A Hybrid Model Integrating Variational Mode Decomposition and Intelligent Optimization for Vegetable Price Prediction by Gao Wang, Shuang Xu, Zixu Chen, Youzhu Li

“…The model employs VMD for multi-scale decomposition of original price series and utilizes the FOA for adaptive optimization of the GRU’s critical parameters, effectively addressing the challenges of high volatility and nonlinearity in agricultural price forecasting. Empirical analysis conducted on daily price data of six major vegetables, specifically, Chinese cabbage, cucumber, beans, tomato, chili, and radish, from 2014 to 2024 reveals that the proposed model significantly outperforms traditional methods, single deep learning models, and other hybrid models in predictive performance. …”
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Machine learning-based prediction of CO2 solubility in methyldiethanolamine solutions: A comparative study by Sajjad Fazeli, Mohammad Amin Moradkhani, Behrouz Bayati

“…Finally, the order of significance of influential factors in controlling solubility was determined based on a sensitivity analysis.…”
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Model and training method for aerial image object detector with optimization of both robustness and computational efficiency by Alona Moskalenko, Mykola Zaretskyi, Maksym Vynohradov, Vladyslav Babych

“…The subject of research is Neural network-based object detectors, which are widely used for video image analysis. An increasing number of tasks now demand data processing directly at the source, which limits the available computational resources. …”
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Predicting Relative Density of Pure Magnesium Parts Produced by Laser Powder Bed Fusion Using XGBoost by Kristijan Šket, Snehashis Pal, Janez Gotlih, Mirko Ficko, Igor Drstvenšek

“…The XGBoost model showed high predictive power, achieving an R² test result of 0.835, a mean absolute error (MAE) of 0.728 and a root mean square error (RMSE) of 0.982. …”
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Enhanced Visible Light Communication for Real-Time Audio With Interference-Resilient Protocols by Kanchana Chathurangi Ahangama, Hemali Pasqual, Chinthaka Premachandra, H. Waruna Haripriya Premachandra

“…Despite its potential for high-speed data transmission, visible light communication (VLC) technology faces significant challenges in achieving reliable real-time audio streaming due to interference, signal degradation, and synchronization issues. …”
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A Hybrid Gaze Distance Estimation via Cross-Reference of Vergence and Depth by Dae-Yong Cho, Min-Koo Kang

“…Presently, various MR devices employ small eye-tracking cameras to capture both eyes and infer the gaze distance based on vergence angle data. However, this technique faces significant challenges, as it is highly sensitive to several human errors, such as strabismus, blinking, and fatigue of the eyes due to prolonged use. …”
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Presenting and evaluating a new empirical relationship for estimating the rate of infiltration in trenches by Mojtaba Hassanpour, Hossein Khozeymehnezhad, Abalfazl Akbarpour

“…This equation incorporates factors such as the wetted perimeter, mean soil particle diameter, trench length, and a coefficient. A comparative analysis between the observed data from nine Iranian earthen canals and the values calculated using the proposed equation revealed an average relative error of 15% between the two datasets. …”
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Application of meta-heuristic hybrid models in estimating the average air temperature of Caspian sea coast of Iran by Hamidreza Babaali, Reza Dehghani, Fatemeh Dehghani

“…To assess and compare model performances, several criteria were employed including correlation coefficient, root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and percentage bias. …”
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A comparative performance study on the development of hybrid extreme gradient boosting models for predicting rock layer subsidence in subsea gold mine by Weijun Liu, Zhixiang Liu, Meng Wang, Shuangxia Zhang

“…The coefficient of determination (R 2), root mean square error (RMSE), and mean absolute error (MAE) were obtained for the four models. …”
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Design of a Demodulation Algorithm for UWOC based on Improved Manchester Coding by ZHANG You, HU Fangren, ZHAO Xiaoyan, ZHOU Jun, WANG Qilong

“…Finally, high-speed communication system with low BER is realized by using Manchester encoding sub-frame headers and Reed-Solomon (RS) error-correcting codes in the data part. The algorithm is designed and implemented on a Field Programmable Gate Array (FPGA), and a UWOC system using this method is built to conduct BER tests under different environments and distances.…”
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GENOMICON-Seq enables realistic simulation of amplicon and exome sequencing for low-frequency mutation detection by Milan S. Stosic, Jean-Marc Costanzi, Ole Herman Ambur, Trine B. Rounge

“…GENOMICON-Seq is thus a flexible, reproducible framework for assessing new protocols, benchmarking variant callers, and refining data analysis pipelines, ultimately reducing costly trial-and-error in the laboratory. …”
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Research on future trends of electricity consumption based on conditional generative adversarial network considering dual‐carbon target by Jinghua Li, Zibei Qin, Yichen Luo, Jianfeng Chen, Shanyang Wei

“…The results demonstrate that the authors’ method achieves lower root mean square error and mean absolute percentage error values, specifically 0.177% and 2.39%, respectively, outperforming established advanced methods such as SVM and LSTM.…”
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Method of troubleshooting in the neural network environment of intellectual decision supporting systems by О.І. Тимочко, С.В. Осієвський, О.О. Тімочко, П.В. Бєляєв

“…This reduces the likelihood of errors of this type for these systems. The classification and analysis of algorithms for sampling knowledge from an artificial neural network are given. …”
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Permeability Correction Method of Sandy Conglomerate Reservoir Based on Flow Unit Division by MAO Chenfei, GAO Yanwu, XIAO Hua, CHEN Guojun, LIU Haiming, WU Wei, GAO Ming

“…The results show that the permeability model with comprehensive flow unit division and clay content correction has higher calculation accuracy, smaller relative error and better agreement with experimental data. …”
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Machine learning assisted noncontact neonatal anthropometry using FMCW radar by Jun Byung Park, Jae Yoon Na, Seung Hyun Kim, Jinjoo Choi, Jihyun Keum, Sung Ho Cho, Hyun-Kyung Park

“…The model achieved a mean absolute error (MAE) of 1.34 cm, a root mean square error (RMSE) of 1.55 cm, and an intraclass correlation coefficient (ICC) of 0.78 (p value < 0.001) in height measurements and an MAE of 0.23 kg, RMSE of 0.28 kg, and ICC of 0.85 (p value < 0.001) in weight measurements. …”
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Localization of mobile robot in prior 3D LiDAR maps using stereo image sequence by I.V. Belkin, A.A. Abramenko, V.D. Bezuglyi, D.A. Yudin

“…It shows a stable absolute translation error of about 0.11 – 0.13 m. and a rotation error of 0.42 – 0.62 deg. …”
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An advanced CNN-attention model with IFTTA optimization for prediction air consumption of relay nozzles by Shen Min, Shao Ning, Cao Yongbo, Xiong Xiaoshuang, Yang Xuezheng, Wang Zhen, Yu Lianqing

“…An in-depth analysis of predicted data reveals that the outlet diameter is the most sensitive factor affecting the airflow rate, followed by inlet diameters and cone angle of the relay nozzle. …”
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Synchronous Control of a Dual-Motor Driving Rack and Pinion Module for Steer-by-Wire System by Insu Chung, Sehoon Oh, Kanghyun Nam

“…The algorithm was constructed using Matlab/Simulink, and the actual equipment was controlled using a data acquisition (DAQ) board. In order to compare the existing synchronous control structures with the proposed control structure, a model-based controller based on dynamic analysis was designed, and the results were derived through two commands. …”
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Evaluation of Smart Building Integration into a Smart City by Applying Machine Learning Techniques by Mustafa Muthanna Najm Shahrabani, Rasa Apanaviciene

“…To fill these gaps, this paper introduces a novel machine learning model to predict smart building integration into smart city levels and assess their impact on smart city performance by leveraging data from 147 smart buildings in 13 regions. Six optimised machine learning algorithms (K-Nearest Neighbours (KNNs), Support Vector Regression (SVR), Random Forest, Adaptive Boosting (AdaBoost), Decision Tree (DT), and Extra Tree (ET)) were employed to train the model and perform feature engineering and permutation importance analysis. …”
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Predicting the Performance of Students Using Deep Ensemble Learning by Bo Tang, Senlin Li, Changhua Zhao

“…The automation of processes and the management of large datasets generated by technology-enhanced learning tools can facilitate the analysis and processing of these data, which provides crucial insights into the knowledge of students and their engagement with academic endeavors. …”
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