Performance Evaluation of Long Short-Term Memory for Chili Price Prediction by Fata Nabil Fikri, Nurochman Nurochman

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Data driven decisions in education using a comprehensive machine learning framework for student performance prediction by Muhammad Nadeem Gul, Waseem Abbasi, Muhammad Zeeshan Babar, Abeer Aljohani, Muhammad Arif

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An optimized approach for predicting water quality features and a performance evaluation for mapping surface water potential zones based on Discriminant Analysis (DA), Geographical... by Abhijeet Das

“…The DA's spatial distribution map is created with the assistance of inverse distance weighted (IDW) method, that is by-default performed by Arc GIS. Proceeding further, ANN model showed exceptional accuracy in water quality prediction, highlighting the dependability and accuracy of errors in the current work, that includes MAE, MSE, MAD, and RMSE, but also record highest in the validation of goodness-of-fit metrics (NSE and R2). …”
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THE COMPARISON OF EXTENDED AND ENSEMBLE KALMAN FILTERS IN MODELING ENVIRONMENTAL POLLUTION INFLUENCES ON ACUTE RESPIRATORY INFECTION DYNAMICS (ISPA) by Yolanda Norasia, Dinni Rahma Oktaviani, Devi Marita Putri

“…Simulation results show that both methods can produce accurate estimations, but EnKF demonstrates superior performance in terms of RMSE compared to EKF. It predicts more accurately for susceptible (X) and infected (Y) populations with EnKF than with EKF. …”
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Comparing the neural network with path analysis in fitting regression models by Fereshteh Aard, Ayyub Sheikhi

“…The criterion for comparing the two methods is RMSE. The results of the analysis showed that both models are over fitted and the RMSE train and test of neural network are less different from the path analysis. …”
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A FUSION MODEL FOR STOCK MARKET PREDICTION USING PROPHET AND LONG SHORT-TERM MEMORY NEURAL NETWORKS by Shruti Mishra, Manjunathan N, Parangat Singh, Sandeep Kumar Satapathy, Sung-Bae Cho, Sachi Nandan Mohanty

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NH4 Modelling with ARIMA and LSTM by Hanna Arini Parhusip, Suryasatriya Trihandaru, Johanes Dian Kurniawan

“…Despite its use, ARIMA's Root Mean Square Error (RMSE) performance was found lacking compared to more advanced methods. …”
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Implementation and Evaluation of a Mobile Indoor Positioning System Using the Frequency Switching Model by Youssef Ibnatta, Mohammed Khaldoun, Mohammed Sadik

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A data mining approach for proposing a relationship to predict self-compaction concrete crack width after the self-healing period by Saeid Hosseini, Ali Seyedkazemi, Abdullah Davoudi-Kia, Saman Soleimani Kutanaei

“…Also, compared to the GEP model, the reductions for RMSE, MAE, and StD were 23.9 %, 12.7 %, and 62.8 %, respectively, indicating its superior performance in predicting CWA. …”
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Enhancement Ear-based Biometric System Using a Modified AdaBoost Method by Abdulkareem Merhej Radhi, Subhi Aswad Mohammed

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Influence of ambient light on the accuracy of different face scanning methods: an in-vitro study by Paul Ulrich Keil, Florian Beuer, Alexey Unkovskiy, Ece Atay, Marie-Elise Jennes

“…The root-mean-square-error (RMSE) was employed as a measure. Separated by trueness and precision, a one-way ANOVA was performed with post hoc Games-Howell tests for each scanning method. …”
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Optimizing Precipitation Forecasting and Agricultural Water Resource Allocation Using the Gaussian-Stacked-LSTM Model by Maofa Wang, Bingcheng Yan, Yibo Zhang, Lu Zhang, Pengcheng Wang, Jingjing Huang, Weifeng Shan, Haijun Liu, Chengcheng Wang, Yimin Wen

“…We evaluate Stacked Long Short-Term Memory (LSTM), Transformer, and Support Vector Regression (SVR) models, with Stacked-LSTM showing the best performance in terms of accuracy and stability, as measured by the Root Mean Square Error (RMSE). …”
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Satellite imagery, big data, IoT and deep learning techniques for wheat yield prediction in Morocco by Abdelouafi Boukhris, Antari Jilali, Abderrahmane Sadiq

“…We used GRU deep learning model for the best performance, the RMSE and R2 for this model was 0.00036 and 0.99 respectively.The main contribution of our paper is the development of a new system that can predict several crop yields, such as wheat, maize, etc., using IoT, satellite imagery for spatial data and the use of sensors for temporal data. …”
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Optimized Partial Band Preamble and Timing Synchronization Algorithm for Orthogonal Time Frequency Space System by Wenchao Zhang, Xiaochen Lu, Guangliang Ren, Lirui Shen

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Shoulder–Elbow Joint Angle Prediction Using COANN with Multi-Source Information Integration by Siyu Zong, Wei Li, Dawen Sun, Zhuoda Jia, Zhengwei Yue

“…Experimental results demonstrate that the COANN achieves significant performance improvements: compared with RBF neural networks, it reduces the root mean square error (RMSE) by 24.32% (ΔR² + 18.75%) with a 22.6% shorter system runtime; relative to conventional ANNs, it decreases the RMSE by 31.59% (ΔR² + 12.15%) while reducing computational time by 35.1%; compared with CNN neural networks, it reduces the root mean square error (RMSE) by 14.9% (ΔR² + 3.84%); and relative to conventional LSTM, it decreases the RMSE by 15.31% (ΔR² + 4.86%). …”
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Multijunction solar cell parameter estimation based on metaheuristic algorithms by Marwa M. Elzalabani, Doaa M. Atia, Aref Y. Eliwa, Belal A. Abou Zalam, Mahmoud S. AbouOmar

“…In terms of errors, and execution time, the TSA algorithm performs the poorest in all three circumstances with RMSE value greater than 0.16.…”
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Enhanced Single-Diode Solar Cell Model: Analytical Solutions Using Lambert W Function and Circuit Innovations by Martin Calasan, Snezana Vujosevic, Kristina Bakic

“…Results demonstrated the models’ accuracy and robustness, with Root Mean Square Error (RMSE) analysis showing superior alignment between simulated and experimental I-V curves compared to existing single-, double-, and triple-diode solar cell models from the literature. …”
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Evaluation of Traditional and Data-Driven Algorithms for Energy Disaggregation Under Sampling and Filtering Conditions by Carlos Rodriguez-Navarro, Francisco Portillo, Isabel Robalo, Alfredo Alcayde

“…The results confirm that no universally superior algorithm exists, and performance varies depending on the type of appliance and signal conditions. …”
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Investigating the Two Optimization Algorithms (GWO and ACO) Coupling with Radial Basis Neural Network to Estimate the Pile Settlement by Ehsanolah Assareh, Reza Poultangari

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Investigating the Reliability of Empirical Path Loss Models over Digital Terrestrial UHF Channels in Ikorodu and Akure, Southwestern Nigeria by Akinsanmi Akinbolati, Bolanle T. Abe

“…The results further indicate that the Hata model had the best performance with the lowest RMSE of 10.812 in Ikorodu, while COST-231 had the best performance in Akure, with the lowest RMSE of 9.877. …”
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