LP-HENN: fully homomorphic encryption accelerator with high energy efficiency by Zhuoyu Tian, Lei Chen, Shengyu Fan, Xianglong Deng, Rui Hou, Dan Meng, Mingzhe Zhang

“…The energy efficiency of LP-HENN is comparable to that of F1, the state-of-the-art ASIC FHE accelerator, while featuring a low power design suitable for edge computing.…”
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FECI-RTDETR a Lightweight Unmanned Aerial Vehicle Infrared Small Target Detector Algorithm Based on RT-DETR by Renzheng Xue, Shijie Hua, Haiqiang Xu

“…Initially, we introduce a lightweight RFConv-Block module that enhances spatial feature extraction capabilities while reducing computational redundancy. …”
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Design of a Drivable Area Segmentation Network Using a Field Programmable Gate Array Based on Light Detection and Ranging by Xue-Qian Lin, Jyun-Yu Jhang, Cheng-Jian Lin, Sheng-Fu Liang

“…The proposed DASNet utilizes depthwise separable convolution as a basis/platform for feature extraction to enable features to be efficiently extracted to reduce both the computational load and required network parameters. …”
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Machine learning and complex network analysis of drug effects on neuronal microelectrode biosensor data by Manuel Ciba, Marc Petzold, Caroline L. Alves, Francisco A. Rodrigues, Yasuhiko Jimbo, Christiane Thielemann

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NeuroTutor: Neural Decoding of Student Engagement During Virtual and Robotic Tutoring by Shahd Elgammal, Shaymaa Alsereidi, Mahra Almansoori, Shamma Alblooshi, Abdelhadi Hireche, Abdelkader Nasreddine Belkacem

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Breast mass lesion area detection method based on an improved YOLOv8 model by Yihua Lan, Yingjie Lv, Jiashu Xu, Yingqi Zhang, Yanhong Zhang

“…The YOLOv8-P2 algorithm significantly reduces the number of necessary parameters by streamlining the number of channels in the feature map. …”
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A Modified MobileNetv3 Coupled With Inverted Residual and Channel Attention Mechanisms for Detection of Tomato Leaf Diseases by Rubina Rashid, Waqar Aslam, Romana Aziz, Ghadah Aldehim

“…Additionally, inverted residual connections are incorporated to expand the model’s receptive field. To maximize feature utilization, cross-layer connections are introduced between the two parallel streams, integrating the Efficient Channel Attention (ECA) module to reduce the number of parameters. …”
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OE-YOLO: An EfficientNet-Based YOLO Network for Rice Panicle Detection by Hongqing Wu, Maoxue Guan, Jiannan Chen, Yue Pan, Jiayu Zheng, Zichen Jin, Hai Li, Suiyan Tan

“…Second, the backbone network is redesigned with EfficientNetV2, leveraging its compound scaling strategy to balance multi-scale feature extraction and computational efficiency. Third, a C3k2_DConv module improved by dynamic convolution is introduced, enabling input-adaptive kernel fusion to amplify discriminative features while suppressing background interference. …”
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SageNet: Fast Neural Network Emulation of the Stiff-amplified Gravitational Waves from Inflation by Fan Zhang, Yifang Luo, Bohua Li, Ruihan Cao, Wenjin Peng, Joel Meyers, Paul R. Shapiro

“…The dual capability of learning both physical and artificial features of the numerical GW spectra establishes SageNet as a robust alternative to exact numerical methods. …”
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A Novel Dangerous Goods Detection Network Based on Multi-Layer Attention Mechanism in X-Ray Baggage Images by Xu Yang, Ting Lan, Yili Xu

“…Compared with some state-of-the-art methods, our network improves performance by 5–10% while reducing parameters and increasing computational efficiency. …”
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ACM-YOLOv10: Research on Classroom Learning Behavior Recognition Algorithm Based on Improved YOLOv10 by Beichen Qin, Haoyan Hu, Shaowen Du

“…This module, with its lightweight design, optimizes convolution operations to reduce computational complexity and parameter quantity, thereby accelerating the model’s inference speed. …”
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Development of a data-driven neural network model for electron thermal transport in NSTX by H. Chung, C.Y. Lee, G.J. Choi, S.M. Kaye, B.P. LeBlanc, J.W. Berkery, Y.-S. Na

“…A data-driven electron thermal transport neural network (ETT-NN) model, trained on TRANSP interpretative analysis results of National Spherical Torus Experiment (NSTX), was developed to enable faster and more accurate ETT computation for spherical tokamaks (STs). The model incorporates both convolutional NNs and recurrent NNs, allowing it to simultaneously account for the spatial and temporal non-localities and multi-scale features of turbulent transport, which have been considered only in a limited manner in conventional models. …”
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Underground personnel detection and tracking using improved YOLOv7 and DeepSORT by Weiqiang FAN, Xuejin WANG, Yinghui ZHANG, Xiaoyu LI

“…After that, in order to be able to further improve the tracking accuracy of personnel targets while reducing the number of model parameters and network complexity, the ShuffleNetV2 lightweight module is introduced into the feature extraction network of DeepSORT, and the improved DeepSORT model is used to encode and track downhole personnel targets. …”
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An advanced deep learning method for pepper diseases and pests detection by Xuewei Wang, Jun Liu, Qian Chen

“…Built upon YOLOv10n, YOLO-Pepper incorporates four major innovations: (1) an Adaptive Multi-Scale Feature Extraction (AMSFE) module that improves feature capture through multi-branch convolutions; (2) a Dynamic Feature Pyramid Network (DFPN) enabling context-aware feature fusion; (3) a specialized Small Detection Head (SDH) tailored for minute targets; and (4) an Inner-CIoU loss function that enhances localization accuracy by 18% compared to standard CIoU. …”
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Preoperative prediction of pulmonary ground-glass nodule infiltration status by CT-based radiomics combined with neural networks by Kun Mei, Zikang Feng, Hui Liu, Min Wang, Chao Ce, Shi Yin, Xiaoying Zhang, Bin Wang

“…Regions of interest (ROIs) within the CT lung window level were manually delineated using ITK-SNAP software, enabling the extraction of relevant CT imaging features, including morphological descriptors, first-order statistical parameters, texture attributes, and high-order characteristics. …”
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Advanced holographic convolutional dense networks and Tangent runner optimization for enhanced polycystic ovarian disease classification by Prathibanandhi Jeyashanker, Annie Grace Vimala Georgewilliam Sundaram, Padmakala Sadagopan, Abid Yahya, Ravi Samikannu, Irfan Anjum Badruddin, Sarfaraz Kamangar, Mohamed Ghouse Shukur

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Vehicle detection in drone aerial views based on lightweight OSD-YOLOv10 by Yang Zhang, Xiaobing Chen, Su Sun, Hongfeng You, Yuanyuan Wang, Jianchu Lin, Jiacheng Wang

“…The proposed algorithm incorporates several key innovations: First, we employ online convolutional reparameterization to construct the OCRConv module and design a lightweight feature extraction structure, SPCC, to replace the conventional C2f module, thereby reducing computational load and parameter count. …”
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Efficient Urban Tree Species Classification via Multirepresentation Fusion of Mobile Laser Scanning Data by Yinchi Ma, Peng Luan, Yujie Zhang, Bo Liu, Lijie Zhang

“…Quantitative assessment yielded 98.57% F1-score and 98.77% overall accuracy with moderate computational resources (2.25M parameters, 1.11G FLOPs), demonstrating significant improvements over existing methods. …”
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VMMT-Net: A Dual-Branch Parallel Network Combining Visual State Space Model and Mix Transformer for Land–Sea Segmentation of Remote Sensing Images by Jiawei Wu, Zijian Liu, Zhipeng Zhu, Chunhui Song, Xinghui Wu, Haihua Xing

“…The model maintains reasonable computational complexity, with only 28.24 M parameters and 25.21 GFLOPs, striking a favorable balance between accuracy and efficiency. …”
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Hybrid Swin-CSRNet: A Novel and Efficient Fish Counting Network in Aquaculture by Jintao Liu, Alfredo Tolón-Becerra, José Fernando Bienvenido-Barcena, Xinting Yang, Kaijie Zhu, Chao Zhou

“…Meanwhile, compared to the original network, the parameter size and computational complexity of Swin-CSRNet were reduced by 70.17% and 79.05%, respectively. …”
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