Enhancing vector control: AI-based identification and counting of Aedes albopictus (Diptera: Culicidae) mosquito eggs by Minghao Wang, Yibin Zhou, Shenjun Yao, Jianping Wu, Minhui Zhu, Linjuan Dong, Dunjia Wang

“…The model’s performance was evaluated in terms of precision, recall, and F1 score, and counting accuracy was assessed using R-squared and root mean square error (RMSE). Results The experimental results revealed the model’s remarkable identification capabilities, achieving precision of 0.977, recall of 0.978, and an F1 score of 0.977. …”
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Clinical Decision Support System for Liver Fibrosis Prediction in Hepatitis Patients: A Case Comparison of Two Soft Computing Techniques by Shaker El-Sappagh, Farman Ali, Amjad Ali, Abdeltawab Hendawi, Farid A. Badria, Doug Young Suh

“…The ANFIS model is designed using trial and error based on the analysis of various experiments. …”
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Real-world safety profile of live attenuated Japanese encephalitis vaccine before and after the vaccine administration law. by Chao Zhang, Yan Liu, Xiaofu Luo, Liping Han, Jianyong Shen

“…AEFI incidence rates are calculated/10,000 vaccine doses and a reporting odds ratio -1.96 standard error (ROR-1.96SE) >1 defined a positive signal between AEFI and vaccine. …”
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MultiCycPermea: accurate and interpretable prediction of cyclic peptide permeability using a multimodal image-sequence model by Zixu Wang, Yangyang Chen, Yifan Shang, Xiulong Yang, Wenqiong Pan, Xiucai Ye, Tetsuya Sakurai, Xiangxiang Zeng

“…In the in-distribution setting of the CycPeptMPDB dataset, MultiCycPermea reduced the mean squared error (MSE) by approximately 44.83% compared to the latest model Multi_CycGT (0.29 vs 0.16). …”
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Wireless Channel Prediction Using Artificial Intelligence With Imperfect Datasets by Gowhar Javanmardi, Ramiro Samano Robles

“…Results show that neural networks (NNs), particularly deep learning (DL), continuously reduce the mean square error (MSE) as the length of the set increases. They quickly outperform LR, even in sets near the undersampling condition with low SNR. …”
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NEURAL NETWORK FORECASTING OF ENERGY CONSUMPTION OF A METALLURGICAL ENTERPRISE by Anna Bakurova, Olesia Yuskiv, Dima Shyrokorad, Anton Riabenko, Elina Tereschenko

“…The LSTM network turned out to be the most effective among the considered neural networks, for which the indicator of the maximum prediction error had the minimum value. Conclusions: analysis of forecasting results using the developed models showed that the chosen approach with experimentally selected architectures and learning algorithms meets the necessary requirements for forecast accuracy when developing a forecasting model based on artificial neural networks. …”
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High-Resolution Eye-Tracking System for Accurate Measurement of Short-Latency Ocular Following Responses: Development and Observational Study by Aleksandar Miladinović, Christian Quaia, Simone Kresevic, Miloš Ajčević, Laura Diplotti, Paola Michieletto, Agostino Accardo, Stefano Pensiero

“…Head motion compensation was successfully tested, showing a displacement error of less than 5 μm. Finally, robust OFRs were detected in 16 children during recording sessions lasting less than 5 minutes. …”
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Evaluation of the Finis Swimsense® and the Garmin Swim™ activity monitors for swimming performance and stroke kinematics analysis. by Robert Mooney, Leo R Quinlan, Gavin Corley, Alan Godfrey, Conor Osborough, Gearóid ÓLaighin

“…It is reasonable to expect that this level of error would increase when the devices are used by recreational swimmers rather than elite swimmers. …”
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Neurocomputational Mechanisms of Sense of Agency: Literature Review for Integrating Predictive Coding and Adaptive Control in Human–Machine Interfaces by Anirban Dutta

“…Predictive coding frameworks, especially when implemented via Kalman filters and LQG control, provide a mechanistic basis for modeling motor learning, error correction, and adaptive control. Disruptions in these inference processes underlie symptoms in disorders such as functional movement disorder. …”
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MEGDTA: multi-modal drug-target affinity prediction based on protein three-dimensional structure and ensemble graph neural network by Zhanwei Hou, Yijun Li, Haixia Zhai, Junwei Luo, Yulian Ding, Yi Pan

“…The results show that MEGDTA performs strongly in terms of mean squared error (MSE) and concordance index (CI), and r2 m, which demonstrate the effectiveness of MEGDTA.…”
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Heat of reaction in individual metabolic pathways of yeast determined by mechanistic modeling in an insulated bioreactor by Yusmel González-Hernández, Emilie Michiels, Patrick Perré

“…Finally, the model was successfully applied and validated for online training under different operating conditions. Conclusions The model demonstrates remarkable accuracy, with a mean relative error under 0.38% in temperature predictions for both anaerobic and aerobic conditions. …”
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Validation of Enzyme Immunoassay for Preclinical Pharmacokinetic Trials of Rituximab by V. V Pisarev, Maria M Ulyashova, Gelia N Gildeeva

“…The within-run and between-run precision of the assay did not exceed 7.4 %, the total error of the method did not exceed 20.1 %. The linearity of dilution makes it possible to use the assay for the analysis of biological samples with a wide range of rituximab concentrations. …”
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Investigation of mental endurance levels of canoe athletes in sport: a cross-sectional study by Levent Tanyeri, Hakan Kırkbir, İsmet Çinan, Büsra Yilmaz, Serkan Hacıcaferoğlu

“…Statistically, the level of error p<.05 was accepted as Alpha (α). Results. …”
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The benefits and accuracy of real‐time continuous glucose monitoring in children and adolescents with type 1 diabetes attending a summer camp by Tatsuya Kondo, Takafumi Senokuchi, Jun Morinaga, Azusa Miyashita, Mayumi Yano, Haruo Takeda, Kenro Nishida, Naoto Kubota

“…Clinically acceptable zones of the error grid analyses were approximately 96% in total. …”
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Psychometric properties of the Breast Cancer Awareness Measurement among Chinese women: a cross-sectional study by Ping Li, Na Liu, Dan-dan Chen, Wei-jia Sun, Xue-hui Zhang

“…CFA showed that the three-factor model explained 51.56% of the total variance, with a good model fit (likelihood ratio χ2/df=1.86, incremental fit index=0.94, comparative fit index=0.94, goodness-of-fit index=0.84, adjusted goodness-of-fit index=0.80, standardised root mean square error of approximation=0.06 and root mean square residual=0.05).Conclusions The C-BCAM has satisfactory validity and reliability and is a culturally appropriate and reliable tool for evaluating breast cancer awareness among Chinese women. …”
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Melt Density Monitoring of Extruder Extrusion Process Based on Multi-source Data Fusion and Convolutional Long Short-term Memory Neural Network by Binbin ZHANG, Zhuyun CHEN, Fei ZHANG, Gang JIN

“…Empirical evaluations reveal a root mean square error (RMSE) of 0.975 g/cm³ and a coefficient of determination (<italic>R</italic>²) value of 0.0063, underscoring the superior predictive accuracy of this approach compared to conventional methods. …”
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Machine Learning-Based X-Ray Projection Interpolation for Improved 4D-CBCT Reconstruction by Jayroop Ramesh, Donthi Sankalpa, Rohan Mitra, Salam Dhou

“…<italic>Results:</italic> The results show that the Real-Time Intermediate Flow Estimation (RIFE) algorithm outperforms the others in terms of the Structural Similarity Index Method (SSIM): 0.986 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 0.010, Peak Signal to Noise Ratio (PSNR): 44.13 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 2.76, and Mean Square Error (MSE): 18.86 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 206.90 across all datasets. …”
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PARAFFIN: A software tool for Pathology Report Automated Feedback for Improved Education of anatomic pathology trainees by Clarissa E. Jordan, Justin E. Juskewitch, Andrew P. Norgan

“…Many trainees attempt to keep track of their cases and later look up final pathology reports in the laboratory information system (LIS); however, this manual and time-consuming process is prone to error and may prevent them from spending time reviewing and learning from these reports. …”
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Implementation of an information system for tuberculosis in healthcare facilities in Indonesia: evaluation of its effectiveness and challenges by Rita Dian Pratiwi, Bachti Alisjahbana, Yanri Wijayanti Subronto, Sigit Priyanta, Suharna Suharna

“…Results The evaluation indicated that user convenience and timeliness require improvement, as well as the match between the system and the real world and error prevention, as shown in the heuristic evaluation of the SITB user interface. …”
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Ultrasound differential diagnosis of different forms of stomatogenic maxillary sinusitis by S. D. Varzhapetyan

“….), as fractions (percent) and the error of share. Statistical analysis of the absolute values has been determined by Student's method, comparison of share - by xi-square method. …”
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