Prediction of the Characteristics of Concrete Containing Crushed Brick Aggregate by Marijana Hadzima-Nyarko, Miljan Kovačević, Ivanka Netinger Grubeša, Silva Lozančić

“…By testing various minimum leaf sizes and ensemble methods such as Random Forest and TreeBagger, the study evaluates metrics including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). …”
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A New Slice Template Matching Method for Full-Field Temporal–Spatial Deflection Measurement of Slender Structures by Jiayan Zheng, Yongzhi Sang, Haijing Liu, Ji He, Zhixiang Zhou

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Scheme of Recent Advances in the Field of Accounting and Economics: Application of Macro Accounting Theory in Economic Forecasting by Vahid Bekhradi Nasab, Ehsan Kamali, Khadije Ebrahimi Kahrizsangi

“…Also, to study the forecast's accuracy, the methods of the mean absolute value of error, mean of square error, and criterion of the average percentage of the absolute value of error have been used. …”
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Artificial neural networking for computational assessment of ternary hybrid nanofluid flow caused by a stretching sheet: implications of machine-learning approach by Imad Khan, M. Waleed Ahmed Khan

“…Researchers are mainly interested in using soft computing artificial intelligence (AI) methods due to their broad applications in analysis, modelling and simulations. …”
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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

“…The optimal weight is obtained when the error value in the training is less than the expected minimum error or the training iteration has reached the specified maximum iteration. …”
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FORECASTING AUTOMOBILE DEMAND AND SALES IN THE NIGERIAN MARKET: A MACHINE LEARNING APPROACH TO URBAN MOBILITY, MARKET COMPETITION, AND POLICY INSIGHTS by Emmanuel Imuede Oyasor

“…However, in emerging markets like Nigeria, empirical research on automobile demand remains sparse despite its growing relevance. …”
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A Data-Driven Artificial Neural Network Approach to Software Project Risk Assessment by Mohammed Naif Alatawi, Saleh Alyahyan, Shariq Hussain, Abdullah Alshammari, Abdullah A. Aldaeej, Ibrahim Khalil Alali, Hathal Salamah Alwageed

“…We compare the performance of mean squared error (MSE) and mean absolute error (MAE) as error functions and find that MAE yields superior results. …”
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An ensemble deep learning framework for energy demand forecasting using genetic algorithm-based feature selection. by Mohd Sakib, Tamanna Siddiqui, Suhel Mustajab, Reemiah Muneer Alotaibi, Nouf Mohammad Alshareef, Mohammad Zunnun Khan

“…The proposed model demonstrated exceptional precision, achieving a Root Mean Square Error (RMSE) of 130.6, a Mean Absolute Percentage Error (MAPE) of 0.38%, and a Mean Absolute Error (MAE) of 99.41 for weekday data. …”
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Leveraging machine learning and open accessed remote sensing data for precise rainfall forecasting by Bambang Kun Cahyono, Muhammad Hidayatul Ummah, Ruli Andaru, Neil Andika, Adjie Pamungkas, Hepi Hapsari Handayani, Paramita Atmodiwirjo, Rory Nathan

“…Meanwhile, accuracy assessments indicated that Support Vector Regression had the most accurate predictions accompanied by Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), R2, and Coefficient Correlation (CC) at 1.366, 0.947, 1.866, 0.948 and 0.982 respectively. …”
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A novel energy pattern factor-based optimized approach for assessing Weibull parameters for wind power applications by Ghulam Abbas, Arshad Ali, Mohamed Tahar Ben Othman, Muhammad Wasim Nawaz, Ateeq Ur Rehman, Habib Hamam

“…The performance of NOEPFM is measured in terms of five goodness-of-fit indices, namely root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), coefficient of efficiency (CoE), and maximum absolute error (MaxAE). …”
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Scalable earthquake magnitude prediction using spatio-temporal data and model versioning by Rahul Singh, Bholanath Roy

“…Multiple machine learning algorithms, including Gradient Boosting, Light Gradient Boosting Machine (LightGBM), XGBoost, and Random Forest, are evaluated on dataset sizes of 20%, 35%, 65%, and 100%, with performance metrics such as Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, and R 2. …”
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Loss of the Sturm–Liouville Property of Time-Varying Second-Order Differential Equations in the Presence of Delayed Dynamics by Manuel De la Sen

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Evaluation of a High-Accuracy Indoor-Positioning System with Wi-Fi Time of Flight (ToF) and Deep Learning by Doan Perdana, I Made Arya Indra Tanaya, Abdul Aziz Marwan, Fityanul Akhyar

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Assessment of the relationship of number of sertoli cells with number of germ cells, spermatogenic activity and germ cell apoptosis rates in bullsusing different regression models by YAMINA BELKHIRI, SOUHEYLA BENBIA, RAMZI LAMRAOUI

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Prediction of Global Horizontal Irradiance for Composite Climatic Zone in India by Naveen Krishnan, K. Ravi Kumar

“…The current study focused on the prediction of GHI for 14 days ahead in New Delhi, India, by Weather Research Forecasting Solar (WRF-Solar). The forecasting of solar radiation is performed for various seasons in a year. …”
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Driving Profile Optimization for Energy Management in the Formula Student Técnico Prototype by Tomás R. Pires, João F. P. Fernandes, Paulo J. Costa Branco

“…Results showed that the optimized strategy can be implemented with less than 0.5% of error in energy consumption and 6.8% of error in the obtained competing points.…”
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Physical bases for determination of scattering kernels from incomplete data in grid-less X-ray imaging by A. Yu. Danyk, O. O. Sudakov

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Design Particularities of Quadrature Chaos Shift Keying Communication System with Enhanced Noise Immunity for IoT Applications by Darja Cirjulina, Ruslans Babajans, Deniss Kolosovs

“…The study focuses on Colpitts and Vilnius chaos oscillators in different synchronization configurations, and the reliability of the system in the particular configuration is assessed using the bit error rate (BER) estimation. The research considers synchronization imbalances and demonstrates their effect on the accuracy of data detection and overall transmission stability. …”
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Limb movement detection and analysis based on visual recognition of human posture by Zhiguo Xiao, Chunxiang Wang, Tianjiao Ding, Xiangfeng Shen, Xinyuan Li, Dongni Li

“…In Rehab dataset, MAE(Mean Absolute Error, MAE) loss was 1.383 for motion count and 0.508 for motion time. …”
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Enhanced Seafloor Topography Inversion Using an Attention Channel 1D Convolutional Network Based on Multiparameter Gravity Data: Case Study of the Mariana Trench by Qiang Wang, Ziyin Wu, Zhaocai Wu, Mingwei Wang, Dineng Zhao, Taoyong Jin, Qile Zhao, Xiaoming Qin, Yang Liu, Yifan Jiang, Puchen Zhao, Ning Zhang

“…Seafloor topography data are fundamental for marine resource development, oceanographic research, and maritime rights protection. However, approximately 75% of the ocean remains unsurveyed for bathymetry. …”
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