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

“…It includes an Efficient Processing (EP) module based on the novel Partial Depthwise Convolution (PDWConv) and the lightweight feature-sharing decoupled detection head (Fast Head), achieving low-size and low-computation wildfire detection. …”
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LFN-YOLO: precision underwater small object detection via a lightweight reparameterized approach by Mingxin Liu, Mingxin Liu, Yujie Wu, Ruixin Li, Cong Lin, Cong Lin

“…Then, we replaced the standard depthwise convolution in the feature extraction network with SPD-Conv, which includes an additional pooling layer to mitigate detail loss. …”
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Research on Asphalt Pavement Surface Distress Detection Technology Coupling Deep Learning and Object Detection Algorithms by Hong Zhang, Yuanshuai Dong, Yun Hou, Xiangjun Cheng, Peiwen Xie, Keming Di

“…To address the challenges posed by the vast scale of highway maintenance in China and the high costs associated with traditional inspection vehicles. …”
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A Method for Selecting the Appropriate Source Domain Buildings for Building Energy Prediction in Transfer Learning: Using the Euclidean Distance and Pearson Coefficient by Chuyi Luo, Liang Xia, Sung-Hugh Hong

“…Long Short-Term Memory, the Gated Recurrent Unit, and the Convolutional Neural Network were applied to verify the appropriateness of the source domain buildings. …”
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MFENet: A Multi-Feature Extraction Network for Enhanced Emotion Detection Using EEG and STFT by N. Ramesh Babu, Viswanathan Vadivel

“…MFENet employs multi-feature extraction blocks and grouped convolutions to enhance representational diversity without incurring high computational costs. …”
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Detection of Typical Transient Signals in Water by XGBoost Classifier Based on Shape Statistical Features: Application to the Call of Southern Right Whale by Zemin Zhou, Yanrui Qu, Boqing Zhu, Bingbing Zhang

“…We also address the challenges posed by deep learning models, such as high data labeling costs and extensive computational requirements. By extracting shape statistical features from audio and using the XGBoost classifier, our method not only outperforms the traditional convolutional neural network (CNN) method in accuracy but also reduces the dependence on labeled data, thus improving the detection efficiency. …”
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Accuracy Assessment of Tomato Harvest Working Time Predictions from Panoramic Cultivation Images by Hiroki Naito, Tomohiko Ota, Kota Shimomoto, Fumiki Hosoi, Tokihiro Fukatsu

“…The scale of horticultural facilities in Japan is expanding, making the efficient management of labor costs essential, particularly in large-scale tomato production. …”
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Advances in Online Grain Quality Assessment: Near-Infrared Spectroscopic Modeling and Transfer Strategies by CUI Chen-hao, FAN Chen

“…Traditional laboratory methods, while accurate, are time-consuming and costly, making them unsuitable for online, real-time monitoring. …”

Physics-Based Noise Modeling and Deep Learning for Denoising Permanently Shadowed Lunar Images by Haiyan Pan, Binbin Chen, Ruyan Zhou

“…While significant advancements have been made in denoising techniques based on deep learning, the direct acquisition of paired clean and noisy images from the PSRs of the Moon is costly, making dataset acquisition expensive and hindering network training. …”
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Improvement of Multiclass Classification of Pavement Objects Using Intensity and Range Images by Elham Eslami, Hae-Bum Yun

“…Motivated by the need for an improved pavement objects classification, we present Dual Attention Convolutional Neural Network (DACNN) to improve the performance of multiclass classification using intensity and range images collected with 3D laser imaging devices. …”
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Federated Learning for Brain Tumor Diagnosis: Methods, Challenges and Future Prospects by Ma Yuhan

“…This paper analyzes various Convolutional Neural Network (CNN) models, including VGG16, ResNet50, DenseNet121, and EfficientNet, exploring their integration within the FL framework to enhance diagnostic accuracy while preserving patient data privacy. …”
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Evaluation of data driven low-rank matrix factorization for accelerated solutions of the Vlasov equation. by Bhavana Jonnalagadda, Stephen Becker

“…Low-rank methods have shown success in accelerating simulations of a collisionless plasma described by the Vlasov equation, but still rely on computationally costly linear algebra every time step. We propose a data-driven factorization method using artificial neural networks, specifically with convolutional layer architecture, that trains on existing simulation data. …”
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Research on Enhanced Dynamic Pig Counting Based on YOLOv8n and Deep SORT by Peng Shen, Keyu Mei, Haori Xue, Tenglong Li, Guoqing Zhang, Yongxiang Zhao, Wei Luo, Liang Mao

“…The ELA attention mechanism, GSConv, and VoVGSCSP lightweight convolution modules are introduced in YOLOv8n, which improve detection accuracy and speed for pig target recognition. …”
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Dynamic UAV data fusion and deep learning for improved maize phenological-stage tracking by Ziheng Feng, Jiliang Zhao, Liunan Suo, Heguang Sun, Huiling Long, Hao Yang, Xiaoyu Song, Haikuan Feng, Bo Xu, Guijun Yang, Chunjiang Zhao

“…These methods are not applicable on the unmanned aerial vehicle (UAV) platform due to the high cost of acquiring time–series UAV images and the shortage of UAV–based phenological monitoring methods. …”
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A Fuzzy Neural Network-Based Intelligent Prediction Model for Useful Lifespan of Lithium-Ion Batteries by Yaowei Xu, Zhenxing Hao, Wei Li, Angang Cao

“…With the rapid development of the new energy industry, accurate prediction of the service life of lithium batteries as core energy storage components is of great significance for optimizing energy configuration and reducing operation and maintenance costs. Traditional prediction methods are difficult to accurately capture their dynamic characteristics. …”
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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

“…This study investigates several DNN architectures, namely Feed-Forward Multilayer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), for solving the IK problem, using the TIAGo robotic arm with seven Degrees of Freedom (DOFs). …”
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ImageOP: The Image Dataset with Religious Buildings in the World Heritage Town of Ouro Preto for Deep Learning Classification by André Luiz Carvalho Ottoni, Lara Toledo Cordeiro Ottoni

“…MobileNet V2 and EfficientNet B0 are architectures of convolutional neural networks designed for computer vision applications aiming at low computational cost, real-time classification on mobile devices. …”
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AI-Based Anomaly Detection and Optimization Framework for Blockchain Smart Contracts by Hassen Louati, Ali Louati, Elham Kariri, Abdulla Almekhlafi

“…The framework integrates Neural Architecture Search (NAS) to automatically design optimal Convolutional Neural Network (CNN) architectures tailored to blockchain data, enabling effective anomaly detection. …”
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Industrial-Grade CNN-Based System for the Discrimination of Music Versus Non-Music in Radio Broadcast Audio by Valerio Cesarini, Vincenzo Addati, Giovanni Costantini

“…We implemented multiple data augmentation strategies, including FM-like pre-processing, trained a custom Convolutional Neural Network, and then built a live inference platform capable of continuously monitoring web radio streams. …”
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Prediction of the packaging chemical migration into food and water by cutting-edge machine learning techniques by Behzad Vaferi, Mohsen Dehbashi, Reza Yousefzadeh, Ali Hosin Alibak

“…Abstract Chemicals transfer from the packaging materials and their dissolution in food and water can create health risks. Due to the costly and time-intensive nature of experimental measurements, employing artificial intelligence (AI) methodologies is beneficial. …”
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