Fuzzy panel data analysis by Muhammet Oguzhan Yalcin, Nevin Güler Dincer, Serdar Demir

“…In order to validate the performance of efficiency of FPDA, FPDA and PDA are applied to panel data consisted of gross domestic production data from five country groups between the years of 2005-2013 and the prediction performances of them are compared by using three criteria such mean absolute percentage error, root mean square error and variance accounted. …”
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Navigation control of unmanned aerial vehicles in dynamic collaborative indoor environment using probability fuzzy logic approach by Sameer Agrawal, Bhumeshwar K. Patle, Sudarshan Sanap

“…The development of drones in various applications makes it essential to address the critical issue of providing collision-free and optimal navigation in uncertain environments. …”
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Development of low-cost portable spectrometer equipped with 18-band spectral sensors using deep learning model for evaluating moisture content of rubber sheets by Amorndej Puttipipatkajorn, Amornrit Puttipipatkajorn

“…The 1D-CNN model had a better prediction accuracy, with a coefficient of determination (R2) of 0.962, a root mean squared error of prediction (RMSEP) of 0.410 %, a prediction-to-deviation ratio (RPD) of 5.2, and an error range ratio (RER) of 18.0. …”
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Machine learning based prediction modeling of micro-EDM of Ti–29Nb–13Ta–4.6Zr (TNTZ) by Shahid Ali, Didier Talamona, Asma Perveen

“…It has the highest R2 of 0.99, the lowest NRMSE of 0.0245, and the percentage of prediction error is less than 5%.…”
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A Physically‐Based Reverse‐Stage Routing Model Considering Lateral Flow for Establishing Normal Rating Curves at Ungauged Upstream River Sections by Ashutosh Pati, Bhabagrahi Sahoo, Muthiah Perumal

“…A novel criterion is also developed for checking the applicability of the RSR model. Reverse routing experiments, carried out by the RSR model using different hypothetical downstream stage hydrographs in a number of hypothetical trapezoidal and rectangular channel reaches to reproduce the benchmark upstream stage and discharge hydrographs, demonstrate the good performance of the RSR model with the Nash–Sutcliffe Efficiency >98%, absolute volume conservation error <1.5%, and an absolute error in peak ≤1%. …”
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Accuracy assessment for Landsat 8 thermal bands in measuring sea surface temperature over Kuwait and North West Arabian Gulf by Jasem A Albanai, Sara A. Abdelfatah

“…The accuracy of Landsat 8 to monitor the SST of Kuwait and north-west Arabian Gulf waters was validated by calculating the root mean square error (RMSE) and the mean absolute percentage error (MAPE). …”
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A hybrid approach to time series forecasting: Integrating ARIMA and prophet for improved accuracy by Sherly A, Mary Subaja Christo, Jesi V Elizabeth

“…A key aspect of this work involves evaluating the performance of these models using RMSE (Root Mean Squared Error) and MAE (Mean Absolute Error. Experimental results are presented to showcase the effectiveness and comparative performance of the discussed forecasting techniques across diverse simulated and real-world datasets relevant to edge computing applications.…”
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Calculation of Neural Network Weights and Biases Using Particle Swarm Optimization by Jerin Paul Selvan, Girish Pandurang Potdar

“…The process of iteration involves changing the weight of a neural network in response to the rate of error observed in the preceding epoch. The error rates can be reduced and the applicability of the model increased, both of which will increase the model’s dependability. …”
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SYNTHESIS METHODS OF ALGEBRAIC NORMAL FORM OF MANY-VALUED LOGIC FUNCTIONS by A. V. Sokolov, O. N. Zhdanov, O. A. Ayvazian

“…The results could be the basis for further theoretical research and practical applications such as: the development of new cryptographic primitives, error-correcting codes, algorithms of data compression, signal structures, and algorithms of block and stream encryption, all based on the perspective principles of many-valued logic. …”
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Optimization of thermal conductivity in coir fibre-reinforced PVC composites using advanced computational techniques by Saksham Anand, Venkatachalam Gopalan, Shenbaga Velu Pitchumani

“…DFO delivers consistent results with slightly higher error rates, while PSO demonstrates rapid convergence but greater variability. …”
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New Insights into Meteorological and Hydrological Drought Modeling: A Comparative Analysis of Parametric and Non-Parametric Distributions by Ahmad Abu Arra, Eyüp Şişman

“…For SDI, it ranged between 0.09 and 0.143. The Mean Absolute Error (MAE) for SPEI was approximately 0.05 for all timescales. …”
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Short-Term Energy Consumption Forecasting Analysis Using Different Optimization and Activation Functions with Deep Learning Models by Mehmet Tahir Ucar, Asim Kaygusuz

“…Finally, the 10 most successful applications are listed.…”
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A Chebyshev Spectral Collocation Method-Based Series Approach for Boundary Layer Flow and Heat Transfer in a Micropolar Fluid past a Permeable Flat Plate by T. M. Agbaje, G. Makanda

“…Further accuracy validation is displayed by using residual error and solution error analysis on the governing PDEs and their underlying solutions. …”
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A Calculation Method for the Sloshing Impact Pressure Imposed on the Roof of a Passive Water Storage Tank of AP1000 by Daogang Lu, Xiaojia Zeng, Junjie Dang, Yu Liu

“…The experimental result showed that this method overpredicted the impact pressure with a certain error of no more than 35%. By the experiment, we conclude that this method is conservative and applicable for the engineering design.…”
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Digital Twins for Intelligent Vehicle-to-Grid Systems: A Multi-Physics EV Model for AI-Based Energy Management by Michela Costa, Gianluca Del Papa

“…Validated using real-world data from a Citroën Ami operating on urban routes in Naples, Italy, it achieved exceptional accuracy with a root mean square error (RMSE) of 1.28% for dynamic state of charge prediction. …”
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Optimization of High-Performance Computing Job Scheduling Based on Offline Reinforcement Learning by Shihao Li, Wei Dai, Yongyan Chen, Bo Liang

“…However, their trial-and-error learning approach can lead to suboptimal performance or resource wastage in the early stages. …”
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Eigenstate Fokker-Planck equation for magnetic nanostructures by Zhuonan Lin, Vitaliy Lomakin

“…The formulation of using the eigenstate-based FPE is applicable to many types of physics.…”
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A Low-Complexity Approach for Improving the Accuracy of Sensor Networks by Angelo Coluccia

“…Remarkably, the resulting cooperative algorithm does not require prior knowledge of the (hyper)parameters and is able to provide a “denoised” version of the monitored field without losing accuracy in detecting extreme (less frequent) values, which can be very important for a number of applications. A novel performance metric is also introduced to suitably quantify the capability to both reduce the measurement error and retain highly-informative characteristics at the same time. …”
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State-of-Charge Estimation of Medium- and High-Voltage Batteries Using LSTM Neural Networks Optimized with Genetic Algorithms by Romel Carrera, Leonidas Quiroz, Cesar Guevara, Patricia Acosta-Vargas

“…The novelty of this approach lies in the integration of machine learning and physical modeling, optimized via evolutionary algorithms, to address limitations of standalone methods in real-time applications. The hybrid model achieved a mean absolute error (MAE) of 0.181%, outperforming conventional estimation strategies. …”
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High-precision indoor localization in multi-level buildings by Chokri Ajroud, Jamel Hattay, Mohsen Machhout

“…Accurate localization in multi-floor indoor environments is essential for applications such as large-scale inventory management, healthcare, and security systems. …”
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