Evaluating the performance of Random Forest, Decision Tree, Support Vector Regression and Gradient Boosting for streamflow prediction by Osahon Idemudia, Jacob Odeh Ehiorobo, Christopher Osadolor Izinyon, Idowu Ilaboya

“… This study utilized a range of machine learning algorithms to predict the hourly streamflow in the Ikpoba River. …”
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Evaluating the performance of Random Forest, Decision Tree, Support Vector Regression and Gradient Boosting for streamflow prediction by Osahon Idemudia, Jacob Odeh Ehiorobo, Christopher Osadolor Izinyon, Idowu Ilaboya

“… This study utilized a range of machine learning algorithms to predict the hourly streamflow in the Ikpoba River. …”
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Enhancing Security of Proof-of-Learning Against Spoofing Attacks Using Feature-Based Model Watermarking by Ozgur Ural, Kenji Yoshigoe

“…The rapid advancement of machine learning (ML) technologies necessitates robust security frameworks to protect the integrity of ML model training processes. …”
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Sustainable strengthening of concrete deep beams with openings using ECC and Bamboo: An equation and data-driven approach through abaqus modeling and GEP by Fayiz Amin, Ijaz Ali, Ali Husnain, Muhammad Faisal Javed, Hisham Alabduljabbar, Asher Junaid

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Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithm by Anis Ben Ghorbal, Azedine Grine, Ibrahim Elbatal, Ehab M. Almetwally, Marwa M. Eid, El-Sayed M. El-Kenawy

“…NiOA is utilized to tune those parameters; as a result, the prediction accuracy is quite spectacular. …”
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MindLift: AI-powered mental health assessment for students by Shanky Goyal, RishiRaj Dutta, Saurabh Dev, Kola Narasimha Raju, Mohammed Wasim Bhatt

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Collision Detection Algorithms for Autonomous Loading Operations of LHD-Truck Systems in Unstructured Underground Mining Environments by Mingyu Lei, Pingan Peng, Liguan Wang, Yongchun Liu, Ru Lei, Chaowei Zhang, Yongqing Zhang, Ya Liu

“…This study addresses collision detection in the unmanned loading of ore from load-haul-dump (LHD) machines into mining trucks in underground metal mines. …”
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Temperature Field Prediction of Glulam Timber Connections Under Fire Hazard: A DeepONet-Based Approach by Jing Luo, Guangxin Tian, Chen Xu, Shijie Zhang, Zhen Liu

“…This research establishes a complete workflow from fundamental heat transfer analysis to efficient data generation and machine learning prediction, providing structural engineers with practical tools for the performance-based fire safety design of timber connections. …”
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Neural network analysis for predicting metrics of fragmented laminar artifacts: a case study from MPPNB sites in the Southern Levant by Eugenio Nobile, Maurizio Troiano, Fabio Mangini, Marco Mastrogiuseppe, Jacob Vardi, Fabrizio Frezza, Cecilia Conati Barbaro, Avi Gopher

“…Our suggested approach is based on readily accessible machine learning (artificial intelligence) and neural network analysis. …”
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Effect of Angle Between Center-Mounted Blades and Disc on Particle Trajectory Correction in Side-Throwing Centrifugal Spreaders by Yongtao Xie, Hongxin Liu, Jiajie Shang, Lifeng Guo, Guoxiang Zheng

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SpyKing—Privacy-preserving framework for Spiking Neural Networks by Farzad Nikfam, Alberto Marchisio, Maurizio Martina, Muhammad Shafique

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Path Planning for Fully Autonomous UAVs-A Taxonomic Review and Future Perspectives by Geeta Sharma, Sanjeev Jain, Radhe Shyam Sharma

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Enhanced Conditional GAN for High-Quality Synthetic Tabular Data Generation in Mobile-Based Cardiovascular Healthcare by Malak Alqulaity, Po Yang

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A Prediction Method for Frictional Resistance in Long-Distance Rectangular Pipe Jacking Considering Complex Contact States by Xiaoxu Tian, Zhanping Song, Kangbao Lun, Jiangsheng Xie, Peng Ma

“…In long-distance, large-section rectangular pipe jacking operations, machine deviation is an inevitable factor that poses substantial challenges to the accurate prediction of frictional resistance. …”
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Artificial intelligence in geoenergy: bridging petroleum engineering and future-oriented applications by Sungil Kim, Tea-Woo Kim, Suryeom Jo

“…Results highlight the extensive use of machine learning (ML) and deep learning (DL) for tasks such as proxy modeling, dimensionality reduction, data generation, and optimization. …”
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A comparative study of deep reinforcement learning for crop production management by Joseph Balderas, Dong Chen, Yanbo Huang, Li Wang, Ren-Cang Li

“…To ensure a fair comparison, we used consistent default parameters, identical reward functions, and the same environment settings. …”
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State-of-the-Art Deep Learning Algorithms for Internet of Things-Based Detection of Crop Pests and Diseases: A Comprehensive Review by Jean Pierre Nyakuri, Celestin Nkundineza, Omar Gatera, Kizito Nkurikiyeyezu

“…Therefore, to tackle these challenges, harnessing advanced technologies such as artificial intelligence (AI), Machine Learning/Deep Learning (ML/DL), and Internet of Things (IoT) is essential for managing and mitigating agriculture hazards. …”
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Biogas Production Using Flexible Biodigester to Foster Sustainable Livelihood Improvement in Rural Households by Charles David, Venkata Krishna Kishore Kolli, Karpagaraj Anbalagan

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ZWDX: a global zenith wet delay forecasting model using XGBoost by Laura Crocetti, Matthias Schartner, Marcus Franz Wareyka-Glaner, Konrad Schindler, Benedikt Soja

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Understanding and Addressing Variability Through Recalibration in a Brain–Computer Interface for Intelligent Vehicle Control Using Long-Term Implanted Human Neural Data: A C... by Yicong Xiao, Spencer Kellis, Christopher. F Reiche, Florian Solzbacher

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