Weather identification using models based on deep learning by Afroza Nahar, Rifat Al Mamun Rudro, Bakhtiar Atiq Faisal, Md. Faruk Abdullah Al Sohan, Laveet Kumar

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Investigating the Model Hypothesis Space: Benchmarking Automatic Model Structure Identification With a Large Model Ensemble by Diana Spieler, Niels Schütze

“…The Automatic Model Structure Identification (AMSI) method simultaneously calibrates model structural choices and model parameters, which reduces the workload of comparing different models. …”
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Innovative Ghost Channel Spatial Attention Network with Adaptive Activation for Efficient Rice Disease Identification by Yang Zhou, Yang Yang, Dongze Wang, Yuting Zhai, Haoxu Li, Yanlei Xu

“…To address the computational complexity and deployment challenges of traditional convolutional neural networks in rice disease identification, this paper proposes an efficient and lightweight model: Ghost Channel Spatial Attention ShuffleNet with Mish-ReLU Adaptive Activation Function (GCA-MiRaNet). …”
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RRBM-YOLO: Research on Efficient and Lightweight Convolutional Neural Networks for Underground Coal Gangue Identification by Yutong Wang, Ziming Kou, Cong Han, Yuchen Qin

“…Coal gangue identification is the primary step in coal flow initial screening, which mainly faces problems such as low identification efficiency, complex algorithms, and high hardware requirements. …”
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Efficient Cache Performance Equivalent 2D-Texel to Memory Mapping Identification for Embedded GPUs by Ahmed El-Mahdy, Marwa K. Elteir, Kholoud Shata

“…This paper presents, for the first time, a parameterized model capable of describing the underlying multidimensional tiling layouts governing this mapping. …”
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A Efficient Drilling Fluid Loss Location Identification Method Based on Temperature and Flow Logging by NIU Buneng, CHEN Haojun, WANG Lu, SUN Yaping, WEI Baojun, ZHAO Pan

“…By optimizing the string combination of logging tools, designing fast wellhead tools and improving the technological process, the combined logging tool can be used to carry out dynamic and static measurement in open hole wells to realize efficient identification of drilling fluid loss location. …”
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Machine learning-based identification of efficient and restrictive physiological subphenotypes in acute respiratory distress syndrome by Gabriela Meza-Fuentes, Iris Delgado, Mario Barbé, Ignacio Sánchez-Barraza, Mauricio A. Retamal, René López

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Toward Automatic Streetside Building Identification With an Integrated YOLO Model for Building Detection and a Vision Transformer for Identification by Ossama Krawi, Lavdie Rada

“…This research initiates the development of an automated Streetside Building Identification System (SBIS). Leveraging the comprehensive coverage of Google Street View images across major cities worldwide, the research integrates a YOLO model for building detection with a Vision Transformer (ViT) model for building identification, supported by Transfer Learning. …”
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HyperspectralViTs: General Hyperspectral Models for On-Board Remote Sensing by Vit Ruzicka, Andrew Markham

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System Elements Identification Method for Heat Transfer Modelling in MBSE by Patrick Jagla, Georg Jacobs, Vincent Derpa, Lukas Irnich, Gregor Höpfner, Stefan Wischmann, Joerg Berroth

“…MBSE, therefore, enhances the efficient development of complex systems by promoting model reuse in interdisciplinary architectural modelling. …”
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Efficient identification of photovoltaic cell parameters via Bayesian neural network-artificial ecosystem optimization algorithm by Bo Yang, Ruyi Zheng, Yucun Qian, Boxiao Liang, Jingbo Wang

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Identification of High-Photosynthetic-Efficiency Wheat Varieties Based on Multi-Source Remote Sensing from UAVs by Weiyi Feng, Yubin Lan, Hongjian Zhao, Zhicheng Tang, Wenyu Peng, Hailong Che, Junke Zhu

“…The ultimate goal is to differentiate high-photosynthetic-efficiency wheat varieties through model-based predictions. …”
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An improved multiscale fusion dense network with efficient multiscale attention mechanism for apple leaf disease identification by Hui LIU, Dandan DAI

“…Third, the convolution layers and bottlenecks were modified without performance degradation, reducing half of the computational load compared with the original models. Incept_EMA_DenseNet, as proposed in this paper, has an accuracy of 96.76%, being 2.93%, 3.44%, and 4.16% better than Resnet50, DenseNet_121 and GoogLeNet, respectively, proved to be reliable and beneficial, and can effectively and conveniently assist apple growers with leaf disease identification in the field.…”
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Identification of truncation frequencies for BIW structures based on modal contribution analysis by Feitan Peng, Huabin Li, Fuwu Yan, Junyi Ye

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Design of miner type identification mechanism based on reputation management model by Jiaren YU, Youliang TIAN, Hui LIN

“…In the public mining pool, miners can freely enter the mining pool to submit proof of work to obtain rewards, and there are no conditions to restrict different types of miners.There will be malicious miners submitting invalid workloads and miners not submitting workloads in the mining pool, occupying the verification computing resources of the mining pool, reducing the verification efficiency of the mining pool, and causing the mining pool system to collapse.Aiming at the problem that it is difficult to distinguish the type of miners in the mining pool, which leads to the collapse of the mining pool system, a reputation management mechanism was introduced to measure the behavior of miners, and contracts were deployed to prevent miners from colluding with the pool manager.A design of miner type identification mechanism based on reputation management model was proposed.A reputation mechanism was constructed to measure the behavior of miners.When a miner conducts malicious behavior, the miner's reputation value would be lowered.When the miner's reputation value was less than the reputation threshold of the mining pool, the system would remove the miner, so that the miner can no longer enter the mining pool to submit proof of work and get rewards.The miners in the mining pool were dynamically updated by Markov process, so that the miners in the mining pool were conducting honest behaviors and submitting proof of work.At the same time, a reward system was designed to motivate the miners in the mining pool, and smart contracts were deployed in the mining pool to prevent miners from collusion with the mining pool manager.Finally, analyzing the scheme from the perspective of security and performance, the proposed scheme was not only safe in the process of miners submitting proof of workload, but also solved the problem of identifying miner types in public mining pools, thereby solving the problem of malicious miners submitting invalid workloads, eliminating malicious miners, and avoiding mining pools verifying invalid workloads, to improve the verification efficiency of the mining pool.…”
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Incorporating Prior Information in Latent Structures Identification for Panel Data Models by Yi Li, Xingxing Luo, Mengqi Liao

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Fault Identification Model Using Convolutional Neural Networks with Transformer Architecture by Yongxin Fan, Yiming Dang, Yangming Guo

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Identification model of mine water inrush source based on XGBoost and SHAP by Bencong Kou, Tingxin Wen

“…The Sparrow Search Algorithm combines Tent chaos mapping and Levy flight strategy (CLSSA), which makes the optimization process better balance the global search ability and local search ability, so as to improve the efficiency and effect of parameter optimization. Specifically, CLSSA is used to optimize key parameters of XGBoost, including the number of weak estimators (NE), tree depth (TD), model learning rate (LR), and then establishes a mine water inrush source identification model based on the CLSSA-XGBoost. …”
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Real time weed identification with enhanced mobilevit model for mobile devices by Xiaoyan Liu, Qingru Sui, Zhihui Chen

“…Abstract Deep learning model optimization have notably enhanced weed identification accuracy. …”
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A large language model for multimodal identification of crop diseases and pests by Yiqun Wang, Fahai Wang, Wenbai Chen, Bowen Lv, Mengchen Liu, Xiangyuan Kong, Chunjiang Zhao, Zhaocen Pan

“…This ensures the precise capture and efficient identification of crop pest and disease characteristics, greatly enhancing the model’s application flexibility and accuracy in the field of pest and disease recognition. …”
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