Enhanced Swine Behavior Detection with YOLOs and a Mixed Efficient Layer Aggregation Network in Real Time by Ji-hyeon Lee, Yo Han Choi, Han-sung Lee, Hyun Ju Park, Jun Seon Hong, Ji Hwan Lee, Soo Jin Sa, Yong Min Kim, Jo Eun Kim, Yong Dae Jeong, Hyun-chong Cho

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ECAN-Detector: An Efficient Context-Aggregation Network for Small-Object Detection by Gaofeng Xing, Zhikang Xu, Yulong He, Hailong Ning, Menghao Sun, Chunmei Wang

“…To address this issue, we propose the ECAN-Detector, an efficient context-aggregation method designed to enrich the feature representation of shallow layers, which are particularly beneficial for small-object detection. …”
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A dual layer secure and energy-efficient model for border surveillance using sea lion inspired strategy in wireless sensor networks by Jayachandran J, Vimaladevi K

“…In sensor networks, security related measure will often lead to an increase in energy consumption, hence, designing efficient energy conserving protocols with robust data aggregation methods are crucial in line with better duty-cycling idle nodes in the network. …”
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A Cross-Layer Optimization Approach for Energy Efficient Wireless Sensor Networks: Coalition-Aided Data Aggregation, Cooperative Communication, and Energy Balancing by Qinghai Gao, Junshan Zhang, Xuemin (Sherman) Shen, Bryan Larish

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YOLO-EFM: Efficient traffic flow monitoring algorithm with enhanced multi-level information fusion by Shizhou Xu, Kaidi Cui

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Efficient secure federated learning aggregation framework based on homomorphic encryption by Shengxing YU, Zhong CHEN

“…In order to solve the problems of data security and communication overhead in federated learning, an efficient and secure federated aggregation framework based on homomorphic encryption was proposed.In the process of federated learning, the privacy and security issues of user data need to be solved urgently.However, the computational cost and communication overhead caused by the encryption scheme would affect the training efficiency.Firstly, in the case of protecting data security and ensuring training efficiency, the Top-K gradient selection method was used to screen model gradients, reducing the number of gradients that need to be uploaded.A candidate quantization protocol suitable for multi-edge terminals and a secure candidate index merging algorithm were proposed to further reduce communication overhead and accelerate homomorphic encryption calculations.Secondly, since model parameters of each layer of neural networks had characteristics of the Gaussian distribution, the selected model gradients were clipped and quantized, and the gradient unsigned quantization protocol was adopted to speed up the homomorphic encryption calculation.Finally, the experimental results show that in the federated learning scenario, the proposed framework can protect data privacy, and has high accuracy and efficient performance.…”
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Enhancing the Efficiency of Unsupervised Network Alignment Using Quotient Graph by Lei Zhang, Feng Qian

“…To address these limitations, this paper proposes ENAMOR (Efficient Network AlignMent via Quotient gRaph), an unsupervised framework incorporating three key components: 1) multi-scale representation learning that hierarchically aggregates local and global structural patterns through GNN layers; 2) embedding-driven graph coarsening via hashing-based quotient graph construction, reducing computational complexity by 60–80% while preserving topological and attribute information; and 3) Matched Neighborhood Consistency (MNC) optimization, which iteratively refines alignment matrices by enforcing structural congruence constraints. …”
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Hybrid optimized data aggregation for fog computing devices in internet of things by M. Jalasri, S. Manikandan, Arthur Davis Nicholas, S. Gobimohan, Naarisetti Srinivasa Rao

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Relayer Aggregation Using Chainless Multi-Layer Consensus by Takaaki Yanagihara, Akihiro Fujihara

“…The increasing adoption of blockchain technology has underscored the need for cross-chain systems that enable seamless communication among multiple BC networks. Achieving cross-chain interoperability, which ensures secure and efficient data storage and transfer across BCs, remains a critical technical challenge. …”
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Image Quality Predictor with Highly Efficient Fully Convolutional Neural Network by Cao Yu-Dong, Liao Xin-Lin, Liu Hai-Yan

“…A highly efficient deep fully convolutional neural network (DFCN) for image quality assessment (IQA) is designed in this paper. …”
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Energy-Efficient and Survivable Optical Access Network Based on Multilayer-Ring Structure by Yunxin Lv, Ning Jiang, Chenpeng Xue, Kun Qiu

“…An energy-efficient and survivable optical access network structure is proposed and investigated. …”
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Benthos-DETR: a high-precision efficient network for benthic organisms detection by Weibo Rao, Gang Chen, Yifei Zhang, Jue Cang, Shusen Chen, Chenyang Wang

“…This study proposes Benthos-DETR, a benthic organisms detection network based on the RT-DETR network. In the backbone of Benthos-DETR network, the Efficient Block with the C2f module reinforces the shallow feature extraction operation in Benthos-DETR, enhancing the algorithm’s multi-scale perception. …”
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Nano-TiO₂-treated recycled aggregate-based photocatalytic mortar: Efficient NOx removal and mathematical modeling across diverse operational scenarios by Xue-Fei Chen, Xiu-Cheng Zhang, Xiangping Xian, S Thomas Ng

“…This innovative mortar demonstrated an exceptional NOx removal efficiency of c.a. 90.75%. A predictive model was developed using the Back Propagation Neural Network, integrating experimental and theoretical data to optimize mortar performance across various scenarios. …”
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WCANet: An Efficient and Lightweight Weight Coordinated Adaptive Detection Network for UAV Inspection of Transmission Line Accessories by Jiawei Chen, Pengfei Shi, Mengyao Xu, Yuanxue Xin, Xinnan Fan, Jinbo Zhang

“…The network is designed with a plug-and-play WCA module that can effectively identify dense small targets, retain information in each channel, and reduce computational overheads, while incorporating Sim-AFPN with a skip-connection structure into the network aggregate feature information layer by layer, enhancing the ability to capture key features, and achieving a lightweight network structure. …”
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Optimizing security and energy efficiency in IoT-Based health monitoring systems for wireless body area networks by Mohammed Naif Alatawi

“…To address these issues, a novel multi-layered framework is proposed. First, an energy-efficient mechanism is introduced using a threshold-based transmission controller. …”
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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

“…In this paper, we therefore propose an Efficient Hierarchical Co-occurrence Graph Convolution Network (EHC-GCN). …”
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Energy efficient gateway based routing with maximized node coverage in a UAV assisted wireless sensor network. by Bilal Ahmad, Masroor Ahmed, Nadeem Anjum, Masood Ur Rehman, Naeem Ramzan

“…Ad-hoc wireless sensor networks face challenges of optimized node deployment for maximizing coverage and efficiently routing data to control centers in post disaster events. …”
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Renewable Energy Consumption Strategies for Electric Vehicle Aggregators Based on a Two-Layer Game by Xiu Ji, Mingge Li, Zheyu Yue, Haifeng Zhang, Yizhu Wang

“…In order to address the above problems, this paper proposes an electric vehicle aggregator (EVA) scheduling strategy based on a two-layer game by constructing a two-layer game model between renewable energy generators (REG) and EVA, where the REG formulates time-sharing tariff strategies in the upper layer to guide the charging and discharging behaviors of electric vehicles, and the EVA respond to the price signals in the lower layer to optimize the large-scale electric vehicle scheduling. …”
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A High-Efficiency Deep-Learning-Based Antivibration Hammer Defect Detection Model for Energy-Efficient Transmission Line Inspection Systems by Fangrong Zhou, Gang Wen, Guochao Qian, Yutang Ma, Hao Pan, Jing Liu, Jiaying Li

“…Secondly, an antivibration hammer defect detector (AVHDD) model is proposed that uses a deep layer aggregation-based feature extraction network and a highly effective weighted bidirectional feature fusion network to replace the original ResNet and FPN on the modified Cascade RCNN to further enhance the model performance. …”
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A Lightweight Semantic- and Graph-Guided Network for Advanced Optical Remote Sensing Image Salient Object Detection by Jie Liu, Jinpeng He, Huaixin Chen, Ruoyu Yang, Ying Huang

“…To further efficiently aggregate multi-level features and preserve the integrity and complexity of overall object shape, we introduce a Graph-Based Region Awareness Module (GRAM). …”
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