Lightweight visible damage detection algorithm for embedded systems applied to pipeline automation equipment by Jiale Xiao, Lei Xu, Changyun Li, Ling Tang, Guogang Gao

“…The backbone of the algorithm, CSPHet, employs efficient combinatorial convolution and heterogeneous kernel convolution, incorporates a lightweight convolutional structure SL in the neck of the network, enhances the nonlinear representation and feature processing capability through channel shuffling, utilizes the lightweight self-attention mechanism Detect_SA for prediction, and employs a multilayered GhostConv to improve the computational efficiency. …”
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An improved EAE-DETR model for defect detection of server motherboard by Jian Chi, Mingke Zhang, Puhon Zhang, Guowang Niu, Zhihao Zheng

“…Lastly, we constructed the EUCB-SC upsampling module, which integrates depth convolution and channel shuffling strategies to enhance feature reconstruction efficiency. …”
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Mobile malware detection method using improved GhostNetV2 with image enhancement technique by Yao Du, CaiXia Gao, Xi Chen, MengTian Cui, LiLi Xu, AoJi Ning

“…Second, we make three improvements to GhostNetV2 to more effectively identify malicious code, including introducing channel shuffling in the Ghost module, replacing the squeeze and excitation mechanism with a more efficient channel attention mechanism, and optimizing the activation function. …”
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DA-YOLOv7: A Deep Learning-Driven High-Performance Underwater Sonar Image Target Recognition Model by Zhe Chen, Guohao Xie, Xiaofang Deng, Jie Peng, Hongbing Qiu

“…New modules such as the Omni-Directional Convolution Channel Prior Convolutional Attention Efficient Layer Aggregation Network (OA-ELAN), Spatial Pyramid Pooling Channel Shuffling and Pixel-level Convolution Bilat-eral-branch Transformer (SPPCSPCBiFormer), and Ghost-Shuffle Convolution Enhanced Layer Aggregation Network-High performance (G-ELAN-H) are central to its design, which reduce the computational burden and enhance the accuracy in detecting small targets and capturing local features and crucial information. …”
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Enhanced CATBraTS for Brain Tumour Semantic Segmentation by Rim El Badaoui, Ester Bonmati Coll, Alexandra Psarrou, Hykoush A. Asaturyan, Barbara Villarini

“…E-CATBraTS integrates convolutional neural networks and Swin Transformer, incorporating channel shuffling and attention mechanisms to effectively segment brain tumours in multi-modal MRI. …”
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Adeno-associated virus vector modification based on directed evolution technology for gene therapy targeting head and neck squamous cell carcinoma by Yiyuan Zhu, Wei Ji, Qi Zhang, Yanbo Dong, Liangfa Liu

“…IntroductionAdeno-associated virus (AAV) vectors are promising tools for cancer gene therapy, yet their clinical application in head and neck squamous cell carcinoma (HNSCC) is hindered by suboptimal transduction efficiency and off-target risks. Bioengineered AAV capsids require optimization to enhance tumor-specific targeting while minimizing systemic toxicity.MethodsWe employed a directed evolution strategy combining DNA shuffling and site-directed mutagenesis to generate AAV variants. …”
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MPJ-SPARK Integration-Based Technique to Enhance Big Data Analytics in High Performance Computing Environments by Sakhr A. Saleh, Maher A. Khemakhem, Fathy E. Eassa

“…This eliminates remote shuffling and improves network efficiency. A key-value data structure was developed to facilitate data exchange and to convert Resilient Distributed Dataset (RDD) into contiguous arrays for MPJ. …”
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A lightweight privacy-preserving truth discovery mechanism for IoT by Yingjie HE, Qiwei LI, Han SUN, Di GAO, Jianfeng DONG, Shuhua YANG

“…In order to solve diverse quality and privacy leakage of perceived data in fog-cloud integrated internet of things (IoT), a streaming encryption-based privacy-preserving truth discovery mechanism for IoT was proposed.Firstly, by utilizing shuffling and streaming encryption algorithms, the ground truths and the weights were anonymously updated on the cloud server and fog server, respectively, so that the collusion attacks between malicious attackers and cloud or fog servers could be resisted to defend against privacy leakage of IoT devices.Secondly, by adopting the Softmax function, the device weights were calculated on fog server, which reduces the error rate for calculating the ground truths.Finally, the theoretical analysis proved that the mechanism could protect privacy of the devices.And, the experimental results demonstrate that the proposed mechanism is an effective privacy-preserving trust discovery mechanism for large-scale IoT devices, which can outperform existing ones in computing efficiency.…”
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Enhancing image security via chaotic maps, Fibonacci, Tribonacci transformations, and DWT diffusion: a robust data encryption approach by Mohammad Mazyad Hazzazi, Mujeeb Ur Rehman, Arslan Shafique, Amer Aljaedi, Zaid Bassfar, Aminu Bello Usman

“…Balancing these two aspects poses a challenge, aiming to achieve efficient encryption without compromising security. …”
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YOLOv8-LSW: A Lightweight Bitter Melon Leaf Disease Detection Model by Shuang Liu, Haobin Xu, Ying Deng, Yixin Cai, Yongjie Wu, Xiaohao Zhong, Jingyuan Zheng, Zhiqiang Lin, Miaohong Ruan, Jianqing Chen, Fengxiang Zhang, Huiying Li, Fenglin Zhong

“…It also integrates the ShuffleAttention mechanism, strengthening the feature response in lesion areas through channel shuffling and spatial attention dual pathways. …”
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Research on Fine-Tuning Optimization Strategies for Large Language Models in Tabular Data Processing by Xiaoyong Zhao, Xingxin Leng, Lei Wang, Ningning Wang

“…Experimental results indicate that decimal truncation reduces data noise, thereby enhancing the model’s learning efficiency. Additionally, multi-dataset mixing improves the model’s generalization and stability, while the random shuffling of key–value pair orders increases the model’s adaptability to changes in data structure. …”
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Application of K-means supported by clustered systems in big data association rule mining by Lihua Liu

“…By combining the distributed computing capabilities of Hadoop clusters with the improved k-means++ clustering algorithm, this method effectively solves the scalability problem in processing large datasets and significantly improves the efficiency of clustering analysis and frequent itemset mining.…”
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GCSA-SegFormer: Transformer-Based Segmentation for Liver Tumor Pathological Images by Jingbin Wen, Sihua Yang, Weiqi Li, Shuqun Cheng

“…The module combines channel attention, channel shuffling, and spatial attention to capture global dependencies within feature maps. …”
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A Quantum Key Distribution Routing Scheme for a Zero-Trust QKD Network System: A Moving Target Defense Approach by Esraa M. Ghourab, Mohamed Azab, Denis Gračanin

“…The proposed approach enhances the security of end-to-end key distribution by dynamically shuffling key exchange routes, enabling secure multipath key distribution. …”
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Deteksi Covid-19 pada Citra Sinar-X Dada Menggunakan Deep Learning yang Efisien by Novanto Yudistira, Agus Wahyu Widodo, Bayu Rahayudi

“…The memory required by each CNN architecture to perform one detection is linearly related to the number of parameters where ShuffleNet only requires GPU memory of 0.646 GB or 0.43 times that of ResNet50, 0.2 times of EfficientNet, and 0.53 times of FullConv. …”
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Comparison of Transfer Learning Model Performance for Breast Cancer Type Classification in Mammogram Images by Cahya Bagus Sanjaya, Muhammad Imron Rosadi, Moch. Lutfi, Lukman Hakim

“…This work conducted a thorough comparison analysis of eight prevalent pre-trained CNN algorithms (VGG16, ResNet50, AlexNet, MobileNetV2, ShuffleNet, EfficientNet-b0, EfficientNet-b1, and EfficientNet-b2) for breast cancer classification. …”
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A Metaheuristic Framework for Cost-Effective Renewable Energy Planning: Integrating Green Bonds and Fiscal Incentives by Juan D. Saldarriaga-Loaiza, Johnatan M. Rodríguez-Serna, Jesús M. López-Lezama, Nicolás Muñoz-Galeano, Sergio D. Saldarriaga-Zuluaga

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Application of Multiple Deep Learning Architectures for Emotion Classification Based on Facial Expressions by Cheng Qian, João Alexandre Lobo Marques, Auzuir Ripardo de Alexandria, Simon James Fong

“…In contrast, lightweight models such as MobileNet V1 and ShuffleNet V2, while excelling in computational efficiency, faced limitations in accuracy, particularly in challenging emotion categories like “fear” and “disgust”. …”
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LGWheatNet: A Lightweight Wheat Spike Detection Model Based on Multi-Scale Information Fusion by Zhaomei Qiu, Fei Wang, Tingting Li, Chongjun Liu, Xin Jin, Shunhao Qing, Yi Shi, Yuntao Wu, Congbin Liu

“…This study aims to improve the accuracy and efficiency of wheat spike detection, enabling efficient crop monitoring under resource-constrained conditions. …”
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An Explainable Deep Learning Network for Environmental Microorganism Classification Using Attention-Enhanced Semi-Local Features by R. Karthik, Armaano Ajay, Akshaj Singh Bisht, Jaehyuk Cho, V. E. Sathishkumar

“…This research introduces a novel network for EM classification using Dense network, Efficient Hierarchical Channel Refinement (EHCR) block, Dilated Adaptive Shuffled-Attention (DASA) block and Neighbourhood Attention (NA). …”
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