The Fruit Recognition and Evaluation Method Based on Multi-Model Collaboration by Mingzheng Huang, Dejin Chen, Dewang Feng

“…In this study, we propose a fruit recognition and evaluation method based on multi-model collaboration. Firstly, the detection model was used to accurately locate and crop the fruit area, and then the cropped image was input into the classification module for detailed classification. …”
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Fault prediction method of large forging press based on a multi scale and multi model integrated method by Chao Yuan, Tianmin Zhang, Yuxi Tang, Huize Li, Hao Zhang, Yunhan Ling, Nan Zhang, Lidong Pan, Yeyingnan Cao, Yiqing Shi

“…To address these issues, this paper proposes a multi-scale Autoregressive-Support Vector Regression-Long Short-Term Memory (AR-SVR-LSTM) multi-model ensemble prediction approach. This method leverages the AR model, SVR model, and LSTM neural network model to predict the states of critical components in large forging presses. …”
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Heavy Precipitation Forecasts Based on Multi-model Ensemble Members by Zhi Xiefei, Zhao Chen

Subjects: “…frequency matching method…”
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Prediction method of sugarcane important phenotype data based on multi-model and multi-task. by Jihong Sun, Chen Sun, Zhaowen Li, Ye Qian, Tong Li

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TAL-SRX: an intelligent typing evaluation method for KASP primers based on multi-model fusion by Xiaojing Chen, Xiaojing Chen, Jingchao Fan, Jingchao Fan, Shen Yan, Longyu Huang, Longyu Huang, Longyu Huang, Guomin Zhou, Guomin Zhou, Jianhua Zhang, Jianhua Zhang

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Ensemble learning methods with single and multi-model deep learning approaches for cephalometric landmark annotation by S. Rashmi, S. Srinath, R. Rakshitha, B. V. Poornima

“…Abstract The study explores end-to-end deep learning frameworks and ensemble methods to enhance the accuracy of anatomical landmark identification in cephalometric radiographs, crucial for precise cephalometric analysis and effective orthodontic treatment planning. …”
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Research and implementation of text classification method for customer service orders based on multi-model fusion by Liang ZHANG, Xiaoju DAI, Rong ZHENG, Tongze He

“…Due to the large amount of order categories and their hierarchical associations, traditional manual order classification method of customer service in telecom call center has the problems of long archiving time, low efficiency and unsustainable accuracy.To solve this problem, a novel text classification algorithm based on multi-model fusion was proposed, which intelligently classify orders with multiple models based on data characteristics and their hierarchical associations, the effectiveness of this method was verified.The current manual operation process was optimized and operation efficiency was enhanced, which support the intelligent transformation and upgradation of existing customer service system.…”
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Research on load frequency control system attack detection method based on multi-model fusion by Feng Zheng, Weixun Li, Huifeng Li, Libo Yang, Zengjie Sun

“…Abstract Load frequency control (LFC) in power systems faces increasingly complex cyber-physical attack threats, while existing detection methods have limited capability to identify intelligent attacks. …”
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Uncertainties in global permafrost area extent estimates from different methods by Xuan-Jia Li, Xiao-Qing Peng, Oliver W. Frauenfeld, Guang-Shang Yang, Wei-Wei Tian, Yuan Huang, Gang Wei, Guan-Qun Chen, Cui-Cui Mu, Hao Sun

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Malicious Traffic Detection Method for Power Monitoring Systems Based on Multi-Model Fusion Stacking Ensemble Learning by Hao Zhang, Ye Liang, Yuanzhuo Li, Sihan Wang, Huimin Gong, Junkai Zhai, Hua Zhang

“…To solve these issues, this paper proposes a malicious traffic detection method based on multi-model fusion, using the stacking strategy to integrate models. …”
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Multi‐Model Assessment of PCA‐Informer Hybrid Model Against Empirical and Deep Learning Methods in TEC Forecasting by Yang Lin, Hanxian Fang, Die Duan, Ding Yang, Hongtao Huang, Chao Xiao, Ganming Ren

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A causality-based method for multi-model comparison: application to relationships between atmospheric, oceanic and marine biogeochemical variables by G. Bénard, M. Gehlen, M. Vrac

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Multi-view adversarial attack defending method for host intrusion detection by WANG Fei, QIAN Kehan, LYU Mingqi, ZHU Tiantian, CHEN Honglong

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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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Global Ionospheric TEC Forecasting for Geomagnetic Storm Time Using a Deep Learning‐Based Multi‐Model Ensemble Method by Xiaodong Ren, Pengxin Yang, Dengkui Mei, Hang Liu, Guozhen Xu, Yue Dong

“…In this study, we developed a new deep learning‐based multi‐model ensemble method (DLMEM) to forecast geomagnetic storm‐time ionospheric TEC that combines the Random Forest (RF) model, the Extreme Gradient Boosting (XGBoost) algorithm, and the Gated Recurrent Unit (GRU) network with the attention mechanism. …”
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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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Effectiveness of a multi-model teaching strategy to train emergency medicine residents to use point-of-care ultrasound (POCUS) for assessment of shock by Quanle Liu, Xintong Ma, Shuang Li, Zunjiang Li, Zhaofan Mo, Yihui Lin, Huazhen Xie, Banghan Ding

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Video Temporal Grounding with Multi-Model Collaborative Learning by Yun Tian, Xiaobo Guo, Jinsong Wang, Bin Li, Shoujun Zhou

“…Current research predominantly centers on enhancing the performance of individual models, thereby overlooking the extensive possibilities afforded by multi-model synergy. While knowledge flow methods have been adopted for multi-model and cross-modal collaborative learning, several critical concerns persist, including the unidirectional transfer of knowledge, low-quality pseudo-label generation, and gradient conflicts inherent in cooperative training. …”
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Pseudo-Labeling Domain Adaptation Using Multi-Model Learning by Victor Akihito Kamada Tomita, Ricardo Marcondes Marcacini

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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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