Journal articles: 'Linear Scoring'

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Parry, Matthew. "Linear scoring rules for probabilistic binary classification." Electronic Journal of Statistics 10, no. 1 (2016): 1596–607. http://dx.doi.org/10.1214/16-ejs1150.

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Shultz, Kenneth S. "Increasing Alpha Reliabilities of Multiple-Choice Tests with Linear Polychotomous Scoring." Psychological Reports 77, no. 3 (1995): 760–62. http://dx.doi.org/10.2466/pr0.1995.77.3.760.

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Little research has been conducted on the use of linear polychotomous scoring of multiple-choice test items. Therefore, several tests were analyzed using both dichotomous and polychotomous scoring of test items to assess how the alpha reliabilities of the tests change based on the type of scoring used. In each case, the alpha reliabilities of the tests increased, with the same or fewer number of items in each test, when polychotomous (vs dichotomous) scoring of multiple-choice test items was used.

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Trajdos, Pawel, and Robert Burduk. "Probability-driven scoring functions in combining linear classifiers." JUCS - Journal of Universal Computer Science 28, no. 3 (2022): 269–91. http://dx.doi.org/10.3897/jucs.80747.

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Although linear classifiers are one of the oldest methods in machine learning, they are still very popular in the machine learning community. This is due to their low computational complexity and robustness to overfitting. Consequently, linear classifiers are often used as base classifiers of multiple ensemble classification systems. This research is aimed at building a new fusion method dedicated to the ensemble of linear classifiers. The fusion scheme uses both measurement space and geometrical space. Namely, we proposed a probability-driven scoring function which shape depends on the orientation of the decision hyperplanes generated by the base classifiers. The proposed fusion method is compared with the reference method using multiple benchmark datasets taken from the KEEL repository. The comparison is done using multiple quality criteria. The statistical analysis of the obtained results is also performed. The experimental study shows that, under certain conditions, some improvement may be obtained.

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Shim, Ju-Hyun, and Young-K. Lee. "Generalized Partially Linear Additive Models for Credit Scoring." Korean Journal of Applied Statistics 24, no. 4 (2011): 587–95. http://dx.doi.org/10.5351/kjas.2011.24.4.587.

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Lev, Omer, and Jeffrey S. Rosenschein. "Convergence of Iterative Scoring Rules." Journal of Artificial Intelligence Research 57 (December 11, 2016): 573–91. http://dx.doi.org/10.1613/jair.5187.

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In multiagent systems, social choice functions can help aggregate the distinct preferences that agents have over alternatives, enabling them to settle on a single choice. Despite the basic manipulability of all reasonable voting systems, it would still be desirable to find ways to reach plausible outcomes, which are stable states, i.e., a situation where no agent would wish to change its vote. One possibility is an iterative process in which, after everyone initially votes, participants may change their votes, one voter at a time. This technique, explored in previous work, converges to a Nash equilibrium when Plurality voting is used, along with a tie-breaking rule that chooses a winner according to a linear order of preferences over candidates. In this paper, we both consider limitations of the iterative voting method, as well as expanding upon it. We demonstrate the significance of tie-breaking rules, showing that no iterative scoring rule converges for all tie-breaking. However, using a restricted tie-breaking rule (such as the linear order rule used in previous work) does not by itself ensure convergence. We prove that in addition to plurality, the veto voting rule converges as well using a linear order tie-breaking rule. However, we show that these two voting rules are the only scoring rules that converge, regardless of tie-breaking mechanism.

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Hu, Tianming, Sam Yuan Sung, Jing Sun, Xiao-Wei Ai, and Peter A. Ng. "A linear transform scheme for building weighted scoring rules1." Intelligent Data Analysis 16, no. 3 (2012): 383–407. http://dx.doi.org/10.3233/ida-2012-0530.

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Hu, C., X. Li, and J. Liang. "Developing optimal non-linear scoring function for protein design." Bioinformatics 20, no. 17 (2004): 3080–98. http://dx.doi.org/10.1093/bioinformatics/bth369.

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Ustun, Berk, and Cynthia Rudin. "Supersparse linear integer models for optimized medical scoring systems." Machine Learning 102, no. 3 (2015): 349–91. http://dx.doi.org/10.1007/s10994-015-5528-6.

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Lee, J. W., S. H. Um, J. B. Lee, J. Mun, and H. Cho. "Scoring and Staging Systems Using Cox Linear Regression Modeling and Recursive Partitioning." Methods of Information in Medicine 45, no. 01 (2006): 37–43. http://dx.doi.org/10.1055/s-0038-1634034.

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Summary Objectives: Scoring and staging systems are used to determine the order and class of data according to predictors. Systems used for medical data, such as the Child-Turcotte-Pugh scoring and staging systems for ordering and classifying patients with liver disease, are often derived strictly from physicians’ experience and intuition. We construct objective and data-based scoring/staging systems using statistical methods. Methods: We consider Cox linear regression modeling and recursive partitioning techniques for censored survival data. In particular, to obtain a target number of stages we propose cross-validation and amalgamation algorithms. We also propose an algorithm for constructing scoring and staging systems by integrating local Cox linear regression models into recursive partitioning, so that we can retain the merits of both methods such as superior predictive accuracy, ease of use, and detection of interactions between predictors. The staging system construction algorithms are compared by cross-validation evaluation of real data. Results: The data-based cross-validation comparison shows that Cox linear regression modeling is somewhat better than recursive partitioning when there are only continuous predictors, while recursive partitioning is better when there are significant categorical predictors. The proposed local Cox linear recursive partitioning has better predictive accuracy than Cox linear modeling and simple recursive partitioning. Conclusions: This study indicates that integrating local linear modeling into recursive partitioning can significantly improve prediction accuracy in constructing scoring and staging systems.

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Koh, Wei Yang Calvin, Hong Qi Tan, Khong Wei Ang, Sung Yong Park, Wen Siang Lew, and James Cheow Lei Lee. "Standardizing Monte Carlo simulation parameters for a reproducible dose-averaged linear energy transfer." British Journal of Radiology 93, no. 1112 (2020): 20200122. http://dx.doi.org/10.1259/bjr.20200122.

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Objective: Dose-averaged linear energy transfer (LETD) is one of the factors which determines relative biological effectiveness (RBE) for treatment planning in proton therapy. It is usually determined from Monte Carlo (MC) simulation. However, no standard simulation protocols were established for sampling of LETD. Simulation parameters like maximum step length and range cut will affect secondary electrons production and have an impact on the accuracy of dose distribution and LETD. We aim to show how different combinations of step length and range cut in GEANT4 will affect the result in sampling of LETD using different MC scoring methods. Methods: In this work, different step length and range cut value in a clinically relevant voxel geometry were used for comparison. Different LETD scoring methods were established and the concept of covariance between energy deposition per step and step length is used to explain the differences between them. Results: We recommend a maximum step length of 0.05 mm and a range cut of 0.01 mm in MC simulation as this yields the most consistent LETD value across different scoring methods. Different LETD scoring methods are also compared and variation up to 200% can be observed at the plateau of 80 MeV proton beam. Scoring Method one has one of the lowest percentage differences compared across all simulation parameters. Conclusion: We have determined a set of maximum step length and range cut parameters to be used for LETD scoring in a 1 mm voxelized geometry. LETD scoring method should also be clearly defined and standardized to facilitate cross-institutional studies. Advances in knowledge: Establishing a standard simulation protocol for sampling LETD would reduce the discrepancy when comparing data across different centres, and this can improve the calculation for RBE.

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Van Gestel, Tony, Bart Baesens, Peter Van Dijcke, Johan Suykens, and Joao Garcia. "Linear and non-linear credit scoring by combining logistic regression and support vector machines." Journal of Credit Risk 1, no. 4 (2005): 31–60. http://dx.doi.org/10.21314/jcr.2005.025.

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Munkhdalai, Lkhagvadorj, Keun Ho Ryu, Oyun-Erdene Namsrai, and Nipon Theera-Umpon. "A Partially Interpretable Adaptive Softmax Regression for Credit Scoring." Applied Sciences 11, no. 7 (2021): 3227. http://dx.doi.org/10.3390/app11073227.

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Credit scoring is a process of determining whether a borrower is successful or unsuccessful in repaying a loan using borrowers’ qualitative and quantitative characteristics. In recent years, machine learning algorithms have become widely studied in the development of credit scoring models. Although efficiently classifying good and bad borrowers is a core objective of the credit scoring model, there is still a need for the model that can explain the relationship between input and output. In this work, we propose a novel partially interpretable adaptive softmax (PIA-Soft) regression model to achieve both state-of-the-art predictive performance and marginally interpretation between input and output. We augment softmax regression by neural networks to make it adaptive for each borrower. Our PIA-Soft model consists of two main components: linear (softmax regression) and non-linear (neural network). The linear part explains the fundamental relationship between input and output variables. The non-linear part serves to improve the prediction performance by identifying the non-linear relationship between features for each borrower. The experimental result on public benchmark datasets shows that our proposed model not only outperformed the machine learning baselines but also showed the explanations that logically related to the real-world.

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Mohamed, Ateesha F., A. Brett Hauber, F. Reed Johnson, and Cheryl D. Coon. "Patient Preferences and Linear Scoring Rules for Patient-Reported Outcomes." Patient: Patient-Centered Outcomes Research 3, no. 4 (2010): 217–27. http://dx.doi.org/10.2165/11537880-000000000-00000.

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Columbu, Silvia, Valentina Mameli, Monica Musio, and Philip Dawid. "The Hyvärinen scoring rule in Gaussian linear time series models." Journal of Statistical Planning and Inference 212 (May 2021): 126–40. http://dx.doi.org/10.1016/j.jspi.2020.08.004.

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Noonan, F., and N. McGowan. "Linear Scoring Models In Evaluating Improvements For Manufacturing System Availability." International Journal of Modelling and Simulation 15, no. 4 (1995): 158–62. http://dx.doi.org/10.1080/02286203.1995.11760268.

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Lovie, A. D., and P. Lovie. "The flat maximum effect and linear scoring models for prediction." Journal of Forecasting 5, no. 3 (1986): 159–68. http://dx.doi.org/10.1002/for.3980050303.

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Hardy, William E., and John L. Adrian. "A linear programming alternative to discriminant analysis in credit scoring." Agribusiness 1, no. 4 (1985): 285–92. http://dx.doi.org/10.1002/1520-6297(198524)1:4<285::aid-agr2720010406>3.0.co;2-m.

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Billiet, Lieven, Sabine Van Huffel, and Vanya Van Belle. "Interval Coded Scoring: a toolbox for interpretable scoring systems." PeerJ Computer Science 4 (April 2, 2018): e150. http://dx.doi.org/10.7717/peerj-cs.150.

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Over the last decades, clinical decision support systems have been gaining importance. They help clinicians to make effective use of the overload of available information to obtain correct diagnoses and appropriate treatments. However, their power often comes at the cost of a black box model which cannot be interpreted easily. This interpretability is of paramount importance in a medical setting with regard to trust and (legal) responsibility. In contrast, existing medical scoring systems are easy to understand and use, but they are often a simplified rule-of-thumb summary of previous medical experience rather than a well-founded system based on available data. Interval Coded Scoring (ICS) connects these two approaches, exploiting the power of sparse optimization to derive scoring systems from training data. The presented toolbox interface makes this theory easily applicable to both small and large datasets. It contains two possible problem formulations based on linear programming or elastic net. Both allow to construct a model for a binary classification problem and establish risk profiles that can be used for future diagnosis. All of this requires only a few lines of code. ICS differs from standard machine learning through its model consisting of interpretable main effects and interactions. Furthermore, insertion of expert knowledge is possible because the training can be semi-automatic. This allows end users to make a trade-off between complexity and performance based on cross-validation results and expert knowledge. Additionally, the toolbox offers an accessible way to assess classification performance via accuracy and the ROC curve, whereas the calibration of the risk profile can be evaluated via a calibration curve. Finally, the colour-coded model visualization has particular appeal if one wants to apply ICS manually on new observations, as well as for validation by experts in the specific application domains. The validity and applicability of the toolbox is demonstrated by comparing it to standard Machine Learning approaches such as Naive Bayes and Support Vector Machines for several real-life datasets. These case studies on medical problems show its applicability as a decision support system. ICS performs similarly in terms of classification and calibration. Its slightly lower performance is countered by its model simplicity which makes it the method of choice if interpretability is a key issue.

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Magnusson, Thor. "Scoring with Code: Composing with algorithmic notation." Organised Sound 19, no. 3 (2014): 268–75. http://dx.doi.org/10.1017/s1355771814000259.

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Computer code is a form of notational language. It prescribes actions to be carried out by the computer, often by systems called interpreters. When code is used to write music, we are therefore operating with programming language as a relatively new form of musical notation. Music is a time-based art form and the traditional musical score is a linear chronograph with instructions for an interpreter. Here code and traditional notation are somewhat at odds, since code is written as text, without any representational timeline. This can pose problems, for example for a composer who is working on a section in the middle of a long piece, but has to repeatedly run the code from the beginning or make temporary arrangements to solve this difficulty in the compositional process. In short: code does not come with a timeline but is rather the material used for building timelines. This article explores the context of creating linear ‘code scores’ in the area of musical notation. It presents theThrenoscopeas an example of a system that implements both representational notation and a prescriptive code score.

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OVERSTREET, GEORGE A., EDWIN L. BRADLEY, and ROBERT S. KEMP. "The flat-maximum effect and generic linear scoring models: a test." IMA Journal of Management Mathematics 4, no. 1 (1992): 97–109. http://dx.doi.org/10.1093/imaman/4.1.97.

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Mohamed, AF, AB Hauber, and FR Johnson. "PR4 LINEAR SCORING RULES FOR PATIENT REPORTED OUTCOMES AND PATIENT PREFERENCES." Value in Health 12, no. 7 (2009): A232. http://dx.doi.org/10.1016/s1098-3015(10)74131-0.

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Suzuki, Takeshi, Ryochi Yoshida, Akiko Okamoto, and Yu Seri. "Semiquantitative Evaluation of Extrasynovial Soft Tissue Inflammation in the Shoulders of Patients with Polymyalgia Rheumatica and Elderly-Onset Rheumatoid Arthritis by Power Doppler Ultrasound." BioMed Research International 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/4272560.

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Objectives. To develop a scoring system for evaluating the extrasynovial soft tissue inflammation of the shoulders in patients with polymyalgia rheumatica (PMR) and elderly-onset rheumatoid arthritis with PMR-like onset (pm-EORA) using ultrasound.Methods. We analyzed stored power Doppler (PD) images obtained by the pretreatment examination of 15 PMR patients and 15 pm-EORA patients. A semiquantitative scoring system for evaluating the severity of PD signals adjacent to the anterior aspect of the subscapularis tendon was designed.Results. A four-point scale scoring for the hyperemia on the subscapularis tendon was proposed as follows in brief: 0 = absent or minimal flow, 1 = single vessel dots or short linear-shape signals, 2 = long linear-shape signals or short zone-shape signals, or 3 = long zone-shape signals. This scoring system showed good intra- and interobserver reliability and good correlation to quantitative pixel-counting evaluation. By using it, we demonstrated that inflammation in PMR is dominantly localized in extrasynovial soft tissue as compared with pm-EORA.Conclusions. We proposed a reliable semiquantitative scoring system using ultrasound for the evaluation of extrasynovial soft tissue inflammation of the shoulders in patients with both PMR and pm-EORA. This system is simple to use and can be utilized in future investigations.

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Krieter, J., and U. Presuhn. "Fundamentskriterien als Selektionsmerkmale in Schweinezuchtprogrammen (Kurzmitteilung)." Archives Animal Breeding 42, no. 6 (1999): 611–18. http://dx.doi.org/10.5194/aab-42-611-1999.

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Abstract. Title of the paper: Linear scoring System of legs as selection tool in swine breeding programmes (short communication) A linear scoring system was used to describe leg traits of two pure breed lines of a swine breeding program. Seven Traits were defined on a scale of −3 to +3. Standard deviations, min- and max-values indicate a high variation of the traits. The estimated heritabilities ränge from h2 = 0.10 to 0.25. The genetic correlations between performance traits (live daily gain, backfat thickness) and exterior traits are antagonistic. Therefore further selection pressure for better Performance traits should take account selection for leg traits.

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Borsa, Paul A., Scott M. Lephart, and James J. Irrgang. "Sport-Specificity of Knee Scoring Systems to Assess Disability in Anterior Cruciate Ligament-Deficient Athletes." Journal of Sport Rehabilitation 7, no. 1 (1998): 44–60. http://dx.doi.org/10.1123/jsr.7.1.44.

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We compared the outcome measures of three knee scoring systems currently used to measure disability in anterior cruciate ligament (ACL)–deficient athletes. Twenty-nine ACL-deficient athletes completed three scoring systems (the Lysholm Knee Scoring System, a modified version of the Cincinnati Knee Scoring System, and the Knee Outcome Survey). Results demonstrate statistically significant mean differences and linear relationships between the outcome measures for the three scoring systems. The Knee Outcome Survey appears to provide valid measures of disability and indicates that our subjects functioned well with activities of daily living but became symptomatic and functionally limited with sports. The outcome measures also indicate that the Lysholm system is more specific to activities of daily living, while the modified Cincinnati is more specific to sports. We recommend that standard scoring systems be developed to provide measures of functional disability in athletes who experience knee injuries.

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Daraei, Paul P., Hardik Doshi, and Louis M. DeJoseph. "Medial Nasal Bone Scoring: A Novel Technique for Improving Osteotomies During Rhinoplasty." American Journal of Cosmetic Surgery 37, no. 3 (2019): 138–42. http://dx.doi.org/10.1177/0748806819895682.

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Nasal bone osteotomies are commonly performed in rhinoplasty to manipulate the upper third of the nose. In patients with a dorsal hump, reduction is often performed, followed by lateral osteotomies to medialize the nasal bone complex. However, fracture patterns are often unknown in vivo. We intend to map nasal bone osteotomies and describe a novel and minimally invasive method of performing medial scoring osteotomies to improve fracture patterns and surgical speed, and decrease complications. In total, 19 formalin-fixed cadavers were dissected to reveal nasal bone architecture. The nasal dorsum (bony and cartilaginous) was reduced to form an open roof deformity. Osteotomies were then performed on all 19 specimens, followed by digital infracture. Medial scoring osteotomies were performed unilaterally, along with a lateral osteotomy. On the contralateral side, only lateral osteotomies were performed. Fracture patterns were mapped and compared. Of 19 cadaver specimens, 38 sides were examined in total: 19 sides underwent medial scoring and 19 sides served as controls. Fracture patterns were linear with less comminution on sides with medial scoring osteotomies. Sides with medial scoring osteotomies also achieved uniform closure of the open roof deformity, compared with 73% in sides without. Rocker deformity was not seen in either group. Osteotomies are integral to nasal dorsum reduction and modification of the upper third of the nose in rhinoplasty. Scoring of the medial nasal bone prior to lateral osteotomy and digital infracture allows for an increased rate of open roof closure. Furthermore, medial scoring osteotomies create smooth, linear fracture patterns that prevent bony spicules, comminution, and irregularities that may be evident in patients with thin nasal skin. Performing the medial scoring osteotomy is a fast, safe method of achieving consistent nasal bone infracture during rhinoplasty.

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Camilli, Gregory. "IRT Scoring and Test Blueprint Fidelity." Applied Psychological Measurement 42, no. 5 (2018): 393–400. http://dx.doi.org/10.1177/0146621618754897.

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This article focuses on the topic of how item response theory (IRT) scoring models reflect the intended content allocation in a set of test specifications or test blueprint. Although either an adaptive or linear assessment can be built to reflect a set of design specifications, the method of scoring is also a critical step. Standard IRT models employ a set of optimal scoring weights, and these weights depend on item parameters in the two-parameter logistic (2PL) and three-parameter logistic (3PL) models. The current article is an investigation of whether the scoring models reflect an intended set of weights defined as the proportion of item falling into each cell of the test blueprint. The 3PL model is of special interest because the optimal scoring weights depend on ability. Thus, the concern arises that for examinees of low ability, the intended weights are implicitly altered.

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Han, Kyung (Chris) T., Dimiter M. Dimitrov, and Faisal Al-Mashary. "Developing Multistage Tests Using D-Scoring Method." Educational and Psychological Measurement 79, no. 5 (2019): 988–1008. http://dx.doi.org/10.1177/0013164419841428.

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The D-scoring method for scoring and equating tests with binary items proposed by Dimitrov offers some of the advantages of item response theory, such as item-level difficulty information and score computation that reflects the item difficulties, while retaining the merits of classical test theory such as the simplicity of number correct score computation and relaxed requirements for model sample sizes. Because of its unique combination of those merits, the D-scoring method has seen quick adoption in the educational and psychological measurement field. Because item-level difficulty information is available with the D-scoring method and item difficulties are reflected in test scores, it conceptually makes sense to use the D-scoring method with adaptive test designs such as multistage testing (MST). In this study, we developed and compared several versions of the MST mechanism using the D-scoring approach and also proposed and implemented a new framework for conducting MST simulation under the D-scoring method. Our findings suggest that the score recovery performance under MST with D-scoring was promising, as it retained score comparability across different MST paths. We found that MST using the D-scoring method can achieve improvements in measurement precision and efficiency over linear-based tests that use D-scoring method.

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Overstreet, G. "Applicability of generic linear scoring models in the U.S. credit-union environment." IMA Journal of Management Mathematics 7, no. 4 (1996): 291. http://dx.doi.org/10.1093/imaman/7.4.291.

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Danforth, Marie Elaine, Kristrina Shuler Herndon, and Kathryn B. Propst. "A preliminary study of patterns of replication in Scoring linear enamel hypoplasias." International Journal of Osteoarchaeology 3, no. 4 (1993): 297–302. http://dx.doi.org/10.1002/oa.1390030408.

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Spenciere, Barbara, Liana Chaves Mendes-Santos, Christina Borges-Lima, and Helenice Charchat-Fichman. "Qualitative analysis and identification of pattern of errors in Clock Drawing Tests of community-dwelling older adults." Dementia & Neuropsychologia 12, no. 2 (2018): 181–88. http://dx.doi.org/10.1590/1980-57642018dn12-020011.

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Abstract The Clock Drawing Test (CDT) is a frequently employed screening tool with different scoring systems. Quantitative and semi-quantitative scoring systems, such as Sunderland’s et al. (1989), do not discriminate different error patterns. Thus, the same score can represent a number of different neuropsychological profiles. Therefore, the use of a scoring method that emphasizes qualitative aspects to determine specific error patterns is fundamental. Objective: To use a qualitative scale to analyze error patterns in the CDTs of older adults who scored 5 in a previous study. Methods: 49 CDTs with score of 5 were analyzed using the qualitative scale. Linear regression and hierarchical and non-hierarchical cluster analyses were performed. Results: The linear regression showed a significant association between the total score and all the error patterns of the qualitative scale. The hierarchical cluster yielded three groups. However, due to the heterogeneity observed among the groups, a non-hierarchical cluster analysis was performed to better understand the results. Three groups were determined with different neuropsychological profiles and patterns of errors. Conclusion: The qualitative scoring of the CDT is important when examining and analyzing specific neuropsychological domains in older adults, especially executive functions.

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Rahayu, Endah Yulia. "THE ANONYMOUS TEACHERS’ FACTORS OF ASSESSING PARAGRAPH WRITING." Journal of English for Academic and Specific Purposes 3, no. 1 (2020): 1. http://dx.doi.org/10.18860/jeasp.v3i1.9208.

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<p> </p><p>Scoring writing is very subjective and mainly relies on a lot on teachers as raters. They play a significant role to meticulously carry out writing evaluations to adjudicate the linguistic and rhetorical features of their students' written responses. Based on the previous studies, the teachers’ factors of knowledge of basic writing assessment, efficacy in selecting assessment method, efficacy in scoring accuracy, and perception in practicing writing assessment can contribute a lot to the quality in teachers’ writing assessment. The 56 junior high school English teachers having at least five years of teaching experience, was invited to fill out the questionnaire and scoring paragraph writing. The results were examined with Multiple Linear Regression analysis. Amongst these factors, only the efficacy in scoring writing accuracy predicts the teachers’ scoring paragraph writing.</p>

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Alizadeh Savareh, Behrouz, Azadeh Bashiri, Ali Behmanesh, Gholam Hossein Meftahi, and Boshra Hatef. "Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis." PeerJ 6 (July 25, 2018): e5247. http://dx.doi.org/10.7717/peerj.5247.

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IntroductionSleep scoring is an important step in the treatment of sleep disorders. Manual annotation of sleep stages is time-consuming and experience-relevant and, therefore, needs to be done using machine learning techniques.MethodsSleep-EDF polysomnography was used in this study as a dataset. Support vector machines and artificial neural network performance were compared in sleep scoring using wavelet tree features and neighborhood component analysis.ResultsNeighboring component analysis as a combination of linear and non-linear feature selection method had a substantial role in feature dimension reduction. Artificial neural network and support vector machine achieved 90.30% and 89.93% accuracy, respectively.Discussion and ConclusionSimilar to the state of the art performance, the introduced method in the present study achieved an acceptable performance in sleep scoring. Furthermore, its performance can be enhanced using a technique combined with other techniques in feature generation and dimension reduction. It is hoped that, in the future, intelligent techniques can be used in the process of diagnosing and treating sleep disorders.

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Parganas, Petros, Roman Liasko, and Christos Anagnostopoulos. "Scoring goals in multiple fields." Sport, Business and Management: An International Journal 7, no. 2 (2017): 197–215. http://dx.doi.org/10.1108/sbm-11-2016-0072.

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Purpose Professional football clubs currently strive for a number of concurrent goals, ranging from on-field success to profit maximization to fan expansion and engagement. The purpose of this paper, theoretically informed by the social penetration theory, is to analyze the economics behind such goals and examine the association between team performance, commercial success, and social media followers in professional team sports. Design/methodology/approach A data set relating to 20 European professional football clubs that combines financial (revenues and costs), sporting, and digital-reach measures for three consecutive football seasons (2013/2014 to 2015/2016) was used. In addition, to elaborate on this data in terms of a descriptive study, the study constructs a range of correlation statistical tests and linear modeling techniques to obtain quantitative results. Findings The results indicate that all the three main sources of club revenues (match-day, commercial/sponsorship, and broadcasting) are positive drivers for Facebook followers. Staff investments (staff costs) are also positively related to Facebook followers, albeit to a lesser extent, while higher-ranked clubs seem to follow a constant approach in terms of their revenues and cost structure. Originality/value This study seeks to bridge the communication and sport economic research, providing evidence that Facebook followers are part of the cyclical phenomenon of team revenues and team performance. In doing so, it initiates a debate on the relationship between the digital expansion of a football club and its sports and financial indicators.

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Ricart, Glenn, Abiodun T. Atoloye, Carrie M. Durward, and Patricia M. Guenther. "New Exponential Scoring Functions for Diet Quality Indexes Solve Problems Caused by Truncation." Journal of Nutrition 152, no. 4 (2021): 1168–73. http://dx.doi.org/10.1093/jn/nxab431.

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ABSTRACT Background Diet quality indexes, including the Healthy Eating Index, assess diets based on usual dietary intakes and a scoring function. Nearly all diet quality indexes use scoring functions that have floors and ceilings, thereby truncating the scores and losing information about intakes outside the scoring range. This score truncation has 2 important impacts: 1) the index does not reflect all intakes; and 2) the assumption that measurement error in intake reporting has a neutral impact on the diet quality score cannot be upheld. Objectives Our main objective was to devise new diet quality scoring functions that eliminate truncation and its attendant problems. Methods Seven desirable properties of a new scoring function were identified: 1) avoid truncations in component scoring to prevent information loss and to provide scoring sensitivity in the currently truncated regions; 2) reduce dependency on the accuracy of dietary standards; 3) minimize measurement error bias and subsequent misclassification; 4) relate plausibly to biological processes; 5) possess desirable mathematical and statistical properties; 6) have simple representations that are easy to calculate and add minimum artifacts of their own; and 7) otherwise closely mimic existing scoring functions. Results The recommended replacement for piecewise-linear scoring is a family of scoring functions based on exponentials. For components where higher intakes are better, the function is a single exponential. For components where lower intakes are better, the function is a concave-convex mirrored pair of exponentials. The proposed exponential scoring functions have all 7 desired properties. Conclusions The proposed exponential scoring functions will improve the usefulness of dietary scoring indexes by eliminating truncations. Compared to existing scoring functions, the use of exponentials makes the scores more inclusive of very high and very low intakes, reduces measurement error bias, and is less sensitive to the exact placement of the scoring standards.

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Hébert-Losier, Kim, Ivana Hanzlíková, Chen Zheng, Lee Streeter, and Michael Mayo. "The ‘DEEP’ Landing Error Scoring System." Applied Sciences 10, no. 3 (2020): 892. http://dx.doi.org/10.3390/app10030892.

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The Landing Error Scoring System (LESS) is an injury-risk screening tool used in sports; but scoring is time consuming, clinician-dependent, and generally inaccessible outside of elite sports. Our aim is to evidence that LESS scores can be automated using deep-learning-based computer vision combined with machine learning and compare the accuracy of LESS predictions using different video cropping and machine learning methods. Two-dimensional videos from 320 double-leg drop-jump landings with known LESS scores were analysed in OpenPose. Videos were cropped to key frames manually (clinician) and automatically (computer vision), and 42 kinematic features were extracted. A series of 10 × 10-fold cross-validation experiments were applied on full and balanced datasets to predict LESS scores. Random forest for regression outperformed linear and dummy regression models, yielding the lowest mean absolute error (1.23) and highest correlation (r = 0.63) between manual and automated scores. Sensitivity (0.82) and specificity (0.77) were reasonable for risk categorization (high-risk LESS ≥ 5 errors). Experiments using either a balanced (versus unbalanced) dataset or manual (versus automated) cropping method did not improve predictions. Further research on the automation would enhance the strength of the agreement between clinical and automated scores beyond its current levels, enabling quasi real-time scoring.

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Singh, Sangeeta, and Shari McMahan. "Evaluation of the Relationship between AcademicPerformance and Physical Fitness Measures in California Schools." Californian Journal of Health Promotion 4, no. 2 (2006): 207–14. http://dx.doi.org/10.32398/cjhp.v4i2.1946.

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The purpose of this study was to evaluate the relationship between academic achievement and physical fitness in California schools. Data from the academic year 2004-2005 Fitnessgram were compared to reading, mathematics and science scores on the California Standards test (CST) of 253 elementary schools in the Orange County School District. Physical education teachers from the 10 lowest scoring and 10 highest scoring schools were interviewed regarding content of the physical education classes in their school. Simple correlation coefficients revealed a positive linear relationship between academic scores and physical fitness scores. The interview with the teachers revealed that most of the 10 lowest scoring schools did not have a designated physical education teacher. All of the 10 highest scoring schools had designated physical education teachers and followed the physical education guidelines recommended by the California Education Board.

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Azaïs, J. M., A. Bardin, and T. Dhorne. "Minqe, maximum likelihood estimation and fisher scoring algorithm for non linear variance models." Statistics 24, no. 3 (1993): 205–13. http://dx.doi.org/10.1080/02331888308802408.

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S. Hatzopoulos, J. Petruccelli, G. "A TEOAE Screening Protocol Based on Linear Click Stimuli: Performance and Scoring Criteria." Acta Oto-Laryngologica 119, no. 2 (1999): 135–39. http://dx.doi.org/10.1080/00016489950181521.

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Van Steenbergen, E. J. "Description and evaluation of a linear scoring system for exterior traits in pigs." Livestock Production Science 23, no. 1-2 (1989): 163–81. http://dx.doi.org/10.1016/0301-6226(89)90012-2.

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Yao, Lili, Shelby J. Haberman, and Mo Zhang. "Prediction of Writing True Scores in Automated Scoring of Essays by Best Linear Predictors and Penalized Best Linear Predictors." ETS Research Report Series 2019, no. 1 (2019): 1–27. http://dx.doi.org/10.1002/ets2.12248.

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Darby, Charlotte A., Ravi Gaddipati, Michael C. Schatz, and Ben Langmead. "Vargas: heuristic-free alignment for assessing linear and graph read aligners." Bioinformatics 36, no. 12 (2020): 3712–18. http://dx.doi.org/10.1093/bioinformatics/btaa265.

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Abstract Motivation Read alignment is central to many aspects of modern genomics. Most aligners use heuristics to accelerate processing, but these heuristics can fail to find the optimal alignments of reads. Alignment accuracy is typically measured through simulated reads; however, the simulated location may not be the (only) location with the optimal alignment score. Results Vargas implements a heuristic-free algorithm guaranteed to find the highest-scoring alignment for real sequencing reads to a linear or graph genome. With semiglobal and local alignment modes and affine gap and quality-scaled mismatch penalties, it can implement the scoring functions of commonly used aligners to calculate optimal alignments. While this is computationally intensive, Vargas uses multi-core parallelization and vectorized (SIMD) instructions to make it practical to optimally align large numbers of reads, achieving a maximum speed of 456 billion cell updates per second. We demonstrate how these ‘gold standard’ Vargas alignments can be used to improve heuristic alignment accuracy by optimizing command-line parameters in Bowtie 2, BWA-maximal exact match and vg to align more reads correctly. Availability and implementation Source code implemented in C++ and compiled binary releases are available at https://github.com/langmead-lab/vargas under the MIT license. Supplementary information Supplementary data are available at Bioinformatics online.

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Du, Wei, Jingwen Li, and Feiyu Li. "Research on The Offensive Characteristics of La Liga Team Based on Social Network Analysis." SHS Web of Conferences 155 (2023): 01003. http://dx.doi.org/10.1051/shsconf/202315501003.

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To explore the difference of social network parameters between the network of passing before scoring and the network of passing before missing the goal, and to explore the correlation between social network parameters and team performance, this paper establishes the offensive pass network of 20 teams in the La Liga from 2017 to 2018, and 11 social network parameters are calculated. The Pearson correlation test is used to explore the linear correlation between 11 social network parameters and team performance. The results show that the linear correlation between the network parameters of passing before scoring and team performance is stronger than the network parameters of passing before missing the goal. According to the results, we can provide reliable and effective information to the football coaches to help improve the performance of football matches.

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Yao, Jian-Rong, and Jia-Rui Chen. "A New Hybrid Support Vector Machine Ensemble Classification Model for Credit Scoring." Journal of Information Technology Research 12, no. 1 (2019): 77–88. http://dx.doi.org/10.4018/jitr.2019010106.

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Credit scoring plays important role in the financial industry. There are different ways employed in the field of credit scoring, such as the traditional logistic regression, discriminant analysis, and linear regression; methods used in the field of machine learning include neural network, k-nearest neighbors, genetic algorithm, support vector machines (SVM), decision tree, and so on. SVM has been demonstrated with good performance in classification. This paper proposes a new hybrid RF-SVM ensemble model, which uses random forest to select important variables, and employs ensemble methods (bagging and boosting) to aggregate single base models (SVM) as a robust classifier. The experimental results suggest that this new model could achieve effective improvement, and has promising potential in the field of credit scoring.

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Li, Jianwu, Haizhou Wei, and Wangli Hao. "Weight-Selected Attribute Bagging for Credit Scoring." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/379690.

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Assessment of credit risk is of great importance in financial risk management. In this paper, we propose an improved attribute bagging method, weight-selected attribute bagging (WSAB), to evaluate credit risk. Weights of attributes are first computed using attribute evaluation method such as linear support vector machine (LSVM) and principal component analysis (PCA). Subsets of attributes are then constructed according to weights of attributes. For each of attribute subsets, the larger the weights of the attributes the larger the probabilities by which they are selected into the attribute subset. Next, training samples and test samples are projected onto each attribute subset, respectively. A scoring model is then constructed based on each set of newly produced training samples. Finally, all scoring models are used to vote for test instances. An individual model that only uses selected attributes will be more accurate because of elimination of some of redundant and uninformative attributes. Besides, the way of selecting attributes by probability can also guarantee the diversity of scoring models. Experimental results based on two credit benchmark databases show that the proposed method, WSAB, is outstanding in both prediction accuracy and stability, as compared to analogous methods.

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Saleh, Ahmed M., Mohamed M. Elsharkawy, Mohamed A. E. AbdelRahman, and Sayed M. Arafat. "Evaluation of Soil Quality in Arid Western Fringes of the Nile Delta for Sustainable Agriculture." Applied and Environmental Soil Science 2021 (September 18, 2021): 1–17. http://dx.doi.org/10.1155/2021/1434692.

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Egypt is currently witnessing an extensive desert greening plan with a target of adding one and a half million feddans to the agricultural area. The present study evaluates the soil quality in the western desert fringes of the Nile Delta using three indicator datasets, which involve the total dataset (TDS), the minimum dataset (MDS), and the expert dataset (EDS). Three quality index models are included: the Additive Soil Quality Index (SQI-A), the Weighted Additive Soil Quality Index (SQI-W), and the Nemoro Soil Quality Index (SQI-N). Linear and nonlinear scoring functions are evaluated for scoring soil and terrain indicators. Thirteen soil quality indicators and three terrain indicators were measured in 397 sampling sites for soil quality evaluation. Factor analyses determined five soil and terrain indicators for the minimum dataset and their associated weights. The linear scoring functions reflected the soil system functions more than nonlinear scoring functions. Soil quality estimation by the minimum dataset (MDS) and Weighted Additive Soil Quality Index (SQI-W) is more sensitive than that by SQI-A and SQI-N quality models to explain soil quality indicators. The moderate soil quality grade is the largest quality grade in the studied area. The minimum dataset of soil quality indicators could assist in reducing time and cost of evaluating soil quality and monitoring the temporal changes in soil quality of the region due to the increased agricultural development.

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Lin, Chin, Yung-Tsai Lee, Feng-Jen Wu, et al. "The Application of Projection Word Embeddings on Medical Records Scoring System." Healthcare 9, no. 10 (2021): 1298. http://dx.doi.org/10.3390/healthcare9101298.

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Medical records scoring is important in a health care system. Artificial intelligence (AI) with projection word embeddings has been validated in its performance disease coding tasks, which maintain the vocabulary diversity of open internet databases and the medical terminology understanding of electronic health records (EHRs). We considered that an AI-enhanced system might be also applied to automatically score medical records. This study aimed to develop a series of deep learning models (DLMs) and validated their performance in medical records scoring task. We also analyzed the practical value of the best model. We used the admission medical records from the Tri-Services General Hospital during January 2016 to May 2020, which were scored by our visiting staffs with different levels from different departments. The medical records were scored ranged 0 to 10. All samples were divided into a training set (n = 74,959) and testing set (n = 152,730) based on time, which were used to train and validate the DLMs, respectively. The mean absolute error (MAE) was used to evaluate each DLM performance. In original AI medical record scoring, the predicted score by BERT architecture is closer to the actual reviewer score than the projection word embedding and LSTM architecture. The original MAE is 0.84 ± 0.27 using the BERT model, and the MAE is 1.00 ± 0.32 using the LSTM model. Linear mixed model can be used to improve the model performance, and the adjusted predicted score was closer compared to the original score. However, the project word embedding with the LSTM model (0.66 ± 0.39) provided better performance compared to BERT (0.70 ± 0.33) after linear mixed model enhancement (p < 0.001). In addition to comparing different architectures to score the medical records, this study further uses a mixed linear model to successfully adjust the AI medical record score to make it closer to the actual physician’s score.

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ZHOU, LIGANG, KIN KEUNG LAI, and JEROME YEN. "CREDIT SCORING MODELS WITH AUC MAXIMIZATION BASED ON WEIGHTED SVM." International Journal of Information Technology & Decision Making 08, no. 04 (2009): 677–96. http://dx.doi.org/10.1142/s0219622009003582.

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Credit scoring models are very important tools for financial institutions to make credit granting decisions. In the last few decades, many quantitative methods have been used for the development of credit scoring models with focus on maximizing classification accuracy. This paper proposes the credit scoring models with the area under receiver operating characteristics curve (AUC) maximization based on the new emerged support vector machines (SVM) techniques. Three main SVM models with different features weighted strategies are discussed. The weighted SVM credit scoring models are tested using 10-fold cross validation with two real world data sets and the experimental results are compared with other six traditional methods including linear regression, logistic regression, k nearest neighbor, decision tree, and neural network. Results demonstrate that weighted 2-norm SVM with radial basis function (RBF) kernel function and t-test feature weighting strategy has the overall better performance with very narrow margin than other SVM models. However, it also consumes more computational time. In considering the balance of performance and time, least squares support vector machines (LSSVM) with RBF kernel maybe a better choice for large scale credit scoring applications.

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Enam, Syed Ather, Fauzan Alam Hashmi, Sanam Mir Ghazi, et al. "NCOG-31. GIANT PITUITARY ADENOMA (GPA) SCORE: A NOVEL SCORING SYSTEM TO PREDICT POSTOPERATIVE OUTCOMES OF GIANT PITUITARY ADENOMAS." Neuro-Oncology 23, Supplement_6 (2021): vi158. http://dx.doi.org/10.1093/neuonc/noab196.621.

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Abstract BACKGROUND Giant pituitary adenomas (GPA) are uncommon and highly variable in morphology and extension. There is no scoring system that considers all the dimensions of adenoma invasion. We developed a new Giant Pituitary Adenoma score and report our surgical experience and evaluate outcomes after resection of these tumors in accordance with the preoperative score. METHODS We developed a novel scoring system for classifying giant pituitary adenomas, and 11-year data of GPA surgery at our center was collected retrospectively, based on this scoring system. GPA Score considered tumor’s parasellar extension, encasement of cavernous internal carotid artery (ICA), suprasellar extension &gt; 2 cm, suprasellar extension &gt; 4cm and retrosellar extension. Maximum possible score was 9. The scoring system was applied to 53 patients of GPA who underwent surgical resection between January 1, 2006, and December 2017. The Lundin-Pederson (ABC/2) method was used to calculate the tumor volume both pre- and post-resection and linear regression was used to assess the relationship between extent of tumor resection and GPA score. RESULTS The median age of the study population was 42.08 ± 16.49 years. The mean maximum diameter of the pituitary adenomas was 5.0 cm (range 4.0 cm-8.5cm) while the mean volume of the adenomas was 27.3 cm3 (range 10 cm3-149 cm3). There were 3 cases of score 2, 5 cases of score 3, 13 cases of score 4, 20 cases of score 5, 9 cases of score 6 and 3 cases of score 7. The range of tumor volumes of tumors for scores from 2-7 was 17.3 cm3 to 65.8 cm3 and GPA score was correlated with the percent residual tumor using linear regression that was statistically significant (p= 0.001). CONCLUSION GPA Score is a reliable scoring system to predict the extent and subsequent difficulty in tumor resection in GPA.

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Liu, Ming, Yuqi Wang, Weiwei Xu, and Li Liu. "Automated Scoring of Chinese Engineering Students' English Essays." International Journal of Distance Education Technologies 15, no. 1 (2017): 52–68. http://dx.doi.org/10.4018/ijdet.2017010104.

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The number of Chinese engineering students has increased greatly since 1999. Rating the quality of these students' English essays has thus become time-consuming and challenging. This paper presents a novel automatic essay scoring algorithm called PSO-SVR, based on a machine learning algorithm, Support Vector Machine for Regression (SVR), and a computational intelligence algorithm, Particle Swarm Optimization, which optimizes the parameters of SVR kernel functions. Three groups of essays, written by chemical, electrical and computer science engineering majors respectively, were used for evaluation. The study result shows that this PSO-SVR outperforms traditional essay scoring algorithms, such as multiple linear regression, support vector machine for regression and K Nearest Neighbor algorithm. It indicates that PSO-SVR is more robust in predicting irregular datasets, because the repeated use of simple content words may result in the low score of an essay, even though the system detects higher cohesion but no spelling error.

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Kocherlakota, Prabhakar, Edward C. Qian, Vishwas C. Patel, et al. "A New Scoring System for the Assessment of Neonatal Abstinence Syndrome." American Journal of Perinatology 37, no. 03 (2019): 333–40. http://dx.doi.org/10.1055/s-0039-3400310.

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Abstract Objective This study aimed to evaluate the concordance of a new scoring system for neonatal abstinence syndrome (NAS) and NAS scores to the traditional Modified Finnegan Neonatal Abstinence Scoring Tool (M-FNAST) score. The NAS score is based on the physiology of withdrawal, with equal emphasis on behavior, and neurological signs. Study Design The NAS scores for a control group of 202 healthy, term neonates were compared with those for 45 term neonates with NAS. The NAS and M-FNAST scores obtained simultaneously in 45 term neonates with NAS were compared using correlation, linear regression, and receiver operating characteristic curve analysis to determine the validity, reliability, and specificity of the NAS scores. Results The association between the NAS and M-FNAST scores was high (Spearman's correlation, 83%; linear regression, 83%), with an area under the curve of the NAS score of 1.00 (p < 0.01). A cut-off NAS score ≥4 identified NAS neonates with a sensitivity of 100% and specificity of 96%. The values of intraclass correlation, interrater agreement, and Cronbach's α were 0.63, 0.88, and 0.63, respectively. Conclusion The new NAS scoring system is valid, reliable, physiologically based, and correlates closely with the M-FNAST score. The NAS scores may require further validation before its use in clinical practice.

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Journal articles on the topic 'Linear Scoring'

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