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CTDI</title> <meta name="description" content="Search results for: weighted CTDI"> <meta name="keywords" content="weighted CTDI"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler 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id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="weighted CTDI"> <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> 568</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: weighted CTDI</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">568</span> Comparison of Computed Tomography Dose Index, Dose Length Product and Effective Dose Among Male and Female Patients From Contrast Enhanced Computed Tomography Pancreatitis Protocol</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Babina%20Aryal">Babina Aryal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: The diagnosis of pancreatitis is generally based on clinical and laboratory findings; however, Computed Tomography (CT) is an imaging technique of choice specially Contrast Enhanced Computed Tomography (CECT) shows morphological characteristic findings that allow for establishing the diagnosis of pancreatitis and determining the extent of disease severity which is done along with the administration of appropriate contrast medium. The purpose of this study was to compare Computed Tomography Dose Index (CTDI), Dose Length Product (DLP) and Effective Dose (ED) among male and female patients from Contrast Enhanced Computed Tomography (CECT) Pancreatitis Protocol. Methods: This retrospective study involved data collection based on clinical/laboratory/ultrasonography diagnosis of Pancreatitis and has undergone CECT Abdomen pancreatitis protocol. data collection involved detailed information about a patient's Age and Gender, Clinical history, Individual Computed Tomography Dose Index and Dose Length Product and effective dose. Results: We have retrospectively collected dose data from 150 among which 127 were males and 23 were females. The values obtained from the display of the CT screen were measured, calculated and compared to determine whether the CTDI, DLP and ED values were similar or not. CTDI for females was more as compared to males. The differences in CTDI values for females and males were 32.2087 and 37.1609 respectively. DLP values and Effective dose for both the genders did not show significant differences. Conclusion: This study concluded that there were no more significant changes in the DLP and ED values among both the genders however we noticed that female patients had more CTDI than males. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computed%20tomography" title="computed tomography">computed tomography</a>, <a href="https://publications.waset.org/abstracts/search?q=contrast%20enhanced%20computed%20tomography" title=" contrast enhanced computed tomography"> contrast enhanced computed tomography</a>, <a href="https://publications.waset.org/abstracts/search?q=computed%20tomography%20dose%20index" title=" computed tomography dose index"> computed tomography dose index</a>, <a href="https://publications.waset.org/abstracts/search?q=dose%20length%20product" title=" dose length product"> dose length product</a>, <a href="https://publications.waset.org/abstracts/search?q=effective%20dose" title=" effective dose"> effective dose</a> </p> <a href="https://publications.waset.org/abstracts/175402/comparison-of-computed-tomography-dose-index-dose-length-product-and-effective-dose-among-male-and-female-patients-from-contrast-enhanced-computed-tomography-pancreatitis-protocol" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/175402.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">118</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">567</span> Notes on Frames in Weighted Hardy Spaces and Generalized Weighted Composition Operators</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shams%20Alyusof">Shams Alyusof</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work is to enrich the studies of the frames due to their prominent role in pure mathematics as well as in applied mathematics and many applications in computer science and engineering. Recently, there are remarkable studies of operators that preserve frames on some spaces, and this research could be considered as an extension of such studies. Indeed, this paper is to we characterize weighted composition operators that preserve frames in weighted Hardy spaces on the open unit disk. Moreover, it shows that this characterization does not apply to generalized weighted composition operators on such spaces. Nevertheless, this study could be extended to provide more specific characterizations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=frames" title="frames">frames</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20weighted%20composition%20operators" title=" generalized weighted composition operators"> generalized weighted composition operators</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20Hardy%20spaces" title=" weighted Hardy spaces"> weighted Hardy spaces</a>, <a href="https://publications.waset.org/abstracts/search?q=analytic%20functions" title=" analytic functions"> analytic functions</a> </p> <a href="https://publications.waset.org/abstracts/156372/notes-on-frames-in-weighted-hardy-spaces-and-generalized-weighted-composition-operators" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156372.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">121</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">566</span> Some Results for F-Minimal Hypersurfaces in Manifolds with Density</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Abdelmalek">M. Abdelmalek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we study the hypersurfaces of constant weighted mean curvature embedded in weighted manifolds. We give a condition about these hypersurfaces to be minimal. This condition is given by the ellipticity of the weighted Newton transformations. We especially prove that two compact hypersurfaces of constant weighted mean curvature embedded in space forms and with the intersection in at least a point of the boundary must be transverse. The method is based on the calculus of the matrix of the second fundamental form in a boundary point and then the matrix associated with the Newton transformations. By equality, we find the weighted elementary symmetric function on the boundary of the hypersurface. We give in the end some examples and applications. Especially in Euclidean space, we use the above result to prove the Alexandrov spherical caps conjecture for the weighted case. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=weighted%20mean%20curvature" title="weighted mean curvature">weighted mean curvature</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20manifolds" title=" weighted manifolds"> weighted manifolds</a>, <a href="https://publications.waset.org/abstracts/search?q=ellipticity" title=" ellipticity"> ellipticity</a>, <a href="https://publications.waset.org/abstracts/search?q=Newton%20transformations" title=" Newton transformations"> Newton transformations</a> </p> <a href="https://publications.waset.org/abstracts/160174/some-results-for-f-minimal-hypersurfaces-in-manifolds-with-density" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160174.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">93</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">565</span> Post-Contrast Susceptibility Weighted Imaging vs. Post-Contrast T1 Weighted Imaging for Evaluation of Brain Lesions</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sujith%20Rajashekar%20Swamy">Sujith Rajashekar Swamy</a>, <a href="https://publications.waset.org/abstracts/search?q=Meghana%20Rajashekara%20Swamy"> Meghana Rajashekara Swamy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Although T1-weighted gadolinium-enhanced imaging (T1-Gd) has its established clinical role in diagnosing brain lesions of infectious and metastatic origins, the use of post-contrast susceptibility-weighted imaging (SWI) has been understudied. This observational study aims to explore and compare the prominence of brain parenchymal lesions between T1-Gd and SWI-Gd images. A cross-sectional study design was utilized to analyze 58 patients with brain parenchymal lesions using T1-Gd and SWI-Gd scanning techniques. Our results indicated that SWI-Gd enhanced the conspicuity of metastatic as well as infectious brain lesions when compared to T1-Gd. Consequently, it can be used as an adjunct to T1-Gd for post-contrast imaging, thereby avoiding additional contrast administration. Improved conspicuity of brain lesions translates directly to enhanced patient outcomes, and hence SWI-Gd imaging proves useful to meet that endpoint. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=susceptibility%20weighted" title="susceptibility weighted">susceptibility weighted</a>, <a href="https://publications.waset.org/abstracts/search?q=T1%20weighted" title=" T1 weighted"> T1 weighted</a>, <a href="https://publications.waset.org/abstracts/search?q=brain%20lesions" title=" brain lesions"> brain lesions</a>, <a href="https://publications.waset.org/abstracts/search?q=gadolinium%20contrast" title=" gadolinium contrast"> gadolinium contrast</a> </p> <a href="https://publications.waset.org/abstracts/160957/post-contrast-susceptibility-weighted-imaging-vs-post-contrast-t1-weighted-imaging-for-evaluation-of-brain-lesions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/160957.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">564</span> Accuracy of Computed Tomography Dose Monitor Values: A Multicentric Study in India</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adhimoolam%20Saravana%20Kumar">Adhimoolam Saravana Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20N.%20Govindarajan"> K. N. Govindarajan</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Devanand"> B. Devanand</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20Rajakumar"> R. Rajakumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The quality of Computed Tomography (CT) procedures has improved in recent years due to technological developments and increased diagnostic ability of CT scanners. Due to the fact that CT doses are the peak among diagnostic radiology practices, it is of great significance to be aware of patient’s CT radiation dose whenever a CT examination is preferred. CT radiation dose delivered to patients in the form of volume CT dose index (CTDIvol) values, is displayed on scanner monitors at the end of each examination and it is an important fact to assure that this information is accurate. The objective of this study was to estimate the CTDIvol values for great number of patients during the most frequent CT examinations, to study the comparison between CT dose monitor values and measured ones, as well as to highlight the fluctuation of CTDIvol values for the same CT examination at different centres and scanner models. The output CT dose indices measurements were carried out on single and multislice scanners for available kV, 5 mm slice thickness, 100 mA and FOV combination used. The 100 CT scanners were involved in this study. Data with regard to 15,000 examinations in patients, who underwent routine head, chest and abdomen CT were collected using a questionnaire sent to a large number of hospitals. Out of the 15,000 examinations, 5000 were head CT examinations, 5000 were chest CT examinations and 5000 were abdominal CT examinations. Comprehensive quality assurance (QA) was performed for all the machines involved in this work. Followed by QA, CT phantom dose measurements were carried out in South India using actual scanning parameters used clinically by the hospitals. From this study, we have measured the mean divergence between the measured and displayed CTDIvol values were 5.2, 8.4, and -5.7 for selected head, chest and abdomen procedures for protocols as mentioned above, respectively. Thus, this investigation revealed an observable change in CT practices, with a much wider range of studies being performed currently in South India. This reflects the improved capacity of CT scanners to scan longer scan lengths and at finer resolutions as permitted by helical and multislice technology. Also, some of the CT scanners have used smaller slice thickness for routine CT procedures to achieve better resolution and image quality. It leads to an increase in the patient radiation dose as well as the measured CTDIv, so it is suggested that such CT scanners should select appropriate slice thickness and scanning parameters in order to reduce the patient dose. If these routine scan parameters for head, chest and abdomen procedures are optimized than the dose indices would be optimal and lead to the lowering of the CT doses. In South Indian region all the CT machines were routinely tested for QA once in a year as per AERB requirements. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CT%20dose%20index" title="CT dose index">CT dose index</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20CTDI" title=" weighted CTDI"> weighted CTDI</a>, <a href="https://publications.waset.org/abstracts/search?q=volumetric%20CTDI" title=" volumetric CTDI"> volumetric CTDI</a>, <a href="https://publications.waset.org/abstracts/search?q=radiation%20dose" title=" radiation dose"> radiation dose</a> </p> <a href="https://publications.waset.org/abstracts/65985/accuracy-of-computed-tomography-dose-monitor-values-a-multicentric-study-in-india" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/65985.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">257</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">563</span> Radiation Dose and Associated Exposure Parameters in Selected MDCT Scanners in Multiphase Scan of Abdomen-Pelvic Region: A Clinical Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Sathyathas">P. Sathyathas</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20M.%20I.%20S.%20W.%20Herath"> H. M. I. S. W. Herath</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Amalraj"> T. Amalraj</a>, <a href="https://publications.waset.org/abstracts/search?q=U.%20J.%20M.%20A.%20L.%20Jayasinghe"> U. J. M. A. L. Jayasinghe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Over two thirds of medical radiation can now be attributed to Computed Tomography (CT). There is little information on amount of radiation received from multiphase CT scan of abdomen- pelvic region in clinical practice. We sought to estimate the radiation dose and associated exposure parameters in the multiphase abdomen - pelvic scan of Multideteror Computed Tomography (MDCT) studies in clinical practice. This was a retrospective cross sectional studies describing radiation dose associated with main exposure parameters in diagnostic multiphase abdomen - pelvic scans performed on 152 consecutive patients by two different sixteen slice CT scanners. Patient information, exposure parameters of CTDI (volume), DLP, kVp, mAs and pitch were recorded for every phases of abdomen- a pelvic study from dose report of MDCT scanners (MDCTs). Age of patients range from 14 years to 87 years in both MDCT scanners. Overall CTDI (volume) median was 63.8 (±10.4) mGy for a multiphase abdominal-pelvic scan with scanner A while it was 35.4 (±15.6) mGy for scanner B. Patients' effective dose for multiphase abdomen - pelvic CT scan range from 8.2 mSv to 58 mSv. Median effective dose for patients, who underwent multiphase abdomen- pelvis scan with scanner A and B were 38.5 (± 8.2) mSv and 21.3 (± 8.6) mSv respectively. Median value of exposure parameters of mAs, kVp and pitch, were 150 (±29.7), 130 (±15.3) and 1.3 (±0.1) respectively in scanner A. In scanner B; they were 60 (±14.5), 120 and 1. The median effective dose for patients between multiphase abdomen-pelvic scan of both MDCT, a significant different (P<0.05) was observed. Multiphase abdomen – pelvic scan of clinical study shows significant different of effective dose with reference level of phantom studies (8-14mSv) and it depends on the type of vendors. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=abdomen-pelvic%20region" title="abdomen-pelvic region">abdomen-pelvic region</a>, <a href="https://publications.waset.org/abstracts/search?q=computed%20tomography" title=" computed tomography"> computed tomography</a>, <a href="https://publications.waset.org/abstracts/search?q=exposure%20parameters" title=" exposure parameters"> exposure parameters</a>, <a href="https://publications.waset.org/abstracts/search?q=radiation%20dose" title=" radiation dose"> radiation dose</a> </p> <a href="https://publications.waset.org/abstracts/46083/radiation-dose-and-associated-exposure-parameters-in-selected-mdct-scanners-in-multiphase-scan-of-abdomen-pelvic-region-a-clinical-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46083.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">327</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">562</span> Variogram Fitting Based on the Wilcoxon Norm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hazem%20Al-Mofleh">Hazem Al-Mofleh</a>, <a href="https://publications.waset.org/abstracts/search?q=John%20Daniels"> John Daniels</a>, <a href="https://publications.waset.org/abstracts/search?q=Joseph%20McKean"> Joseph McKean</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Within geostatistics research, effective estimation of the variogram points has been examined, particularly in developing robust alternatives. The parametric fit of these variogram points which eventually defines the kriging weights, however, has not received the same attention from a robust perspective. This paper proposes the use of the non-linear Wilcoxon norm over weighted non-linear least squares as a robust variogram fitting alternative. First, we introduce the concept of variogram estimation and fitting. Then, as an alternative to non-linear weighted least squares, we discuss the non-linear Wilcoxon estimator. Next, the robustness properties of the non-linear Wilcoxon are demonstrated using a contaminated spatial data set. Finally, under simulated conditions, increasing levels of contaminated spatial processes have their variograms points estimated and fit. In the fitting of these variogram points, both non-linear Weighted Least Squares and non-linear Wilcoxon fits are examined for efficiency. At all levels of contamination (including 0%), using a robust estimation and robust fitting procedure, the non-weighted Wilcoxon outperforms weighted Least Squares. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=non-linear%20wilcoxon" title="non-linear wilcoxon">non-linear wilcoxon</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20estimation" title=" robust estimation"> robust estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=variogram%20estimation" title=" variogram estimation"> variogram estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=wilcoxon%20norm" title=" wilcoxon norm"> wilcoxon norm</a> </p> <a href="https://publications.waset.org/abstracts/50377/variogram-fitting-based-on-the-wilcoxon-norm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50377.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">458</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">561</span> A Comparative Analysis of Grade Weighted Average and Comprehensive Examination Result of Non Board Passers and Board Passers</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rob%20Gesley%20Capistrano">Rob Gesley Capistrano</a>, <a href="https://publications.waset.org/abstracts/search?q=Jasper%20James%20Isaac"> Jasper James Isaac</a>, <a href="https://publications.waset.org/abstracts/search?q=Rose%20Mae%20Moralda"> Rose Mae Moralda</a>, <a href="https://publications.waset.org/abstracts/search?q=Therese%20Anne%20Peleo"> Therese Anne Peleo</a>, <a href="https://publications.waset.org/abstracts/search?q=Danica%20Rillo"> Danica Rillo</a>, <a href="https://publications.waset.org/abstracts/search?q=Maria%20Virginia%20Santillian"> Maria Virginia Santillian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the valuable things that shows the intelligence among individuals is the academic background specifically their Grade Weighted Average and the significant result of the Comprehensive Examination. The general objective of the researchers to this study is to determine if there is a significant difference between General Weighted Average and Comprehensive Examination Result of Psychometrician Board Passers and Non-Board Passers. The respondents of this study composed of board passers and non-board passers. The researchers used purposive sampling technique. The result utilized by using T-test Independent Sample to determine the comparison of General Weighted Average and Comprehensive Examination Result of Board Passers and Non Board Passers. At the end, it concluded that the General Weighted Average of Board Passers and Non-Board Passers shows that there is no significant difference, but the average showed a minimal variation. The Comprehensive Examination Result of Board Passers and Non-Board Passers result revealed that there is a significant difference. The performance of comprehensive examination that will test the overall knowledge of an individual and will determine whose more proficient will likely to have a higher score. The result of the comprehensive examination had an impact in the passing performance of board examination. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=board%20passers" title="board passers">board passers</a>, <a href="https://publications.waset.org/abstracts/search?q=comprehensive%20examination%20result" title=" comprehensive examination result"> comprehensive examination result</a>, <a href="https://publications.waset.org/abstracts/search?q=grade%20weighted%20average" title=" grade weighted average"> grade weighted average</a>, <a href="https://publications.waset.org/abstracts/search?q=non%20board%20passers" title=" non board passers"> non board passers</a> </p> <a href="https://publications.waset.org/abstracts/101314/a-comparative-analysis-of-grade-weighted-average-and-comprehensive-examination-result-of-non-board-passers-and-board-passers" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/101314.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">191</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">560</span> Weighted G2 Multi-Degree Reduction of Bezier Curves</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salisu%20ibrahim">Salisu ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdalla%20Rababah"> Abdalla Rababah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this research, we use Weighted G2-Multi-degree reduction of Bezier curve of degree n to a Bezier curve of degree m, m < n. The degree reduction of Bezier curves is used to represent a given Bezier curve of n by a Bezier curve of degree m, m < n. Exact degree reduction is not possible, and degree reduction is approximate process in nature. We derive a weighted degree reducing method that is geometrically continuous at the end points. Different norms will be considered, several error minimizations will be given. The proposed methods produce error function that are less than the errors of existing methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bezier%20curves" title="Bezier curves">Bezier curves</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20degree%20reduction" title=" multiple degree reduction"> multiple degree reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=geometric%20continuity" title=" geometric continuity"> geometric continuity</a>, <a href="https://publications.waset.org/abstracts/search?q=error%20function" title=" error function"> error function</a> </p> <a href="https://publications.waset.org/abstracts/18669/weighted-g2-multi-degree-reduction-of-bezier-curves" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18669.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">482</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">559</span> Solving Process Planning, Weighted Earliest Due Date Scheduling and Weighted Due Date Assignment Using Simulated Annealing and Evolutionary Strategies</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Halil%20Ibrahim%20Demir">Halil Ibrahim Demir</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdullah%20Hulusi%20Kokcam"> Abdullah Hulusi Kokcam</a>, <a href="https://publications.waset.org/abstracts/search?q=Fuat%20Simsir"> Fuat Simsir</a>, <a href="https://publications.waset.org/abstracts/search?q=%C3%96zer%20Uygun"> Özer Uygun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traditionally, three important manufacturing functions which are process planning, scheduling and due-date assignment are performed sequentially and separately. Although there are numerous works on the integration of process planning and scheduling and plenty of works focusing on scheduling with due date assignment, there are only a few works on integrated process planning, scheduling and due-date assignment. Although due-dates are determined without taking into account of weights of the customers in the literature, here weighted due-date assignment is employed to get better performance. Jobs are scheduled according to weighted earliest due date dispatching rule and due dates are determined according to some popular due date assignment methods by taking into account of the weights of each job. Simulated Annealing, Evolutionary Strategies, Random Search, hybrid of Random Search and Simulated Annealing, and hybrid of Random Search and Evolutionary Strategies, are applied as solution techniques. Three important manufacturing functions are integrated step-by-step and higher integration levels are found better. Search meta-heuristics are found to be very useful while improving performance measure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=process%20planning" title="process planning">process planning</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20scheduling" title=" weighted scheduling"> weighted scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20due-date%20assignment" title=" weighted due-date assignment"> weighted due-date assignment</a>, <a href="https://publications.waset.org/abstracts/search?q=simulated%20annealing" title=" simulated annealing"> simulated annealing</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20strategies" title=" evolutionary strategies"> evolutionary strategies</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20searches" title=" hybrid searches"> hybrid searches</a> </p> <a href="https://publications.waset.org/abstracts/67706/solving-process-planning-weighted-earliest-due-date-scheduling-and-weighted-due-date-assignment-using-simulated-annealing-and-evolutionary-strategies" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67706.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">462</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">558</span> Solving Process Planning, Weighted Apparent Tardiness Cost Dispatching, and Weighted Processing plus Weight Due-Date Assignment Simultaneously Using a Hybrid Search</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Halil%20Ibrahim%20Demir">Halil Ibrahim Demir</a>, <a href="https://publications.waset.org/abstracts/search?q=Caner%20Erden"> Caner Erden</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdullah%20Hulusi%20Kokcam"> Abdullah Hulusi Kokcam</a>, <a href="https://publications.waset.org/abstracts/search?q=Mumtaz%20Ipek"> Mumtaz Ipek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Process planning, scheduling, and due date assignment are three important manufacturing functions which are studied independently in literature. There are hundreds of works on IPPS and SWDDA problems but a few works on IPPSDDA problem. Integrating these three functions is very crucial due to the high relationship between them. Since the scheduling problem is in the NP-Hard problem class without any integration, an integrated problem is even harder to solve. This study focuses on the integration of these functions. Sum of weighted tardiness, earliness, and due date related costs are used as a penalty function. Random search and hybrid metaheuristics are used to solve the integrated problem. Marginal improvement in random search is very high in the early iterations and reduces enormously in later iterations. At that point directed search contribute to marginal improvement more than random search. In this study, random and genetic search methods are combined to find better solutions. Results show that overall performance becomes better as the integration level increases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=process%20planning" title="process planning">process planning</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20search" title=" hybrid search"> hybrid search</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20search" title=" random search"> random search</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20due-date%20assignment" title=" weighted due-date assignment"> weighted due-date assignment</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20scheduling" title=" weighted scheduling"> weighted scheduling</a> </p> <a href="https://publications.waset.org/abstracts/68613/solving-process-planning-weighted-apparent-tardiness-cost-dispatching-and-weighted-processing-plus-weight-due-date-assignment-simultaneously-using-a-hybrid-search" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68613.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">363</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">557</span> Approximation by Generalized Lupaş-Durrmeyer Operators with Two Parameter α and β</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Preeti%20Sharma">Preeti Sharma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with the Stancu type generalization of Lupaş-Durrmeyer operators. We establish some direct results in the polynomial weighted space of continuous functions defined on the interval [0, 1]. Also, Voronovskaja type theorem is studied. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lupas-Durrmeyer%20operators" title="Lupas-Durrmeyer operators">Lupas-Durrmeyer operators</a>, <a href="https://publications.waset.org/abstracts/search?q=polya%20distribution" title=" polya distribution"> polya distribution</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20approximation" title=" weighted approximation"> weighted approximation</a>, <a href="https://publications.waset.org/abstracts/search?q=rate%20of%20convergence" title=" rate of convergence"> rate of convergence</a>, <a href="https://publications.waset.org/abstracts/search?q=modulus%20of%20continuity" title=" modulus of continuity"> modulus of continuity</a> </p> <a href="https://publications.waset.org/abstracts/47660/approximation-by-generalized-lupas-durrmeyer-operators-with-two-parameter-a-and-v" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/47660.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">346</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">556</span> Integrating Process Planning, WMS Dispatching, and WPPW Weighted Due Date Assignment Using a Genetic Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Halil%20Ibrahim%20Demir">Halil Ibrahim Demir</a>, <a href="https://publications.waset.org/abstracts/search?q=Tar%C4%B1k%20Cakar"> Tarık Cakar</a>, <a href="https://publications.waset.org/abstracts/search?q=Ibrahim%20Cil"> Ibrahim Cil</a>, <a href="https://publications.waset.org/abstracts/search?q=Muharrem%20Dugenci"> Muharrem Dugenci</a>, <a href="https://publications.waset.org/abstracts/search?q=Caner%20Erden"> Caner Erden</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Conventionally, process planning, scheduling, and due-date assignment functions are performed separately and sequentially. The interdependence of these functions requires integration. Although integrated process planning and scheduling, and scheduling with due date assignment problems are popular research topics, only a few works address the integration of these three functions. This work focuses on the integration of process planning, WMS scheduling, and WPPW due date assignment. Another novelty of this work is the use of a weighted due date assignment. In the literature, due dates are generally assigned without considering the importance of customers. However, in this study, more important customers get closer due dates. Typically, only tardiness is punished, but the JIT philosophy punishes both earliness and tardiness. In this study, all weighted earliness, tardiness, and due date related costs are penalized. As no customer desires distant due dates, such distant due dates should be penalized. In this study, various levels of integration of these three functions are tested and genetic search and random search are compared both with each other and with ordinary solutions. Higher integration levels are superior, while search is always useful. Genetic searches outperformed random searches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=process%20planning" title="process planning">process planning</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20scheduling" title=" weighted scheduling"> weighted scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20due-date%20assignment" title=" weighted due-date assignment"> weighted due-date assignment</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20search" title=" random search"> random search</a> </p> <a href="https://publications.waset.org/abstracts/51544/integrating-process-planning-wms-dispatching-and-wppw-weighted-due-date-assignment-using-a-genetic-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51544.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">555</span> Solving Weighted Number of Operation Plus Processing Time Due-Date Assignment, Weighted Scheduling and Process Planning Integration Problem Using Genetic and Simulated Annealing Search Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Halil%20Ibrahim%20Demir">Halil Ibrahim Demir</a>, <a href="https://publications.waset.org/abstracts/search?q=Caner%20Erden"> Caner Erden</a>, <a href="https://publications.waset.org/abstracts/search?q=Mumtaz%20Ipek"> Mumtaz Ipek</a>, <a href="https://publications.waset.org/abstracts/search?q=Ozer%20Uygun"> Ozer Uygun</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traditionally, the three important manufacturing functions, which are process planning, scheduling and due-date assignment, are performed separately and sequentially. For couple of decades, hundreds of studies are done on integrated process planning and scheduling problems and numerous researches are performed on scheduling with due date assignment problem, but unfortunately the integration of these three important functions are not adequately addressed. Here, the integration of these three important functions is studied by using genetic, random-genetic hybrid, simulated annealing, random-simulated annealing hybrid and random search techniques. As well, the importance of the integration of these three functions and the power of meta-heuristics and of hybrid heuristics are studied. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=process%20planning" title="process planning">process planning</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20scheduling" title=" weighted scheduling"> weighted scheduling</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20due-date%20assignment" title=" weighted due-date assignment"> weighted due-date assignment</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20search" title=" genetic search"> genetic search</a>, <a href="https://publications.waset.org/abstracts/search?q=simulated%20annealing" title=" simulated annealing"> simulated annealing</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20meta-heuristics" title=" hybrid meta-heuristics"> hybrid meta-heuristics</a> </p> <a href="https://publications.waset.org/abstracts/57629/solving-weighted-number-of-operation-plus-processing-time-due-date-assignment-weighted-scheduling-and-process-planning-integration-problem-using-genetic-and-simulated-annealing-search-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57629.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">469</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">554</span> Weighted Rank Regression with Adaptive Penalty Function</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kang-Mo%20Jung">Kang-Mo Jung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of regularization for statistical methods has become popular. The least absolute shrinkage and selection operator (LASSO) framework has become the standard tool for sparse regression. However, it is well known that the LASSO is sensitive to outliers or leverage points. We consider a new robust estimation which is composed of the weighted loss function of the pairwise difference of residuals and the adaptive penalty function regulating the tuning parameter for each variable. Rank regression is resistant to regression outliers, but not to leverage points. By adopting a weighted loss function, the proposed method is robust to leverage points of the predictor variable. Furthermore, the adaptive penalty function gives us good statistical properties in variable selection such as oracle property and consistency. We develop an efficient algorithm to compute the proposed estimator using basic functions in program R. We used an optimal tuning parameter based on the Bayesian information criterion (BIC). Numerical simulation shows that the proposed estimator is effective for analyzing real data set and contaminated data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20penalty%20function" title="adaptive penalty function">adaptive penalty function</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20penalized%20regression" title=" robust penalized regression"> robust penalized regression</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20selection" title=" variable selection"> variable selection</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20rank%20regression" title=" weighted rank regression"> weighted rank regression</a> </p> <a href="https://publications.waset.org/abstracts/79449/weighted-rank-regression-with-adaptive-penalty-function" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79449.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">475</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">553</span> Bag of Words Representation Based on Weighting Useful Visual Words</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatma%20Abdedayem">Fatma Abdedayem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The most effective and efficient methods in image categorization are almost based on bag-of-words (BOW) which presents image by a histogram of occurrence of visual words. In this paper, we propose a novel extension to this method. Firstly, we extract features in multi-scales by applying a color local descriptor named opponent-SIFT. Secondly, in order to represent image we use Spatial Pyramid Representation (SPR) and an extension to the BOW method which based on weighting visual words. Typically, the visual words are weighted during histogram assignment by computing the ratio of their occurrences in the image to the occurrences in the background. Finally, according to classical BOW retrieval framework, only a few words of the vocabulary is useful for image representation. Therefore, we select the useful weighted visual words that respect the threshold value. Experimentally, the algorithm is tested by using different image classes of PASCAL VOC 2007 and is compared against the classical bag-of-visual-words algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BOW" title="BOW">BOW</a>, <a href="https://publications.waset.org/abstracts/search?q=useful%20visual%20words" title=" useful visual words"> useful visual words</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20visual%20words" title=" weighted visual words"> weighted visual words</a>, <a href="https://publications.waset.org/abstracts/search?q=bag%20of%20visual%20words" title=" bag of visual words"> bag of visual words</a> </p> <a href="https://publications.waset.org/abstracts/14009/bag-of-words-representation-based-on-weighting-useful-visual-words" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14009.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">552</span> Forecasting Unusual Infection of Patient Used by Irregular Weighted Point Set</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seema%20Vaidya">Seema Vaidya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mining association rule is a key issue in data mining. In any case, the standard models ignore the distinction among the exchanges, and the weighted association rule mining does not transform on databases with just binary attributes. This paper proposes a novel continuous example and executes a tree (FP-tree) structure, which is an increased prefix-tree structure for securing compacted, discriminating data about examples, and makes a fit FP-tree-based mining system, FP enhanced capacity algorithm is used, for mining the complete game plan of examples by illustration incessant development. Here, this paper handles the motivation behind making remarkable and weighted item sets, i.e. rare weighted item set mining issue. The two novel brightness measures are proposed for figuring the infrequent weighted item set mining issue. Also, the algorithm are handled which perform IWI which is more insignificant IWI mining. Moreover we utilized the rare item set for choice based structure. The general issue of the start of reliable definite rules is troublesome for the grounds that hypothetically no inciting technique with no other person can promise the rightness of influenced theories. In this way, this framework expects the disorder with the uncommon signs. Usage study demonstrates that proposed algorithm upgrades the structure which is successful and versatile for mining both long and short diagnostics rules. Structure upgrades aftereffects of foreseeing rare diseases of patient. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=association%20rule" title="association rule">association rule</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title=" data mining"> data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=IWI%20mining" title=" IWI mining"> IWI mining</a>, <a href="https://publications.waset.org/abstracts/search?q=infrequent%20item%20set" title=" infrequent item set"> infrequent item set</a>, <a href="https://publications.waset.org/abstracts/search?q=frequent%20pattern%20growth" title=" frequent pattern growth"> frequent pattern growth</a> </p> <a href="https://publications.waset.org/abstracts/32862/forecasting-unusual-infection-of-patient-used-by-irregular-weighted-point-set" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32862.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">399</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">551</span> Enhanced Weighted Centroid Localization Algorithm for Indoor Environments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=I.%20Ni%C5%BEeti%C4%87%20Kosovi%C4%87">I. Nižetić Kosović</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Jagu%C5%A1t"> T. Jagušt</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Lately, with the increasing number of location-based applications, demand for highly accurate and reliable indoor localization became urgent. This is a challenging problem, due to the measurement variance which is the consequence of various factors like obstacles, equipment properties and environmental changes in complex nature of indoor environments. In this paper we propose low-cost custom-setup infrastructure solution and localization algorithm based on the Weighted Centroid Localization (WCL) method. Localization accuracy is increased by several enhancements: calibration of RSSI values gained from wireless nodes, repetitive measurements of RSSI to exclude deviating values from the position estimation, and by considering orientation of the device according to the wireless nodes. We conducted several experiments to evaluate the proposed algorithm. High accuracy of ~1m was achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=indoor%20environment" title="indoor environment">indoor environment</a>, <a href="https://publications.waset.org/abstracts/search?q=received%20signal%20strength%20indicator" title=" received signal strength indicator"> received signal strength indicator</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20centroid%20localization" title=" weighted centroid localization"> weighted centroid localization</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20localization" title=" wireless localization"> wireless localization</a> </p> <a href="https://publications.waset.org/abstracts/11878/enhanced-weighted-centroid-localization-algorithm-for-indoor-environments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11878.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">232</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">550</span> Statistical and Analytical Comparison of GIS Overlay Modelings: An Appraisal on Groundwater Prospecting in Precambrian Metamorphics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tapas%20Acharya">Tapas Acharya</a>, <a href="https://publications.waset.org/abstracts/search?q=Monalisa%20Mitra"> Monalisa Mitra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Overlay modeling is the most widely used conventional analysis for spatial decision support system. Overlay modeling requires a set of themes with different weightage computed in varied manners, which gives a resultant input for further integrated analysis. In spite of the popularity and most widely used technique; it gives inconsistent and erroneous results for similar inputs while processed in various GIS overlay techniques. This study is an attempt to compare and analyse the differences in the outputs of different overlay methods using GIS platform with same set of themes of the Precambrian metamorphic to obtain groundwater prospecting in Precambrian metamorphic rocks. The objective of the study is to emphasize the most suitable overlay method for groundwater prospecting in older Precambrian metamorphics. Seven input thematic layers like slope, Digital Elevation Model (DEM), soil thickness, lineament intersection density, average groundwater table fluctuation, stream density and lithology have been used in the spatial overlay models of fuzzy overlay, weighted overlay and weighted sum overlay methods to yield the suitable groundwater prospective zones. Spatial concurrence analysis with high yielding wells of the study area and the statistical comparative studies among the outputs of various overlay models using RStudio reveal that the Weighted Overlay model is the most efficient GIS overlay model to delineate the groundwater prospecting zones in the Precambrian metamorphic rocks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20overlay" title="fuzzy overlay">fuzzy overlay</a>, <a href="https://publications.waset.org/abstracts/search?q=GIS%20overlay%20model" title=" GIS overlay model"> GIS overlay model</a>, <a href="https://publications.waset.org/abstracts/search?q=groundwater%20prospecting" title=" groundwater prospecting"> groundwater prospecting</a>, <a href="https://publications.waset.org/abstracts/search?q=Precambrian%20metamorphics" title=" Precambrian metamorphics"> Precambrian metamorphics</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20overlay" title=" weighted overlay"> weighted overlay</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20sum%20overlay" title=" weighted sum overlay "> weighted sum overlay </a> </p> <a href="https://publications.waset.org/abstracts/119978/statistical-and-analytical-comparison-of-gis-overlay-modelings-an-appraisal-on-groundwater-prospecting-in-precambrian-metamorphics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/119978.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">549</span> Humeral Head and Scapula Detection in Proton Density Weighted Magnetic Resonance Images Using YOLOv8</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aysun%20Sezer">Aysun Sezer</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Magnetic Resonance Imaging (MRI) is one of the advanced diagnostic tools for evaluating shoulder pathologies. Proton Density (PD)-weighted MRI sequences prove highly effective in detecting edema. However, they are deficient in the anatomical identification of bones due to a trauma-induced decrease in signal-to-noise ratio and blur in the traumatized cortices. Computer-based diagnostic systems require precise segmentation, identification, and localization of anatomical regions in medical imagery. Deep learning-based object detection algorithms exhibit remarkable proficiency in real-time object identification and localization. In this study, the YOLOv8 model was employed to detect humeral head and scapular regions in 665 axial PD-weighted MR images. The YOLOv8 configuration achieved an overall success rate of 99.60% and 89.90% for detecting the humeral head and scapula, respectively, with an intersection over union (IoU) of 0.5. Our findings indicate a significant promise of employing YOLOv8-based detection for the humerus and scapula regions, particularly in the context of PD-weighted images affected by both noise and intensity inhomogeneity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=YOLOv8" title="YOLOv8">YOLOv8</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=humerus" title=" humerus"> humerus</a>, <a href="https://publications.waset.org/abstracts/search?q=scapula" title=" scapula"> scapula</a>, <a href="https://publications.waset.org/abstracts/search?q=IRM" title=" IRM"> IRM</a> </p> <a href="https://publications.waset.org/abstracts/175663/humeral-head-and-scapula-detection-in-proton-density-weighted-magnetic-resonance-images-using-yolov8" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/175663.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">66</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">548</span> Exercise Training for Management Hypertensive Patients: 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=Noor%20F.%20Ilias">Noor F. Ilias</a>, <a href="https://publications.waset.org/abstracts/search?q=Mazlifah%20Omar"> Mazlifah Omar</a>, <a href="https://publications.waset.org/abstracts/search?q=Hashbullah%20Ismail"> Hashbullah Ismail</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Exercise training has been shown to improve functional capacity and is recommended as a therapy for management of blood pressure. Our purpose was to establish whether different exercise capacity produces different effect size for Cardiorespiratory Fitness (CRF), systolic (SBP) and diastolic (DBP) blood pressure in patients with hypertension. Exercise characteristic is required in order to have optimal benefit from the training, but optimal exercise capacity is still unwarranted. A MEDLINE search (1985 to 2015) was conducted for exercise based rehabilitation trials in hypertensive patients. Thirty-seven studies met the selection criteria. Of these, 31 (83.7%) were aerobic exercise and 6 (16.3%) aerobic with additional resistance exercise, providing a total of 1318 exercise subjects and 819 control, the total of subjects was 2137. We calculated exercise volume and energy expenditure through the description of exercise characteristics. 4 studies (18.2%) were 451kcal - 900 kcal, 12 (54.5%) were 900 kcal – 1350 kcal and 6 (27.3%) >1351kcal per week. Peak oxygen consumption (peak VO2) increased by mean difference of 1.44 ml/kg/min (95% confidence interval [CI]: 1.08 to 1.79 ml/kg/min; p = 0.00001) with weighted mean 21.2% for aerobic exercise compare to aerobic with additional resistance exercise 4.50 ml/kg/min (95% confidence interval [CI]: 3.57 to 5.42 ml/kg/min; p = 0.00001) with weighted mean 14.5%. SBP was clinically reduce for both aerobic and aerobic with resistance training by mean difference of -4.66 mmHg (95% confidence interval [CI]: -5.68 to -3.63 mmHg; p = 0.00001) weighted mean 6% reduction and -5.06 mmHg (95% confidence interval [CI]: -7.32 to -2.8 mmHg; p = 0.0001) weighted mean 5% reduction respectively. Result for DBP was clinically reduce for aerobic by mean difference of -1.62 mmHg (95% confidence interval [CI]: -2.09 to -1.15 mmHg; p = 0.00001) weighted mean 4% reduction and aerobic with resistance training reduce by mean difference of -3.26 mmHg (95% confidence interval [CI]: -4.87 to -1.65 mmHg; p = 0.0001) weighted mean 6% reduction. Optimum exercise capacity for 451 kcal – 900 kcal showed greater improvement in peak VO2 and SBP by 2.76 ml/kg/min (95% confidence interval [CI]: 1.47 to 4.05 ml/kg/min; p = 0.0001) with weighted mean 40.6% and -16.66 mmHg (95% confidence interval [CI]: -21.72 to -11.60 mmHg; p = 0.00001) weighted mean 9.8% respectively. Our data demonstrated that aerobic exercise with total volume of 451 kcal – 900 kcal/ week energy expenditure may elicit greater changes in cardiorespiratory fitness and blood pressure in hypertensive patients. Higher exercise capacity weekly does not seem better result in management hypertensive patients. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blood%20Pressure" title="blood Pressure">blood Pressure</a>, <a href="https://publications.waset.org/abstracts/search?q=exercise" title=" exercise"> exercise</a>, <a href="https://publications.waset.org/abstracts/search?q=hypertension" title=" hypertension"> hypertension</a>, <a href="https://publications.waset.org/abstracts/search?q=peak%20VO2" title=" peak VO2"> peak VO2</a> </p> <a href="https://publications.waset.org/abstracts/44456/exercise-training-for-management-hypertensive-patients-a-systematic-review-and-meta-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/44456.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">282</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">547</span> Automatic Seizure Detection Using Weighted Permutation Entropy and Support Vector Machine</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Noha%20Seddik">Noha Seddik</a>, <a href="https://publications.waset.org/abstracts/search?q=Sherine%20Youssef"> Sherine Youssef</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Kholeif"> Mohamed Kholeif</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The automated epileptic seizure detection research field has emerged in the recent years; this involves analyzing the Electroencephalogram (EEG) signals instead of the traditional visual inspection performed by expert neurologists. In this study, a Support Vector Machine (SVM) that uses Weighted Permutation Entropy (WPE) as the input feature is proposed for classifying normal and seizure EEG records. WPE is a modified statistical parameter of the permutation entropy (PE) that measures the complexity and irregularity of a time series. It incorporates both the mapped ordinal pattern of the time series and the information contained in the amplitude of its sample points. The proposed system utilizes the fact that entropy based measures for the EEG segments during epileptic seizure are lower than in normal EEG. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram%20%28EEG%29" title="electroencephalogram (EEG)">electroencephalogram (EEG)</a>, <a href="https://publications.waset.org/abstracts/search?q=epileptic%20seizure%20detection" title=" epileptic seizure detection"> epileptic seizure detection</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20permutation%20entropy%20%28WPE%29" title=" weighted permutation entropy (WPE)"> weighted permutation entropy (WPE)</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine%20%28SVM%29" title=" support vector machine (SVM)"> support vector machine (SVM)</a> </p> <a href="https://publications.waset.org/abstracts/12444/automatic-seizure-detection-using-weighted-permutation-entropy-and-support-vector-machine" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12444.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">372</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">546</span> Reliability and Probability Weighted Moment Estimation for Three Parameter Mukherjee-Islam Failure Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ariful%20Islam">Ariful Islam</a>, <a href="https://publications.waset.org/abstracts/search?q=Showkat%20Ahmad%20Lone"> Showkat Ahmad Lone</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Mukherjee-Islam Model is commonly used as a simple life time distribution to assess system reliability. The model exhibits a better fit for failure information and provides more appropriate information about hazard rate and other reliability measures as shown by various authors. It is possible to introduce a location parameter at a time (i.e., a time before which failure cannot occur) which makes it a more useful failure distribution than the existing ones. Even after shifting the location of the distribution, it represents a decreasing, constant and increasing failure rate. It has been shown to represent the appropriate lower tail of the distribution of random variables having fixed lower bound. This study presents the reliability computations and probability weighted moment estimation of three parameter model. A comparative analysis is carried out between three parameters finite range model and some existing bathtub shaped curve fitting models. Since probability weighted moment method is used, the results obtained can also be applied on small sample cases. Maximum likelihood estimation method is also applied in this study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=comparative%20analysis" title="comparative analysis">comparative analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20likelihood%20estimation" title=" maximum likelihood estimation"> maximum likelihood estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=Mukherjee-Islam%20failure%20model" title=" Mukherjee-Islam failure model"> Mukherjee-Islam failure model</a>, <a href="https://publications.waset.org/abstracts/search?q=probability%20weighted%20moment%20estimation" title=" probability weighted moment estimation"> probability weighted moment estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=reliability" title=" reliability"> reliability</a> </p> <a href="https://publications.waset.org/abstracts/74517/reliability-and-probability-weighted-moment-estimation-for-three-parameter-mukherjee-islam-failure-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74517.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">274</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">545</span> Solving Single Machine Total Weighted Tardiness Problem Using Gaussian Process Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wanatchapong%20Kongkaew">Wanatchapong Kongkaew</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes an application of probabilistic technique, namely Gaussian process regression, for estimating an optimal sequence of the single machine with total weighted tardiness (SMTWT) scheduling problem. In this work, the Gaussian process regression (GPR) model is utilized to predict an optimal sequence of the SMTWT problem, and its solution is improved by using an iterated local search based on simulated annealing scheme, called GPRISA algorithm. The results show that the proposed GPRISA method achieves a very good performance and a reasonable trade-off between solution quality and time consumption. Moreover, in the comparison of deviation from the best-known solution, the proposed mechanism noticeably outperforms the recently existing approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20process%20regression" title="Gaussian process regression">Gaussian process regression</a>, <a href="https://publications.waset.org/abstracts/search?q=iterated%20local%20search" title=" iterated local search"> iterated local search</a>, <a href="https://publications.waset.org/abstracts/search?q=simulated%20annealing" title=" simulated annealing"> simulated annealing</a>, <a href="https://publications.waset.org/abstracts/search?q=single%20machine%20total%20weighted%20tardiness" title=" single machine total weighted tardiness"> single machine total weighted tardiness</a> </p> <a href="https://publications.waset.org/abstracts/6433/solving-single-machine-total-weighted-tardiness-problem-using-gaussian-process-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6433.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">544</span> A Weighted Approach to Unconstrained Iris Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yao-Hong%20Tsai">Yao-Hong Tsai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a weighted approach to unconstrained iris recognition. Nowadays, commercial systems are usually characterized by strong acquisition constraints based on the subject’s cooperation. However, it is not always achievable for real scenarios in our daily life. Researchers have been focused on reducing these constraints and maintaining the performance of the system by new techniques at the same time. With large variation in the environment, there are two main improvements to develop the proposed iris recognition system. For solving extremely uneven lighting condition, statistic based illumination normalization is first used on eye region to increase the accuracy of iris feature. The detection of the iris image is based on Adaboost algorithm. Secondly, the weighted approach is designed by Gaussian functions according to the distance to the center of the iris. Furthermore, local binary pattern (LBP) histogram is then applied to texture classification with the weight. Experiment showed that the proposed system provided users a more flexible and feasible way to interact with the verification system through iris recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=authentication" title="authentication">authentication</a>, <a href="https://publications.waset.org/abstracts/search?q=iris%20recognition" title=" iris recognition"> iris recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=adaboost" title=" adaboost"> adaboost</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20binary%20pattern" title=" local binary pattern"> local binary pattern</a> </p> <a href="https://publications.waset.org/abstracts/3876/a-weighted-approach-to-unconstrained-iris-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3876.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">225</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">543</span> X̄ and S Control Charts based on Weighted Standard Deviation Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Derya%20Karag%C3%B6z">Derya Karagöz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A Shewhart chart based on normality assumption is not appropriate for skewed distributions since its Type-I error rate is inflated. This study presents X̄ and S control charts for monitoring the process variability for skewed distributions. We propose Weighted Standard Deviation (WSD) X̄ and S control charts. Standard deviation estimator is applied to monitor the process variability for estimating the process standard deviation, in the case of the W SD X̄ and S control charts as this estimator is simple and easy to compute. Unlike the Shewhart control chart, the proposed charts provide asymmetric limits in accordance with the direction and degree of skewness to construct the upper and lower limits. The performances of the proposed charts are compared with other heuristic charts for skewed distributions by using Simulation study. The Simulation studies show that the proposed control charts have good properties for skewed distributions and large sample sizes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=weighted%20standard%20deviation" title="weighted standard deviation">weighted standard deviation</a>, <a href="https://publications.waset.org/abstracts/search?q=MAD" title=" MAD"> MAD</a>, <a href="https://publications.waset.org/abstracts/search?q=skewed%20distributions" title=" skewed distributions"> skewed distributions</a>, <a href="https://publications.waset.org/abstracts/search?q=S%20control%20charts" title=" S control charts"> S control charts</a> </p> <a href="https://publications.waset.org/abstracts/45730/x-and-s-control-charts-based-on-weighted-standard-deviation-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45730.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">399</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">542</span> Hybrid Fuzzy Weighted K-Nearest Neighbor to Predict Hospital Readmission for Diabetic Patients </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Soha%20A.%20Bahanshal">Soha A. Bahanshal</a>, <a href="https://publications.waset.org/abstracts/search?q=Byung%20G.%20Kim"> Byung G. Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor. <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=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20fuzzy%20weighted%20k-nearest%20neighbor" title=" hybrid fuzzy weighted k-nearest neighbor"> hybrid fuzzy weighted k-nearest neighbor</a>, <a href="https://publications.waset.org/abstracts/search?q=diabetic%20hospital%20readmission" title=" diabetic hospital readmission"> diabetic hospital readmission</a> </p> <a href="https://publications.waset.org/abstracts/129397/hybrid-fuzzy-weighted-k-nearest-neighbor-to-predict-hospital-readmission-for-diabetic-patients" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129397.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">186</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">541</span> A Ratio-Weighted Decision Tree Algorithm for Imbalance Dataset Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Doyin%20Afolabi">Doyin Afolabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Phillip%20Adewole"> Phillip Adewole</a>, <a href="https://publications.waset.org/abstracts/search?q=Oladipupo%20Sennaike"> Oladipupo Sennaike</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most well-known classifiers, including the decision tree algorithm, can make predictions on balanced datasets efficiently. However, the decision tree algorithm tends to be biased towards imbalanced datasets because of the skewness of the distribution of such datasets. To overcome this problem, this study proposes a weighted decision tree algorithm that aims to remove the bias toward the majority class and prevents the reduction of majority observations in imbalance datasets classification. The proposed weighted decision tree algorithm was tested on three imbalanced datasets- cancer dataset, german credit dataset, and banknote dataset. The specificity, sensitivity, and accuracy metrics were used to evaluate the performance of the proposed decision tree algorithm on the datasets. The evaluation results show that for some of the weights of our proposed decision tree, the specificity, sensitivity, and accuracy metrics gave better results compared to that of the ID3 decision tree and decision tree induced with minority entropy for all three datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=imbalance%20dataset" title=" imbalance dataset"> imbalance dataset</a> </p> <a href="https://publications.waset.org/abstracts/157609/a-ratio-weighted-decision-tree-algorithm-for-imbalance-dataset-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157609.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">137</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">540</span> Econophysics: The Use of Entropy Measures in Finance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Sheraz">Muhammad Sheraz</a>, <a href="https://publications.waset.org/abstracts/search?q=Vasile%20Preda"> Vasile Preda</a>, <a href="https://publications.waset.org/abstracts/search?q=Silvia%20Dedu"> Silvia Dedu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Concepts of econophysics are usually used to solve problems related to uncertainty and nonlinear dynamics. In the theory of option pricing the risk neutral probabilities play very important role. The application of entropy in finance can be regarded as the extension of both information entropy and the probability entropy. It can be an important tool in various financial methods such as measure of risk, portfolio selection, option pricing and asset pricing. Gulko applied Entropy Pricing Theory (EPT) for pricing stock options and introduced an alternative framework of Black-Scholes model for pricing European stock option. In this article, we present solutions to maximum entropy problems based on Tsallis, Weighted-Tsallis, Kaniadakis, Weighted-Kaniadakies entropies, to obtain risk-neutral densities. We have also obtained the value of European call and put in this framework. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=option%20pricing" title="option pricing">option pricing</a>, <a href="https://publications.waset.org/abstracts/search?q=Black-Scholes%20model" title=" Black-Scholes model"> Black-Scholes model</a>, <a href="https://publications.waset.org/abstracts/search?q=Tsallis%20entropy" title=" Tsallis entropy"> Tsallis entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaniadakis%20entropy" title=" Kaniadakis entropy"> Kaniadakis entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20entropy" title=" weighted entropy"> weighted entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=risk-neutral%20density" title=" risk-neutral density"> risk-neutral density</a> </p> <a href="https://publications.waset.org/abstracts/55546/econophysics-the-use-of-entropy-measures-in-finance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55546.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">303</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">539</span> Real-Time Lane Marking Detection Using Weighted Filter</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ayhan%20Kucukmanisa">Ayhan Kucukmanisa</a>, <a href="https://publications.waset.org/abstracts/search?q=Orhan%20Akbulut"> Orhan Akbulut</a>, <a href="https://publications.waset.org/abstracts/search?q=Oguzhan%20Urhan"> Oguzhan Urhan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, advanced driver assistance systems (ADAS) have become popular, since they enable safe driving. Lane detection is a vital step for ADAS. The performance of the lane detection process is critical to obtain a high accuracy lane departure warning system (LDWS). Challenging factors such as road cracks, erosion of lane markings, weather conditions might affect the performance of a lane detection system. In this paper, 1-D weighted filter based on row filtering to detect lane marking is proposed. 2-D input image is filtered by 1-D weighted filter considering four-pixel values located symmetrically around the center of candidate pixel. Performance evaluation is carried out by two metrics which are true positive rate (TPR) and false positive rate (FPR). Experimental results demonstrate that the proposed approach provides better lane marking detection accuracy compared to the previous methods while providing real-time processing performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=lane%20marking%20filter" title="lane marking filter">lane marking filter</a>, <a href="https://publications.waset.org/abstracts/search?q=lane%20detection" title=" lane detection"> lane detection</a>, <a href="https://publications.waset.org/abstracts/search?q=ADAS" title=" ADAS"> ADAS</a>, <a href="https://publications.waset.org/abstracts/search?q=LDWS" title=" LDWS"> LDWS</a> </p> <a href="https://publications.waset.org/abstracts/90804/real-time-lane-marking-detection-using-weighted-filter" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90804.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> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=weighted%20CTDI&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=weighted%20CTDI&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=weighted%20CTDI&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=weighted%20CTDI&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=weighted%20CTDI&page=6">6</a></li> <li class="page-item"><a class="page-link" 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