DP-YOLO: A Lightweight Real-Time Detection Algorithm for Rail Fastener Defects by Lihua Chen, Qi Sun, Ziyang Han, Fengwen Zhai

“…First, we design a Depthwise Separable Convolution Stage Partial (DSP) module that integrates depthwise separable convolution with a CSP residual connection strategy, reducing model parameters while enhancing recognition accuracy. …”
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Predicting the Temperature of a Permanent Magnet Synchronous Motor: A Comparative Study of Artificial Neural Network Algorithms by Nabil El Bazi, Nasr Guennouni, Mohcin Mekhfioui, Adil Goudzi, Ahmed Chebak, Mustapha Mabrouki

“…The intent is to identify the most favorable model that balances high accuracy with low computational cost.…”
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YOLO-DAFS: A Composite-Enhanced Underwater Object Detection Algorithm by Shengfu Luo, Chao Dong, Guixin Dong, Rongmin Chen, Bing Zheng, Ming Xiang, Peng Zhang, Zhanwei Li

“…The detection head adopts DyHead-GDC, integrating ghost depthwise separable convolution with DyHead for greater efficiency. Furthermore, the ADown module replaces conventional feature extraction and downsampling convolutions, reducing parameters and FLOPs by 14%. …”
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Enhanced Neural Architecture for Real-Time Deep Learning Wavefront Sensing by Jianyi Li, Qingfeng Liu, Liying Tan, Jing Ma, Nanxing Chen

“…To achieve real-time deep learning wavefront sensing (DLWFS) of dynamic random wavefront distortions induced by atmospheric turbulence, this study proposes an enhanced wavefront sensing neural network (WFSNet) based on convolutional neural networks (CNN). We introduce a novel multi-objective neural architecture search (MNAS) method designed to attain Pareto optimality in terms of error and floating-point operations (FLOPs) for the WFSNet. …”
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Intelligent Sports Weights by Olga dos Santos Duarte, Gustavo Jacinto, Mário Véstias, Rui Policarpo Duarte

“…The system is based on a low-power embedded System-on-a-Chip to perform the classification of the correctness of physical exercises using a Convolutional Neural Network with data from the embedded IMU. …”
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On the Extrapolation of Generative Adversarial Networks for Downscaling Precipitation Extremes in Warmer Climates by Neelesh Rampal, Peter B. Gibson, Steven Sherwood, Gab Abramowitz

“…Abstract While deep‐learning downscaling algorithms can generate fine‐scale climate projections cost‐effectively, it is unclear how effectively they extrapolate to unobserved climates. …”
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A Sensory Glove With a Limited Number of Sensors for Recognition of the Finger Alphabet of Polish Sign Language by Jakub Piskozub, Pawel Strumillo

“…This is because existing designs often prioritize accuracy at the cost of ergonomics, accessibility, and affordability. …”
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Faster Dynamic Graph CNN: Faster Deep Learning on 3D Point Cloud Data by Jinseok Hong, Keeyoung Kim, Hongchul Lee

“…However, it has been difficult to apply such data as input to a convolutional neural network (CNN) or recurrent neural network (RNN) because of their unstructured and unordered features. …”
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Lightweight framework for misbehavior detection in internet of vehicles by Yujing Gong, Bin-Jie Hu

“…We compare two recently introduced models—the liquid time-constant (LTC) network and the closed-form continuous-time (CFC) neural network—with the established convolutional neural network-long short-term memory (CNN-LSTM) model. …”
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Artificial Intelligence Driving Innovation in Textile Defect Detection by Ahmet Ozek, Mine Seckin, Pinar Demircioglu, Ismail Bogrekci

“…It delves into the types of defects occurring at various production stages, assesses the strengths and weaknesses of conventional and automated approaches, and underscores the pivotal role of deep learning models, especially Convolutional Neural Networks (CNNs), in achieving high precision in defect identification. …”
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Impact of Pretrained Deep Neural Networks for Tomato Leaf Disease Prediction by Mohamed Bouni, Badr Hssina, Khadija Douzi, Samira Douzi

“…This article identifies tomato leaf disease using a deep convolutional neural network (CNN) and transfer learning. …”
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Hybrid Capsule Network for precise and interpretable detection of malaria parasites in blood smear images by Bader Alawfi

“…IntroductionRapid and precise malaria diagnosis is critical in resource-constrained settings to enable timely treatment and reduce mortality. Existing convolutional neural network (CNN) and capsule network hybrids, although effective, often suffer from high computational demands and limited generalizability across datasets.MethodsWe propose Hybrid Capsule Network (Hybrid CapNet), a lightweight architecture combining CNN-based feature extraction with dynamic capsule routing for accurate parasite identification and life-cycle stage classification. …”
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A soil organic carbon mapping method based on transfer learning without the use of exogenous data by Jingfeng Han, Mujie Wu, Yanlong Qi, Xiaoning Li, Xiao Chen, Jing Wang, Jinlong Zhu, Qingliang Li

“…Accurate and cost-effective mapping of soil organic carbon (SOC) is critical for understanding carbon dynamics and informing sustainable land management. …”
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Enhancing Lidar-Based 3D Classification Through an Improved Deep Learning Framework With Residual Connections by Shih-Lin Lin, Jun-Yi Wu

“…Through systematic adjustments—such as increasing convolutional kernel size and quantity, incorporating dropout and Batch Normalization layers, and integrating residual connections—we achieve substantial accuracy gains. …”
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Leaf disease detection and classification in food crops with efficient feature dimensionality reduction. by Khasim Syed, Shaik Salma Asiya Begum, Anitha Rani Palakayala, G V Vidya Lakshmi, Sateesh Gorikapudi

“…Dimensionality reduction techniques are employed to enhance computational performance by reducing the dimensionality of inner layers. Convolutional Neural Networks (CNNs), originally designed to recognize critical image components, now learn features across multiple layers. …”
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FruitNet: Lightweight CNN for High-Throughput Image-Based Fruit Yield Estimation by Yadav Kamlesh Kumar, Tandan Gajendra

“…Innovations in this work include the most lightweight Convolutional Neural Network (CNN) named FruitNet proposed for achieving high throughput and image based estimation offrait yield. …”
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Machine Learning-Based Approaches for Breast Density Estimation from Mammograms: A Comprehensive Review by Khaldoon Alhusari, Salam Dhou

“…The most commonly utilized models are support vector machines (SVMs) and convolutional neural networks (CNNs), with classification accuracies ranging from 76.70% to 98.75%. …”
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Equivariant spherical CNNs for accurate fiber orientation distribution estimation in neonatal diffusion MRI with reduced acquisition time by Haykel Snoussi, Davood Karimi

“…In this study, we propose a rotationally equivariant Spherical Convolutional Neural Network (sCNN) framework tailored for neonatal dMRI. …”
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Recent Advancements in Hyperspectral Image Reconstruction from a Compressive Measurement by Xian-Hua Han, Jian Wang, Huiyan Jiang

“…Furthermore, we review benchmark datasets, evaluation metrics, and prevailing challenges including spectral distortion, computational cost, and generalizability across diverse conditions. …”
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LiDAR Sensor Parameter Augmentation and Data-Driven Influence Analysis on Deep-Learning-Based People Detection by Lukas Haas, Florian Sanne, Johann Zedelmeier, Subir Das, Thomas Zeh, Matthias Kuba, Florian Bindges, Martin Jakobi, Alexander W. Koch

“…However, to design these systems cost- and energy-efficiently, the relationship between the measurement data and final object detection output with deep neural networks (DNNs) has to be elaborated. …”
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