Tire Pressure Monitoring System Using Feature Fusion and Family of Lazy Classifiers by Arpit Pandey, Sridharan Naveen Venkatesh, Prabhakaranpillai Sreelatha Anoop, B. R. Manju, Vaithiyanathan Sugumaran

“…Empirically, the research achieved 97.92% accuracy using the local weighted learning (LWL) algorithm, demonstrating the effectiveness of combined statistical, histogram, and ARMA features in enhancing TPMS predictive capabilities.…”
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A Wi-Fi Indoor Localization Strategy Using Particle Swarm Optimization Based Artificial Neural Networks by Nan Li, Jiabin Chen, Yan Yuan, Xiaochun Tian, Yongqiang Han, Mingzhe Xia

“…Thus, with the combined strategy, we can reduce the positioning error and shorten the prediction time. We implement the proposed system on a mobile phone and the positioning results show that our algorithm can provide a higher localization accuracy and significantly improves the prediction speed.…”
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Machine vision and learning for evaluating different rancidity grades of Prunus mandshurica (Maxim.) Koehne by Yashun Wang, Huirong Chen, Jianting Gong, Yang Cui, Huiqin Zou, Yonghong Yan

“…Discrimination and prediction models based on color features combined with multiple machine learning algorithms were established using 10-fold cross-validation and external test set validation. …”
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Data-Driven and Mechanistic Soil Modeling for Precision Fertilization Management in Cotton by Miltiadis Iatrou, Panagiotis Tziachris, Fotis Bilias, Panagiotis Kekelis, Christos Pavlakis, Aphrodite Theofilidou, Ioannis Papadopoulos, Georgios Strouthopoulos, Georgios Giannopoulos, Dimitrios Arampatzis, Evangelos Vergos, Christos Karydas, Dimitris Beslemes, Vassilis Aschonitis

“…By comparing the Mean Absolute Error (MAE) between predicted and observed cotton yield values across three ML algorithms, i.e., Random Forest (RF), XGBoost, and LightGBM, the RF model achieved the lowest error (422.6 kg/ha), outperforming XGBoost (446 kg/ha) and LightGBM (449 kg/ha). …”
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Aerodynamic Optimization of Morphing Airfoil by PCA and Optimization-Guided Data Augmentation by Ao Guo, Jing Wang, Miao Zhang, Han Wang

“…A Multi-Island Genetic Algorithm (MIGA) efficiently explores the reduced design space, while iterative retraining of the surrogate model enhances prediction accuracy, particularly in high-performance regions. …”
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Grassland management and phenology affect trait retrieval accuracy from remote sensing observations by Maksim Iakunin, Franziska Taubert, Reimund Goss, Severin Sasso, Hannes Feilhauer, Daniel Doktor

“…This study combines radiative transfer model (RTM) and machine learning algorithms to assess the efficacy of the model inversion in predicting plant functional traits under different grassland management regimes. …”
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Adaptively Receding Galerkin Optimal Control for a Nonlinear Boiler-Turbine Unit by Gang Zhao, Zhi-gang Su, Jun Zhan, Hongxia Zhu, Ming Zhao

“…With the help of a mathematical predictive model, optimal control law is then obtained based on a Galerkin optimization algorithm. …”
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Using random forests to forecast daily extreme sea level occurrences at the Baltic Coast by K. Bellinghausen, B. Hünicke, E. Zorita

“…<p>We have designed a machine learning method to predict the occurrence of daily extreme sea level at the Baltic Sea coast with lead times of a few days. …”
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Applying interpretable machine learning to assess intraspecific trait divergence under landscape‐scale population differentiation by Sambadi Majumder, Chase M. Mason

“…Methods Recursive feature elimination was applied to functional trait data from the HeliantHOME database, followed by the application of the Boruta algorithm to detect the traits that are most predictive of ecoregion. …”
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Traffic congestion forecasting using machine learning methods by Ramil R. Zagidullin, Almaz N. Khaybullin

“…To identify patterns in the data, additive time series decomposition, spectral analysis based on the fast Fourier transform, and autocorrelation analysis were applied. The predictive model was implemented using a two-stage approach: the classical ARIMA algorithm was used for baseline forecasting, while an LSTM architecture with two recurrent layers and regularization was trained on 24-hour sequences. …”
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An Adaptive Evolutionary Causal Dynamic Factor Model by Qian Wei, Heng-Guo Zhang

“…Results: The experimental results show that the AcNowcasting algorithm can extract common factors that reflect macroeconomic fluctuations better, and the prediction accuracy of the AcNowcasting algorithm is more accurate than that of traditional nowcasting models. …”
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Analyzing Student Graduation and Dropout Patterns Using Artificial Intelligence and Survival Strategies by Behrouz Alefy, Vahid Babazadeh

“…The project explores approaches targeted at improving dropout survival rates and utilizing predictive modeling techniques to assess critical student outcomes, including graduation and dropout. …”
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Full Information Numerical Simulation of Two-Dimensional Steady Ground Temperature Field by Fan Jiang, Shikun Dai, Ying Zhang, Hongjun Tian

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An Intelligent System for Medical Oxygen Consumption Management Using Oximetry and Barometry by Abbas Izadi, Hadi Ghasemifard, Mohamad Amin Bakhshali, Nadia Roudsarabi, Omid Sarrafzadeh

“…The system also utilizes an artificial neural network model to predict future oxygen consumption levels, enabling proactive supply chain management. …”
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Research on Mechanical Properties of Steel Tube Concrete Columns Reinforced with Steel–Basalt Hybrid Fibers Based on Experiment and Machine Learning by Bohao Zhang, Xiao Xu, Wenxiu Hao

“…On the basis of the experiments, a parametric expansion analysis of several structural parameters of the specimen was carried out by using ABAQUS finite element software, and a combined model NRBO-XGBoost, based on the Newton-Raphson optimization algorithm (NRBO), and the advanced machine learning model XGBoost was proposed for the prediction of the BSFCFST’s ultimate carrying capacity. …”
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Time series forecasting of Valley fever infection in Maricopa County, AZ using LSTMResearch in context by Xueting Jin, Fangwu Wei, Srinivasa Srivatsav Kandala, Tejas Umesh, Kayleigh Steele, John N. Galgiani, Manfred D. Laubichler

“…Two models with different lengths of forecasting periods, 10 days and 30 days, are identified with good prediction. Interpretation: LSTM algorithms, combined with traditional statistical methods, could help with the forecasting of CM cases. …”
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Intelligent Grinding System for Medium/Thick Plate Welding Seams in Construction Machinery Using 3D Laser Measurement and Deep Learning by Qifeng Liu, Rencheng Zheng, Pengchao Li, Chao Liu, Deyuan Mi, Jian Wang, Wenli Xie

“…Experiments were conducted to verify the feasibility and accuracy of the 3D line laser measurement technology for weld seam inspections, and that the deep learning algorithm can effectively identify the type and location of the weld seam, thus predicting the trimming strategy. …”
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The influence of pH and temperature on benthic chlorophyll-a: Insights from SHAP-XGBoost and random forest models by Sangar Khan, Noël P.D. Juvigny-Khenafou, Tatenda Dalu, Paul J. Milham, Yasir Hamid, Kamel Mohamed Eltohamy, Habib Ullah, Bahman Jabbarian Amiri, Hao Chen, Naicheng Wu

“…There is little information on machine learning predictive models of benthic chl–a and input parameters in lotic ecosystems, and to fill this gap, we predict benthic chl–a levels in China's Thousand Islands Lake (TIL) watershed using machine learning algorithms. …”
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End-Point Static Control of Basic Oxygen Furnace (BOF) Steelmaking Based on Wavelet Transform Weighted Twin Support Vector Regression by Chuang Gao, Minggang Shen, Xiaoping Liu, Lidong Wang, Maoxiang Chu

“…Finally, the results of proposed prediction models show that the prediction error bound with 0.005% in carbon content and 10°C in temperature can achieve a hit rate of 92% and 96%, respectively. …”
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Statistics release and privacy protection method of location big data based on deep learning by Yan YAN, Yiming CONG, Mahmood Adnan, Quanzheng SHENG

“…Aiming at the problems of the unreasonable structure and the low efficiency of the traditional statistical partition and publishing of location big data, a deep learning-based statistical partition structure prediction method and a differential publishing method were proposed to enhance the efficacy of the partition algorithm and improve the availability of the published location big data.Firstly, the two-dimensional space was intelligently partitioned and merged from the bottom to the top to construct a reasonable partition structure.Subsequently, the partition structure matrices were organized as a three-dimensional spatio-temporal sequence, and the spatio-temporal characteristics were extracted via the deep learning model in a bid to realize the prediction of the partition structure.Finally, the differential privacy budget allocation and Laplace noise addition were implemented on the prediction partition structure to realize the privacy protection of the statistical partition and publishing of location big data.Experimental comparison of the real location big data sets proves the advantages of the proposed method in improving the querying accuracy of the published location big data and the execution efficiency of the publishing algorithm.…”
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