Data-Driven Gas Holdup Correlation in Bubble Column Reactors Considering Alcohol Concentration and Carbon Number by Salar Helchi, Mir Mehrshad Emamshoushtari, Farshid Pajoum Shariati, Babak Bonakdarpour, Bahram Haddadi

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Parametric deep learning model for predicting bearing capacity of strip foundation via neural operator by Tongtong Niu, Maosong Huang, Jian Yu

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Improved estimation of forage nitrogen in alpine grassland by integrating Sentinel-2 and SIF data by Yongkang Zhang, Jinlong Gao, Dongmei Zhang, Tiangang Liang, Zhiwei Wang, Xuanfan Zhang, Zhanping Ma, Jinhuan Yang

“…The proposed method provides a feasible framework for the spatiotemporal prediction of the key forage growth parameters of forage and offers a theoretical basis for determining the rational utilization of grassland resources and studying the nutritional balance between grassland and livestock.…”
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Determining boride layer thicknesses formed on XC38 steel with artificial neural network by Yassine El Guerri, Bendaoud Mebarek

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Formal Verification- and AI/ML-Assisted Radio Resource Allocation for Open RAN Compliant 5G/6G Networks by Tariq Mumtaz, Shahabuddin Muhammad, Faouzi Bouali

“…The proposed RRM methodology incorporates formal verification capabilities to generate vast Pareto optimality datasets for specific RAN design parameters, establishing a foundation for rigorous RRM strategy selection. …”
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PACKETCLIP: multi-modal embedding of network traffic and language for cybersecurity reasoning by Ryozo Masukawa, Sanggeon Yun, Sungheon Jeong, Wenjun Huang, Yang Ni, Ian Bryant, Nathaniel D. Bastian, Mohsen Imani

“…With a 95% mean AUC, an 11.6% improvement over baselines, and a 92% reduction in intrusion detection training parameters, it is ideally suited for real-time anomaly detection. …”
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Robust extreme gradient boosting model for predicting the behavior of RC slabs under impact loading: key influencing factors and performance insights by Ammar Babiker

“… Abstract This study presents an advanced approach to analyzing the impact behavior of reinforced concrete (RC) slabs, utilizing an optimized extreme gradient boosting (XGB) machine learning algorithm. Supported by a comprehensive dataset of 143 records drawn from diverse sources, the methodology effectively pinpoints and evaluates critical parameters affecting the model's predictions. …”
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Analysis of Cogging Torque Ripple Based on Multiple Square-Wave Radial Superposition MMF for Wound-Field Synchronous Motors by Tingting Hou, Jiakuan Xia, Jin Chen, Meijun Qi

“…Then, the relationship between the cogging torque ripple and the structural parameters of the distributed salient-pole wound rotor is analyzed. …”
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Assessing habitat suitability for aoudad (Ammotragus lervia) reintroduction in southeastern morocco to promote ecotourism by Lahbib Naimi, El Mahi Bouziane, Lamya Benaddi, Abdeslam Jakimi, Mohamed Manaouch

“…To begin with, an extensive inventory of 88 remaining sites where these Barbary sheep still living was conducted, and precise measurements of three topographical parameters were collected at each site. Subsequently, a machine learning algorithm called Bagging was employed to develop a predictive model. …”
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Real-Time Milk Quality Control Using Multi-Spectral Sensing and Edge Computing: Advancing On-Site Detection of Milk Components with XGBoost by Mahmut Durgun

“…The collected data were processed using advanced machine learning models, where XGBoost and other regression models were assessed for their accuracy in predicting protein and fat content. …”
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Fidan: a predictive service demand model for assisting nursing home health-care robots by Feng Zhou, Xin Du, WenLi Li, Zhihui Lu, Shih-Chia Huang

“…We optimise the model parameters based on Grid Search during the training process. …”
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A Pipeline for Multivariate Time Series Forecasting of Gas Consumption in Pelletization Process by Thadeu Pezzin Melo, Jefferson Andrade, Karin Satie Komati

“…The pipeline comprises: (i) data preprocessing, (ii) converting the dataset into a tabular format using a sliding window technique, (iii) applying feature selection methods, and (iv) employing machine learning tuned via AutoML. The methodology was tested on a dataset with 45 operational parameters collected over 90 days from an industrial plant, with predictions evaluated using Root Mean Squared Error (RMSE). …”
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Modeling and Prediction of Mixed Errors in Feed Systems Based on Digital Twins by HUANG Hua, MEI Le, ZHI Xiaobo, ZHANG Huiwang

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FedQP: Large-Scale Private and Flexible Federated Query Processing by Hussain M. J. Almohri, Layne T. Watson

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XGBoost Algorithm for Cervical Cancer Risk Prediction: Multi-dimensional Feature Analysis by Sudi Suryadi, Masrizal

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Error Compensation for Dead Reckoning Based on SVM by Xin LI, Xiaoming WANG, Jianguo WU, Jiwei ZHAO, Jiacheng XIN, Kai CHEN, Bin ZHANG

“…In the use of machine learning methods for error compensation in dead reckoning of an autonomous undersea vehicle(AUV), the neural network algorithm is commonly used. …”
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Data-driven seismic mechanical performance evaluation of RC columns based on adaptive optimization ensemble learning method integrating random forest and back propagation neural ne... by Ayong Jiao, Ziqing Jiao, Bin Gao

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Soil Texture Mapping in the Permafrost Region: A Case Study on the Eastern Qinghai–Tibet Plateau by Wangping Li, Yadong Liu, Xiaodong Wu, Lin Zhao, Tonghua Wu, Guojie Hu, Defu Zou, Yongping Qiao, Xiaoying Fan, Xiaoxian Wang

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Degradation prediction of PEM water electrolyzer under constant and start-stop loads based on CNN-LSTM by Boshi Xu, Wenbiao Ma, Wenyan Wu, Yang Wang, Yang Yang, Jun Li, Xun Zhu, Qiang Liao

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Optimization of the Traceability of Perishable Products Through Light Blockchain and IoT in the Food Industry by William Villegas-Ch, Jaime Govea, Pablo Santiago Moncayo-Moncayo, Alexandra Maldonado Navarro, Carlos Chavez-Pirca

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