Enhanced and predictive modelling of direct shoot regeneration of Evolvulus alsinoides (L.) using ANN machine learning model and genetic stability studies by Collince Omondi Awere, Kasinathan Rakkammal, Andaç Batur Çolak, Mustafa Bayrak, Ogolla Fredrick, Valentine Chikaodili Anadebe, Manikandan Ramesh

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Predicting the evolution of pH and total soluble solids during coffee fermentation using near-infrared spectroscopy coupled with chemometrics by Vicente Tirado-Kulieva, Carlos Quijano-Jara, Himer Avila-George, Wilson Castro

“…Currently, coffee fermentation is visually operated, which results in incomplete or excessive processes and coffees with undesirable characteristics. …”
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Computer simulation of microwave-assisted drying: Coupled influence of microwave power and pulse ratio on product and process characteristics by Jalal Dehghannya, Mahdi Habibi-Ghods

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Skin hyperspectral imaging and machine learning to accurately predict the muscular poly-unsaturated fatty acids contents in fish by Yi-Ming Cao, Yan Zhang, Qi Wang, Ran Zhao, Mingxi Hou, Shuang-Ting Yu, Kai-Kuo Wang, Ying-Jie Chen, Xiao-Qing Sun, Shijing Liu, Jiong-Tang Li

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A Near-Real-Time Model for Predicting Electricity Disruptions in Texas During Winter Storms by Jangjae Lee, Sangkeun Lee, Supriya Chinthavali, Stephanie Paal

“…For model optimization, Bayesian optimization was employed using Root Mean Squared Error (RMSE) as the objective function. …”
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Prediction of air temperature and humidity in greenhouses via artificial neural network. by Caixia Yan, Ta Na, Qi Zhen, Yunfeng Sun, Kunyu Liu

“…Results show that the MLP model with Levenberg-Marquardt optimization performs best in predicting the current temperature and humidity, with an RMSE of 0.439°C and R2 of 0.997 for temperature prediction and an RMSE of 1.141% and R2 of 0.996 for relative humidity prediction. …”
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Random Forest-Based Machine Learning Model Design for 21,700/5 Ah Lithium Cell Health Prediction Using Experimental Data by Sid-Ali Amamra

“…The random forest model, which is a novel strategy for SoH prediction application, was trained using experimental features, including current (A), potential (V), and temperature (°C), and tuned through a grid search for performance optimization. …”
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Precision forecasting for hybrid energy systems using five deep learning algorithms for meteorological parameter prediction by Ceren Ceylan, Zehra Yumurtacı

“…Key results demonstrate that GRU achieves superior performance in wind speed prediction (RMSE: 0.049 m/s, R2: 0.8634) and solar radiation forecasting (RMSE: 0.146 W/m2, R2: 0.6643), while CNN-LSTM excels in ambient temperature prediction (RMSE: 0.011 °C, R2: 0.9976). …”
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Machine Learning-Based Lithium Battery State of Health Prediction Research by Kun Li, Xinling Chen

“…Results indicate that the optimized models achieved significant improvements in prediction accuracy, with RMSE and MAE reduced by over 0.5%, a minimum reduction of 38% in MAPE, and R² exceeding 0.8, demonstrating strong fitting capabilities and validating the effectiveness of the PSO strategy. …”
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The Fermentation Degree Prediction Model for Tieguanyin Oolong Tea Based on Visual and Sensing Technologies by Yuyan Huang, Jian Zhao, Chengxu Zheng, Chuanhui Li, Tao Wang, Liangde Xiao, Yongkuai Chen

“…Finally, the Sparrow Search Algorithm (SSA) was applied to optimize the data fusion models. After optimization, the models exhibited a MAE ranging from 1.703 to 2.078, a RMSE from 2.258 to 3.230, and R² values between 0.988 and 0.994 on the test set. …”
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An Improved Whale Algorithm for Setting Standard Scheduled Block Time Based on the Airline Fairness by Qian Wang, Yong Tian, Lili Lin, Ratnaji Vanga, Lina Ma

“…The objective of this paper is to develop and solve a mathematical model for standard SBT setting with consideration of both fairness and reliability. We use whale optimization algorithm (WOA) and an improved version of the whale optimization algorithm (IWOA) to solve the SBT setting problem. …”
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A framework of crop water productivity estimation from UAV observations: A case study of summer maize by Minghan Cheng, Ni Song, Josep Penuelas, Matthew F. McCabe, Xiyun Jiao, Yuping Lv, Chengming Sun, Xiuliang Jin

“…This investigation establishes Crop Water Productivity (CWP) - quantified as yield per unit water consumption (kg/m³) - as a pivotal metric for agricultural water resource optimization. However, current methodologies face limitations in estimation accuracy and operational efficiency due to the multidisciplinary complexity integrating agronomic and hydrological expertise. …”
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Integrating Environmental Variables into Geostatistical Interpolation: Enhancing Soil Mapping for the MEDALUS Model in Montenegro by Stefan Miletić, Jelena Beloica, Predrag Miljković

“…The results were validated by the coefficient of determination (R²) and root mean square error (RMSE). For the clay, EBKRP (empirical Bayesian kriging regression prediction) achieved R² = 0.35 and RMSE = 6.95%, for the sand, it achieved R² = 0.34 and RMSE = 17.38%, for the humus, it achieved R² = 0.50 and RMSE = 3.80%, and for the soil depth, it achieved R² = 0.76 and RMSE = 5.36 cm. …”
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STNet: Prediction of Underwater Sound Speed Profiles with an Advanced Semi-Transformer Neural Network by Wei Huang, Junpeng Lu, Jiajun Lu, Yanan Wu, Hao Zhang, Tianhe Xu

“…The proposed network architecture incorporates an optimized self-attention mechanism to effectively capture long-range temporal dependencies within historical sound velocity time-series data, facilitating an accurate estimation of current SSPs or prediction of future SSPs. …”
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Federated Learning for Surface Roughness by Kai-Lun Cheng, Yu-Hung Ting, Wen-Ren Jong, Shia-Chung Chen, Zhe-Wei Zhou

“…Data balancing techniques improved prediction accuracy, achieving performance comparable to centralized models. After optimizing the training dataset through balancing and augmentation, the federated model achieved a Root Mean Square Error (RMSE) of 0.076, which closely approaches the 0.074 RMSE obtained by the centralized model. …”
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Revolutionizing the construction industry by cutting edge artificial intelligence approaches: a review by Eliezer Zahid Gill, Daniela Cardone, Alessia Amelio

“…This study evaluates various AI and ML models, including Artificial Neural Networks (ANNs) and Support Vector Machines SVMs, as well as optimization techniques like whale and moth flame optimization. …”
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PSO Tuned Super-Twisting Sliding Mode Controller for Trajectory Tracking Control of an Articulated Robot by Zewdalem Abebaw Ayinalem, Abrham Tadesse Kassie

“…Intelligent particle swarm optimization (PSO) is employed to obtain optimal parameter values for STSMC, ensuring consistency, stability, and robustness. …”
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LSTM time series prediction of soil moisture content in kiwifruit root zone based on meteorological data fusion by Jingyuan He, Weifeng Li, Shijia Pan, Nikolaos Sygrimis, Zijie Niu, Dongyan Zhang, Dong Han, Petro A. Roussos

“…Accurate prediction of soil moisture content (SMC) is important for water resource management, irrigation optimization, and drought monitoring. Current research focuses on inverse monitoring of SMC, but management decisions based on inverse results often suffer from lags. …”
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Chemometrics to connect feedstock quality, process settings and calorific value of hydrochar through infrared spectra by Álvaro Amado-Fierro, Tim Offermans, Jeroen Jansen, Teresa A. Centeno, María A. Díez

“…The current urgent need for clean energy has sparked interest in hydrothermal carbonization (HTC) as a sustainable avenue to convert high moisture biomass wastes into an efficient bioenergy source. …”
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Resource Time Series Analysis and Forecasting in Large-Scale Virtual Clusters by Yue Lin, Jiamin Wen, Xudong Zhang, Yan Liang, Jianjiang Li

“…Recent small-batch data are employed for fine-tuning model parameters to better adapt to current data patterns. Comparative experiments are conducted between the proposed model and other baseline models, demonstrating notable improvements in Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R2 metrics by up to 35.2%, 56.1%, 32.5%, and 10.3%, respectively. …”
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