Real-Time Corn Variety Recognition Using an Efficient DenXt Architecture with Lightweight Optimizations by Jin Zhao, Chengzhong Liu, Junying Han, Yuqian Zhou, Yongsheng Li, Linzhe Zhang

“…In this study, we established a dataset of maize plants at the elongation stage containing 31,000 images of 40 different types, including corn leaves, staminates, and root caps, and proposed a DenXt framework model. Representative Batch Normalization (RBN) is introduced into the DenseNet-121 model to improve the generalization ability of the model, and the SE module and deep separable convolution are integrated to enhance the feature representation and reduce the computational complexity, and the Dropout regularization is introduced to further improve the generalization ability of the model and reduce the overfitting. …”
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Carbon Emission Reduction in Traffic Control: A Signal Timing Optimization Method Based on Rainbow DQN by Juan Lv, Zhaowei Wang, Jianxiao Ma

“…The algorithm uses Rainbow DQN as the core framework, incorporating vehicle position, speed, and acceleration information into the state space. …”
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Monitoring Pine Wilt Disease Using High-Resolution Satellite Remote Sensing at the Single-Tree Scale with Integrated Self-Attention by Wenhao Lv, Junhao Zhao, Jixia Huang

“…Within the unified semantic segmentation framework MMSegmentation, we implemented four single-head attention models—NLNet, CCNet, DANet, and GCNet—and two multi-head attention models—Swin Transformer and SegFormer—for the accurate semantic segmentation of infected trees. …”
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MSU-Net: A Synthesized U-Net for Exploiting Multi-Scale Features in OCT Image Segmentation by Dejie Chen, Xiangping Chen, Hao Gu, Su Zhao, Hao Jiang

“…To address these issues, this study introduces MSU-Net, a novel neural network for OCT-based choroidal segmentation. The proposed framework enhances performance through two innovations: 1) replacement of standard encoder blocks with a multi-branch module combining heterogeneous convolutions to achieve multi-scale receptive field diversification; 2) redesign of skip connections through a pyramid fusion module with spatial attention for adaptive multi-level feature weighting. …”
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Spatio-Temporal Collaborative Perception-Enabled Fault Feature Graph Construction and Topology Mining for Variable Operating Conditions Diagnosis by Jiaxin Zhao, Xing Wu, Chang Liu, Feifei He

“…Finally, we develop a graph residual convolutional network to mine topological information from multi-source spatio-temporal features under complex operating conditions. …”
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NPI-WGNN: A Weighted Graph Neural Network Leveraging Centrality Measures and High-Order Common Neighbor Similarity for Accurate ncRNA–Protein Interaction Prediction by Fatemeh Khoushehgir, Zahra Noshad, Morteza Noshad, Sadegh Sulaimany

“…Experimental results on three benchmark datasets demonstrate that NPI-WGNN outperforms state-of-the-art methods, achieving up to 96.1% accuracy, 97.5% sensitivity, and an F1-score of 0.96, positioning it as a robust and accurate framework for ncRNA–protein interaction prediction.…”
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Deep learning model for grading carcinoma with Gini-based feature selection and linear production-inspired feature fusion by Shreyan Kundu, Souradeep Mukhopadhyay, Rahul Talukdar, Dmitrii Kaplun, Alexander Voznesensky, Ram Sarkar

“…To enhance the grading accuracy for liver and renal cell carcinoma, this research introduces a novel feature selection and fusion framework inspired by economic theories, incorporating attention mechanisms into three Convolutional Neural Network (CNN) architectures-MobileNetV2, DenseNet121, and InceptionV3-as foundational models. …”
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Novel load balancing mechanism for cloud networks using dilated and attention-based federated learning with Coati Optimization by Atul B. Kathole, Viomesh Kumar Singh, Ankur Goyal, Shiv Kant, Amit Sadanand Savyanavar, Swapnaja Amol Ubale, Prince Jain, Mohammad Tariqul Islam

“…This study proposes a novel approach to resource prediction for cloud network load balancing, incorporating federated learning within a blockchain framework for secure and distributed management. The model leverages Dilated and Attention-based 1-Dimensional Convolutional Neural Networks with bidirectional long short-term memory (DA-DBL) to predict resource needs based on factors such as processing time, reaction time, and resource availability. …”
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Deep Learning Frontiers in 3D Object Detection: A Comprehensive Review for Autonomous Driving by Ambati Pravallika, Mohammad Farukh Hashmi, Aditya Gupta

“…This analysis explores the pivotal function of three-dimensional object detection in improving AV safety and performance, underscoring its importance within the larger framework of self-driving vehicle systems. We offer a thorough examination of techniques, including deep learning frameworks such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to assess their advantages and drawbacks in 3D object detection. …”
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Construction and scheduling optimization of renewable energy consumption forecasting system for twisted tire porcelain manufacturing industry based on deep learning by Fangrong Liao, Runlin Ran, Jiayi Zhang

“…At the same time, based on the convolutional neural network model Alexnet, the cross-entropy loss function is selected as the training loss, and the twisted tire porcelain pattern classification algorithm under the deep learning framework is designed. …”
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A User-Friendly Machine Learning Pipeline for Automated Leaf Segmentation in by Michelle Lynn Yung, Kamila Murawska-Wlodarczyk, Alicja Babst-Kostecka, Raina Margaret Maier, Nirav Merchant, Aikseng Ooi

“…By focusing on practical deployment in addition to model performance, this study provides an open-source, scalable framework for plant science applications and addresses real-world challenges in automation and usability by the end-researcher.…”
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The research on recognition of passenger abnormal behavior in urban rail transit stations based on conditional generative adversarial networks by Jing Zuo, Zexin Li, Ming He, Qiaoli Yang

“…Second, the framework incorporates a Patch GAN local discriminator network combined with Lite FlowNet to rectify local and motion distortions in generated frame, thereby enhancing passenger behavioral modeling accuracy. …”
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AI-driven smart agriculture using hybrid transformer-CNN for real time disease detection in sustainable farming by Zhuo Zeng, Tariq Mahmood, Yu Wang, Amjad Rehman, Muhammad Akram Mujahid

“…This study introduces the AttCM-Alex model, a novel deep-learning framework designed to boost the detection and classification of plant diseases under challenging environmental conditions. …”
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geodl: An R package for geospatial deep learning semantic segmentation using torch and terra. by Aaron E Maxwell, Sarah Farhadpour, Srinjoy Das, Yalin Yang

“…Convolutional neural network (CNN)-based deep learning (DL) methods have transformed the analysis of geospatial, Earth observation, and geophysical data due to their ability to model spatial context information at multiple scales. …”
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Segmented Curve-Fitting Method for Continuum Removal in CRISM MTRDR data by P. Kumari, S. Soor, A. Shetty, S. G. Koolagudi

“…The identification score is improved by around 8% for the similarity matching method Weighted Sum of Spectrum Correlation and by around 1.5% for a Convolutional Neural Network. Furthermore, an SCF-based mineral identification framework demonstrates its effectiveness in identifying the dominant minerals on CRISM MTRDR hyperspectral data collected from different locations on the Martian surface.…”
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Design of Chinese traditional Jiaoyi (Folding chair) based on Kansei Engineering and CNN-GRU-attention by Xinyan Yang, Nan Zhang, Jiufang Lv, Jiufang Lv, Jiufang Lv

“…BackgroundsThis study innovatively enhances personalized emotional responses and user experience quality in traditional Chinese folding armchair (Jiaoyi chair) design through an interdisciplinary methodology.GoalTo systematically extract user emotional characteristics, we developed a hybrid research framework integrating web-behavior data mining.Methods1) the KJ method combined with semantic crawlers extracts emotional descriptors from multi-source social data; 2) expert evaluation and fuzzy comprehensive assessment reduce feature dimensionality; 3) random forest and K-prototype clustering identify three core emotional preference factors: “Flexible Refinement,” “Uncompromising Quality,” and “ergonomic stability.”DiscussionA CNN-GRU-Attention hybrid deep learning model was constructed, incorporating dynamic convolutional kernels and gated residual connections to address feature degradation in long-term semantic sequences. …”
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TDR-Model: Tomato Disease Recognition Based on Image Dehazing and Improved MobileNetV3 Model by Zhixiang Zhang, Tong Liu, Jinyu Gao, Meng Yang, Wenjun Luo, Fanqiang Lin

“…Additionally, we provide extensive ablation studies to evaluate effectiveness of the proposed framework.…”
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Medical Report Generation With Knowledge Distillation and Multi-Stage Hierarchical Attention in Vision Transformer Encoder and GPT-2 Decoder by Hilya Tsaniya, Chastine Fatichah, Nanik Suciati, Takashi Obi, Joong-Sun Lee

“…In this study, we propose a novel framework that integrates knowledge distillation and multi-stage hierarchical attention mechanisms to enhance the generation of comprehensive and accurate medical reports. …”
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Adaptive Outdoor Cleaning Robot with Real-Time Terrain Perception and Fuzzy Control by Raul Fernando Garcia Azcarate, Akhil Jayadeep, Aung Kyaw Zin, James Wei Shung Lee, M. A. Viraj J. Muthugala, Mohan Rajesh Elara

“…This paper proposes a terrain-aware cleaning framework that dynamically adjusts robot behavior based on real-time surface classification and slope estimation. …”
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Attention Feature Fusion Network via Knowledge Propagation for Automated Respiratory Sound Classification by Ida A. P. A. Crisdayanti, Sung Woo Nam, Seong Kwan Jung, Seong-Eun Kim

“…<italic>Methods:</italic> We developed a deep convolutional neural network (CNN) model that utilizes spectrographic representations of respiratory sounds within an image classification framework. …”
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