Deep learning-based approach for extracting inflorescence morphology features in cut chrysanthemum by Shanpeng Xu, Jingshan Lu, Yin Wu, Huahao Liu, Fadi Chen, Fei Zhang, Sumei Chen, Weimin Fang, Zhiyong Guan

“…These results demonstrate a scalable and efficient solution for floral trait analysis, supporting high-throughput phenotyping in ornamental horticulture.…”
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GC-Like LDPC Code Construction and its NN-Aided Decoder Implementation by Yu-Lun Hsu, Li-Wei Liu, Yen-Chin Liao, Hsie-Chia Chang

“…Different from existing model-driven methodologies only suitable for short codes, a Globally-Coupled Like (GC-like) LDPC code construction is presented to enable efficient training with simplified neural networks for longer LDPC codes. …”
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Dry fruit image classification using stacking ensemble model by Maheen Islam, Mujahidul Islam, Alfe Suny, Abdullah Al Rafi, Abdullahi Chowdhury, Mohammad Manzurul Islam, Saleh Masum, Md Sawkat Ali, Taskeed Jabid, Md Mostofa Kamal Rasel

“…Precise and efficient classification of dry fruit images is critical for enhancing quality control, efficiency, and safety in the agricultural and food industries. …”
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VGG-MFO-orange for sweetness prediction of Linhai mandarin oranges by Chun Fang, Runhong Shen, Meiling Yuan, ZhengXu, Wangyi Ye, Sheng Dai, Di Wang

“…Therefore, our model can provide an efficient means of fruit grading for agricultural production, contribute to agricultural modernization, and enhance the competitiveness of agricultural products in the market.…”
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Physics-inspired time-frequency feature extraction and lightweight neural network for power quality disturbance classification by Zhiwen Hou, Boyu Wang, Jingrui Liu, Yumeng He, Yuxuan Yao

“…Compared to other state-of-the-art models, PowerMobileNet outperforms KELM (97.4%), SqueezeNet (99.0%), ShuffleNet V2 (98.6%), and AlexNet (98.3%) in terms of classification accuracy. …”
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STar-DETR: A Lightweight Real-Time Detection Transformer for Space Targets in Optical Sensor Systems by Yao Xiao, Yang Guo, Qinghao Pang, Xu Yang, Zhengxu Zhao, Xianlong Yin

“…Second, group shuffle convolution (GSConv) is incorporated into the efficient hybrid encoder, which reduces convolution parameters while facilitating information exchange between channels. …”
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A Fault Diagnosis Model for Rotating Machinery Using VWC and MSFLA-SVM Based on Vibration Signal Analysis by Lei You, Wenjie Fan, Zongwen Li, Ying Liang, Miao Fang, Jin Wang

“…As demonstrated by the results, the VWC method is efficient in extracting vibration signal features of rotating machinery. …”
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Hand–Eye Separation-Based First-Frame Positioning and Follower Tracking Method for Perforating Robotic Arm by Handuo Zhang, Jun Guo, Chunyan Xu, Bin Zhang

“…The vision arm (“eye”) provides real-time position data to the drilling arm (“hand”), ensuring accurate and efficient operation. The study employs an RFBNet model for initial frame localization, replacing the original VGG16 backbone with ShuffleNet V2. …”
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NRGS-Net: A Lightweight Uformer with Gated Positional and Local Context Attention for Nighttime Road Glare Suppression by Ruoyu Yang, Huaixin Chen, Sijie Luo, Zhixi Wang

“…The upsampling layers incorporate a residual PixelShuffle module to achieve effective restoration in glare-affected regions. …”
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A lightweight and optimized deep learning model for detecting banana bunches and stalks in autonomous harvesting vehicles by Duc Tai Nguyen, Phuoc Bao Long Do, Doan Dang Khoa Nguyen, Wei-Chih Lin

“…Specifically, the standard convolution layers are upgraded with a lightweight group-shuffle convolution module, reducing complexity while preserving efficiency. …”
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Deep learning-based automatic diagnosis of rice leaf diseases using ensemble CNN models by Prameetha Pai, S. Amutha, Seema Patil, T. Shobha, Mustafa Basthikodi, B. M. Ahamed Shafeeq, Ananth Prabhu Gurpur

“…We evaluated seven advanced deep learning architectures—MobileNetV2, GoogLeNet, EfficientNet, ResNet-34, DenseNet-121, VGG16, and ShuffleNetV2—across a range of performance metrics including precision, recall, and overall diagnostic accuracy. …”
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LightYOLO: Lightweight model based on YOLOv8n for defect detection of ultrasonically welded wire terminations by Jianshu Xu, Lun Zhao, Yu Ren, Zhigang Li, Zeshan Abbas, Lan Zhang, Md Shafiqul Islam

“…Secondly, Group-Shuffle Convolution (GSConv) is used to construct the feature fusion structure of the neck, which enhances the fusion efficiency of multi-level features. …”
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Advancing blood cell detection and classification: performance evaluation of modern deep learning models by Shilpa Choudhary, Sandeep Kumar, Pammi Sri Siddhaarth, Guntu Charitasri, Monali Gulhane, Nitin Rakesh, Feslin Anish Mon, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene

“…In terms of real-time performance, YOLOv10 outperforms other object detection models with better detection rates and classification accuracy. But MobileNetV2 and ShuffleNetV2 are more computationally efficient, which becomes more appropriate for resource-constrained environments. …”
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FIDC-YOLO: Improved YOLO for Detecting Pine Wilt Disease in UAV Remote Sensing Images via Feature Interaction and Dependency Capturing by Zekun Xu, Yipeng Zhou, Shiting Wen, Weipeng Jing

“…First, to effectively extract the discriminative features of PWD targets, the shuffle efficient layer aggregation network is proposed to promote information interaction between features, improving the model’s learning capability. …”
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Crop-Free-Ridge Navigation Line Recognition Based on the Lightweight Structure Improvement of YOLOv8 by Runyi Lv, Jianping Hu, Tengfei Zhang, Xinxin Chen, Wei Liu

“…First, this method reduces the parameters and computational complexity of the model by replacing the YOLOv8 backbone network with MobileNetV4 and the feature extraction module C2f with ShuffleNetV2, thereby improving the real-time segmentation of crop-free ridges. …”
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An intelligent non-destructive method to identify the quality of self-compacting concrete based on convolutional neural networks via image recognition by Zhong Xiao, Zixuan Liu, Xuying Guo, Jun Liu

“…In order to correlate the image recognition of SCC with its characteristics, the fluidity, compressive strength and chloride ion diffusion coefficient of SCC were tested while images of SCC were acquired using a smartphone. The efficiency of CNN models including GoogleNet, ResNet18, ResNet34, ResNet50 and ShuffleNet were compared in terms of prediction accuracy and computation time, and the results demonstrated superiority of the ResNet18 model over all competing models. …”
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Assessment of using transfer learning with different classifiers in hypodontia diagnosis by Tansel Uyar, Didem Sakaryalı Uyar

“…Pretrained convolutional neural network models (AlexNet, DarkNet-19, DarkNet-53, DenseNet-201, EfficientNet, GoogLeNet, InceptionV3, IncResV2, MobileNetV2, NasNet-Mobile, Places365, ResNet-18, ResNet-50, ResNet-101, ShuffleNet, SqueezeNet, VGG-16, VGG-19, and Xception) were used for training with the fine-tuning method and different machine learning classifiers (decision trees, discriminant analysis, logistic regression, naive Bayes, support vector machines, nearest neighbor, ensemble method, and artificial neural network). …”
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GDText-VM: an arbitrary-shaped scene text detector based on globally deformable VMamba by Yingnan Zhao, Zheng Hu, Fangqi Ding, Jielin Jiang, Xiaolong Xu

“…The results indicate that GDText-VM outperforms the state-of-the-art methods in terms of precision, recall, and F-measure, while maintaining efficient computation with 25.88M parameters and 40.83G FLOPs. …”
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An advanced fire detection system for assisting visually challenged people using recurrent neural network and sea-horse optimizer algorithm by Fahd N. Al-Wesabi, Abeer A. K. Alharbi, Ishfaq Yaseen

“…Furthermore, the fusion of feature extraction comprises three methods, EfficientNetB7, CapsNet, and ShuffleNetV2. Furthermore, the SFDAB-ARNNSHO model performs fire detection and classification using stacked two-layer bidirectional long short-term memory with attention mechanism (SBiLSTM-AM) technique. …”
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Deep Learning-Based Automatic Estimation of Live Coral Cover from Underwater Video for Coral Reef Health Monitoring by Zechen Li, Shuqi Zhao, Yuxian Lu, Cheng Song, Rongyong Huang, Kefu Yu

“…By automating the estimation of LCC, this deep learning-based approach can greatly enhance efficiency, thereby contributing significantly to global conservation efforts by enabling more scalable and efficient monitoring and management of coral reef ecosystems.…”
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