A Drone-Deployable Remote Current Sensor for Non-Invasive Overhead Transmission Lines Monitoring by Khaled Osmani, Detlef Schulz

“…Laboratory tests demonstrated a maximum average error of 5.22% under extreme conditions (e.g., Total Harmonic Distortions (THD) of 40% in the injected current signals) and a minimum error of 2.54% under standard TL operating conditions. …”
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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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Performance of M-QAM MIMO PNC Based on MRC and ZR Techniques by Alaa A. Yassin, Ebtihal H. G. Yousif, Rashid A. Saeed, Hashim Elshafie, Abdullah Alenizi, Hesham Alhumyani

“…The targetted performance metrics are the error probability, improvement of uplink capacity gain and downlink achievable sum rate. …”
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Analysis of the Influence of Different Turbulence Models on the Prediction of Vehicle Aerodynamic Performance by Luwei Wang, Xingjun Hu, Peng Guo, Zirui Wang, Jingyu Wang, Yuqi Wang, Yan Ma, Ying Li, Jing Zhao, Xu Yang, Ruixing Ma, Yinan Zhu, Jianjiao Deng

“…The k-ε model best predicts the steady-state drag coefficient (Cd) (error 0.0009). DES excels in transient conditions (Cd error −0.4%, lift coefficient Cl matching experiments). …”
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Analysis of the Effects of Infusion Drips on Flow Rate and Volume Determination in IV Systems by Halid Ceylan, Ogan Karabaş, Kadri Süleyman Yiğit

“… In this study, drop form flow examinations, which form the basis of IV applications used for therapeutic purposes, were performed. …”
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Novel Fusion Technique for High-Performance Automated Crop Edge Detection in Smart Agriculture by F. Martinez, James B. Romaine, P. Johnson, A. Cardona Ruiz, Pablo Millan Gata

“…Throughout the comparison, a custom metric is used to evaluate performance both in training and inference, balancing computational cost and area error, making it applicable in agriculture. Performance metric is associated with computational cost factor and accuracy factor whose value are respectively 65% and 35%, ensuring applicability for autonomous agricultural devices. …”
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PERBANDINGAN IMPLEMENTASI METODE WEIGHTED MOVING AVERAGE DAN METODE SINGLE EXPONENTIAL SMOOTHING PADA PENENTUAN PERSEDIAAN OBAT by I Gede Bawa Aprilyanta, Ariesta Lestari, Sherly Christina

“…The results of the study resulted in the Weighted Moving Average method as the method that has the smallest Error value where from the 4 drugs that were used for forecasting, 3 drugs showed the Weighted Moving Average method as the best method which has the smallest Mean Absolute Deviation value and the smallest Mean Absolute Percentage Error value compared to Single Exponential Smoothing.…”
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Kinematics Model Estimation of 4W Skid-Steering Mobile Robots Using Visual Terrain Classification by Yang Chen, Yao Wu, Wei Zeng, Shaoyi Du

“…Experiments on a real skid-steering mobile robot show that this method can quickly estimate the kinematics model of the robot in the case of terrain changes, and can meet the needs of practical applications. The average error of odometer estimation based on visual terrain classification is 0.06 m, while the average error of odometer estimation without terrain classification is 0.14 m.…”
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Prediction of Landslide Displacement Based on GreyRelational Analysis and VMD-SES-BP Model by AN Bei, JIANG Yanan, ZENG Qifei

“…Aiming at the “stepped” landslide displacement in the Three Gorges area,this paper proposes a new landslide displacement time series prediction model,namely VMD-SES-BP prediction model by combining variational mode decomposition (VMD),second exponential smoothing (SES),and BP neural network (BPNN);conducts the VMD of the GPS monitoring displacement data of landslide at Baishuihe River of the Three Gorges through this model to obtain the trend component and other sub-sequence components;makes rolling predictions of trend components by the SES,determines the influencing factors of other displacement components of the landslide through gray relational analysis (GRA),and learns and predicts by considering it as the training sample of BPNN.Comparing the prediction results of each component with the true value,the average relative error of prediction is 0.78%,the mean square error is 3.14 cm,and the correlation coefficient is 0.986.The experimental results show that the model is well applicable to the prediction of “stepped” landslide displacement,with high prediction accuracy,which provides a certain reference value for landslide displacement prediction.…”
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The role of face regions in remote photoplethysmography for contactless heart rate monitoring by Maksym Bondarenko, Carlo Menon, Mohamed Elgendi

“…Machine learning-based approaches outperform traditional methods under motion artifacts and poor lighting, achieving Mean Absolute Error and Root Mean Square Error below 1.0 for some datasets. …”
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Approaches, Relevant Topics, and Internal Method for Uncertainty Evaluation in Predictions of Thermal-Hydraulic System Codes by Alessandro Petruzzi, Francesco D'Auria

“…Namely, the propagations of code input error and calculation output error constitute the keywords for identifying the methods of current interest for industrial applications, while the adjoint sensitivity-analysis procedure and the global adjoint sensitivity-analysis procedure, extended to performing uncertainty evaluation in conjunction with concepts from data adjustment and assimilation, constitute the innovative approach. …”
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Peak-to-average power ratio reduction of orthogonal frequency division multiplexing signals using improved salp swarm optimization-based partial transmit sequence model by Vandana Tripathi, Prabhat Patel, Prashant Kumar Jain, Shailja Shukla

“…Three evaluation measures, namely, the complementary cumulative distribution function (CCDF), bit error rate (BER), and symbol error rate (SER), demonstrate the efficacy of the PTS-ISSA model, which achieves a lower PAPR of 3.47 dB and is superior to other optimization algo-rithms using the PTS method. …”
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Robust Position Control of VTOL UAVs Using a Linear Quadratic Rate-Varying Integral Tracker: Design and Validation by Omer Saleem, Muhammad Kazim, Jamshed Iqbal

“…Experimental results demonstrate significant improvements over the nominal LQI tracking controller, achieving 17.9%, 61.6%, 83.4%, 43.7%, 35.8%, and 6.8% enhancement in root mean squared error, settling time, overshoot during start-up, overshoot under impulsive disturbance, disturbance recovery time, and control energy expenditure, respectively, underscoring the controller’s effectiveness for potential UAV and drone applications under exogenous disturbances.…”
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Long-distance target localization optimization algorithm based on single robot moving path planning by Yourong Chen, Ke Wu, Yidan Guo, Kehua Zhao, Liyuan Liu

“…Additionally, an improved hierarchical density-Based spatial clustering of applications with noise (HDBSCAN) algorithm is developed to fuse the relative coordinates of multiple targets. …”
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The Short-Term Wind Power Forecasting by Utilizing Machine Learning and Hybrid Deep Learning Frameworks by Sunku V.S., Namboodiri V., Mukkamala R.

“…Key performance metrics—namely, mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and the coefficient of determination (R²)—were employed to assess the efficacy of each model. …”
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State-of-Health Estimation for Lithium-Ion Batteries Based on Lightweight DimConv-GFNet by Kehao Huang, Jianqiang Kang, Jing V. Wang, Qian Wang, Oukai Wu

“…Although the model shows slightly lower accuracy compared to the DimConv-Transformer baseline, it delivers competitive performance with a root mean squared error (RMSE) of 0.335%, mean absolute error (MAE) of 0.233% and a mean absolute percentage error (MAPE) of 0.230%. …”
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Modeling of the mass attenuation coefficients of X ray beams using deep neural networks (DNN) and NIST database by GUSTAVO BERNARDES DA SILVA, VIVIANE RODRIGUES BOTELHO, Carla Diniz Lopes Becker, Cassiana Viccari, Thatiane A. Pianoschi

“…Attenuation coefficients are essential physical parameters for many applications, such as the calculation of photon penetration and energy deposition to evaluate biological shielding. …”
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Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring by Rinaldi Anwar Buyung, Alhadi Bustamam, Muhammad Remzy Syah Ramazhan

“…The experimental results demonstrate that incorporating a multimodal approach enhances model performance, with the random forest model achieving superior results, yielding a mean absolute error (MAE) of 3.057 bpm, a root mean squared error (RMSE) of 10.532 bpm, and a mean absolute percentage error (MAPE) of 4.2% that outperforms the state-of-the-art rPPG methods. …”
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Performance Evaluation of a Radial Distribution Network Under Emerging Load Prediction Modeling Approach and DG Integration Using a Particle Swarm Optimization Algorithm by Demsew Mitiku Teferra

“…The accuracy of these forecasts is quantified by mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and the coefficient of determination (R2), where ANFIS demonstrates superior performance with a MAE = 7.7611, MAPE = 0.14401, MSE = 0.6399, RMSE = 0.79993, and R2 = 0.99993, in contrast to ANN’s MAE = 31.4114, MAPE = 1.631%, MSE = 109.55, RMSE = 10.467, and R2 = 0.98797. …”
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Breaking the Standard: Can Oxford Nanopore Technologies Sequencing Compete With Illumina in Protistan Amplicon Studies? by Dana Bludau, Guido Sieber, Manan Shah, Aman Deep, Jens Boenigk, Daniela Beisser

“…While Illumina still dominates the sequencing market and offers high accuracy with low error rates, it is limited by shorter read lengths. …”
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