Thermal optimization of nickel-carbon nanodots using neural network and numerical simulation by Sohail Ahmad, Hessa A. Alsalmah

“…The ongoing research presents an in-depth analysis of the dynamics of nickel-carbon nanodots suspended in glycerol. …”
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YED-Net: Yoga Exercise Dynamics Monitoring with YOLOv11-ECA-Enhanced Detection and DeepSORT Tracking by Youyu Zhou, Shu Dong, Hao Sheng, Wei Ke

“…Against the backdrop of the deep integration of national fitness and sports science, this study addresses the lack of standardized movement assessment in yoga training by proposing an intelligent analysis system that integrates an improved YOLOv11-ECA detector with the DeepSORT tracking algorithm. A dynamic adaptive anchor mechanism and an Efficient Channel Attention (ECA) module are introduced, while the depthwise separable convolution in the C3k2 module is optimized with a kernel size of 2. …”
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deepBBQ: A Deep Learning Approach to the Protein Backbone Reconstruction by Justyna D. Kryś, Maksymilian Głowacki , Piotr Śmieja , Dominik Gront

“…Coarse-grained models have provided researchers with greatly improved computational efficiency in modeling structures and dynamics of biomacromolecules, but, to be practically useful, they need fast and accurate conversion methods back to the all-atom representation. …”
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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

“…Therefore, this paper proposes an improved algorithm based on You Only Look Once (YOLO) v9, which enhances feature capture capability while reducing parameters by 33.6%. First, a dynamic local shuffle module (DLSConv) is proposed, which utilizes convolutions and adaptive shuffling, effectively enhancing information interaction and feature richness. …”
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A Multi-Source Domain Adaptation Method for Bearing Fault Diagnosis with Dynamically Similarity Guidance on Incomplete Data by Juan Tian, Shun Zhang, Gang Xie, Hui Shi

“…To address these issues, the Dynamic Similarity-guided Multi-source Domain Adaptation Network (DS-MDAN) is proposed. …”
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A Blueberry Maturity Detection Method Integrating Attention-Driven Multi-Scale Feature Interaction and Dynamic Upsampling by Haohai You, Zhiyi Li, Zhanchen Wei, Lijuan Zhang, Xinhua Bi, Chunguang Bi, Xuefang Li, Yunpeng Duan

“…Built on the YOLOv8n architecture, ADE-YOLO features a dimensionality-reducing convolution at the backbone’s end, reducing computational complexity while optimizing input features. …”
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A Multiscale Mixed-Graph Neural Network Based on Kinematic and Dynamic Joint Features for Human Motion Prediction by Rongyong Zhao, Bingyu Wei, Lingchen Han, Yuxin Cai, Yunlong Ma, Cuiling Li

“…The extraction of joint features at each scale is facilitated by a single-scale mixed-graph convolution module. And to effectively integrate the extracted kinematic and dynamic features, a KD-fused Graph-GRU (Kinematic and Dynamics Fused Graph Gate Recurrent Unit) predictor is designed to fuse them. …”
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TCN–Transformer Spatio-Temporal Feature Decoupling and Dynamic Kernel Density Estimation for Gas Concentration Fluctuation Warning by Yanping Wang, Longcheng Zhang, Zhenguo Yan, Jun Deng, Yuxin Huang, Zhixin Qin, Yuqi Cao, Yiyang Wang

“…The transient fluctuation characteristics of gas concentration are captured using causal dilation convolution, while a multi-head self-attention mechanism is used to analyze the cross-scale correlation of geological mining parameters. …”
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Transfer learning-enhanced CNN-GRU-attention model for knee joint torque prediction by Hao Xie, Yingpeng Wang, Tingting Liu, Songhua Yan, Jizhou Zeng, Kuan Zhang

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Optimizing deep belief network for concrete crack detection via a modified design of ideal gas molecular dynamics by Tan Qin, Gongxing Yan, Huaguo Jiang, Minqi Shen, Andrea Settanni

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Deep learning based model predictive control of active filter inverter as interface for photovoltaic generation by Amin Rasoulian, Hadi Saghafi, Mohammadali Abbasian, Majid Delshad

“…To overcome this problem, this study develops an artificial intelligence‐based on one‐dimensional convolutional neural network (1D‐CNN) based MPCs. While 1D‐CNN benefits from the inherent strong feature extraction/selection capability and lower computational complexity than other deep methods, it still cannot properly track the dynamic changes due to fixed weights during the training process. …”
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Knowledge graph construction and talent competency prediction for human resource management by Bowen Yang, Zhixuan Shen

“…To address these challenges, we propose a hybrid model that integrates Graph Convolutional Networks (GCN), Reinforcement Learning (RL), and Deep Collaborative Filtering (DCF). …”
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Remaining Useful Life Prediction of Aero-Engine Based on Improved GWO and 1DCNN by Lihua Shen, Yucheng Wang, Baorui Du, Hailong Yang, He Fan

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A data-driven approach for predicting remaining intra-surgical time and enhancing operating room efficiency by Saleem Ramadan, Mohammad Abu-Shams, Sameer Al-Dahidi, Ibrahim Odeh, Najat Almasarwah

“…In this regard, this paper introduces an innovative method that employs Convolutional Neural Networks (CNNs) to predict the remaining intra-surgical time through binary classification for the Gallbladder Dissection phase and to dynamically manage OR schedules. …”
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Encrypted traffic identification method based on deep residual capsule network with attention mechanism by Guozhen SHI, Kunyang LI, Yao LIU, Yongjian YANG

“…With the improvement of users’ security awareness and the development of encryption technology, encrypted traffic has become an important part of network traffic, and identifying encrypted traffic has become an important part of network traffic supervision.The encrypted traffic identification method based on the traditional deep learning model has problems such as poor effect and long model training time.To address these problems, the encrypted traffic identification method based on a deep residual capsule network (DRCN) was proposed.However, the original capsule network was stacked in the form of full connection, which lead to a small model coupling coefficient and it was impossible to build a deep network model.The DRCN model adopted the dynamic routing algorithm based on the three-dimensional convolutional algorithm (3DCNN) instead of the fully-connected dynamic routing algorithm, to reduce the parameters passed between each capsule layer, decrease the complexity of operations, and then build the deep capsule network to improve the accuracy and efficiency of recognition.The channel attention mechanism was introduced to assign different weights to different features, and then the influence of useless features on the recognition results was reduced.The introduction of the residual network into the capsule network layer and the construction of the residual capsule network module alleviated the gradient disappearance problem of the deep capsule network.In terms of data pre-processing, the first 784byte of the intercepted packets was converted into images as input of the DRCN model, to avoid manual feature extraction and reduce the labor cost of encrypted traffic recognition.The experimental results on the ISCXVPN2016 dataset show that the accuracy of the DRCN model is improved by 5.54% and the training time of the model is reduced by 232s compared with the BLSTM model with the best performance.In addition, the accuracy of the DRCN model reaches 94.3% on the small dataset.The above experimental results prove that the proposed recognition scheme has high recognition rate, good performance and applicability.…”
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Optimized image-based grouping of e-commerce products using deep hierarchical clustering by Yuliana Melita Pranoto, Anik Nur Handayani, Heru Wahyu Herwanto, Yosi Kristian

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A Hybrid Deep Learning Approach for Integrating Transient Electromagnetic and Magnetic Data to Enhance Subsurface Anomaly Detection by Zhijie Qu, Yuan Gao, Shiyan Li, Xiaojuan Zhang

“…Trained on synthetic datasets generated through forward modeling, MagEMNet leverages the adaptive moment estimation (Adam) optimizer and a dynamic learning rate strategy to enhance convergence. …”
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Trajectory Prediction and Intention Recognition Based on CNN-GRU by Jinghao Du, Dongdong Lu, Fei Li, Ke Liu, Xiaolan Qiu

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End-Edge Collaborative Lightweight Secure Federated Learning for Anomaly Detection of Wireless Industrial Control Systems by Chi Xu, Xinyi Du, Lin Li, Xinchun Li, Haibin Yu

“…Specifically, we first design a residual multihead self-attention convolutional neural network for local feature learning, where the variability and dependence of spatial-temporal features can be sufficiently evaluated. …”
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An Advanced Spatio-Temporal Graph Neural Network Framework for the Concurrent Prediction of Transient and Voltage Stability by Chaoping Deng, Liyu Dai, Wujie Chao, Junwei Huang, Jinke Wang, Lanxin Lin, Wenyu Qin, Shengquan Lai, Xin Chen

“…In contrast, a temporal convolutional network captures the system’s dynamic behavior over time. …”
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