A novel approach for joint indoor localization and activity recognition using a hybrid CNN-GRU and MRF framework. by Sarmad Sohaib, Syed Mohsin Bokhari, Muhammad Shafi, Anas Alhashmi

“…This work proposes a new hybrid model for joint indoor localization and activity recognition by combining a Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) model with a Markov Random Field (MRF) for better classification. …”
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Deep Learning-Based Video Anomaly Detection Using Optimised Attention-Enhanced Autoencoders by Anjali S, Don S

“…Through the reconstruction of normal patterns and the computation of reconstruction error in relation to ground truth, convolutional autoencoders detect anomalies. Frames with errors above a threshold are flagged as abnormal. …”
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Design and realization of compressor data abnormality safety monitoring and inducement traceability expert system. by Yuan Wang, Shaolin Hu

“…The results show that this method effectively overcomes the problems of false alarms and missed alarms based on fixed threshold alarm methods, and achieves 100% classification of two types of faults: non starting of the drive machine and low oil pressure by constructing a PCA (Principal Component Analysis)-SPE (Square Prediction Error)-CNN (Convolutional Neural Network) classifier. Combined with dynamic knowledge graph and NLP (Natural Language Processing) inference, it achieves good diagnostic results.…”
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Research on Optimized Algorithm for Deep Learning Based Recognition of Sediment Particles in Turbulent Flow by WANG Hao, YANG Feiqi, ZHANG Lei, WU Wei, XIE Haonan, ZHAO Lin

“…It addresses the challenge of detecting multiple moving sediment particles and provides a useful reference for research in fluvial dynamics.…”
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On the added value of sequential deep learning for the upscaling of evapotranspiration by B. Kraft, B. Kraft, B. Kraft, J. A. Nelson, S. Walther, F. Gans, U. Weber, G. Duveiller, M. Reichstein, W. Zhang, M. Rußwurm, D. Tuia, M. Körner, Z. Hamdi, M. Jung

“…., non-sequential) models (extreme gradient boosting (XGBoost) and a fully connected neural network (FCN)) with sequential models (a long short-term memory (LSTM) model and a temporal convolutional network (TCN)) for the modeling and upscaling of ET. …”
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Advanced Hydro-Informatic Modeling Through Feedforward Neural Network, Federated Learning, and Explainable AI for Enhancing Flood Prediction by Shahariar Hossain Mahir, Md Tanjum An Tashrif, Md Ahsan Karim, Dipanjali Kundu, Anichur Rahman, Md. Amir Hamza, Fahmid Al Farid, Abu Saleh Musa Miah, Sarina Mansor

“…Explainable AI techniques, such as SHAP, sheds light on the most significant role played by upstream rainfall and river dynamics, particularly from Cherrapunji and the Surma-Kushiyara river system, in driving flood events in Sylhet. …”
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Advanced investing with deep learning for risk-aligned portfolio optimization. by Minh Duc Nguyen

“…We combine two prediction models, Long Short-Term Memory (LSTM) and One-Dimensional Convolutional Neural Network (1D-CNN), with three portfolio frameworks: Mean-Variance with Forecasting (MVF), Risk Parity Portfolio (RPP), and Maximum Drawdown Portfolio (MDP). …”
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Face Detection Method based on Lightweight Network and Weak Semantic Segmentation Attention Mechanism by Xiaoyan Wu

“…A dynamic enhancement attachment based on weak semantic segmentation is added in front of the detector head, whose output is used as the spatial weight distribution to correct the activation area, to suppress the false detection and missed detection caused by the decrease of extraction ability evoked by the pursuit of lightweight. …”
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CSI feedback algorithm for massive MIMO systems based on SFNet by ZHANG Yun, HUANG Jingwei, XU Sunwu, GAO Gui, YU Shujuan, ZHAO Shengmei

“…Fast Fourier convolution and a dynamic feature fusion mechanism were utilized to activate more input information, adjust the receptive field, selectively highlight spatially correlated features, suppress interference, and allow the network to achieve advanced performance with extremely low computational complexity. …”
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Harnessing Deep Learning for Lung Cancer Detection and Prevention: A Comprehensive Survey by Tidke Dipika, Banait Satish S.

“…Deep learning models have had some success in accurately classifying lung nodules and other CT abnormalities using convolutional neural networks (CNNs), as well as advanced architecture such as ResNet and U-Net. …”
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Optimizing Sensor Placement for Event Detection: A Case Study in Gaseous Chemical Detection by Priscile Fogou Suawa, Christian Herglotz

“…In dynamic industrial environments, strategic sensor placement is key to accurately monitoring equipment and detecting critical events. …”
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Research on autonomous obstacle avoidance of mountainous tractors based on semantic neural network and laser SLAM. by Ningjie Chang, Xianghai Yan, Bingxin Chen, Yiwei Wu, Liyou Xu

“…The accuracy and consistency of obstacle avoidance map construction are poor in complex and changeable dynamic environment. In order to improve the driving safety of mountain tractors in complex mountain environment, an autonomous obstacle avoidance method for mountain tractors based on semantic neural network and laser SLAM was studied. …”
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Transferable Deep Learning Models for Accurate Ankle Joint Moment Estimation during Gait Using Electromyography by Amged Elsheikh Abdelgadir Ali, Dai Owaki, Mitsuhiro Hayashibe

“…The best-performing intrasubject models were recurrent convolutional neural networks trained using signal energy features. …”
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Research on Data Repair of Pile-Type Adjustable Wind Turbine Foundation Monitoring Based on FST-ATTNet by WEI Huanwei, ZHAO Jizhang, ZHENG Xiao, TAN Fang, LIU Cong

“…In the spatial domain, the Temporal Convolutional Network (TCN) models long-range dependencies by expanding causal convolutions, thereby capturing local and global spatial relationships. …”
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Faster-Than-Nyquist Signal Design for Multiuser Multicell Indoor Visible Light Communications by Yi-Jun Zhu, Wen-Ya Wang, Gang Xin

“…To reduce the complexity of the maximum-likelihood sequence estimation decoding algorithm, a convolutional code structure with dynamic weights and a fast iterative receiver for FTNMM are developed. …”
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An Ultra-Precision Smoothing Polishing Model for Optical Surface Fabrication with Morphology Gradient Awareness by Guohao Liu, Yonghong Deng, Zhibin Li

“…To improve the surface morphology quality of ultra-precision optical components, particularly in the suppression of mid-spatial frequency (MSF) errors, this paper proposes a morphology gradient-aware spatiotemporal coupled smoothing model based on convolutional material removal. By introducing the Laplacian curvature into the surface evolution framework, a curvature-sensitive “peak-priority” mechanism is established to dynamically guide the local dwell time. …”
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Study on Novel Surface Defect Detection Methods for Aeroengine Turbine Blades Based on the LFD-YOLO Framework by Wei Deng, Guixiong Liu, Jun Meng

“…The proposed approach employs the LDconv model to adjust the size and shape of convolutional kernels dynamically, integrates the deformable attention mechanism (DAT) to capture minute defect features effectively, and uses Focaler-CIoU to optimize the bounding box loss function of the detection network. …”
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YOLOP-MVF: A Multi-Task Autonomous Driving Perception Detection Method Based on Multi Scale Feature Weighted Fusion by Yanqiu Niu, Jing Zhang

“…To improve feature extraction flexibility, deformable convolutions are incorporated, enabling dynamic sampling based on input characteristics. …”
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Visual Episodic Memory-based Exploration by Jack Vice, Natalie Ruiz-Sanchez, Pamela K Douglas, Gita Sukthankar

“…When guiding robotic exploration, our proposed method outperforms the Curiosity-driven Variational Autoencoder (CVAE) at finding dynamic anomalies.…”
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CGFTNet: Content-Guided Frequency Domain Transform Network for Face Super-Resolution by Yeerlan Yekeben, Shuli Cheng, Anyu Du

“…CGCAF dynamically adjusts feature fusion based on image content, enhancing detail restoration. …”
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