Leveraging Machine Learning for Exchange Rate Prediction: A Business and Financial Management Perspective in Nigeria by Adedeji Daniel Gbadebo

“…Findings The findings indicate that the Random Forest (RF) model outperforms other approaches in predicting Nigeria’s exchange rate against the US dollar, demonstrating the lowest prediction errors (MAE, MSE, RMSE, and MAPE). …”
Get full text

Machine learning based prediction of geotechnical parameters affecting slope stability in open-pit iron ore mines in high precipitation zone by John Gladious, Partha Sarathi Paul, Manas Mukhopadhyay

“…The models’ accuracies were evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared (R2), Mean Bias Deviation (MBD), and Willmott’s Index of Agreement (d). …”
Get full text

Prediction rotary drilling penetration rate in lateritic soils using machine learning models by Eugène Gatchouessi Kamdem, Franck Ferry Kamgue Tiam, Luc Leroy Mambou Ngueyep, Olivier Wounabaissa, Hugues Richard Lembo Nnomo, Abraham Kanmogne

“…The key performance metrics including correlation coefficient (R2), mean absolute error (MAE) and root mean square error (RMSE) were calculated for each method to evaluate the accuracy of the predictions. …”
Get full text

Predicting Landing Position Deviation in Low-Visibility and Windy Environment Using Pilots’ Eye Movement Features by Xiuyi Li, Yue Zhou, Weiwei Zhao, Chuanyun Fu, Zhuocheng Huang, Nianqian Li, Haibo Xu

“…RF also performs well as per the RMSE metric, as it is suitable for predicting landing position errors of outliers.…”
Get full text

Performance prediction using educational data mining techniques: a comparative study by Yaosheng Lou, Kimberly F. Colvin

“…The results indicated that generalized linear regression consistently outperformed decision tree and random forest regression in terms of both predictive accuracy and error. …”
Get full text

Bridging the Gap Between Machine Learning and Medicine: A Critical Evaluation of the Dworak Regression Grade in Rectal Cancer by Camille Raets, Chaimae El Aisati, Amir L. Rifi, Mark De Ridder, Koen Putman, Johan De Mey, Alexandra Sermeus, Kurt Barbe

“…This study investigated the consistency between Random Forest’s prediction for rectal cancer regression grades and doctors’ opinion based on clinical data. …”
Get full text

A Measure–Correlate–Predict Approach for Transferring Wind Speeds from MERRA2 Reanalysis to Wind Turbine Hub Heights by José A. Carta, Diana Moreno, Pedro Cabrera

“…The method includes the fitting of 21 vertical wind profile models using data at 2 m, 10 m, and 50 m, with model selection based on the minimum mean square error. The approach was applied to seven wind-prone locations in the Canary Islands, selected for their strategic relevance in current or planned wind energy development. …”
Get full text

A case study comparing approaches to mask satellite-derived bathymetry by Galen Richardson, Anders Knudby, Yulun Wu, Mohsen Ansari

“…We then compared the different approaches to masking unsuitable pixels in terms of the mean absolute error (MAE) of the retained predictions and the total mapped area. …”
Get full text

Integrating Temporal Vegetation and Inundation Dynamics for Elevation Mapping Across the Entire Turbid Estuarine Intertidal Zones Using ICESat-2 and Sentinel-2 Data by Siqi Yao, Jianrong Zhu, Wanying Zhang, Bo Tian, Weiwei Sun, Weiguo Zhang, Weiming Xie, Pengjie Tao, Chunpeng Chen, Kai Tan

“…A case study conducted on the muddy intertidal zones of the islands in the Yangtze River Estuary from 2019 to 2023 reveals that the average root mean square errors are 0.33 and 0.69 m for high-mid and mid-low intertidal zones, respectively. …”
Get full text

An Interpretable Machine Learning Framework for Unraveling the Dynamics of Surface Soil Moisture Drivers by Zahir Nikraftar, Esmaeel Parizi, Mohsen Saber, Mahboubeh Boueshagh, Mortaza Tavakoli, Abazar Esmaeili Mahmoudabadi, Mohammad Hassan Ekradi, Rendani Mbuvha, Seiyed Mossa Hosseini

“…SMAP outperformed the others, showing the highest median correlation and the lowest Root Mean Square Error (RMSE). Using SMAP, we estimated SSM across 609 catchments employing the Random Forest (RF) algorithm. …”
Get full text

Application of machine learning and neural network models based on experimental evaluation of dissimilar resistance spot-welded joints between grade 2 titanium alloy and AISI 304 s... by Marwan T. Mezher, Alejandro Pereira, Rusul Ahmed Shakir, Tomasz Trzepieciński

“…Models from gradient boosting, CatBoost, and random forest machine learning (ML) algorithms were used to guarantee an accurate analysis, along with the artificial neural network regressions. …”
Get full text

Harvester Maintenance Prediction Tool: Machine Learning Model Based on Mechanical Features by Rodrigo Oliveira Almeida, Richardson Barbosa Gomes da Silva, Danilo Simões

“…In default mode, we ran 25 popular algorithms through the database and compared them based on accuracy and error metrics. Although the combination models performed well, the Random Forest model performed better in the default mode with an accuracy of 0.933. …”
Get full text

Estimating actual crop evapotranspiration by using satellite images coupled with hybrid deep learning-based models in potato fields by Larona Keabetswe, Yiyin He, Chao Li, Zhenjiang Zhou

“…Notably CNN-RF1 model yielded the best results, achieving a normalized root mean squared error (nRMSE) of 11.7 and 31.5 %; Nash–Sutcliffe Efficiency (NSE) of 0.97 and 0.80; Correlation coefficient (CC) of 0.99 and 0.91 at training and testing phases respectively. …”
Get full text

Using intelligence techniques to automate Oracle testing by Nour Sulaiman, safwan hasson

“…Oracle testing is necessary to determine if a particular test case detects an error because the purpose of testing is to ensure that the application adheres to its specifications. …”
Get full text

Gait-based Parkinson’s disease diagnosis and severity classification using force sensors and machine learning by Navita, Pooja Mittal, Yogesh Kumar Sharma, Anjani Kumar Rai, Sarita Simaiya, Umesh Kumar Lilhore, Vimal Kumar

“…The crucial evaluation metrics used for evaluating model performance include accuracy, mean absolute error, and root mean square error. The findings indicate that the suggested model significantly surpasses current methodologies. …”
Get full text

Stacked ensemble model for accurate crop yield prediction using machine learning techniques by Ramesh V, Kumaresan P

“…The ensemble model’s performance was assessed using several metrics, including a Mean Absolute Error of 7.20 tons/hectare, Mean Square Error of 15570.32 tons ^2 /hectare ^2 , Root Mean Square Error of 124.78 tons/hectare, and Coefficient of Determination (R ^2 Score) of 0.98. …”
Get full text

Compositional modeling of solution gas–oil ratio (Rs): a comparative study of tree-based models, neural networks, and equations of state by Aydin Larestani, Sara Sahebalzamani, Abdolhossein Hemmati-Sarapardeh, Ali Naseri

Get full text

A new method for determining factors Influencing productivity of deep coalbed methane vertical cluster wells by HUANG Li, XIONG Xianyue, WANG Feng, SUN Xiongwei, ZHANG Yixin, ZHAO Longmei, SHI Shi, ZHANG Wen, ZHAO Haoyang, JI Liang, DENG Lin

“…The results showed that: 1） The Beggs & Bill model and Gray model exhibited poor applicability for predicting the bottom-hole flowing pressure of deep CBM wells, while the single-phase gas model demonstrated reduced overall error as water production declined. Predictions using the neural network method were more accurate, with a relative error of less than 10% compared to measured values. 2） Using Kendall's tau-b correlation analysis, the discrete dominant factor was identified as the microstructural position, primarily located in uplifted positive structural zones, with the secondary factor being fracture development, categorized mainly as “well-developed” or “developed.” 3） By combining lasso regression-random forest- decision tree algorithm to iteratively eliminate irrelevant factors, the continuous dominant factors influencing productivity were ranked in descending order as: ash content, average construction discharge rate, total sand volume pumped, flowback rate at gas breakthrough, net pay thickness, acoustic travel time, gamma ray log value, average construction pressure, percentage of 100-mesh sand, and average gas measurement value. …”
Get full text

Comparative analysis of machine learning models for predicting water quality index in Dhaka’s rivers of Bangladesh by Mosaraf Hosan Nishat, Md. Habibur Rahman Bejoy Khan, Tahmeed Ahmed, Syed Nahin Hossain, Amimul Ahsan, M. M. El-Sergany, Md. Shafiquzzaman, Monzur Alam Imteaz, Mohammad T. Alresheedi

“…Among the evaluated ML models, ANN and Random Forest Regressor performed the best. The ANN model demonstrated superior predictive capability, achieving a Root Mean Squared Error (RMSE) of 2.34, a Mean Absolute Error (MAE) of 1.24, a Nash–Sutcliffe Efficiency (NSE) of 0.97, and a Coefficient of Determination (R2) of 0.97. …”
Get full text

Does digital soil mapping prediction performance of soil texture improve when adding uncertain field texture estimates? A study based on clay content by Anne C. Richer-de-Forges, Songchao Chen, Gerard B.M. Heuvelink, Stephan van der Westhuizen, Thomas G. Orton, Hocine Bourennane, Dominique Arrouays

Get full text