Federated Learning for Multimodal Sentiment Analysis: Advancing Global Models With an Enhanced LinkNet Architecture by P. Vasanthi, V. Madhu Viswanatham

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UAV Target Segmentation Based on Depse Unet++ Modeling by Zhaoqi Hou, Yiqing Gu, Zhen Zheng, Yueqiang Li, Haojie Li

“…By introducing Squeeze-and-Excitation, the model’s ability to discriminate camouflaged targets in high-similarity backgrounds is improved; by incorporating a depth-separated convolutional design, the parameters and computational requirements for embedded device applications are significantly reduced; and employing Dropout technique to prevent overfitting with limited sample sizes, thus boosting the model’s adaptability and generalization across environments. …”
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DeepGenMon: A Novel Framework for Monkeypox Classification Integrating Lightweight Attention-Based Deep Learning and a Genetic Algorithm by Abdulqader M. Almars

“…Compared to the state-of-the-art (SOTA) models, DeepGenMon features a lightweight design that requires significantly lower computational resources and is easier to train with few parameters. …”
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Improved RT-DETR Framework for Railway Obstacle Detection by Peng Li, Yanhui Peng, Su-Mei Wang, Cheng Zhong

“…Building upon the RT-DETR framework, this study proposes a Multiscale Separable Deformable (MSD) module that integrates depthwise convolution with deformable convolution to enhance feature extraction capabilities while reducing computational load. …”
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Modeling Short-Term Drought for SPEI in Mainland China Using the XGBoost Model by Fanchao Zeng, Qing Gao, Lifeng Wu, Zhilong Rao, Zihan Wang, Xinjian Zhang, Fuqi Yao, Jinwei Sun

“…The CPSO-XGBoost’s superiority stems from synergistic optimization: binary particle swarm feature selection enhances input relevance while adaptive parameter tuning improves computational efficiency, collectively addressing climate variability challenges across diverse terrains. …”
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Improved YOLOv10n model for enhanced cotton recognition in complex environments by Yutao Gong, Wenwen Ding, Nenghui Huang, Tao Li, Juntao Zhou

“…To address this, this study improves the YOLOv10n model and proposes an enhanced YOLOv10n - based cotton recognition model.Firstly, the SimAM attention mechanism is integrated into the backbone network to strengthen the model's ability to focus on key features of multi - category cotton. Secondly, partial convolution (PConv) replaces standard convolution in the C2f module, creating a lightweight C2f - PConv structure to reduce computational redundancy.Test results indicate that the improved YOLOv10n model achieves 94 % detection accuracy, 89.7 % recall, and 94.8 % mAP@0.5. …”
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A Parts Detection Network for Switch Machine Parts in Complex Rail Transit Scenarios by Jiu Yong, Jianwu Dang, Wenxuan Deng

“…However, in the detection of complex rail transit switch machine parts such as augmented reality and automatic inspection, existing algorithms have problems such as insufficient feature extraction, large computational complexity, and high demand for hardware resources. …”
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State of the Art in Automated Operational Modal Identification: Algorithms, Applications, and Future Perspectives by Hasan Mostafaei, Mahdi Ghamami

“…Additionally, the review covers frequency-domain methods like Frequency Domain Decomposition (FDD) and Enhanced Frequency Domain Decomposition (EFDD), highlighting their application in spectral analysis and modal parameter extraction. Techniques based on machine learning (ML), deep learning (DL), and artificial intelligence (AI) are explored for their ability to automate feature extraction, classification, and decision making in large-scale SHM systems. …”
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CAML-PSPNet: A Medical Image Segmentation Network Based on Coordinate Attention and a Mixed Loss Function by Yuxia Li, Peng Li, Hailing Wang, Xiaomei Gong, Zhijun Fang

“…Finally, the lightweight MobilenetV2 is utilized in backbone feature extraction, which largely reduces the model’s parameter count and enhances computation speed. …”
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EGRN-YOLO: An Enhanced Multi-View Remote Sensing Detection Algorithm for Onshore Wind Turbines Based on YOLOv7 by Renzheng Xue, Haiqiang Xu, Qianlong Wu

“…Firstly, the lightweight network EfficientNetV2 is utilized as the feature extraction backbone to reduce the number of model parameters and computational load. …”
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Reservoir Stochastic Simulation Based on Octave Convolution and Multistage Generative Adversarial Network by Xuechao Wu, Wenyao Fan, Shijie Peng, Bing Qin, Qing Wang, Mingjie Li, Yang Li

“…Abstract For finely representation of complex reservoir units, higher computing overburden and lower spatial resolution are limited to traditional stochastic simulation. …”
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Multimodal bearing fault classification under variable conditions: A 1D CNN with transfer learning by Tasfiq E. Alam, Md Manjurul Ahsan, Shivakumar Raman

“…While this approach attains excellent accuracy across varied conditions, it requires more computational time due to its greater number of trainable parameters. …”
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A Corn Point Cloud Stem-Leaf Segmentation Method Based on Octree Voxelization and Region Growing by Qinzhe Zhu, Ming Yu

“…Plant phenotyping is crucial for advancing precision agriculture and modern breeding, with 3D point cloud segmentation of plant organs being essential for phenotypic parameter extraction. Nevertheless, although existing approaches maintain segmentation precision, they struggle to efficiently process complex geometric configurations and large-scale point cloud datasets, significantly increasing computational costs. …”
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Intrusion Detection Using Machine Learning for Risk Mitigation in IoT-Enabled Smart Irrigation in Smart Farming by Abhishek Raghuvanshi, Umesh Kumar Singh, Guna Sekhar Sajja, Harikumar Pallathadka, Evans Asenso, Mustafa Kamal, Abha Singh, Khongdet Phasinam

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LoG-staging: a rectal cancer staging method with LoG operator based on maximization of mutual information by Ge Zhang, Hao Dang, Qian Zuo, Zhen Tian

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Multi-Task Water Quality Colorimetric Detection Method Based on Deep Learning by Shenlan Zhang, Shaojie Wu, Liqiang Chen, Pengxin Guo, Xincheng Jiang, Hongcheng Pan, Yuhong Li

“…Subsequently, to effectively improve detection accuracy while reducing model parameters and computational load, we implemented several improvements to the deep learning algorithm, including the MGFF (Multi-Scale Grouped Feature Fusion) module, the LSKA-SPPF (Large Separable Kernel Attention-Spatial Pyramid Pooling-Fast) module, and the GNDCDH (Group Norm Detail Convolution Detection Head). …”
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Learning the factors controlling mineral dissolution in three-dimensional fracture networks: applications in geologic carbon sequestration by Aleksandra A. Pachalieva, Aleksandra A. Pachalieva, Jeffrey D. Hyman, Daniel O’Malley, Gowri Srinivasan, Hari Viswanathan

“…This study is a first step towards characterizing the parameters that control carbon mineralization using an approach with integrates computational physics and machine learning.…”
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Use of Smartphone-Based Experimental Data for the Calibration of Biodynamic Spring-Mass-Damper (SMD) Pedestrian Models by Chiara Bedon, Martina Sciomenta, Alessandro Mazelli

“…Among various walking features, the vertical reaction force that a pedestrian transfers to the supporting structure during motion is a key input for design, but results from the combination of multiple influencing parameters and dynamic interactions. …”
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A Novel Phase Error Estimation Method for TomoSAR Imaging Based on Adaptive Momentum Optimizer and Joint Criterion by Muhan Wang, Silin Gao, Xiaolan Qiu, Zhe Zhang

“…Compared to conventional phase error calibration methods in a two-step iterative framework, our proposed method considers image features and parameter coupling relationships, thus achieving higher precision estimation while saving computational costs. …”
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Distributional properties of the entropy transformed Weibull distribution and applications to various scientific fields by Tabassum Naz Sindhu, Anum Shafiq, Showkat Ahmad Lone, Qasem M. Al-Mdallal, Tahani A. Abushal

“…Some of its core characteristics, such as its statistical and computational features, are simply and clearly presented. …”
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