FCDNet: A Lightweight Network for Real-Time Wildfire Core Detection in Drone Thermal Imaging by Linfeng Wang, Oualid Doukhi, Deok Jin Lee

“…Compared to the state-of-the-art YOLOv11n, FCDNet reduces parameters, computation, and model size by 26.9%, 20.6%, and 27.3%, respectively. …”
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Microstrip Patch Antenna Design Using a Four-Layer Feed Forward Artificial Neural Network Trained by Levenberg-Marquardt Algorithm by Jitu Prakash Dhar, Maodudul Hasan, Eisuke Nishiyama, Ichihiko Toyoda

“…Therefore, patch antennas with three fundamental geometrical shapes can be designed using the same ANN removing computational complexity for the designers. The ANN contains a multi-layered network architecture that learns and generalizes complex patterns through the LM algorithm and weight optimization based on the datasets without any feature extraction like Deep Neural Network (DNN). …”
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EGRN-YOLO: An Enhanced Multi-View Remote Sensing Detection Algorithm for Onshore Wind Turbines Based on YOLOv7 by Renzheng Xue, Haiqiang Xu, Qianlong Wu

“…Firstly, the lightweight network EfficientNetV2 is utilized as the feature extraction backbone to reduce the number of model parameters and computational load. …”
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Benchmarking 21 Open-Source Large Language Models for Phishing Link Detection with Prompt Engineering by Arbi Haza Nasution, Winda Monika, Aytug Onan, Yohei Murakami

“…Additionally, our analysis highlights smaller models (7B–27B parameters) offering strong performance with substantially reduced computational costs. …”
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Multimodal bearing fault classification under variable conditions: A 1D CNN with transfer learning by Tasfiq E. Alam, Md Manjurul Ahsan, Shivakumar Raman

“…While this approach attains excellent accuracy across varied conditions, it requires more computational time due to its greater number of trainable parameters. …”
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Numerical Simulation of Protective Spraying by Helicopter-Type Unmanned Aerial Vehicles by V. P. Asovsky, A. S. Kuzmenko

“…The paper illustrates the implementation features of the system’s main blocks and modules, including modeling the inductive wave of a multicopter, droplet deposition, working fluid application indicators and fullarea coverage. …”
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A Lightweight CNN for Multi-Class Classification of Handwritten Digits and Mathematical Symbols by Nicholas Abisha, Tita Putri Redytadevi, Sri Nurdiati, Elis Khatizah, Mohamad Khoirun Najib

“…The proposed model, implemented in Julia using the Flux.jl library, features a compact architecture with only two convolutional layers and approximately 55,000 trainable parameters significantly smaller than typical deep CNNs. …”
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Intelligent deep learning-based dual-task approach for robust power quality event classification by Lipsa Ray, Pampa Sinha, Siddhanta Pani, Anshuman Nayak, Kaushik Paul, Chitralekha Jena, Md. Minarul Islam, Taha Selim Ustun

“…The methodology integrates the tunable-Q wavelet transform (TQWT) for signal decomposition, optimizing Q-factor parameters to extract precise features, and morphological component analysis (MCA) with the Split Augmented Lagrangian Shrinkage Algorithm (SALSA) for effective component separation. …”
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Improved YOLOv10n model for enhanced cotton recognition in complex environments by Yutao Gong, Wenwen Ding, Nenghui Huang, Tao Li, Juntao Zhou

“…Compared to YOLOv5, YOLOv7, YOLOv8, and the baseline YOLOv10n models, its mAP@0.5 increases by 6.3, 5.6, 3.9, and 1.3 percentage points, respectively. With 1.45 M parameters and 2.8 G computations, it represents a 5.8 % and 15.2 % reduction from the original YOLOv10n model.In complex farmland settings, the enhanced YOLOv10n model can precisely identify multi - category cotton growth stages, optimize network computational efficiency, and support the development of visual systems for cotton - harvesting robots.…”
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A Novel Phase Error Estimation Method for TomoSAR Imaging Based on Adaptive Momentum Optimizer and Joint Criterion by Muhan Wang, Silin Gao, Xiaolan Qiu, Zhe Zhang

“…Compared to conventional phase error calibration methods in a two-step iterative framework, our proposed method considers image features and parameter coupling relationships, thus achieving higher precision estimation while saving computational costs. …”
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A Corn Point Cloud Stem-Leaf Segmentation Method Based on Octree Voxelization and Region Growing by Qinzhe Zhu, Ming Yu

“…Plant phenotyping is crucial for advancing precision agriculture and modern breeding, with 3D point cloud segmentation of plant organs being essential for phenotypic parameter extraction. Nevertheless, although existing approaches maintain segmentation precision, they struggle to efficiently process complex geometric configurations and large-scale point cloud datasets, significantly increasing computational costs. …”
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LCAT: A Lightweight Color-Aware Transformer With Hierarchical Attention for Leaf Disease Classification in Precision Agriculture by Parkpoom Chaisiriprasert, Khachonkit Chuiad

“…A key contribution of LCAT lies in its use of smaller patch sizes to extract features in high-resolution regions while maintaining shallow depth, which significantly reduces model complexity. …”
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A Lightweight Two-Step Detection Method for Real-Time Small UAV Detection by Sungkyu Jung, Jaeyeon Jang, Chang Ouk Kim

“…Additionally, our compression approach preserves critical UAV features by selectively removing low-importance parameters, significantly reducing the degree of redundancy while minimizing the induced detection performance loss.…”
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Simultaneous Learning Knowledge Distillation for Image Restoration: Efficient Model Compression for Drones by Yongheng Zhang

“…This dual-teacher approach enables the student model to learn from both degraded and clean images simultaneously, achieving robust image restoration while significantly reducing computational complexity. Experimental evaluations across five benchmark datasets and three restoration tasks—deraining, deblurring, and dehazing—demonstrate that, compared to the teacher models, the SLKD student models achieve an average reduction of 85.4% in FLOPs and 85.8% in model parameters, with only a slight average decrease of 2.6% in PSNR and 0.9% in SSIM. …”
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Spatio-temporal activity patterns induced by triadic interactions in an in silico neural medium by Ana P Millán, Hanlin Sun, Joaquín J Torres

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Reservoir Stochastic Simulation Based on Octave Convolution and Multistage Generative Adversarial Network by Xuechao Wu, Wenyao Fan, Shijie Peng, Bing Qin, Qing Wang, Mingjie Li, Yang Li

“…Then, the octave convolution is used to perform multi-frequency feature representation on different feature maps. …”
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A Dual-Branch Residual Network with Attention Mechanisms for Enhanced Classification of Vaginal Lesions in Colposcopic Images by Haima Yang, Yeye Song, Yuling Li, Zubei Hong, Jin Liu, Jun Li, Dawei Zhang, Le Fu, Jinyu Lu, Lihua Qiu

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PHRF-RTDETR: a lightweight weed detection method for upland rice based on RT-DETR by Xianjin Jin, Jinheng Zhang, Fei Wang, Mengyan Zhao, Yunshuang Wang, Jianping Yang, Jinfeng Wu, Bing Zhou

“…Second, we integrate HiLo, a mechanism excluding parameter growth, into the AIFI module to enhance the model’s capability of capturing multi-frequency features. …”
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A Lightweight GCT-EEGNet for EEG-Based Individual Recognition Under Diverse Brain Conditions by Laila Alshehri, Muhammad Hussain

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AS-YOLO: Enhanced YOLO Using Ghost Bottleneck and Global Attention Mechanism for Apple Stem Segmentation by Na Rae Baek, Yeongwook Lee, Dong-hee Noh, Hea-Min Lee, Se Woon Cho

“…The proposed model reduces the number of parameters and enhances the computational efficiency using the ghost bottleneck while improving feature extraction capabilities using the global attention mechanism. …”
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