Evaluation of Lyso-Gb1 as a biomarker for Gaucher disease treatment outcomes using data from the Gaucher Outcome Survey by Ari Zimran, Shoshana Revel-Vilk, Tama Dinur, Majdolen Istaiti, Jaco Botha, Elena Lukina, Pilar Giraldo, Patrick Deegan, Stephan vom Dahl

“…Decreases were greater for all treatment-naive than previously treated patients (median change − 120.5 vs. − 3.3 ng/mL) and for velaglucerase alfa–treated patients vs. the overall treated cohort (–32.6 vs. − 8.6 ng/mL). …”
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Easy Data Augmentation untuk Data yang Imbalance pada Konsultasi Kesehatan Daring by Anisa Nur Azizah, Misbachul Falach Asy'ari, Ifnu Wisma Dwi Prastya, Diana Purwitasari

“…Kemudian, percobaan menyelidiki proses augmentasi kami menggunakan pengklasifikasi Random Forest, Naïve Bayes, dan metode berbasis peningkatan seperti XGBoost dan ADABoost, yang menghasilkan peningkatan akurasi rata-rata sebesar 0,63. …”
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Real-Life Experiences with Telaprevir in the Treatment of Chronic Genotype 1 Hepatitis C — The TEPS Study by T. Berg, P. Buggisch, D. Hueppe, S. Mauss, H. Wedemeyer, G. Teuber, T. Lutz, K. Stein, S. Wegner, H. Hinrichsen

“…To assess the implementation of therapy stopping rules or shortening of the treatment and their impact on sustained virological response (SVR), as well as the safety and efficacy of the TVR-based therapy during routine daily treatment of patients in Germany.Materials and Methods. 802 patients were assessed (272 treatment naïve, 520 pre-treated) in the noninterventional, multi-center study.Results. 56.6 % of the patients achieved SVR. …”
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Pegloticase and Methotrexate Cotherapy in Patients With Uncontrolled Gout With Prior Pegloticase Monotherapy Failure: Findings of an Open‐Label Trial by Orrin M. Troum, John K. Botson, Katie Obermeyer, Bo Chao, Yang Song, Jennifer Zarzoso, Kjerstina Gutierrez, Supra Verma, Lissa Padnick‐Silver, Brian LaMoreaux

“…Methotrexate (MTX) cotherapy increases pegloticase response rates and lowers IR risk in pegloticase‐naïve patients. Therefore, the question of re‐treating patients with previous pegloticase monotherapy failure has arisen. …”
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The association of HIV status and depressive symptoms in the Ndlovu Cohort study by Li Xiang Y. den Boer, Karine Scheuermaier, Hugo A. Tempelman, Roos E. Barth, Walter L. J. M. Devillé, Roel A. Coutinho, Diederick E. Grobbee, Francois Venter, Alinda G. Vos-Seda, Kerstin Klipstein-Grobusch

“…In general, higher odds of depressive symptoms in ART-naïve PLHIV could reflect poor coping with diagnosis of HIV. …”
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Research on Personal Loan Default Risk Assessment Based on Machine Learning by Liu Guangsen

“…This paper analyzes the current research status of other scholars on machine learning in personal loan default risk assessment in recent years, and selects Logistic Regression, Support Vector Machine, Naïve Bayes and Deep Neural Networks as research model. …”
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Interpersonal relations by Llew von Essen

“…But like you, I am not that naïve to realise that it is an easy one free of problems. …”
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Saving Death: Apoptosis for Intervention in Transplantation and Autoimmunity by Alice Li, Okechukwu Ojogho, Alan Escher

“…Processing of apoptotic cells by DCs is of particular interest, because DCs are the only known APCs capable of activating naïve T lymphocytes to become effector or regulatory cells. …”
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A Phosphorous History: William Carlos Williams’ In the American Grain by Antonia Rigaud

“…In the American Grain looks at the “beginners” that have made American history, from the Spanish explorers to Poe, and questions the naïve notion of “beginning”. William Carlos Williams’ In the American Grain is a biography of the American cultural imagination that puts in dialogue the national epic with a perspectival counter history.…”
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A Hybrid Machine Learning Framework for Soccer Match Outcome Prediction: Incorporating Bivariate Poisson Distribution by Chen Zhong An

“…The author utilizes a comprehensive dataset from top European leagues (2014-2022) and employ models including Bivariate Poisson Distribution, Naive Bayes, Neural Networks, Support Vector Machines, Random Forests, and Gradient Boosting. …”
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Comparing Correlation-Based Feature Selection and Symmetrical Uncertainty for Student Dropout Prediction by Haryono Setiadi, Indah Paksi Larasati, Esti Suryani, Dewi Wisnu Wardani, Hasan Dwi Cahyono Wardani, Ardhi Wijayanto

“…We evaluate these methods using three classification algorithms: decision tree (DT), support vector machine (SVM), and naive Bayes (NB). Results show that SU outperforms CFS overall, with SVM achieving the highest accuracy (98.16%) when combined with SU Moreover, this study identifies total credits in the fourth semester, cumulative GPA, gender, and student domicile as key predictors of student dropout. …”
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Design of an Adaptive Distributed Critical-Care Extensive Response Network (AD-CERN) Using Cooperative Overlay Network by V. Akilandeswari, S. Mercy Shalinie

“…In this network, each node will be trained independently based on Naïve Bayes classification algorithm to react against any critical services in the network. …”
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Identificación de las principales enfermedades de la planta del café (Coffea arabica) a través de visión artificial by Oscar Eder Flores Colorado, Jair Cervantes Canales, Farid García-Lamont, José Sergio Ruiz Castilla

“…Por último, se implementaron cuatro algoritmos de clasificación entre los que se encuentran support vector machine, random forest, Naive Bayes y redes neuronales artificiales backpropagation. …”
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Big Data Approach to Sentiment Analysis in Machine Learning-Based Microblogs: Perspectives of Religious Moderation Public Policy in Indonesia by Mhd. Furqan, Ahmad Fakhri Ab. Nasir

“…Sentiment analysis was conducted on three primary microblogs such as Twitter, Instagram and YouTube using six machine learning algorithms. These include Naïve Bayes, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Bagging Classifier, Random Forest, and Gradient Boosting Classifier. …”
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Advanced Cancer Classification Using AI and Pattern Recognition Techniques by Haddou Bouazza Sara, Haddou Bouazza Jihad

“…We applied feature selection techniques such as the F Test, Signal-to-Noise Ratio (SNR), T-test, ReliefF, Correlation Coefficient, Mutual Information, and minimum redundancy maximum relevance, along with classifiers including K-Nearest Neighbors, Support Vector Machines, Linear Discriminant Analysis, Decision Tree Classifiers, and Naive Bayes. Our results demonstrate that the SNR method consistently achieved the highest accuracy in gene selection, particularly when paired with K-means clustering. …”
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Comparative Study for Classification Algorithms Performance in Crop Yields Prediction Systems by Halbast Rashid Ismael, Adnan Mohsin Abdulazeez, Dathar A. Hasan

“…The performance of the decision tree, naive Bayes, random forest, support vector machine and K-nearest neighbour is measured and compared to each other. …”
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Enhancing Spam Filtering: A Comparative Study of Modern Advanced Machine Learning Techniques by Zhang Chenwei

“…As a result, effective spam filtering has become essential. With an emphasis on Naïve Bayes (NB), Decision Trees (DT), and Support Vector Machines (SVM), this study offers a thorough analysis of the major machine learning techniques utilized in contemporary spam filtering. …”
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Le rôle de l’intertexte et du palimpseste dans la création d’une Écosse mythique dans Waverley et Rob Roy de Walter Scott by Céline SABIRON

“…This message of an identity re-creation is relayed by Walter Scott’s Scottish novels, in particular Waverley and Rob Roy which describe Scotland and its inhabitants through the gaze of naive English scholars. Far from being realistic, this textual portrait is made of a superimposition of mythical and literary images, especially when describing the Highlands. …”
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A Closest Resemblance Classifier with Feature Interval Learning and Outranking Measures for Improved Performance by Nabil Belacel

“…We evaluate our classifiers on multiple UCI repository datasets and compare them with established methods, including k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Random Forest (RF), Neural Networks (NNs), Naive Bayes (NB), and Nearest Centroid (NC). The results demonstrate that our methods are robust to imbalanced datasets and irrelevant features, achieving comparable or superior performance in many cases. …”
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Evaluating the Effect of Modelling Errors in Load Identification Using Classical Identification Methods by Michael Vigsø, Rune Brincker, Christos Georgakis

“…Two results are outlined: one where the identification process is completely blindfolded in its most naive form, and one where the spatial distribution of the load is predefined. …”
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