Patient safety culture: Insights from a cross-sectional study among healthcare professionals by Vijay K. Tadia, Neelam Kotwal, Rahul S. Jalaunia

“…Conclusions: The study presented with a plethora of outcomes that can be used for promoting safe healthcare. …”
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A novel ensemble ARIMA‐LSTM approach for evaluating COVID‐19 cases and future outbreak preparedness by Somit Jain, Shobhit Agrawal, Eshaan Mohapatra, Kathiravan Srinivasan

“…Conclusions The proposed ARIMA‐LSTM hybrid model outperforms ARIMA, GRU, LSTM, Prophet, and the ARIMA‐ANN hybrid model when evaluating using metrics like MAPE, symmetric mean absolute percentage error, and median absolute percentage error across all countries analyzed. …”
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Accuracy of Age Estimation using Cameriere’s European Formula and London Atlas Method among the Children of Dakshina Kannada Population – A Comparative Study by Laxmikanth Chatra, Aiswarya Shibu, Rachana V. Prabhu, Prashanth Shenoy, K M Veena, Prathima Shetty

“…The overall RMSE and MAE for Cameriere’s method are 1.627 and 1.267, respectively, while for the London Atlas, the overall RMSE and MAE are 1.447 and 0.962, respectively. Conclusions: The London Atlas method, with its simpler application and minimal error, demonstrated greater accuracy compared to Cameriere’s method for the Dakshina Kannada population.…”
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Compression of Multichannel Signals With Irregular Sampling Rates and Data Gaps by Pablo Cervenansky, Alvaro Martin, Gadiel Seroussi

“…From the results we extract some general conclusions: in the lossless case, TS2Diff and LZMA attain the best compression performance, whereas our adaptation of algorithm APCA is preferred for positive error bounds. …”
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Unveiling the future: Wavelet- ARIMAX analysis of climate and diarrhea dynamics in Bangladesh’s Urban centers by Md. Waliullah, Md. Jamal Hossain, Md. Raqibul Hasan, Abdul Hannan, Mohammad Mafizur Rahman

“…Based on climatic variables, Wavelet-ARIMAX can accurately predict diarrheal occurrence, as indicated by the mean absolute error (MAE), root mean squared error (RMSE), and root mean squared logarithmic error (RMSLE). …”
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Evaluation of the elastic modulus of pavement layers using different types of neural networks models by M. M.M. Elshamy, A. N. Tiraturyan, E. V. Uglova

“…The efficiency measures such as mean absolute error (MAE), the coefficient of multiple determinations R2, Root Mean Square Error (RMSE), Mean Absolute Percent Error (MAPE) values were obtained for all models results.Results. …”
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Using artificial intelligence for the development of a living evidence map: The pharmacopuncture example by Chan-Young Kwon

“…Task-specific performance varied from sample size extraction (0% error rate) to pharmacopuncture name extraction (22.22% error rate), with high accuracy of over 90% in most tasks. …”
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Calibration of parameters in microscopic traffic flow simulation models considering micro-meteorological information. by Jian Ma, Yuchen Zhang, Liyan Zhang, Zongwei Gao, Keyi Cao, Qianlong Fu, Zheng Qian

“…The conclusions are as follows: the average error and standard deviation of the improved I-IDM model are smaller than those of the I-Wiedemann99 model, with the maximum Root Mean Square Percentage Error (RMSPE) for I-IDM model parameter calibration being 0.4568 and the minimum being 0.1324. …”
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High-resolution global modeling of wheat’s water footprint using a machine learning ensemble approach by Murat Emeç, Abdullah Muratoğlu, Muhammed Sungur Demir

“…The model achieved a mean absolute error (MAE) of 108.5 m3/t, mean squared error (MSE) of 239.9 m3/t, and mean absolute percentage error (MAPE) of 1.51, along with a high prediction accuracy evidenced by a test score of 98.49% and an R 2 value of 0.87. …”
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Inner Form of Word as Phenomenon of Ordinary Metalinguistic Consciousness by A. I. Olkhovskaya

“…To identify the principles of its functioning in consciousness of native speakers the author refers to the experiment and processing of the results obtained by four parameters: (1) correctness, (2) consistency, (3) strategy for establishing the inner form of the word, and (4) the type of error in its interpretation. The survey allowed to draw a number of conclusions. …”
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Some Uniqueness Results of the Solutions for Two Kinds of Riccati Equations with Variable Fractional Derivative by Shi-you Lin, Li-sha Chen, Bo-yang Li

“…Additionally, we also point out an error in the work of Kashkari and Syam and correct it.…”
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Assessing the spatial relationship between mandibular third molars and the inferior alveolar canal using a deep learning-based approach: a proof-of-concept study by Wenjie Lyu, Shang Lou, Jieying Huang, Zelun Huang, Haoran Zheng, Huangan Liao, Yu Qiao, Kexiong OuYang

“…The measurement accuracy of the AI system compared to the gold standard was 0.19 for mean error (ME), 0.18 for mean absolute error (MAE), 0.69 for mean square error (MSE), 0.83 for root mean square error (RMSE), and 0.96 for coefficient of determination (R2) (p < 0.01). …”
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Implications of Intra-Individual Variability in Motor Performance on Functional Mobility in Stroke Survivors by Neha Lodha, Prakruti Patel, Evangelos A. Christou, Anjali Tiwari, Manfred Diehl

“…We measured average accuracy (mean endpoint error) and IIV (within-person SD of endpoint error). …”
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Intentional saccadic eye movements in patients with vestibular migraine by Pan Gu, Jing Feng, Lipeng Cai, Huimin Fan, Hailing Wang, Xiaokun Geng, Yuchuan Ding

“…Conclusions: We found abnormalities in the AS and MGS tasks in patients with VM. …”
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How to use learning curves to evaluate the sample size for malaria prediction models developed using machine learning algorithms by Sophie G. Zaloumis, Megha Rajasekhar, Julie A. Simpson

“…The shape of the learning curves indicates that increasing the training dataset size beyond 835 samples is unlikely to significantly reduce the balanced error rates further. Conclusions Learning curves are a simple tool that can be used to determine the minimum sample size required for future prediction modelling studies of different malaria outcomes that use machine learning algorithms for prediction. …”
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Investigating the accuracy of adjusting for examiner differences in multi-centre Objective Structured Clinical Exams (OSCEs). A simulation study of video-based Examiner Score Compa... by Peter Yeates, Gareth McCray

“…We replicated this 1000 times for each permutation to determine average error reduction and the proportion of students whose scores became more accurate. …”
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Estimating Age and Sex from Dental Panoramic Radiographs Using Neural Networks and Vision–Language Models by Salem Shamsul Alam, Nabila Rashid, Tasfia Azrin Faiza, Saif Ahmed, Rifat Ahmed Hassan, James Dudley, Taseef Hasan Farook

“…For age regression, performance was evaluated using mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), R², and mean absolute percentage error (MAPE). …”
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The Role of Artificial Intelligence in Predicting the Progression of Intraocular Hypertension to Glaucoma by Nicoleta Anton, Cătălin Lisa, Bogdan Doroftei, Ruxandra Angela Pîrvulescu, Ramona Ileana Barac, Ionuț Iulian Lungu, Camelia Margareta Bogdănici

“…The performance of the neural models was evaluated using several metrics: Mean Squared Error (MSE), Normalized Mean Squared Error (NMSE), correlation coefficient (r²), and percentage error (Ep). …”
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Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study by Yan Jiang, Canyang Jiang, Bin Shi, You Wu, Shuli Xing, Hao Liang, Jianping Huang, Xiaohong Huang, Li Huang, Lisong Lin

“…To evaluate the accuracy of our algorithm, we determined the mean absolute error along the x-, y-, and z-axes and calculated the mean radial error (MRE) between the reference landmark and predicted landmark, as well as the successful detection rate (SDR). …”
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Study on the vibration prediction model of step group hole blasting considering non-stationarity by LI Hongchao, ZHANG Qipeng, HUANG Guoquan, SHI Yulian, HUANG Yonghui, HAN Haoxuan, YI Jiaxin, QU Chenliang

“…The constructed group hole blasting vibration prediction model, applied to the group hole blasting vibration monitoring experiments, the predicted peak vibration velocity of 11.4649cm/s, and the measured value of 10.9605cm/s error is only 4%; the predicted value of the main frequency is 29.0023Hz, and the measured value of 28.3688Hz error is only 2%, and the measured waveform is highly consistent. …”
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