Enhancing Visitor Forecasting with Target-Concatenated Autoencoder and Ensemble Learning by Ray-I Chang, Chih-Yung Tsai, Yu-Wei Chang

“…Preceding forecasting algorithms primarily focused on time series analysis, often overlooking influential factors such as economic conditions. …”
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The Importance of Distance between Photovoltaic Power Stations for Clear Accuracy of Short-Term Photovoltaic Power Forecasting by Abdelhakim El hendouzi, Abdennaser Bourouhou, Omar Ansari

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Moral Distress Challenges in Nurses During Early Stage of COVID-19 Pandemic: A Qualitative Study by Ebrahim Aliafsari Mamaghani, Farzaneh Soleymani, Mina Hashemiparast

“…Data were collected using individual semi-structured interviews until data saturation and concurrently analyzed by MAXQDA software. …”
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Optimized Detection Algorithm for Vertical Irregularities in Vertical Curve Segments by Rong Xie, Chunjun Chen

“…These were compared with excitation irregularities to identify key factors affecting detection accuracy in vertical curve segments. Through further analysis of abnormal exceedances in detection data, the reference cancellation method was proposed. …”
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AI-driven competency recommendations based on attendance patterns and academic performance by Junaidi, Teguh Wahyono, Irwan Sembiring

“…Gradient Boosting (GB) was the most effective model for weighting discipline and learning outcomes (Mean Squared Error [MSE]: 2.9224, Root Mean Squared Error [RMSE]: 1.4252, Coefficient of Determination [R2]: 0.9667), outperforming three alternatives. …”
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Research on Health Monitoring of Flying-Swallow-Typed Tied Arch Bridge Based on PSO-GRNN Algorithm by Tianpeng Zhang, Long Liu, Pengfei Ji

“…The mean absolute percent error (MAPE), root mean square error (RMSE), coefficient of determination (R2), and Nash-Sutcliffe efficiency (NSE) coefficient were used to evaluate the prediction performance of the model. …”
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Koopman-Driven Grip Force Prediction Through EMG Sensing by Tomislav Bazina, Ervin Kamenar, Maria Fonoberova, Igor Mezic

“…Influential parameters were subsequently identified through sensitivity analysis. Leveraging a novel data-driven Koopman-based approach and problem-specific data lifting, we devised a method for the estimation and short-term prediction of grip force from processed sEMG signals. …”
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Recurrence Multilinear Regression Technique for Improving Accuracy of Energy Prediction in Power Systems by Quota Alief Sias, Rahma Gantassi, Yonghoon Choi, Jeong Hwan Bae

“…All of these variables are derived from detailed energy production data from the various energy sources. The purpose of this paper is to demonstrate that it is possible to analyze energy demand and supply directly together as a way to produce a more in-depth analysis. …”
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A comparative study on quantitative precipitation estimation based on GPM satellite and X-band phased-array weather radar by Yongyan Su, Yongyan Su, Yongyan Su, Di Wang, Wenyu Kong, Bo Zhao, Yan Liu, Xuejiao Chen, Debin Su

“…This study investigates the performance of the Global Precipitation Measurement (GPM) satellite in comparison to the X-band Phased Array Weather Radar (XPAR) regarding precipitation measurement accuracy, focusing on radar echoes. A comparative analysis was conducted on two significant precipitation events that occurred in 2023 in Xiong’ an New Area, Hebei Province, China, utilizing data from XPAR, GPM, and ground-based observations. …”
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Groundwater level prediction using an improved SVR model integrated with hybrid particle swarm optimization and firefly algorithm by Sandeep Samantaray, Abinash Sahoo, Falguni Baliarsingh

“…The SVR-FFAPSO model performs best in GWL forecasting for Khuntuni station, according to the quantitative analysis with correlation coefficient (R) = 0.9978, Nash–Sutcliffe efficiency (NSE) = 0.9933, mean absolute error (MAE) = 0.00025 (m), root mean squared error (RMSE) = 0.00775 (m) during the training phase. …”
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OPGW fault localization method based on transformer and federated learning by Yan Zhigang, Cui Min, Su Xinyue, Wang Jinrui, Ma Xiao, Wu Lijun

“…Finally, the Transformer network was introduced into the model, which greatly improved the processing efficiency of fault data through parallel computing. The simulation experiment results show that the relative error, absolute error, and localization time of the proposed method for fault localization are the smallest on different datasets, with the lowest values being 0.78%, 0.0297 km, and 5.33 µs, respectively.…”
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Information dynamics and the emergence of high-order individuality in ecosystems by Hardik Rajpal, Clem von Stengel, Pedro A. M. Mediano, Fernando E. Rosas, Eduardo Viegas, Pablo A. Marquet, Henrik J. Jensen

“…Here, we capitalise on recent advances in information-theoretic data analysis to advance this state of affairs by investigating the emergence of high-order structures- such as groups of species- in the collective dynamics of the Tangled Nature model of evolutionary ecology. …”
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Evaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors. by Montaser Abdelsattar, Mohamed A Ismeil, Karim Menoufi, Ahmed AbdelMoety, Ahmed Emad-Eldeen

“…Using a dataset of 40,000 observations, the models were assessed based on R-squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). ET achieved the highest performance among ML models, with an R-squared value of 0.7231 and a RMSE of 0.1512. …”
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UaVirBASE: A Public-Access Unmanned Aerial Vehicle Sound Source Localization Dataset by Gabriel Jekateryńczuk, Rafał Szadkowski, Zbigniew Piotrowski

“…Additionally, we describe the software and hardware employed for data acquisition and annotation alongside an analysis of the dataset’s structure. …”
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Artificial intelligence-based optimization and modeling of cadmium reduction via ultraviolet-assisted malathion/sulfite reaction mechanisms by Hossein Azarpira, Parsa Khakzad, Tayebeh Rasolevandi, Amir Sheikhmohammadi

“…In contrast, the SVR model showed better generalization to the test data, with lower error metrics and a consistent R2 of around 0.81 across both datasets. …”
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Predicting Crown-width of Dominant Trees on Teak Plantation from Clonal Seed Orchards in Ngawi Forest Management Unit, East Java by Ronggo Sadono

“…The best model was eventually validated using the following criteria : root mean squared error, aggregate deviation, and relative deviation. …”
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Cognitive flexibility: exploring students’ problem-solving in elementary school mathematics learning by Sri Rahayuningsih, Sirajuddin Sirajuddin, Nasrun Nasrun

“…Problem-solving worksheet, think-aloud records, and interviews are used as data collection instruments. Then, the data were analyzed using a qualitative descriptive approach. …”
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Deep learning framework for interpretable supply chain forecasting using SOM ANN and SHAP by Khandakar Rabbi Ahmed, Md Eahia Ansari, Md. Naimul Ahsan, Arafat Rohan, Md Borhan Uddin, Mir Araf Hossain Rivin

“…The R 2 was found to be 0.92, Root Mean Squared Error (RMSE) 0.936, and Mean Absolute Error (MAE) 0.8459 of the SOM+ANN model estimated the shipping duration. …”
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EDUCATION, LABOUR PRODUCTIVITY AND INCOME INEQUALITY IN NIGERIA by SHARIMAKIN A., OSENI M.S., ADEGBOYE A.C.

“…A dynamic structure is devised for the analysis using data for the period 1981 to 2013. The cointegration and error correction methodology is adopted in the empirical analysis. …”
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汽车同步器装配体倾斜量的一种在线估算方法 by 刘飞飞, 陈涛, 王书昭

“…In view of tilting detection of gear hub and gear sleeves in automobile gearbox synchronizer assembly detection,there are many problems such as low degree of automation,too many uncontrollable factors and so on,the method of soft measurement is applied,taking the influence factors of the amount of tilting as independent variables,based on theoretical analysis and experimental test and under the tooth side centering mode,a soft measurement model of synchronizer gear hub and gear sleeves is built.According to the sample data collected in the field,depending on the predict amount of tilting and detection amount of tilting,put quantity into MATLAB to make simulation analysis.The simulation results show that the actual situation of model of the soft measurement can reflect the amount of tilting,the relative error of the mean value is less than 1.1%,which can meet the production demand of the company.…”
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