameters" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52738.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">309</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">3011</span> Diesel Fault Prediction Based on Optimized Gray Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Han%20Bing">Han Bing</a>, <a href="https://publications.waset.org/abstracts/search?q=Yin%20Zhenjie"> Yin Zhenjie</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to analyze the status of a diesel engine, as well as conduct fault prediction, a new prediction model based on a gray system is proposed in this paper, which takes advantage of the neural network and the genetic algorithm. The proposed GBPGA prediction model builds on the GM (1.5) model and uses a neural network, which is optimized by a genetic algorithm to construct the error compensator. We verify our proposed model on the diesel faulty simulation data and the experimental results show that GBPGA has the potential to employ fault prediction on diesel. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fault%20prediction" title="fault prediction">fault prediction</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=GM%281" title=" GM(1"> GM(1</a>, <a href="https://publications.waset.org/abstracts/search?q=5%29%20genetic%20algorithm" title="5) genetic algorithm">5) genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=GBPGA" title=" GBPGA"> GBPGA</a> </p> <a href="https://publications.waset.org/abstracts/48844/diesel-fault-prediction-based-on-optimized-gray-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48844.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">304</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">3010</span> Application of Interval Valued Picture Fuzzy Set in Medical Diagnosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Palash%20Dutta">Palash Dutta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> More frequently uncertainties are encountered in medical diagnosis and therefore it is the most important and interesting area of applications of fuzzy set theory. In this present study, an attempt has been made to extend Sanchez’s approach for medical diagnosis via interval valued picture fuzzy sets and exhibit the technique with suitable case studies. In this article, it is observed that a refusal can be expressed in the databases concerning the examined objects. The technique is performing diagnosis on the basis of distance measures and as a result, this approach makes it possible to introduce weights of all symptoms and consequently patient can be diagnosed directly. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=medical%20diagnosis" title="medical diagnosis">medical diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty" title=" uncertainty"> uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20set" title=" fuzzy set"> fuzzy set</a>, <a href="https://publications.waset.org/abstracts/search?q=picture%20fuzzy%20set" title=" picture fuzzy set"> picture fuzzy set</a>, <a href="https://publications.waset.org/abstracts/search?q=interval%20valued%20picture%20fuzzy%20set" title=" interval valued picture fuzzy set"> interval valued picture fuzzy set</a> </p> <a href="https://publications.waset.org/abstracts/54935/application-of-interval-valued-picture-fuzzy-set-in-medical-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54935.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">378</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3009</span> A Prediction Model of Adopting IPTV</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jeonghwan%20Jeon">Jeonghwan Jeon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the advent of IPTV in the fierce competition with existing broadcasting system, it is emerged as an important issue to predict how much the adoption of IPTV service will be. This paper aims to suggest a prediction model for adopting IPTV using classification and Ranking Belief Simplex (CaRBS). A simplex plot method of representing data allows a clear visual representation to the degree of interaction of the support from the variables to the prediction of the objects. CaRBS is applied to the survey data on the IPTV adoption. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=prediction" title="prediction">prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=adoption" title=" adoption"> adoption</a>, <a href="https://publications.waset.org/abstracts/search?q=IPTV" title=" IPTV"> IPTV</a>, <a href="https://publications.waset.org/abstracts/search?q=CaRBS" title=" CaRBS"> CaRBS</a> </p> <a href="https://publications.waset.org/abstracts/2971/a-prediction-model-of-adopting-iptv" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2971.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">412</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li 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