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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Fusion of Multi-Task Neurophysiological Data to Enhance the Detection of Attention- Deficit/Hyperactivity Disorder by Kai-Feng Zhang, Shih-Ching Yeh, Eric Hsiao-Kuang Wu, Xiu Xu, Ho-Jung Tsai, Chun-Chuan Chen

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

“…Given an untrimmed video and a natural language query, the video temporal grounding task aims to accurately locate the target segment within the video. …”
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Pruning Bayesian networks for computationally tractable multi-model calibration by Nicolas Gratius, Mario Bergés, Burcu Akinci

“…Bayesian Networks are well suited for multi-model calibration tasks as they can be used to formulate a mathematical abstraction of model components and encode their relationship in a probabilistic and interpretable manner. …”
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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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OncoFusion: Multi-Model Approach for Generalized and Ovarian Cancer Detection with Stacked Ensembles by Zaware Sarika, Neharkar Rutuja, Sayyad Ayesha, Patil Aarya, Mulla Salma

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Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook by Amna Naseeb, Muhammad Zain, Nisar Hussain, Amna Qasim, Fiaz Ahmad, Grigori Sidorov, Alexander Gelbukh

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HTAP3 Fires: towards a multi-model, multi-pollutant study of fire impacts by C. H. Whaley, T. Butler, J. A. Adame, R. Ambulkar, R. Ambulkar, S. R. Arnold, R. R. Buchholz, B. Gaubert, D. S. Hamilton, M. Huang, H. Hung, J. W. Kaiser, J. W. Kaminski, C. Knote, G. Koren, J.-L. Kouassi, M. Lin, T. Liu, J. Ma, K. Manomaiphiboon, E. Bergas Masso, E. Bergas Masso, J. L. McCarty, M. Mertens, M. Parrington, H. Peiro, P. Saxena, S. Sonwani, V. Surapipith, D. Y. T. Tan, D. Y. T. Tan, W. Tang, V. Tanpipat, K. Tsigaridis, K. Tsigaridis, C. Wiedinmyer, O. Wild, Y. Xie, P. Zuidema

“…Coordinated under the auspices of the Task Force on Hemispheric Transport of Air Pollution, the international atmospheric modelling and fire science communities are working towards the common goal of improving global fire modelling and using this multi-model experiment to provide estimates of fire pollution for impact studies. …”
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Prediction of Student Academic Performance Utilizing a Multi-Model Fusion Approach in the Realm of Machine Learning by Wei Zou, Wei Zhong, Junzhen Du, Lingyun Yuan

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A multi-model feature fusion based transfer learning with heuristic search for copy-move video forgery detection by Hessa Alfraihi, Muhammad Swaileh A. Alzaidi, Hamed Alqahtani, Abdulbasit A. Darem, Ali M. Al-Sharafi, Ahmad A. Alzahrani, Menwa Alshammeri, Abdulwhab Alkharashi

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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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Comparing traditional natural language processing and large language models for mental health status classification: a multi-model evaluation by Thomas Kallstenius, Andrea Johansson Capusan, Gerhard Andersson, Adam Williamson

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Smart Grain Storage Solution: Integrated Deep Learning Framework for Grain Storage Monitoring and Risk Alert by Xinze Li, Wenfu Wu, Hongpeng Guo, Yunshandan Wu, Shuyao Li, Wenyue Wang, Yanhui Lu

“…In order to overcome the notable limitations of current methods for monitoring grain storage states, particularly in the early warning of potential risks and the analysis of the spatial distribution of grain temperatures within the granary, this study proposes a multi-model fusion approach based on a deep learning framework for grain storage state monitoring and risk alert. …”
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DP-AMF: Depth-Prior–Guided Adaptive Multi-Modal and Global–Local Fusion for Single-View 3D Reconstruction by Luoxi Zhang, Chun Xie, Itaru Kitahara

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Asking an AI for salary negotiation advice is a matter of concern: Controlled experimental perturbation of ChatGPT for protected and non-protected group discrimination on a context... by R Stuart Geiger, Flynn O'Sullivan, Elsie Wang, Jonathan Lo

“…Empirically, we find many reasons why ChatGPT as a multi-model platform is not robust and consistent enough to be trusted for such a task. …”
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Approximate planning in spatial search. by Marta Kryven, Suhyoun Yu, Max Kleiman-Weiner, Tomer Ullman, Joshua Tenenbaum

“…How people plan is an active area of research in cognitive science, neuroscience, and artificial intelligence. However, tasks traditionally used to study planning in the laboratory tend to be constrained to artificial environments, such as Chess and bandit problems. …”
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The Whale Optimization Algorithm and Markov Chain Monte Carlo-Based Approach for Optimizing Teacher Professional Development in Creative Learning Design with Technology by Kalliopi Rigopouli, Dimitrios Kotsifakos, Yannis Psaromiligkos

“…Finding the best possible training for creativity in learning design with technology is a complex task, as many dynamic and multi-model variables need to be taken into consideration. …”
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Multi-Modal Temporal Dynamic Graph Construction for Stock Rank Prediction by Ying Liu, Zengyu Wei, Long Chen, Cai Xu, Ziyu Guan

“…Stock rank prediction is an important and challenging task. Recently, graph-based prediction methods have emerged as a valuable approach for capturing the complex relationships between stocks. …”
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Efficient tree mapping through deep distance transform (DDT) learning by Jan Schindler, Ziyi Sun, Bing Xue, Mengjie Zhang

“…While previous methods had to combine multi-model and multi-task outputs to create decision surfaces, we developed an efficient UNet-based modelling approach which focusses solely on learning the distance transforms of tree objects as cost surface for watershed segmentation. …”
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Toward Closing the Loop in Image-to-Image Conversion in Radiotherapy: A Quality Control Tool to Predict Synthetic Computed Tomography Hounsfield Unit Accuracy by Paolo Zaffino, Ciro Benito Raggio, Adrian Thummerer, Gabriel Guterres Marmitt, Johannes Albertus Langendijk, Anna Procopio, Carlo Cosentino, Joao Seco, Antje Christin Knopf, Stefan Both, Maria Francesca Spadea

“…The proposed algorithm generates a volumetric map as an output, informing clinicians of the predicted MAE slice-by-slice. A cascading multi-model architecture was used to deal with the complexity of the MAE prediction task. …”
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