Surrogate Modeling for Building Design: Energy and Cost Prediction Compared to Simulation-Based Methods by Navid Shirzadi, Dominic Lau, Meli Stylianou

“…To enhance model interpretability, SHapley Additive exPlanations (SHAP) analysis is used to quantify feature importance, revealing how different model types prioritize design parameters. …”
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S₂Head: Small-Scale Human Head Detection Algorithm by Improved YOLOv8n Architecture by Yuteng Sui, Xinghua Shan, Linlin Dai, Hui Jing, Bo Li, Jianjun Ma

“…Additionally, a small object detection branch and a reparameterizable BiFPN (Rep-BiFPN) structure are incorporated into the neck to improve the model’s sensitivity to small-scale features. Finally, a lightweight MSBlock is also integrated into the head to reduce computational overhead and parameter count without sacrificing detection accuracy. …”
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Development of a Sound Quality Evaluation Model Based on an Optimal Analytic Wavelet Transform and an Artificial Neural Network by Mehdi POURSEIEDREZAEI, Ali LOGHMANI, Mehdi KESHMIRI

“…The feature matrix is fed into the neural network input to determine the psychoacoustic parameters used for sound quality evaluation. …”
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ACM-YOLOv10: Research on Classroom Learning Behavior Recognition Algorithm Based on Improved YOLOv10 by Beichen Qin, Haoyan Hu, Shaowen Du

“…This module, with its lightweight design, optimizes convolution operations to reduce computational complexity and parameter quantity, thereby accelerating the model’s inference speed. …”
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Sustainable energy: Advancing wind power forecasting with grey wolf optimization and GRU models by Zainab Al-Ibraheemi, Samaher Al-Janabi

“…Programming challenges include high computational demands and the trial-and-error nature of parameter determination in deep learning, mitigated by using GWO. …”
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Orga-Dete: An Improved Lightweight Deep Learning Model for Lung Organoid Detection and Classification by Xuan Huang, Qin Gao, Hanwen Zhang, Fuhong Min, Dong Li, Gangyin Luo

“…However, their morphological analysis remains hindered by manual detection inefficiencies and the high computational cost of existing algorithms. To overcome these challenges, this study proposes Orga-Dete—a lightweight, high-precision detection model based on YOLOv11n—which first employs data augmentation to mitigate the small-scale dataset and class imbalance issues, then optimizes via a triple co-optimization strategy: a bi-directional feature pyramid network for enhanced multi-scale feature fusion, MPCA for stronger micro-organoid feature response, and EMASlideLoss to address class imbalance. …”
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Prediction and evaluation of environmental quality for nursing sow buildings via multisource sensor information fusion by Chong Chen, Xingqiao Liu, Chaoji Liu, Chengyang Yu

“…The Random Forest (RF) model was selected for the feature selection. There were six feature factors that were closely related to environmental quality, including temperature, relative humidity, concentrations of NH3, CO2,H2S and air speed. …”
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Research on the lightweight detection method of rail internal damage based on improved YOLOv8 by Xiaochun Wu, Shuzhan Yu

“…Firstly, the GhostHGNetV2 network is adopted as the feature extraction backbone, which reduces computational costs through structural optimization. …”
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A lightweight high-frequency mamba network for image super-resolution by Tao Wu, Wei Xu, Yajuan Wu

“…It can better incorporate local and global information and has linear complexity in the global feature extraction branch. Experiments on multiple benchmark datasets demonstrate that the network outforms recent SOTA methods in SISR while using fewer parameters. …”
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MTMU: Multi-domain Transformation based Mamba-UNet designed for unruptured intracranial aneurysm segmentation by Bing Li, Nian Liu, Jianbin Bai, Jianfeng Xu, Yi Tang, Yan Liu

“…It endows the model with the capability of long-range dependency perceiving while balancing computational cost. Fourier Transform (FT) based connection allows for the enhancement of edge information in feature maps, thereby mitigating the difficulties in feature extraction caused by the small size of the target and the limited number of foreground pixels. …”
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Research on SeaTreasure Target Detection Technology Based on Improved YOLOv7-Tiny by Xiang Shi, Yunli Zhao, Jinrong Guo, Yan Liu, Yongqi Zhang

“…First, based on the YOLOv7-Tiny network, the MAFPN neck structure is used to replace the ELAN structure to achieve the multi-scale capture of semantic information of underwater sea treasures, and to enhance the UPA-YOLO model to accurately locate the targets of underwater sea treasures; second, the P2ELAN module is constructed and added to the backbone network, which makes use of the redundancy information in the feature map and dynamically adjusts the convolution kernel to adapt to data The P2ELAN module is added to the backbone network, using the redundant information in the feature map, dynamically adjusting the convolutional kernel to adapt to the lack of data, reducing the number of parameters in the model, and introducing the MSCA attention mechanism to inhibit the complex and changeable background features underwater, to improve the semantic feature extraction ability of the UPA-YOLO model for underwater targets, adding the MPDiou loss function to the improved algorithm model and completing the data validation of the detection model; finally, based on the TensorRT acceleration framework, the optimisation of the target detection Finally, based on the TensorRT acceleration framework, the target detection model is optimised, and the Jetson Nano edge device is used to complete the localisation deployment and realise the real-time target detection task of underwater sea treasures. …”
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LHB-YOLOv8: An Optimized YOLOv8 Network for Complex Background Drop Stone Detection by Anjun Yu, Hongrui Fan, Yonghua Xiong, Longsheng Wei, Jinhua She

“…Finally, a bidirectional feature pyramid network (BiFPN) is introduced in the neck to effectively reduce the parameters and computational complexity and improve the overall performance of rockfall detection. …”
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An advanced deep learning method for pepper diseases and pests detection by Xuewei Wang, Jun Liu, Qian Chen

“…Built upon YOLOv10n, YOLO-Pepper incorporates four major innovations: (1) an Adaptive Multi-Scale Feature Extraction (AMSFE) module that improves feature capture through multi-branch convolutions; (2) a Dynamic Feature Pyramid Network (DFPN) enabling context-aware feature fusion; (3) a specialized Small Detection Head (SDH) tailored for minute targets; and (4) an Inner-CIoU loss function that enhances localization accuracy by 18% compared to standard CIoU. …”
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Dynamic emotion intensity estimation from physiological signals facilitating interpretation via appraisal theory. by Isabel Barradas, Reinhard Tschiesner, Angelika Peer

“…In our research, we estimate emotion intensity from multiple physiological features associated to the CPM's neurophysiological component using dynamical models with the aim of bringing insights into the relationship between physiological dynamics and perceived emotion intensity. …”
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A fiber channel modeling method based on complex neural networks by Haifeng Yang, Yongjun Wang, Chao Li, Lu Han, Qi Zhang, Xiangjun Xin

“…To address this limitation, we propose a complex-valued conditional generative adversarial network (C-CGAN) in this paper to comprehensively learn channel features. We describe the architecture and parameters of the C-CGAN and employ complex-valued windowed construction for input data. …”
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Multi-step depth enhancement refine network with multi-view stereo. by Yuxuan Ding, Kefeng Li, Guangyuan Zhang, Zhenfang Zhu, Peng Wang, Zhenfei Wang, Chen Fu, Guangchen Li, Ke Pan

“…The MSDER-MVS network leverages the potent capabilities of modern deep learning in conjunction with the geometric intuition of traditional 3D reconstruction techniques, with a particular focus on optimizing the quality of the depth map and the efficiency of the reconstruction process.Our key innovations include a dual-branch fusion structure and a Feature Pyramid Network (FPN) to effectively extract and integrate multi-scale features. …”
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Lightweight YOLOv8 for tongue teeth marks and fissures detection based on C2f_DCNv3 by Chunyang Jin, Delong Zhang, Xiyuan Cao, Zhidong Zhang, Chenyang Xue, Yanjun Zhang

“…Additionally, the model reduces computational cost by approximately one-third in terms of FLOPS, maintaining high accuracy while greatly decreasing the number of parameters, thus offering a more robust and resource-efficient solution. …”
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Hybrid Capsule Network for precise and interpretable detection of malaria parasites in blood smear images by Bader Alawfi

“…Existing convolutional neural network (CNN) and capsule network hybrids, although effective, often suffer from high computational demands and limited generalizability across datasets.MethodsWe propose Hybrid Capsule Network (Hybrid CapNet), a lightweight architecture combining CNN-based feature extraction with dynamic capsule routing for accurate parasite identification and life-cycle stage classification. …”
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RADNet: Adaptive Spatial-Dilation Learning for Efficient Road Crack Detection by Kehao Du, Yifan Dai

“…Experiments on the RDD 2022 dataset demonstrate that RADNet achieves state-of-the-art performance with <inline-formula> <tex-math notation="LaTeX">$86.6\%$ </tex-math></inline-formula> precision, 78.7% recall, and 83.8% mAP50, while maintaining a lightweight architecture of 2.47M parameters and 9.4 GFLOPs at 370 FPS. Ablation studies show that the integration of ASDown and C2f-MSD significantly enhances both detection accuracy and computational efficiency, improving mAP50 by 5.6% over the YOLOv8n baseline while reducing computational complexity.…”
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IMViT: Adjacency Matrix-Based Lightweight Plain Vision Transformer by Qihao Chen, Yunfeng Yan, Xianbo Wang, Jishen Peng

“…Second, we design a multi-receptive attention and interaction mechanism to perceive global and local correlations of the images in every transformer block for effective feature learning for small-sized networks. Extensive experiments show that the proposed lightweight IMViT-B outperforms DeiTIII, this paper IMViT-B(300 epochs) achieves a top accuracy of <inline-formula> <tex-math notation="LaTeX">$82.8~\%$ </tex-math></inline-formula> on ImageNet-1K with only 26M parameters, surpasses the DeiTIII-S(800 epochs) +1.4%, with a similar number of parameters and computation cost. …”
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