Enhancing hand-drawn diagram recognition through the integration of machine learning and deep learning techniques by Vanita Agrawal, MVV Prasad Kantipudi, Jayant Jagtap

“…Abstract For many generations, hand-drawn diagrams were utilized as a common graphical means of interaction in various fields, such as engineering, architecture, and education. …”
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A Study of Futures Price Forecasting with a Focus on the Role of Different Economic Markets by Yongxiang Wang, Qingyang Liu, Yanrong Hu, Hongjiu Liu

“…We then apply the sample convolution and interaction network (SCINet) to perform both single-step and multi-step predictions of futures prices. …”
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Self-organizing expert communities in educational projects by Gennadiy P. Vinogradov, Vladimir N. Kuznetsov

“…The most promising form of education project management is the model of information interaction within the framework of active self-developing network expert environments. …”
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DFT and dynamic simulations of OleuoPectin structures as a potential anticoagulant agent by Mona T. M. Ghanem, Heba D. Hassanein, Ahmed F. El-Sayed, Medhat A. Ibrahim, Ahmed M. Bayoumy

“…Among these ligands, the 2-Oleu(OH)-Pec(OH) emerges as a highly promising candidate, demonstrating exceptional stability with a docking RMSD of 0.68 Å, a robust binding affinity of -6.80 kcal/mol, and an extensive interaction network comprising 18 total bonds, including 12 hydrogen bonds. …”
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Cascaded Machine Learning of Soil Moisture and Salinity Prediction in Estuarine Wetlands Based on In Situ Internet of Things Monitoring by Jie Song, Yujun Yi

“…To address these challenges and improve our ability to predict and manage wetland soil properties, this study employs an in situ Internet of Things (IoT)‐based monitoring network and a interpretable, cascaded machine learning model to predict these critical soil parameters. …”
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Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Algorithm and Fusion of Multiple Health Features by Guoju Liu, Zhihui Deng, Yonghong Xu, Lianfeng Lai, Guoqing Gong, Liang Tong, Hongguang Zhang, Yiyang Li, Minghui Gong, Mengxiang Yan, Zheng Ye

“…The model adopts the collaborative architecture of a convolutional neural network and time series module, strengthens the cross-level feature interaction by introducing a multi-level attention mechanism, then uses the FVIM optimization algorithm to optimize the key parameters to realize the overall model architecture. …”
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Domestication and feedback: bidirectional hijacking in pancreatic ductal adenocarcinoma microenvironment by Yuxi Qiao, Haobo Yin, Yong Zhang, Jingdong Zhang, Qian Dong

“…And we examines the role of cancer cells in the TME, highlighting their ability to manipulate stromal components to serve as collaborators in tumor progression. …”
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Identification and validation of differentially expressed genes in intramuscular fat metabolism in Guizhou yellow chickens using RNA-Seq analysis. by Yingping Tian, Xiaoya Wang, Yongchao Rao, Xiaohong Zhou, Yaozhou Jiang, Qinsong Liu, Sheng Wu, Fuping Zhang

“…The results further showed that COL1A1 is a key gene in the gene regulatory network during fat deposition, and the interaction of these genes affects the proliferation and differentiation of fat cells, thereby reducing the accumulation of fat droplets in muscle fibers. …”
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SR-DETR: Target Detection in Maritime Rescue from UAV Imagery by Yuling Liu, Yan Wei

“…This is largely due to their exceptional ability to move freely and their capacity for wide-area monitoring. …”
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Toward User-Guided Seismic Facies Interpretation With a Pre-Trained Large Vision Model by Joshua Atolagbe, Ardiansyah Koeshidayatullah

“…This is further compounded by the fact that these models do not allow user-guided interaction, which limits the ability of seismic interpreters to perform localized seismic facies segmentation through prompts. …”
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Integrating intracellular dynamics using CompuCell3D and Bionetsolver: applications to multiscale modelling of cancer cell growth and invasion. by Vivi Andasari, Ryan T Roper, Maciej H Swat, Mark A J Chaplain

“…We then apply this multiscale modelling technique to a model of cancer growth and invasion, based on a previously published model of Ramis-Conde et al. (2008) where individual cell behaviour is driven by a molecular network describing the dynamics of E-cadherin and β-catenin. …”
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Life cycle support software components by S. A. Kudzh, V. Ya. Tsvetkov, I. E. Rogov

“…The organization of interaction between software components affects the efficiency of the program. …”
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Information Fusion of Infrared Images and Vibration Signals for Coupling Fault Diagnosis of Rotating Machinery by Tangbo Bai, Jianwei Yang, Dechen Yao, Ying Wang

“…Firstly, data enhancement for infrared images and data visualization for vibration are performed on the dataset by using the principle of graphics and Short-Term Fourier Transform, which increases the diversity of the dataset and enhances the generalization ability of the model. Then, a multichannel convolution neural network-based method is constructed to achieve data-level information fusion and improve the fault diagnosis accuracy. …”
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CLMT: graph contrastive learning model for microbe-drug associations prediction with transformer by Liqi Xiao, Junlong Wu, Liu Fan, Lei Wang, Xianyou Zhu, Xianyou Zhu

“…Firstly, unlike conventional GCN-based models, CLMT leverages a Graph Transformer network with an attention mechanism to model high-order dependencies in the microbe-drug interaction graph, enhancing its ability to capture long-range associations. …”
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YOLO-AMM: A Real-Time Classroom Behavior Detection Algorithm Based on Multi-Dimensional Feature Optimization by Yi Cao, Qian Cao, Chengshan Qian, Deji Chen

“…Firstly, we constructed the Adaptive Efficient Feature Fusion (AEFF) module to enhance the fusion of semantic information between different features and improve the model’s ability to capture detailed features. Then, we designed a Multi-dimensional Feature Flow Network (MFFN), which fuses multi-dimensional features and enhances the correlation information between features through the multi-scale feature aggregation module and contextual information diffusion mechanism. …”
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Predictive Emojis for User Cognitive Response to Dynamic Behaviour of Webpages Using Pupil and Click Reinforcement by Fatima Isiaka

“…A computational test was carried out to determine the reliability of the proposed model (predictive dynamic control) with a standard artificial neural network and the results show compatibility as compared to state-of-the-art models. …”
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Few-Shot Incremental Learning With Context-Aware Spatial Enhancement for Image Recognition by Heng Wu, Ze Yang, Zijun Zheng, Haiyang Wang, Wansong Wang

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Deepening the Modulatory Activity of Bioactive Compounds Against AFB1- and OTA-Induced Neuronal Toxicity Through a Proteomic Approach by Alessandra Cimbalo, Massimo Frangiamone, Lara Manyes

“…Additionally, important metabolic pathways associated with the RHO GTPase family, estrogen-dependent gene expression, and androgen receptor transcription stimulated by PKN1 activation were discovered. Network interaction analysis highlighted the modulation of cytoskeletal dynamics, cell migration, and stress responses. …”
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DMM-YOLO: A high efficiency soil fauna detection model based on an adaptive dynamic shuffle mechanism by Jiehui Ke, Renbo Luo, Guoliang Xu, Yuna Tan, Zhifeng Wu, Liufeng Xiao

“…First, a dynamic local shuffle module (DLSConv) is proposed, which utilizes convolutions and adaptive shuffling, effectively enhancing information interaction and feature richness. Additionally, different efficient modules with multi-branch fusion structures, integrating DLSConv, are adopted for the Backbone and Neck to enhance feature extraction and fusion, while optimizing the feature maps fed into the detection head, thereby improving the network’s ability to extract features and detect targets. …”
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Towards large-scale analyses of settlement patterns in urbanizing landscapes—findings of first studies for India, Egypt, and China by Thanh Thi Nguyen, Thomas Esch, Ellen Hoffmann, Julian Zeidler, Lorenz Gruber, Dennis Kaiser, Andreas Buerkert

“…Abstract Improved ability to assess and categorize the spatial characteristics of settlement patterns is required for a deeper understanding of how urbanization is driving land use and land cover transformation and its effects. …”
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