LayerFold: A Python library to reduce the depth of neural networks by Giommaria Pilo, Nour Hezbri, André Pereira e Ferreira, Victor Quétu, Enzo Tartaglione

“…Large-scale models are the backbone of Computer Vision and Natural Language Processing, and their generalizability allows for transfer learning and deployment in different scenarios. However, their large size means that reducing their computational and memory demands remains a challenge. …”
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Application of Traffic Cone Target Detection Algorithm Based on Improved YOLOv5 by Mingwu Wang, Dan Qu, Zedong Wu, Ao Li, Nan Wang, Xinming Zhang

“…The experimental results show that the network could maintain recognition accuracy and speed values of around 89% and 9 fps under different working conditions such as varying distances, lighting conditions, and occlusions, meeting the technical requirements for deploying and retrieving cones at a speed of 30 cones per minute when the operating vehicle’s speed was 20 km/h. …”
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Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis by Kevin Barrera-Llanga, Jordi Burriel-Valencia, Angel Sapena-Bano, Javier Martinez-Roman

“…This analysis introduces a new approach by demonstrating how different convolutional blocks capture particular features: the first convolutional block captures signal shape, while the second identifies background features. …”
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Deep learning-developed multi-light source discrimination capability of stretchable capacitive photodetector by Su Bin Choi, Jun Sang Choi, Hyun Sik Shin, Jeong-Won Yoon, Youngmin Kim, Jong-Woong Kim

“…It shows high sensitivity at both 448 and 505 nm wavelengths, detecting light sources under mechanical deformations, different wavelengths and frequencies. By integrating a one-dimensional convolutional neural network (1D-CNN) model, we classified the light source power level with 96.52% accuracy even the light of two wavelengths is mixed. …”
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THE EMPIRICAL COMPARISON OF DEEP NEURAL NETWORK OPTIMIZERS FOR BINARY CLASSIFICATION OF OCT IMAGES by R. Loganathan, S. Latha

“…The Adam optimizer could train all binary convolutional neural networks based on these results.…”
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RoBERTa-Based Multi-Feature Integrated BiLSTM and CNN Model for Ceramic Review Analysis by LiHua Yang, Jun Wang, WangRen Qiu

“…To address the limitation that the Robustly Optimized BERT Pretraining Approach (RoBERTa) may not effectively capture local dependencies and salient features within the text, we propose a feature fusion framework based on RoBERTa’s multi-output architecture. By feeding different outputs of RoBERTa into Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks, the model effectively captures both local static patterns and global contextual dependencies, thereby enhancing its capability to handle complex textual inputs. …”
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Inter-turn Short-circuit Fault Diagnosis and Severity Estimation for Five-phase PMSM by Yijia Huang, Wentao Huang, Tinglong Pan, Dezhi Xu

“…Finally, simulations and experiments under different operating points validate the effectiveness of the proposed method.…”
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HDCTfusion: Hybrid Dual-Branch Network Based on CNN and Transformer for Infrared and Visible Image Fusion by Wenqing Wang, Lingzhou Li, Yifei Yang, Han Liu, Runyuan Guo

“…The effectiveness of the proposed method in this paper is verified on different experimental datasets, and it is better than most of the current advanced fusion algorithms.…”
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Inverse Kinematics for Robotic Manipulators via Deep Neural Networks: Experiments and Results by Ana Calzada-Garcia, Juan G. Victores, Francisco J. Naranjo-Campos, Carlos Balaguer

“…Different training datasets, normalization techniques, and orientation representations are tested, and custom metrics are introduced to evaluate position and orientation errors. …”
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CMPF-UNet: a ConvNeXt multi-scale pyramid fusion U-shaped network for multi-category segmentation of remote sensing images by Ning Li, Xiaopeng Yu, Miao Yu

“…Finally, multiple Global Pyramid Guidance (GPG) modules are embedded in the network, aiming to provide different levels of global context information for the decoder by reconstructing skip-connections. …”
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A data-efficient deep transfer learning framework for methane super-emitter detection in oil and gas fields using the Sentinel-2 satellite by S. Zhao, S. Zhao, Y. Zhang, Y. Zhang, S. Zhao, S. Zhao, X. Wang, X. Wang, D. J. Varon

“…We evaluate the ability of the algorithm to discover new methane sources with a suite of transfer tasks, in which training and evaluation data come from different regions. Results show that DSAN (average macro <span class="inline-formula"><i>F</i>₁</span> score 0.86) outperforms four convolutional neural networks (CNNs), MethaNet (average macro <span class="inline-formula"><i>F</i>₁</span> score 0.70), ResNet-50 (average macro <span class="inline-formula"><i>F</i>₁</span> score 0.77), VGG16 (average macro <span class="inline-formula"><i>F</i>₁</span> score 0.73), and EfficientNet-V2L (average macro <span class="inline-formula"><i>F</i>₁</span> score 0.78), in transfer tasks. …”
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Adversarial attacks dataset for low light image enhancementMendeley Data by Axel Martinez, Matthieu Olague, Gustavo Olague, Emilio Hernandez, Julio Cesar Lopez-Arredondo

“…Enhancing images in low-light conditions is a field of research where deep convolutional neural networks have shown considerable effectiveness. …”
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Ocular Disease Detection Using Fundus Images: A Hybrid Approach of Grad-CAM and Multiscale Retinex Preprocessing With VGG16 Deep Features and Fine KNN Classification by Shreemat Kumar Dash, Kante Satyanarayana, Santi Kumari Behera, Sudarson Jena, Ashoka Kumar Ratha, Prabira Kumar Sethy, Aziz Nanthaamornphong

“…This research investigates the application of deep feature extraction for classifying eight different ocular diseases. The VGG16, a pretrained convolutional neural network (CNN) model, was employed for feature extraction, while the fine k-nearest neighbor (KNN) classifier was utilized for classification. …”
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Prescription Recommendation Algorithm Based on Herbal Property-Driven Compatibility Mechanism Semantic Modeling by Geng Xueru, Zhang Jiantong, Luo Tao, Hou Jianchen, Tao Xiaohua

“…This is followed by aggregating higher-order heterogeneous path information of nodes through a graph convolutional network model. Finally, an attention mechanism is employed to fuse information from symptom interaction graphs, symptom-herb interaction graphs, and herb interaction graphs, distinguishing the influence of different dimensions of TCM semantic information. …”
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Presentation Attack Detection using iris periocular visual spectrum images by Andrés Valenzuela, Juan E. Tapia, Violeta Chang, Christoph Busch

“…The analysis was carried out by evaluating the performance of five Convolutional Neural Networks (CNN) using both facial and periocular iris images for PAD with two different attack instruments. …”
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Soft-sediment Deformation Structures and Sand Body Architecture in the Chang 6 Oil Member of the Upper Triassic Yanchang Formation, Southwestern Ordos Basin, China by Qingshao Liang, Jingchun Tian, Feng Wang, Xiang Zhang

“…As a consequence, genetic models of the sand bodies formed by different sedimentation processes are established.…”
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Intelligent Testing Method for Multi-Point Vibration Acquisition of Pile Foundation Based on Machine Learning by Ke Wang, Weikai Zhao, Juntao Wu, Shuang Ma

“…The model’s performance was assessed using statistical error metrics, including the correlation coefficient R², mean absolute error (MAE), and variance accounted for (VAF), with comparative evaluations conducted across different model frameworks. Results show that both the convolutional neural network (CNN) and the long short-term memory neural network (LSTM) consistently achieved high accuracy in identifying the location of the first reflection point in the pile shaft, with R² values greater than 0.98, MAE below 0.41 (m), and VAF greater than 98%. …”
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Limited-angle x-ray nano-tomography with machine-learning enabled iterative reconstruction engine by Chonghang Zhao, Mingyuan Ge, Xiaogang Yang, Yong S. Chu, Hanfei Yan

“…We demonstrate the effectiveness of the proposed approach using various experimental datasets obtained with different x-ray microscopy techniques. All show significantly improved reconstruction even with a missing wedge of over 100 degrees−a scenario where conventional methods fail. …”
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Deep learning in defects detection of PV modules: A review by Katleho Masita, Ali Hasan, Thokozani Shongwe, Hasan Abu Hilal

“…Despite achieving high accuracy, challenges such as the need for large datasets and model generalization across different PV modules and environmental conditions remain. …”
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Ubiquitous UWB Ranging Error Mitigation With Application to Infrastructure-Free Cooperative Positioning by Maija Makela, Martta-Kaisa Olkkonen, Martti Kirkko-Jaakkola, Toni Hammarberg, Tuomo Malkamaki, Jesperi Rantanen, Sanna Kaasalainen

“…This ranging error can be corrected with machine learning (ML) methods, such as convolutional neural networks (CNNs). However, these ML models often generalize poorly between different environments. …”
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