White Blood Cell Detection Based on FBDM-YOLOv8s by Borui Sun, Xiangsuo Fan, Jie Meng, Jinfeng Wang, Huajin Chen, Lei Liu

“…Additionally, to attain diversified feature extraction, this article introduces the DBBNCSPELAN4 module, which can perform diversified feature extraction in a multi-branch manner while lightweighting the model, reducing the number of parameters, and speeding up computation. …”
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Integrating Multiscale Spatial–Spectral Shuffling Convolution With 3-D Lightweight Transformer for Hyperspectral Image Classification by Qinggang Wu, Mengkun He, Qiqiang Chen, Le Sun, Chao Ma

“…The combination of convolutional neural networks and vision transformers has garnered considerable attention in hyperspectral image (HSI) classification due to their abilities to enhance the classification accuracy by concurrently extracting local and global features. However, these accuracy improvements come at the cost of significant demands on storage resources, computational overhead, and extensive training samples. …”
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Analysis of COVID-19 Disease Model: Backward Bifurcation and Impact of Pharmaceutical and Nonpharmaceutical Interventions by Ibad Ullah, Nigar Ali, Ihtisham Ul Haq, Imtiaz Ahmad, Mohammed Daher Albalwi, Md. Haider Ali Biswas

“…An analysis of the model’s qualitative features was conducted, encompassing the computation of the fundamental reproduction number, R0. …”
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An enhanced moth flame optimization extreme learning machines hybrid model for predicting CO2 emissions by Ahmed Ramdan Almaqtouf Algwil, Wagdi M. S. Khalifa

“…Feature importance analysis highlighted economic growth, foreign direct investment, and renewable energy as key predictors. …”
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Optimizing Gammatone Cepstral Coefficients for Gear Fault Detection by Zrar Kh Abdul, Abdulbasit K. Al-Talabani, Wisam Hazim Gwad, Entisar Alkayal, Halgurd S. Maghdid, Safar Maghdid Asaad

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Research on Fabric Defect Detection Algorithm Based on Lightweight YOLOv7-Tiny by Tang Li, Mei Shunqi, Shi Yishan, Zhou Shi, Zheng Quan, Hongkai Jiang, Xu Qiao, Zhang Zhiming

“…The Ghost convolution module is also incorporated to reduce computation and model parameters. The lightweight upsampling technique CARAFE facilitates the flexible extraction of deep features, coupled with their integration with shallow features. …”
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Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine by Debendra Muduli, Rani Kumari, Adnan Akhunzada, Korhan Cengiz, Santosh Kumar Sharma, Rakesh Ranjan Kumar, Dinesh Kumar Sah

“…We proposed learning technique called fast discrete curvelet transform with wrapping (FDCT-WRP) to create feature set. This method is entitled extracting curve-like features and creating a feature set. …”
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High-precision segmentation and quantification of tunnel lining crack using an improved DeepLabV3+ by Zhutian Pan, Xuepeng Zhang, Yujing Jiang, Bo Li, Naser Golsanami, Hang Su, Yue Cai

“…The amount of EfficientNetV2 block computation is reduced and a self-designed shallow feature fusion module is used to merge the layers to enhance parameter utilization efficiency. …”
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Advanced lightweight deep learning vision framework for efficient pavement damage identification by Shuai Dong, Yunlong Wang, Jin Cao, Jia Ma, Yang Chen, Xin Kang

“…Initially, a lightweight feature extraction network, FasterNet, is adopted to reduce the number of parameters and computational complexity. …”
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MDIGCNet: Multidirectional Information-Guided Contextual Network for Infrared Small Target Detection by Luping Zhang, Junhai Luo, Yian Huang, Fengyi Wu, Xingye Cui, Zhenming Peng

“…Furthermore, since both IDConv and MGDC are parallel multiconvolutional kernel structures, reparameterization techniques are used to avoid excessive parameters and computational load. Experimental results on public datasets NUDT-SIRST, IRSTD-1k, and SIRST-Aug demonstrate that our algorithm outperforms other state-of-the-art methods in detection performance.…”
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LMFUNet: A Lightweight Multi-fusion UNet Based on Spiking Neural Systems for Skin Lesion Segmentation by Ningkang Hu, Bing Li, Hong Peng, Zhicai Liu, Jun Wang

“…To cope with this problem, we propose a lightweight multi-fusion network (LMFUNet) with parameters of only 0.100M and GFLOPs of 0.106. LMFUNet uses an Efficient Multi-scale Feature Extraction block (EMFE) in deep stages, which uses grouping of features by convolution with different dilation rates to reduce model complexity and effectively capture multi-scale features. …”
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Stress Concentration Modelling in Internal Stiffeners of Ship-to-Shore Quay Cranes Legs Due to Structural Heightening by José Roberto Castillo Rivera, María Inmaculada Rodríguez-García, María Gema Carrasco-García, Ignacio J. Turias

“…This paper presents a study on the modelling and estimation of stress concentration at the tips of leg stiffeners in ship-to-shore (STS) quay cranes, which is intensified in those on the sea-side leg extensions, which are more prone to crack formation, notably following structural heightening of the cranes. A computer-simulated database was generated, incorporating mechanical parameters and geometric features that impact stress concentration. …”
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Improvement of RT-DETR model for ground glass pulmonary nodule detection. by Siyuan Tang, Qiangqiang Bao, Qingyu Ji, Tong Wang, Naiyu Wang, Min Yang, Yu Gu, Jinliang Zhao, Yuhan Qu, Siriguleng Wang

“…To obtain a more lightweight model, modules are designed for smaller number of parameters and higher computational efficiency. Model are tested on mixed dataset composed of LIDC-IDRI data and clinical data from cooperating hospitals. …”
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Mechanical Properties of Shale-Reservoir Rocks Based on Stress–Strain Curves and Mineral Content by Wen-Tie Sun, Zhong-Hui Li, Yi-Shan Lou, Liang Zhu, Hui-Mei Wu, Arnaud Regis Kamgue Lenwoue, Qin Liu

“…The results showed that (1) the difference in mineral composition leads to easier fracturing of sand shale reservoirs compared with pure shale reservoir. (2) Under uniaxial conditions, the rock mechanical parameters along the vertical bedding direction of sand shale reservoirs are better than pure shale reservoir parameters. …”
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YOLO-DAFS: A Composite-Enhanced Underwater Object Detection Algorithm by Shengfu Luo, Chao Dong, Guixin Dong, Rongmin Chen, Bing Zheng, Ming Xiang, Peng Zhang, Zhanwei Li

“…It remains lightweight, with 6.5 M parameters and a computational cost of 7.1 GFLOPs.…”
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YOLOv8-DBW: An Improved YOLOv8-Based Algorithm for Maize Leaf Diseases and Pests Detection by Xiang Gan, Shukun Cao, Jin Wang, Yu Wang, Xu Hou

“…Based on the original YOLOv8n, the algorithm replaced the Conv module with the DSConv module in the backbone network, which reduced the backbone network parameters and computational load and improved the detection accuracy at the same time. …”
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A deep learning-based algorithm for the detection of personal protective equipment. by Bo Tong, Guan Li, Xiangli Bu, Yang Wang, Xingchen Yu

“…Additionally, structured pruning techniques were applied to the model at varying levels, further reducing computational and parameter loads. Experimental results indicate that at a pruning level of 1.5, mAP@0.5 and mAP@0.5:0.95 improved by 3.9% and 4.6%, respectively, while computational load decreased by 21% and parameter count dropped by 53%. …”
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Instinctive Recognition of Pathogens in Rice Using Reformed Fractional Differential Segmentation and Innovative Fuzzy Logic-Based Probabilistic Neural Network by Anusha Preetham, Sayed Sayeed Ahmad, Ihab Wattar, Pooja Singh, Sandeep Rout, Mejdal A. Alqahtani, Enoch Tetteh Amoatey

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Applying the Least Squares Approach to Solve Fredholm Fuzzy Fractional Integro-Differential Equations by Saif Aldeen M. Jameel, Sharmila karim, Ali Fareed Jameel

“…A new form of LSM is changed to FLSM includes an extra feature known as the convergence control points parameters, regarded as one of its most powerful tools that constructed from the use of some concepts of fuzzy set theory and fractional calculus. …”
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EHC-GCN: Efficient Hierarchical Co-Occurrence Graph Convolution Network for Skeleton-Based Action Recognition by Ying Bai, Dongsheng Yang, Jing Xu, Lei Xu, Hongliang Wang

“…Secondly, we introduce depth-wise separable convolution layers to reduce the model parameters. Additionally, we apply a two-stream branch and attention mechanism to further extract discriminative features. …”
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