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

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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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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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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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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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Piecewise Linear and Nonlinear Window Functions for Modelling of Nanostructured Memristor Device by T.D. Dongale, P.J. Patil, K.P. Patil, S.B. Mullani, K.V. More, S.D. Delekar, P.K. Gaikwad, R.K. Kamat

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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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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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MRD: A Linear-Complexity Encoder for Real-Time Vehicle Detection by Kaijie Li, Xiaoci Huang

“…To address these limitations, this study introduces Mamba RT-DETR (MRD), an optimized architecture featuring three principal innovations: (1) We devise an efficient vehicle detection Mamba (EVDMamba) network that strategically integrates a linear-complexity state space model (SSM) to substantially mitigate computational overhead while preserving feature extraction efficacy. (2) To counteract the constrained receptive fields and suboptimal spatial localization associated with conventional SSM sequence modeling, we implement a multi-branch collaborative learning framework that synergistically optimizes channel dimension processing, thereby augmenting the model’s capacity to capture critical spatial dependencies. (3) Comprehensive evaluations on the BDD100K benchmark demonstrate that MRD architecture achieves a 3.1% enhancement in mean average precision (mAP) relative to state-of-the-art RT-DETR variants, while concurrently reducing parameter count by 55.7%—a dual optimization of accuracy and efficiency.…”
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YOLO-BCD: A Lightweight Multi-Module Fusion Network for Real-Time Sheep Pose Estimation by Chaojie Sun, Junguo Hu, Qingyue Wang, Chao Zhu, Lei Chen, Chunmei Shi

“…Comparative evaluations demonstrate significant improvements over baseline models, achieving 91.7% recognition accuracy with 389 FPS processing speed while maintaining 19.2% parameter reduction and 32.1% lower computational load compared to standard YOLOv8. …”
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Multi-scale friction model for automotive brake system incorporating tribological effects of surface asperities by Jung Yun Won, Hyungjo Seo, Youngjae Kim, Hoejeong Jeong, Joonseok Kyeong, Woojeong Oh, Myoung-Gyu Lee

“…The model primarily focuses on overall material behavior and micromechanical deformation within the brake system while maintaining computational efficiency, providing a mechanics-based, multi-scale representation suitable for large-scale simulations in consideration of pressure, temperature, and topological parameters. …”
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The Value of PET/CT-Based Radiomics in Predicting Adrenal Metastases in Patients with Cancer by Qiujun He, Xiangxing Kong, Xiangxi Meng, Xiuling Shen, Nan Li

“…Logistic regression analysis was employed to build models based on clinical and PET/CT routine parameters. The open-source software Python (version 3.7.11) was utilized to process the regions of interest (ROI) delineated by ITK-SNAP, extracting radiomic features. …”
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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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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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ResTreeNet: A Height-Aware LiDAR Tree Classification Model With Explainable AI for Forestry Applications by Asrat Kaleab Taye, Jeong-Mook Park, Hyung-Ju Cho, Jin-Taek Kang, Yeon-Ok Seo

“…With its lightweight architecture (requiring only 0.47 million parameters) and computational efficiency, ResTreeNet is a practical solution for large-scale ecological research, offering an innovative approach to automated forest monitoring and sustainable resource management.…”
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Single-Scene SAR Image Data Augmentation Based on SBR and GAN for Target Recognition by Shangchen Feng, Xikai Fu, Yanlin Feng, Xiaolei Lv

“…On the other hand, ray tracing simulations offer high geometric accuracy and computational efficiency but struggle with low amplitude correctness, hindering accurate numerical feature extraction. …”
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A Deep Learning Approach for Extracting Cyanobacterial Blooms in Eutrophic Lakes From Satellite Imagery by Nan Wang, Zhenyu Tan, Chen Yang, Jinge Ma, Hongtao Duan

“…MBAUNet was crafted as a lightweight network by integrating elements of MobileNetV3 and UNet, reducing learnable parameters and training time, while the introduction of spatial and channel attentions enhanced feature extraction. …”
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