High‐Throughput Nanorheology of Living Cells Powered by Supervised Machine Learning by Jaime R. Tejedor, Ricardo Garcia

“…The training and the validation of the regressor are performed by using theoretical curves derived from a contact mechanics model that combined power–law rheology with bottom effect corrections and functional data analysis. The regressor predicts the modulus and the fluidity coefficient of mammalian cells with a relative error below 4%.…”
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p < 0,05, ¿Criterio mágico para resolver cualquier problema o leyenda urbana? by Pedro Monterrey Gutiérrez

“…The fundamentals of Fisher´s, Neyman-Pearson´s and Bayesian´s solutions are analyzed and based on them, the inconsistency of the commonly used procedure of determining a p value, compare it to a type I error value (usually 0.05) and get a conclusion is discussed and, on their basis, inconsistencies of the data analysis procedure are identified, procedure consisting in the identification of a P value, the comparison of the P-value with a type-I error value –which is usually considered to be 0.05– and upon this the decision on the conclusions of the analysis. …”
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Empirical likelihood based heteroscedasticity diagnostics for varying coefficient partially nonlinear models by Cuiping Wang, Xiaoshuang Zhou, Peixin Zhao

“…Furthermore, simulation studies and a real data analysis are implemented to evaluate the performances of our proposed approaches.…”
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Forecasting tuberculosis in Ethiopia using deep learning: progress toward sustainable development goal evidence from global burden of disease 1990–2021 by Zinabu Bekele Tadese, Fetlework Gubena Arage, Tigist Kifle Tsegaw, Eyob Akalewold Alemu, Tsegasilassie Gebremariam Abate, Eliyas Addisu Taye

“…The statistical significance level was set at 0.05 to check data stationarity. Model performance was evaluated using Root Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error, and Symmetric Mean Absolute Percentage Error. …”
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Study on Cooperative Multipoint Communication Precoding Algorithm under SLNR-MMSE Framework by Yu Fan

“…The precoding algorithm of SLNR-MMSE is proposed. The simulation analysis shows that the proposed algorithm has certain advantages over other algorithms in terms of bit error rate and system capacity. …”
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An Application of High-Resolution Mobile Mapping in Smart Cities: Gaziosmanpasa Case Study by R. A. Kucak, İ. Yakar, İ. Buyuksalih

“…Since the surface calculation of the digitisations to be made for inventory extraction is important, the accuracy analysis was performed by comparing the point cloud data here with the ground measurement. …”
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Classification of Review Text using Hybrid Convolutional Neural Network and Gated Recurrent Unit Methods by Fiqih Fathor Rachim, Auli Damayanti, Edi Winarko

“…A total of 1000 review texts were divided into 80% training data and 20% test data. The review text is converted into matrix using One Hot Encoding algorithm and then extracted using CNN. …”
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Startup Drift Compensation of MEMS INS Based on PSO–GRNN Network by Songlai Han, Jingyi Xie, Jing Dong

“…With the proposed method, the speed error improved by over 36.4%, and the position error improved by over 41.1%. …”
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RESEARCH ON METHODS OF DETERMINING CUSTOMER LOYALTY AND ASSESSING THEIR LEVEL OF SATISFACTION by Yana Leiba, Mariya Shirokopetleva, Iryna Gruzdo

“…The purpose of the work is the analysis of methods for determining customer loyalty and assessing their level of satisfaction, the development of a unified assessment algorithm based on various types of data. …”
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Valid knowledge of performance provided by a motion capturing system in shot put by Stefan Künzell, Anna Knoblich, Annika Stippler

“…The expert feedback for two participants was sealed in an envelope. In a qualitative analysis of the motion data, the error feedback was then determined and subsequently compared with the experts’ feedback. …”
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Development and Validation of Cloud-based Heart Rate Variability Monitor by Sushma N. Bhat, Ghanshyam D. Jindal, Gajanan D. Nagare

“…Peak positions and spectrum values are validated using Pearson’s correlation, mean error, standard deviation (SD) of error, and range of error. …”
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Evaluating the Performance of SWAN model in forecasting storm surges over the Persian Gulf and Oman Sea in a case study (June 7-10, 2023) by Zahra Ghassabi, Sara Karami, Behzad Layeghi

“…Therefore, it can be concluded that in areas with more complex topography, such as the Strait of Hormuz, a small error in wind data leads to a large error in wave prediction. …”
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Verification of Application of ANN Modelling in Study of Compressive Behaviour of Aluminium Sponges by Anna Małgorzata STRĘK, Marek DUDZIK, Arkadiusz KWIECIEŃ, Krzysztof WAŃCZYK, Barbara LIPOWSKA

“…The research consisted of two phases: first – compression experiments, which in turn provided data for the second phase – the artificial neural network (ANN) analysis. …”
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Damage Identification of Railway Bridge KW51 Conditions Using Deep-Learning-Based 1D CNN Model by Ali Al-Ghalib, Sawsan Mahmoud

“…The maximum value of Type II error (False Negative FN) was 8.9% for data from accelerometer aBD23Ay, whereas it was 4.8% in similar case studies analyzed by the 1D CNN method. …”
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Accuracy of a novel calibratable real-time continuous glucose monitoring device based on FreeStyle libre in- and out-of-hospital by Zhenghao Wu, Zhenghao Wu, Zhaoxiang Liu, Wenhui Zhao, Shaocheng Wang, Liangbiao Gu, Jianzhong Xiao

“…The accuracy of CGMs was assessed through consistency analysis, Bland-Altman analysis, calculation of MARD and MAD, Consensus Error Grids, as well as analysis using the Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA).ResultsIn outpatient setting, 1907 values from 138 users were analyzed. …”
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A Model Proposal for Estimate the Approxımate Costs and Contract Fees of Public Education Buildings (School Buildings) by Latif Onur Uğur, Servet Kesim

“…Conducting similar studies by increasing the number of data may be a solution to minimize the error rate in subsequent modeling.…”
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Estimation the pH of CO2-saturated NaCl solutions using gene expression programming: Implications for CO2 sequestration by Mohammad Rasool Dehghani, Parirokh Ebrahimi, Moein Kafi, Hamed Nikravesh, Yousef Kazemzadeh

“…A cumulative frequency plot of absolute relative error showed all data points had errors below 0.102, with over 90 % below 0.0615. …”
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PREDICTION OF AIRCRAFT NORMAL OVERLOAD EXTREME VALUE BASED ON PEARSON-Ⅲ DISTRIBUTION by LEI XiaoBo, WEN Min, LEI DiYuan, ZHANG XiaMei

“…Calculation method on the deviation coefficients Φ_p of skew coefficients Cswas deduced and established,which eliminated look-up and interpolation errors. In consideration that there was not uniform quantitative judge standard on optional curving fitting when fitting real data by P-Ⅲ distribution on the curve-fitting method,Using the maximum membership principle judged curving fitting level according to fuzziness of flight data,and membership function was deduced in detail. …”
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Neuro-fuzzy inference system and white shark optimization of coagulation-flocculation of aquaculture wastewater treatment by A.F. Mohamed, H. Rezk

“…FINDINGS: The adaptive neuro-fuzzy inference system turbidity removal model has root mean square error values of 3.52e-05 and 1.51 for training and testing data, respectively. …”
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Development of a Real-time Near-surface Air Temperature, Humidity, and Wind Product with 10-min Resolution in China by Liao Zhihong, Han Shuai, Gu Junxia, Shi Chunxiang, Zhang Zhiqiang, Ruan Xin, Zhao Qi, Zhang Tao, Zhang Zhisen, Du Aodong, Xu Bin, Sun Shuai, Pang Zihao, Zhang Yu, Xu Yanqin, Yang Lei

“…National Meteorological Information Center (NMIC), on the basis of operational analysis products of 1-h resolution near-surface temperature, relative humidity and wind in China, eliminates abnormal values of observations from ground stations through the minute-level quality control technology, and resamples 10-min resolution background data from the near-surface model forecast data using spatio-temporal downscale analysis techniques. …”
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