HEALTH CLAIM INSURANCE PREDICTION USING SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION by Syaiful Anam, M. Rafael Andika Putra, Zuraidah Fitriah, Indah Yanti, Noor Hidayat, Dwi Mifta Mahanani

“…Parameter selection of SVM is normally done by trial and error so that the performance is less than optimal. …”
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Interdependence Between River Aquifer Groundwater Flow and Temperature–Depth Profiles: Type Curves Based on Pi Theorem and Numerical Simulations by José Antonio Jiménez-Valera, Iván Alhama, Carlos Duque, David Labat

“…The uncertainties related to hydrogeological features are considered to have the greatest influence on the error.…”
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Predicting Adjustment of students based on School Culture: the mediating role of Quality of life at School by Seyed Alireza Ghasemi, Hossein Khosravi, Minoo Miri, Hadi Pourshafei, Faezeh Khosravi

“…The goodness-of-fit index of the root of the approximation error is equal to0.076 and the standard root of the residual variance is equal to0.054, which indicates the good fit of the model. …”
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Robotic-assisted vs. traditional medial patellofemoral ligament reconstruction: a comparative study of surgical precision and clinical outcomes by Hongzhi Fang, Zhenghui Shang, Tianli Du

“…The primary endpoints included the error margin between the femoral tunnel entry point, a predefined reference landmark, and the quantity of intraoperative fluoroscopic exposures. …”
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Prediction of Urban Construction Land Carbon Effects (UCLCE) Using BP Neural Network Model: A Case Study of Changxing, Zhejiang Province, China by Qinghua Liao, Xiaoping Zhang, Zixuan Cui, Xunxi Yin

“…These contributions carry significant theoretical and practical implications.…”
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MACRO-ECONOMETRIC ANALYSIS OF INWARD CAPITAL FLOWS: A CASE OF NIGERIA IN TIMES OF SECURITY CHALLENGES by Kingsley Onyekachi Onyele, Eberechi Bernadine Ikwuagwu, Happy Chukwudike Ibe

“…The ARDL bounds test technique identified the long-run relationships between macroeconomic dynamics, insecurity, and total capital inflows to Nigeria, while the error correction mechanism (ECM) identified the short-run relationships. …”
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Advanced finite segmentation model with hybrid classifier learning for high-precision brain tumor delineation in PET imaging by K. Murugan, SatheeshKumar Palanisamy, N. Sathishkumar, Tagrid Abdullah N. Alshalali

“…Furthermore, FSM-ICL enhances classification precision to 95.59%, reduces classification error to 5.67%, and minimizes classification time to 572.39 ms, demonstrating a 10.09% improvement in precision and a 10.96% boost in classification rates over state-of-the-art methods. …”
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The two-mode network approach to digital skills and tasks among technology park employees by Anna Ujwary-Gil, Bianka Godlewska-Dzioboń

“…Implications & Recommendations: The findings contribute to the literature on digital skills and shared tasks from a dyadic and organizational perspective by deepening the understanding of the relationship between a pair of employees. …”
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Ensemble machine learning prediction accuracy: local vs. global precision and recall for multiclass grade performance of engineering students by Yagyanath Rimal, Yagyanath Rimal, Navneet Sharma

“…These findings are further corroborated by precision-recall error plots. The grid search for random forest algorithms achieved a score of 79% when optimally tuned; however, the training accuracy was 99%. …”
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Reliability of using CBCT scans to derive the parameters of the facial canal by Shavana Govender, Tania Hanekom, René Human-Baron

“…However, the Bland-Altman plots showed agreement between the two modalities in measuring FC segments. Interobserver error values were 0.963 and 0.950 for the CBCT and dissection groups, respectively, indicating a high repeatability. …”
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Optimizing Screw Fixation in Total Hip Arthroplasty: A Deep Learning and Finite Element Analysis Approach by Jacob Stroud, Emily T. Yan, Jacob Anthony, Kyle Walker, Chung-Hyun Goh

“…The comparative analysis of FEA and DL results showed that the DL-FEA surrogate model successfully replicated deformation patterns, though with a mean squared error (MSE) of 24.06%. While this suggests room for improvement, the model demonstrates potential for streamlining surgical planning. …”
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Multivariate forecasting of dengue infection in Bangladesh: evaluating the influence of data downscaling on machine learning predictive accuracy by Mahadee Al Mobin

“…Comparative analysis reveals that downscaling provided a $$28.5\%$$ 28.5 % improvement in accuracy and an $$89.3\%$$ 89.3 % reduction in mean absolute percentage error (MAPE) over non-downscaled data which has been proven to be statistically significant using the Wilcoxon signed rank test, illustrating the substantial advantages of employing downscaling for effective DENV forecasting. …”
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Measurement of level of PTSD with the International Trauma Questionnaire (ITQ): bias and precision when using full ordinal or dichotomized items by Tine Nielsen, Ask Elklit, Sabrina Brødsgaard Nielsen, Mikkel Auning-Hansen, Svend Kreiner, Maria Louison Vang

“…Measurement by scores over dichotomized items increased the standard error of measurement and reduced the reliability to a level, where psychometric theory would conclude that the measure of PTSD was inapplicable. …”
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Specialized Medical Weight Management Intervention for High-Risk Obesity by Gitanjali Srivastava, Chelsea Paris, Jessica Johnson, Emma Barnes, Brittany L. Cunningham, C. J. Stimson, Kevin D. Niswender, Sabrina J. Poon

“…Mean weight loss from the initial program start date was 6.28% (+/-0.48% standard error of mean [SEM]; 95% confidence interval [CI] 5.34-7.23%). …”
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Assessing the accuracy of adherence and sexual behaviour data in the MDP301 vaginal microbicides trial using a mixed methods and triangulation model. by Robert Pool, Catherine M Montgomery, Neetha S Morar, Oliver Mweemba, Agnes Ssali, Mitzy Gafos, Shelley Lees, Jonathan Stadler, Andrew Nunn, Angela Crook, Richard Hayes, Sheena McCormack

“…The main reasons for inaccuracies are participants forgetting, interviewer error, desirability bias, problems with the definition and delineation of key concepts (e.g. …”
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Physiological stress differentially impacts cognitive performance during—and memory following—simulated police encounters with persons experiencing a mental health crisis by Hannah Marlatte, Hannah Marlatte, Paula M. Di Nota, Judith P. Andersen

“…Increased heart rate during the post-incident debrief was associated with the following: making a lethal force error during the scenario, decreased memory for perceptual aspects of the scenario, and impaired recall of one’s own actions. …”
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Integrating remote sensing and machine learning to evaluate environmental drivers of post-fire vegetation recovery in the Mount Kenya forest by Loventa Anyango Otieno, Terry Amolo Otieno, Brian Rotich, Katharina Löhr, Harison Kiplagat Kipkulei

“…The RF model achieved excellent accuracy with a coefficient of determination (R²) of 0.9013 and a Root Mean Square Error (RMSE) of 0.0280 on the training dataset, and R² of 0.8753 and RMSE of 0.0406 on the validation set. …”
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Technological Advancements and Economic Growth as Key Drivers of Renewable Energy Production in Saudi Arabia: An ARDL and VECM Analysis by Faten Derouez

“…This study examines the short- and long-term effects of various economic, environmental, and policy factors on renewable energy production (REP) in Saudi Arabia from 1990 to 2024, using the Autoregressive Distributed Lag (ARDL) approach and Vector Error Correction Model (VECM) techniques. The analysis focuses on fossil fuel consumption (FFC), renewable energy investment (REI), carbon emissions (CEs), energy prices (EPs), government policies (GPs), technological advancements (TAs), socioeconomic factors (SEFs), and economic growth (EG) as determinants of REP, measured as electricity generated from solar power sources in kilowatt-hours (kWh). …”
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Evidential deep learning-based drug-target interaction prediction by Yanpeng Zhao, Yuting Xing, Yixin Zhang, Yifei Wang, Mengxuan Wan, Duoyun Yi, Chengkun Wu, Shangze Li, Huiyan Xu, Hongyang Zhang, Ziyi Liu, Guowei Zhou, Mengfan Li, Xuanze Wang, Zhengshan Chen, Ruijiang Li, Lianlian Wu, Dongsheng Zhao, Peng Zan, Song He, Xiaochen Bo

“…In addition, our study shows that EviDTI can calibrate prediction errors. More importantly, well-calibrated uncertainty information enhances the efficiency of drug discovery by prioritizing DTIs with higher confident predictions for experimental validation. …”
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Digital twin manifesto for the pathology laboratory by Albino Eccher, Fabio Pagni, Massimo Dominici, Luca Reggiani Bonetti, Stefano Marletta, Enrico Munari, Giorgio Cazzaniga, Anil V Parwani, Vincenzo L’Imperio, Angelo Paolo Dei Tos

“…The framework highlights measurable gains such as up to 90% reduction in labeling errors, 20–30% improvements in slide quality, and 30–50% reductions in diagnostic turnaround time. …”
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