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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A Lightweight Barcode Detection Algorithm Based on Deep Learning by Jingchao Chen, Ning Dai, Xudong Hu, Yanhong Yuan

“…The EfficientViT block based on a linear self-attention mechanism is introduced into the backbone of the original model to enhance the model’s attention to barcode features. In the model’s neck, linear mapping and grouped convolution are used to improve the C2f module, and the ADown convolution block is utilized to modify the model’s downsampling, which reduces the model’s parameters and computational cost while improving the efficiency of model feature fusion. …”
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AnomLite: Efficient binary and multiclass video anomaly detection by Anna K. Zvereva, Mariam Kaprielova, Andrey Grabovoy

“…AnomLite is competitive due to its computational efficiency, requiring only 11 million parameters, and its robustness, achieving a ROC AUC of 0.99, Average Precision of 0.99 and F1-Score (Weighted) of 0.92 and outperforming comparable models in anomaly detection tasks. …”
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DDD++: Exploiting Density map consistency for Deep Depth estimation in indoor environments by Giovanni Pintore, Marco Agus, Alberto Signoroni, Enrico Gobbetti

“…This lightweight architecture requires fewer tunable parameters and computational resources than competing methods. …”
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Spatial-Temporal Cooperative In-Vehicle Network Intrusion Detection Method Based on Federated Learning by Liu Tao, Zhao Xiyang

“…This paper proposes a spatial-temporal collaborative intrusion detection method for IVN based on federated learning (FL), aiming to address the limitations of traditional intrusion detection methods in data privacy protection, temporal modeling, and computational efficiency. The method employs an autoencoder (AE) to achieve feature compression, reducing data dimensionality and extracting core spatial features. …”
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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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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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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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Unlocking the potential of secure communications: A comprehensive systematic review of the network design model for topology control and routing synergy in wireless sensor networks... by Taher Alzahrani, Saima Rashid

“…Dynamics are implemented for researching the behavior of network topologies in delayed parameters. The center manifold theorem applies to figure out the features of expanding recurring fluctuations. …”
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Lightweight defocus deblurring network for curved-tunnel line scanning using wide-angle lenses by Shaojie Qin, Taiyue Qi, Xiaodong Huang, Xiao Liang

“…The proposed depthwise ResBlocks significantly improves the parameter efficiency of the network. Additionally, the proposed feature refinement block captures the structurally similar features to enhance the image details, increasing the peak signal-to-noise ratio (PSNR). …”
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LP-HENN: fully homomorphic encryption accelerator with high energy efficiency by Zhuoyu Tian, Lei Chen, Shengyu Fan, Xianglong Deng, Rui Hou, Dan Meng, Mingzhe Zhang

“…The energy efficiency of LP-HENN is comparable to that of F1, the state-of-the-art ASIC FHE accelerator, while featuring a low power design suitable for edge computing.…”
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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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Temporal Convolutional Network Approach to Secure Open Charge Point Protocol (OCPP) in Electric Vehicle Charging by Ikram Benfarhat, Vik Tor Goh, Chun Lim Siow, Muhammad Sheraz, Teong Chee Chuah

“…Additionally, we conducted a comparative analysis with a hybrid model, demonstrating that the proposed TCN delivers superior performance with lower computational complexity, fewer parameters, a smaller model size, and shorter computation times. …”
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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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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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Enhanced Digital Acoustic Perception in Hearing Aid Device Using Reconfigurable Filter Bank Structure: A Systematic Review with Recommendation by Rekha Karuppaiah, Umadevi Seerengasamy, Nagajayanthi Boobalakrishnan

“…This proposed architecture features reconfigurable sub-band frequencies tailored to the user’s specific hearing loss, utilizing a Coefficient Scanning Mechanism (CSM) and Floating Point-Computation Sharing High-speed Mechanism (FP-CSHM). …”
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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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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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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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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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