Engineering a multi model fallback system for edge devices by Gaurav Kadve, Abishi Chowdhury, Vishal Krishna Singh, Amrit Pal

“…This paper presents a novel multi-model fallback system designed for deployment on resource-constrained edge devices, leveraging the advancements of TinyML. …”
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Pruning Bayesian networks for computationally tractable multi-model calibration by Nicolas Gratius, Mario Bergés, Burcu Akinci

“…Anomaly response in aerospace systems increasingly relies on multi-model analysis in digital twins to replicate the system’s behaviors and inform decisions. …”
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A Multi-Model Ontological System for Intelligent Assistance in Laser Additive Processes by Valeriya Gribova, Yury Kulchin, Alexander Nikitin, Pavel Nikiforov, Artem Basakin, Ekaterina Kudriashova, Vadim Timchenko, Ivan Zhevtun

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ARIMA-Kriging and GWO-BiLSTM Multi-Model Coupling in Greenhouse Temperature Prediction by Wei Zhou, Shuo Liu, Junxian Guo, Na Liu, Zhenglin Li, Chang Xie

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Parallel Multi-Model Energy Demand Forecasting with Cloud Redundancy: Leveraging Trend Correction, Feature Selection, and Machine Learning by Kamran Hassanpouri Baesmat, Zeinab Farrokhi, Grzegorz Chmaj, Emma E. Regentova

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A Predictive Method for Greenhouse Soil Pore Water Electrical Conductivity Based on Multi-Model Fusion and Variable Weight Combination by Jiawei Zhao, Peng Tian, Jihong Sun, Xinrui Wang, Changjun Deng, Yunlei Yang, Haokai Zhang, Ye Qian

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Multi-model MCDM framework for sustainable renewable energy selection in India: integrating CRITIC-EDAS-CODAS-CoCoSo by Shankha Shubhra Goswami, T. Tapankumar, N. Channa Keshava Naik, J. Gowrishankar, Nilesh Bhosle, Amrita Singh, G. Sanyasi Raju, D. Nagesh, A. Johnson Santhosh

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Comparative analysis of deep learning and traditional methods for IoT botnet detection using a multi-model framework across diverse datasets by Saeed Ullah, Junsheng Wu, Zhijun Lin, Mian Muhammad Kamal, Hala Mostafa, Muhammad Sheraz, Teong Chee Chuah

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Research progress in globular fruit picking recognition algorithm based on deep learning by LI Hui, ZHANG Jun, YU Shuochen, LI Zhixin

“…When changing new environments and adding new categories, the fruit detection algorithm based on deep learning can improve the recognition ability and recognition accuracy of the model through transfer learning, data enhancement, multi-model combination, feature fusion and multi-modal data. …”
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Decentralized community-integrated research sites drive higher randomization rates: insights from a large-scale neurodegenerative disease trial by Seyed-Mahdi Khaligh-Razavi, Tyler E. Miller, Tyler E. Miller, Tyler E. Miller, Ralph Passarella, Ahmad Namvargolian

“…These findings highlight the effectiveness of a flexible, multi-model site strategy in addressing recruitment and retention challenges in large-scale Phase 3 neurodegenerative disease trials and suggest that this approach may extend to other therapeutic areas facing similar challenges.…”
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