Handwritten W-Net for High-Frequency Guided Single-Image Super-Resolution by Xiang Yang, Chen Fang, Xiaolan Xie, Minggang Dong

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Real-time motion detection using dynamic mode decomposition by Marco Mignacca, Simone Brugiapaglia, Jason J. Bramburger

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FPG-AI RNN: A Technology-Agnostic Framework for the Automatic Acceleration of LSTM/GRU-Based Models on FPGAs by Tommaso Pacini, Pietro Nannipieri, Silvia Moranti, Luca Fanucci

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Automated Arrhythmia Classification System: Proof-of-Concept With Lightweight Model on an Ultra-Edge Device by Namho Kim, Seongjae Lee, Seungmin Kim, Sung-Min Park

“…The completed edge-computing system featured sufficiently short inference time and low memory usage. …”
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Decoupled pixel-wise correction for abdominal multi-organ segmentation by Xiangchun Yu, Longjun Ding, Dingwen Zhang, Jianqing Wu, Miaomiao Liang, Jian Zheng, Wei Pang

“…Attention-based deep neural networks (ADNNs) fundamentally engage in the iterative computation of gradients for both input layers and weight parameters. …”
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Perceptually Correlated Parameters of Musical Instrument Tones by James W. BEAUCHAMP

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Method for calculating the parameters of the rotary-impact mechanism by L. A. Sladkova, D. I. Skripnikov

“…An analytical method with the use of modern computer technology, fundamental provisions of theoretical mechanics and strength theory are applied in theoretical and experimental research. …”
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Lightweight detection algorithms for small targets on unmanned mining trucks by Shuoqi CHENG, Yilihamu·YAERMAIMAITI, Lirong XIE, Xiyu LI, Ying MA

“…It enhances multi-scale feature fusion capability via weighted feature fusion, significantly reducing parameter count while improving small target detection capability. …”
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A New Efficient Hybrid Technique for Human Action Recognition Using 2D Conv-RBM and LSTM with Optimized Frame Selection by Majid Joudaki, Mehdi Imani, Hamid R. Arabnia

“…Recognizing human actions through video analysis has gained significant attention in applications like surveillance, sports analytics, and human–computer interaction. While deep learning models such as 3D convolutional neural networks (CNNs) and recurrent neural networks (RNNs) deliver promising results, they often struggle with computational inefficiencies and inadequate spatial–temporal feature extraction, hindering scalability to larger datasets or high-resolution videos. …”
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Steel Defect Detection Based on YOLO-SAFD by Feihong Yu, Jinshan Zhang, Dingdiao Mu

“…The proposed model incorporates two key innovations: 1) the Squeezed and Excited Asymptotic Feature Pyramid Network (SAFPN), which enhances multi-scale feature fusion and improves the detection of small defects, increasing the mean Average Precision (mAP) from 0.78 (YOLOv5 baseline) to 0.84; 2) the Diverse Branch Block (DBB), which replaces conventional convolutions to enrich feature diversity while reducing computational complexity, cutting the model parameters from 13.8M to 4.82M. …”
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Lightweight image super-resolution network based on muti-domain information enhancement by KOU Qiqi, LIU Gui, JIANG He, CHEN Liangliang, CHENG Deqiang

“…Aiming to solve the problems that the reconstruction capability of single-domain features was limited and deep convolutional neural networks used in existing single-image super-resolution reconstruction tasks were difficult to deploy on mobile terminals due to the large number of parameters and high computational requirements, a lightweight image super-resolution network based on multi-domain information enhancement was proposed. …”
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Research on UAV aerial imagery detection algorithm for Mining-Induced surface cracks based on improved YOLOv10 by Jiayong An, Siyuan Dong, Xuanli Wang, Chenlei Li, Wenpei Zhao

“…However, detection is challenged by small crack size, complex morphology, large scale variation, and uneven spatial distribution, further exacerbated by UAVs’ limited onboard computational capacity. To tackle these issues, we introduce an efficient and lightweight small-target detection model, namely YOLO-LSN, which is built upon the optimized YOLO architecture.Firstly, we introduce a Lightweight Dynamic Alignment Detection Head (LDADH) for multi-scale feature fusion, precise alignment, and dynamic receptive field adjustment, optimizing crack feature extraction. …”
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DSF-YOLO for weld defect detection in X-ray images with dynamic staged fusion by Meng Zhang, Yanzhu Hu, Binbin Xu, Lisha Luo, Song Wang

“…Additionally, DSF-YOLO significantly reduces computational complexity, achieving a 75% reduction in FLOPs and a 47.5% decrease in parameters compared to YOLOv8-X. …”
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Improved YOLOv8-Based Algorithm for Citrus Leaf Disease Detection by Zhengbing Zheng, Yibang Zhang, Luchao Sun

“…The proposed approach uses YOLOv8n as the base model and introduces adaptive convolution into the Backbone, allowing the model to dynamically prioritize different disease features. This modification increases the model’s parameter count without adding to the computational cost in terms of floating-point operations. …”
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Leveraging machine learning to proactively identify phishing campaigns before they strike by Kun Zhang, Haifeng Wang, Meiyi Chen, Xianglin Chen, Long Liu, Qiang Geng, Yu Zhou

“…Feature selection was conducted using SHapley Additive Explanations (SHAP) and Recursive Feature Elimination (RFE) to enhance interpretability and computational efficiency. …”
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BSE-YOLO: An Enhanced Lightweight Multi-Scale Underwater Object Detection Model by Yuhang Wang, Hua Ye, Xin Shu

“…Firstly, we replace the original neck with an improved Bidirectional Feature Pyramid Network (Bi-FPN) to reduce parameters. …”
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Underwater image enhancement using hybrid transformers and evolutionary particle swarm optimization by Ajay Kumar, Gagandeep Berar, Manmohan Sharma, Sakshi, Ajit Noonia, Gunjan Verma

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Research on multi class pests identification and detection based on fusion attention mechanism with Mask-RCNN-CBAM by Xingwang Wang, Xingwang Wang, Xingwang Wang, Can Hu, Xufeng Wang, Hainie Zha, Xueyong Chen, Shanshan Yuan, Jing Zhang, Jianfeng Liao, Zhangying Ye

“…The framework combines three innovations: (1) a CBAM attention mechanism to amplify pest features while suppressing background noise; (2) a feature-enhanced pyramid network (FPN) for multi-scale feature fusion, enhancing small pest recognition; and (3) a dual-channel downsampling module to minimize detail loss during feature propagation. …”
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Smart Farming: Enhancing Urban Agriculture Through Predictive Analytics and Resource Optimization by Eman Ali Aldhahri, Abdulwahab Ali Almazroi, Monagi Hassan Alkinani, Nasir Ayub, Elham Abdullah Alghamdi, Nourah Fahad Janbi

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Efficient Transformer-Based Road Scene Segmentation Approach with Attention-Guided Decoding for Memory-Constrained Systems by Bartas Lisauskas, Rytis Maskeliunas

“…Accurate object detection and an understanding of the surroundings are key requirements when applying computer vision systems in the automotive or robotics industries, namely with autonomous vehicles or self-driving robots. …”
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