A Reliable Image Watermarking Scheme Based on Redistributed Image Normalization and SVD by Musrrat Ali, Chang Wook Ahn, Millie Pant, Patrick Siarry

“…Empirical analysis of the results has demonstrated the efficiency of the proposed scheme.…”
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SiamAHG: adaptive hierarchical graph attention for lightweight siamese tracking by Na Li, Yaofu Fan, Xuhao Chen, Xinyu Liu, Jinglu He

“…It employs the lightweight network ShuffleNet V2 for feature extraction and a novel adaptive hierarchical graph attention for feature fusion. …”
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Metaheuristic Algorithms for Optimization and Feature Selection in Cloud Data Classification Using Convolutional Neural Network by Nandita Goyal, Munesh Chandra Trivedi

“…A comparison of different metaheuristics algorithms- Particle Swarm Optimization, Shuffled Frog Leap Optimization and Fire fly algorithm for feature optimization is done based on convergence rate and efficiency.…”
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Diversity and determinants of recombination landscapes in flowering plants. by Thomas Brazier, Sylvain Glémin

“…These patterns correspond globally to the underlying gene distribution, which affects how efficiently genes are shuffled at meiosis. These results raised new questions not only on the evolution of recombination rates but also on their distribution along chromosomes.…”
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Lightweight Pyramid Cross-Attention Network for No-Service Rail Surface Defect Detection by Sixu Guo, Jiyou Fei, Liying Wang, Hua Li, Xiaodong Liu

“…Vision-based rail defect detection plays a crucial role in ensuring the safety and efficiency of railway transportation systems. However, many existing methods face challenges such as high parameters, complex computation, slow inspection speed, and low accuracy. …”
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A Novel MoCo-Based Self-Supervised Learning Framework for Solar Panel Defect Detection by Jun Huang, Shamsul Arrieya Ariffin, Yongqiang Chen, Jinghui Lin, Wanting Xu

“…This framework offers a scalable, automated solution for image-level defect detection, poised to enhance efficiency and reduce manual intervention in industrial applications.…”
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Enhanced Occupational Safety in Agricultural Machinery Factories: Artificial Intelligence-Driven Helmet Detection Using Transfer Learning and Majority Voting by Simge Özüağ, Ömer Ertuğrul

“…The following neural networks were employed: MobileNetV2, ResNet50, DarkNet53, AlexNet, ShuffleNet, DenseNet201, InceptionV3, Inception-ResNetV2, and GoogLeNet. …”
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Incorporating Wave-ViT for Breast Cancer Diagnosis Using MRI Imaging by Sahil Mahey, Hamid Usefi

“…These included randomized training and testing splits using the Fisher-Yates shuffle, exploration of different Wave-ViT variants, and testing across multiple training set configurations. …”
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Improved Indoor 3D Point Cloud Semantic Segmentation Method Based on PointNet++ by Xingfei Tan, Yonghua Xia, Bin Wang, Ruidong Zou

“…Additionally, the SA (Shuffle Attention) mechanism is integrated, employing channel grouping and rearrangement to effectively capture both local and global information while ensuring computational efficiency. …”
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CPredictive performance of CT images-based 3D ResNet18 model for identifying lung tuberculosis drug resistance by LI Chunhua, LIU Xueyan, ZHENG Jiaofeng

“…Six 3D deep learning architectures (3D Swin Transformer, 3D ShuffleNet v2, 3D ViT, 3D MobileNet v2, 3D DenseNet, and 3D ResNet18) were employed to evaluate the discriminative efficiency between DS-TB and DR-TB. …”
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Lightweight Interpretable Deep Learning Model for Nutrient Analysis in Mobile Health Applications by Zvinodashe Revesai, Okuthe P. Kogeda

“…We develop a lightweight interpretable deep learning architecture combining depthwise separable convolutions, Shuffle Attention mechanisms, and knowledge distillation with integrated Grad-CAM and LIME explanations for real-time interpretability. …”
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Enhancing Road Scene Segmentation With an Optimized DeepLabV3+ by Zhe Ren, Libao Wang, Tianming Song, Yihang Li, Jian Zhang, Fengfeng Zhao

“…First, the heavy Xception backbone is replaced with the lightweight MobileNetV2, significantly boosting real-time efficiency while maintaining competitive segmentation accuracy. …”
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Underground personnel detection and tracking using improved YOLOv7 and DeepSORT by Weiqiang FAN, Xuejin WANG, Yinghui ZHANG, Xiaoyu LI

“…The coal industry is undergoing a transformation in the concept of intelligent mining with "safety, efficiency, intelligence, and green" as its core. Computer vision, as an emerging technology with high efficiency, intelligence, and low cost, has become an important highlight in the current construction of intelligent mines. …”
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An Explainable Deep Learning Network With Transformer and Custom CNN for Bean Leaf Disease Classification by R. Karthik, R. Aswin, K. S. Geetha, K. Suganthi

“…Importantly, the model’s computational efficiency makes it well-suited for practical application in real-world agricultural scenarios.…”
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LSD-Det: A Lightweight Detector for Small Ship Targets in SAR Images by Zhen Wang, Bin Qin, Shang Gao

“…Then, a Grouped Split Enhanced Channel Attention (GSECA) module is introduced in the backbone, combining average and max pooling with channel shuffle to improve recognition of small targets and suppress background noise. …”
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FA-FENet: A Feature Attention Front-End Network Based on a Lightweight CNN Architecture for Recognizing Abnormal Underwater Illegal Fishing Behavior by Xiang-Rui Huang, Liang-Bi Chen

“…The front end of the backbone network can be directly replaced to improve the learning efficiency and performance of the proposed network. …”
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Image Processing for Tooth Type Classification using Deep Learning by Mahmut Emin Çelik, Mehmet Zahid Genç, Ertuğrul Furkan Savaştaer, Fikret Ulus, Berrin Çelik

“…Methods: The state-of-the-art 6 deep learning classification models -Xception, GoogleNet, ResNet18, ShuffleNet, MobileNetV2, ResNext50- was implemented with transfer learning for model efficiency. …”
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A Low Computational Cost Deep Learning Approach for Localization and Classification of Diseases and Pests in Coffee Leaves by Clecio Elias Silva E. Silva, Jonatan Borges Fragoso, Thuanne Paixao, Ana Beatriz Alvarez, Facundo Palomino-Quispe

“…For the second stage, the InceptionResNetv2, DenseNet169, Resnet50 and ShuffleNet models are being trained and used to classify the detected region, and a modification to a low computational cost classification architecture called SmallPavicNet-MC is being proposed. …”
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Optimized Yolov5s-Im for real-time apple flower detection in drone-based pollination by Shahram Hamza Manzoor, Zhao Zhang, Hongwen Li, Qu Zhang, Kuifan Chen, C. Igathinathane, Tianzhong Li, Wei Li, Muhammad Naveed Tahir, Nabil Mustafa, Mustafa Mhamed

“…Control tests with lightweight models YOLOv5s with ShuffleNet version 2 (YOLOv5-Sh-V2) and YOLOv5s with MobileNet version 2 (YOLOv5s-Mb-V2) as backbones, averaged 37.8 and 30.6 attempts per flight, respectively, with accuracies of 80 % and 82 % mAP and detection speeds of 1.0 FPS and 0.7 FPS, further confirming YOLOv5s-Im’s superior balance of accuracy and efficiency. …”
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YOLOv11-RDTNet: A Lightweight Model for Citrus Pest and Disease Identification Based on an Improved YOLOv11n by Qiufang Dai, Shiyao Liang, Zhen Li, Shilei Lyu, Xiuyun Xue, Shuran Song, Ying Huang, Shaoyu Zhang, Jiaheng Fu

“…Second, the Dynamic Group Shuffle Transformer (DGST) module replaces the original C3k2 module, reducing the model’s parameter count and computational demand, further enhancing efficiency and performance. …”
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