Real-Time Multi-Task Deep Learning Model for Polyp Detection, Characterization, and Size Estimation by Phanukorn Sunthornwetchapong, Kasichon Hombubpha, Kasenee Tiankanon, Satimai Aniwan, Pasit Jakkrawankul, Natawut Nupairoj, Peerapon Vateekul, Rungsun Rerknimitr

“…For the various tasks, the models are trained using datasets with incomplete labels, leading to a comparison of different training strategies. Our model, YOLOv8, achieved an F1-score of 95.96% for the polyp detection task, 85.24% F1-score for the polyp classification task, and 78.41% macro F1-score for the polyp size estimation task. …”
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Optimized Motion Capture for Cricket Shot Classification Using Minimal Hardware and Machine Learning by J. Ishan Randika, Kanishka Rajamanthri, Avishka Kothalawala, Niroshan Gunawardana, Ashan Induranga, Pathum Weerakkody, Kaveendra Maduwantha, B. T. G. S. Kumara, Kaveenga Koswattage

“…Motion data collected from the system was analyzed to extract distinct angle variation patterns associated with different batting shots. These patterns were used to train a hybrid machine learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. …”
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Leveraging two-dimensional pre-trained vision transformers for three-dimensional model generation via masked autoencoders by Muhammad Sajid, Kaleem Razzaq Malik, Ateeq Ur Rehman, Tauqeer Safdar Malik, Masoud Alajmi, Ali Haider Khan, Amir Haider, Seada Hussen

“…In vision, attention is used in conjunction with convolutional networks or to replace individual convolutional network elements while preserving the overall network design. …”
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Vision Foundation Model Guided Multimodal Fusion Network for Remote Sensing Semantic Segmentation by Chen Pan, Xijian Fan, Tardi Tjahjadi, Haiyan Guan, Liyong Fu, Qiaolin Ye, Ruili Wang

“…The fusion of multimodal data presents challenges due to discrepancies in image acquisition mechanisms among different sensors, leading to misalignment issues. …”
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STID-Net: Optimizing Intrusion Detection in IoT with Gradient Descent by James Deva Koresh Hezekiah, Usha Nandini Duraisamy, Kalaichelvi Nallusamy, Avudaiammal Ramalingam, Saranya Chandran, Murugesan Rajeswari Thiyagupriyadharsan, Periasamy Selvaraju, Rajagopal Maheswar

“…Existing methods often struggle in capturing complex and irregular patterns from dynamic intrusion data, making them not suitable for different IoT applications. To address these limitations, this work proposes STID-Net that integrated customized convolutional kernels for spatial feature extraction and LSTM layers for temporal sequence modelling. …”
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Position-Aware Graph Neural Network for Few-Shot SAR Target Classification by Jia Zheng, Ming Li, Peng Zhang, Yan Wu, Hongmeng Chen

“…Synthetic aperture radar (SAR) target classification methods based on convolutional neural networks (CNNs) are susceptible to overfitting due to limited samples. …”
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Temporal waveform denoising using deep learning for injection laser systems of inertial confinement fusion high-power laser facilities by Wei Chen, Xinghua Lu, Wei Fan, Xiaochao Wang

“…During the evaluation of experimental waveforms, the model can obtain different denoised waveforms with contrast greater than 200:1. …”
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Narrowband Radar Micromotion Targets Recognition Strategy Based on Graph Fusion Network Constructed by Cross-Modal Attention Mechanism by Yuanjie Zhang, Ting Gao, Hongtu Xie, Haozong Liu, Mengfan Ge, Bin Xu, Nannan Zhu, Zheng Lu

“…The network first adopts convolutional neural networks (CNNs) to extract unimodal features from RCSs, TF images, and CVDs independently. …”
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A Multi-Spatial Scale Ocean Sound Speed Prediction Method Based on Deep Learning by Yu Liu, Benjun Ma, Zhiliang Qin, Cheng Wang, Chao Guo, Siyu Yang, Jixiang Zhao, Yimeng Cai, Mingzhe Li

“…The core concept involves accounting for the coupling effects among various spatial scales while extracting temporal and spatial information from the data and assigning appropriate weights to different spatiotemporal entities. Furthermore, we introduce an interpolation method for ocean temperature and salinity data based on the KNN algorithm to enhance dataset resolution. …”
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A systematic review of deep learning methods for community detection in social networks by Mohamed El-Moussaoui, Mohamed Hanine, Ali Kartit, Monica Garcia Villar, Monica Garcia Villar, Monica Garcia Villar, Helena Garay, Helena Garay, Helena Garay, Isabel de la Torre Díez

“…It also examines the variety of social networks, datasets, evaluation metrics, and employed frameworks in these studies.DiscussionHowever, the analysis highlights several challenges, such as scalability, understanding how the models work (interpretability), and the need for solutions that can adapt to different types of networks. These issues stand out as important areas that need further attention and deeper research. …”
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Ultra-short-term Probabilistic Forecasting of Distributed Photovoltaic Power Generation Based on Hierarchical Correlation Modeling by Can CHEN, Zinuo SU, Yuan MA, Jialin LIU, Yuqing WANG, Fei WANG

“…On this basis, a hierarchical graph structure is constructed to simultaneously model the intra-subregion and inter-subregion spatio-temporal correlations, enabling effective utilization of correlation information across different hierarchical levels. Then, a probabilistic forecasting model based on hierarchical graph convolutional neural networks (GCNs) is proposed to mine deep spatio-temporal correlation features between PV power stations, thereby enhancing the accuracy of ultra-short-term probabilistic forecasting of regional distributed PV power. …”
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GU-Net3+: A Global-Local Feature Fusion Algorithm for Building Extraction in Remote Sensing Images by Yali Liu, Cui Ni, Peng Wang, Dongqing Yang, Hexin Yuan, Chao Ma

“…Finally, a Global Cross-Attention module is developed to aggregate global features from different spatial locations, which are then fused with local features from skip connections as input to the decoder, enhancing information exchange across multi-scale and hierarchical features to improve building extraction accuracy. …”
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Comparative Analysis of Attention Mechanisms in Densely Connected Network for Network Traffic Prediction by Myeongjun Oh, Sung Oh, Jongkyung Im, Myungho Kim, Joung-Sik Kim, Ji-Yeon Park, Na-Rae Yi, Sung-Ho Bae

“…Recently, STDenseNet (SpatioTemporal Densely connected convolutional Network) showed remarkable performance in predicting network traffic by leveraging the inductive bias of convolution layers. …”
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Fine-Scale Small Water Body Uncovered by GF-2 Remote Sensing and Multifeature Deep Learning Model by Yixin Jiang, Chunlin Wang, Zhaji Huang, Dandan Li, Biao Wang, Yanlan Wu, Hui Liu, Zihan Liu

“…Spatial characteristics of small water bodies in different urban zones and their relationship with overall urban water resources are then analyzed. …”
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Breast Tumor Detection and Diagnosis Using an Improved Faster R-CNN in DCE-MRI by Haitian Gui, Han Jiao, Li Li, Xinhua Jiang, Tao Su, Zhiyong Pang

“…We adopted Faster RCNN as the architecture, introduced ROI aligning to minimize quantization errors and feature pyramid network (FPN) to extract different resolution features, added a bounding box quadratic regression feature map extraction network and three convolutional layers to reduce interference from tumor surrounding information, and extracted more accurate and deeper feature maps. …”
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Validation of a Swine Cough Monitoring System Under Field Conditions by Luís F. C. Garrido, Gabriel S. T. Rodrigues, Leandro B. Costa, Diego J. Kurtz, Ruan R. Daros

“…It is recommended to test the technology in other environments to evaluate the effectiveness in different farm settings.…”
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Novel Neural Networks for Camera Calibration in Underwater Environments by Cristian H. Sanchez-Saquin, Leonardo Barriga-Rodriguez, Leonardo A. Baldenegro-Perez, Guillermo Ronquillo-Lomeli, Noe A. Rodriguez-Olivares

“…Three distinct scenes clean, green and blue waters were used to study the network performance under different lighting and color conditions. For network training, the Mean Squared Error (MSE) was used as the loss function, and the <inline-formula> <tex-math notation="LaTeX">$L2$ </tex-math></inline-formula> norm was applied to the dense layers for 256 epochs. …”
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Reverse design of solid propellant grain based on deep learning: Imaging internal ballistic data by Lin Sun, Xiangyu Peng, Yang Liu, Shu Long, Weihua Hui, Ran Wei, Futing Bao

“…This paper conducts comparative experiments across various neural network models, validating the effectiveness of the feature extraction method that transforms internal ballistic time-series data into images, as well as its generalization capability across different CNN architectures. Ignition tests were performed based on the predicted propellant grain. …”
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An Automated Image-Based Dietary Assessment System for Mediterranean Foods by Fotios S. Konstantakopoulos, Eleni I. Georga, Dimitrios I. Fotiadis

“…The food volume estimation subsystem achieves an overall mean absolute percentage error 10.5&#x0025; for 148 different food dishes. <italic>Conclusions:</italic> The proposed automated image-based dietary assessment system provides the capability of continuous recording of health data in real time.…”
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Synchronous End-to-End Vehicle Pedestrian Detection Algorithm Based on Improved YOLOv8 in Complex Scenarios by Shi Lei, He Yi, Jeffrey S. Sarmiento

“…The motivation behind our design is twofold: first, to address the limitations of traditional methods in handling targets of different scales and severe occlusions, and second, to improve the efficiency and accuracy of real-time detection. …”
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