Deep learning-based time series prediction for precision field crop protection by Tao He, Meijin Li, Dong Jin

“…At its core, our Spatially-Aware Data Fusion Network (SADF-Net) integrates multi-modal data sources, such as satellite imagery, IoT sensor readings, and meteorological forecasts, into a unified predictive model. By combining convolutional layers for spatial feature extraction, recurrent neural networks for temporal modeling, and attention mechanisms for data fusion, SADF-Net captures intricate spatial-temporal dependencies while ensuring robustness to noisy and incomplete data. …”
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Artificial intelligence-driven precipitation downscaling and projections over Thailand using CMIP6 climate models by Muhammad Waqas, Usa Wannasingha Humphries

“…The research evaluates the performance of artificial intelligence (AI)-driven downscaling techniques (Dynamic Neural Network with Memory (DyNN-Mem) and Hybrid Long Short-Term Memory Convolutional Neural Network (LSTM-CNN)) for scaling down CMIP6 Global Climate Models (GCMs) daily precipitation outputs across Thailand. …”
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Intelligent SDN to enhance security in IoT networks by Safi Ibrahim, Aya M. Youssef, Mahmoud Shoman, Sanaa Taha

“…As the popularity of SDN increases, it is crucial to address security vulnerabilities in these dynamic networks. This paper proposes a framework for enhancing security in SDN by utilising three separate Deep Learning models, namely Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM). …”
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Multi-model learning for vessel ETA prediction in inland waterways using multi-attribute data by Abdullah Al Noman, Anton Zitnikov, Aaron Heuermann, Klaus-Dieter Thoben

“…The model integrates Convolutional Neural Networks (CNNs) to extract spatial features, Long Short-Term Memory (LSTM) networks to model sequential dependencies, Transformer-based attention mechanisms to dynamically weigh environmental factors, and a Multi-Layer Perceptron (MLP) for incorporating vessel-specific and other residual features. …”
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Dual-Stream Enhanced Deep Network for Transmission Near-Infrared Dorsal Hand Vein Age Estimation with Attention Mechanisms by Zhenghua Shu, Zhihua Xie, Xiaowei Zou

“…To this end, this paper proposes an efficient dorsal hand vein age estimation model using a deep neural network with attention mechanisms. Specifically, a convolutional neural network (CNN) is developed to extract the expressive features for age estimation. …”
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Multi-Dimensional Feature Fusion and Enhanced Attention Streaming Movie Prediction Algorithm by Hanqing Hu, Tianmu Tian, Chengjing Liu, Xueyuan Bai

“…Second, an attention mechanism was applied to dynamically assign importance to different time steps and features, enabling the model to focus on the most critical information based on time periods and movie types. …”
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DDoS attack detection in intelligent transport systems using adaptive neuro-fuzzy inference system by G. Usha, H. Karthikeyan, Kumar Gautam, Nikhil Pachauri

“…Moreover, Intelligent Transportation Systems is different from the standard vehicular ad hoc network design since it functions in a highly dynamic environment brought on by the quick mobility between the nodes in short connection times. …”
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3D animation design image detail enhancement based on intelligent fuzzy algorithm by Pu Haitao, Pu Yuang

“…The image was divided into superpixel regions using SLIC (Simple Linear Iterative Clustering) algorithm, and local features such as texture, contrast, and edge intensity were extracted; in the SRGAN model, the generator improved image resolution through deep residual blocks and Convolutional Neural Network (CNN), while the discriminator optimized the generated image quality through adversarial training; at the same time, a Fuzzy Logic System (FLS) was constructed to dynamically adjust the image fuzzy degree; channel and spatial attention modules in the generator were integrated to enhance key area details. …”
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A model for tobacco growing area classification based on time series features of thermogravimetric analysis by Jiaxu Xia, Yunong Tian, Xianwei Hao, Yuhan Peng, Guanqun Luo, Zhihua Gan

“…This study combines Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) model to process the derivative thermogravimetric (DTG) data, aiming to uncover the inherent time series properties and the continuous and dynamic relationship between temperatures for classifying tobacco growing areas. …”
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Remote sensing image Super-resolution reconstruction by fusing multi-scale receptive fields and hybrid transformer by Denghui Liu, Lin Zhong, Haiyang Wu, Songyang Li, Yida Li

“…The generator network of the model employs multi-scale convolutional kernels to extract image features and utilizes a multi-head self-attention mechanism to dynamically fuse these features, significantly improving the ability to capture both fine details and global information in remote sensing images. …”
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A deep learning-based algorithm for the detection of personal protective equipment. by Bo Tong, Guan Li, Xiangli Bu, Yang Wang, Xingchen Yu

“…The DCNV2(Deformable Convolutional Networks v2) module was incorporated to handle geometric scale transformations by generating spatial offsets and feature masks from interactive features, thereby improving localization accuracy and dynamically selecting weights to enhance classification precision.The improved model incorporates rich multi-level and multi-scale features, allowing it to better adapt to tasks involving geometric transformations and aligning with the image data distribution in construction scenarios. …”
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A Residual-Corrected Hybrid ARIMA–CNN–LSTM Framework for High-Accuracy Tobacco Sales Forecasting in Regulated Markets by Shiyu Huang, Lili Zhou

“…The ARIMA model is reliable in learning linear or regular relationships, while the deep learn, such as convolutional neural network (CNN) and long short-term memory network (LSTM), is superior when capturing and learning nonlinear relationships. …”
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A Destination Prediction Network Based on Spatiotemporal Data for Bike-Sharing by Jian Jiang, Fei Lin, Jin Fan, Hang Lv, Jia Wu

“…In the final step, DPNst dynamically aggregates the output of the three neural networks based on the given data and generates the predictions. …”
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Hierarchical Multi-Scale Decomposition and Deep Learning Ensemble Framework for Enhanced Carbon Emission Prediction by Yinuo Sun, Zhaoen Qu, Zhuodong Liu, Xiangyu Li

“…An adaptive ensemble mechanism dynamically weights component predictions based on stability and performance metrics. …”
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Spatial–Temporal Transformer for Optimizing Human Health Through Skeleton-Based Body Sports Action Recognition by Faze Liang, Lejia Ou, Zujun Lei, Xiaohong Tu, Kai Xin

“…Despite progress in skeleton-based recognition using Graph Convolutional Networks (GCNs) and Transformers, existing methods often fail to effectively model complex spatial-temporal dependencies, especially in dynamic or subtle actions. …”
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A lightweight trichosanthes kirilowii maxim detection algorithm in complex mountain environments based on improved YOLOv7-tiny. by Zhongjian Xie, Xinwei Chen, Weilin Wu, Yao Xiao, Yuanhang Li, Yaya Zhang, ZhuXuan Wan, Weiqi Chen

“…Secondly, a lightweight convolutional module is introduced to replace the standard convolutions in the Efficient Long-range Aggregation Network (ELAN-A) module, and the channel pruning techniques are applied to further decrease the model's complexity. …”
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YOLOv8-BaitScan: A Lightweight and Robust Framework for Accurate Bait Detection and Counting in Aquaculture by Jian Li, Zehao Zhang, Yanan Wei, Tan Wang

“…The key innovations are as follows: (1) By incorporating the channel prior convolutional attention (CPCA) into the final layer of the backbone, the model efficiently extracts spatial relationships and dynamically allocates weights across the channel and spatial dimensions. (2) The minimum points distance intersection over union (MPDIoU) loss function improves the model’s localization accuracy for bait bounding boxes. (3) The structure of the Neck network is optimized by adding a tiny-target detection layer, which improves the recall rate for small, distant bait targets and significantly reduces the miss rate. (4) We design the lightweight detection head named Detect-Efficient, incorporating the GhostConv and C2f-GDC module into the network to effectively reduce the overall number of parameters and computational cost of the model. …”
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Radar-Based Hand Gesture Recognition With Feature Fusion Using Robust CNN-LSTM and Attention Architecture by Irshad Khan, Young-Woo Kwon

“…This article introduces a novel deep learning approach for hand gesture recognition, leveraging convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and attention mechanisms. …”
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Research on intelligent classification of coastal land cover by integrating remote sensing images and deep learning by Xinhao Lin, Junmiao Hei, Yixiao Wang, Ang Zhang

“…This work highlights the potential of deep learning in enhancing remote sensing analysis for environmental and urban applications, paving the way for intelligent decision-making in dynamic coastal zones.…”
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Failure Management Overview in Optical Networks by Sergio Cruzes

“…Effective management requires the detection of soft failures, accurate bit error rate (BER) predictions, and dynamic network operations to maintain minimal margins. …”
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