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Deep learning

A PPE detection method based on the advanced YOLOv11 architecture with a lightweight refinement block system

Alibek Barlybayev, Nurzada Amangeldy, [ ... ], Ramazan Sadvakassov

Deep learning optimisation for cardiology: Neural Architecture Search-driven arrhythmia classification with electrocardiograms

Erik Vanegas Müller, Arese Joe-Oshodi, Abhirup Banerjee, Mauricio Villarroel

GT-PNID: A lightweight graph-temporal network integrating GCN and transformer for power network intrusion detection

Heng Zhou, Mianting Wu, Weixiong Chen, Qiang Zhou

ACMNet: An attention-convolution mixed neural network for semantic segmentation of high-resolution remote sensing images

Shangkun Yu, Leijuan Yu, [ ... ], Chengxin Wang

Crack identification and detection in mine boreholes using deep learning: A case study of hexi coal mine

Ruifeng Huang, Wanshun He, [ ... ], Wenzhe Li

BanglaHateCorpus: A large-scale bangla benchmark for hate speech classification and a transformer-based deep learning method

Anika Tasnim Rodela, Mahbub E Sobhani, [ ... ], Dewan Md. Farid

Deep-learning side-channel analysis of embedded ML-KEM: Single-trace message recovery, cross-device generalization, and frequency-band leakage characterization

Chung-Wei Kuo, Yu-I Hung, Kai-Lun Lin

Enhancing medical image segmentation with HCE-Net: A heterogeneous feature collaborative extraction network

Guoqing Zhu, Hongzhi Li, [ ... ], Jiaxi Wang

A temperature-adaptive logit-standardized knowledge distillation for lightweight network intrusion detection

XiaoFang Dong, Kai Yang, JingChao Liu, Wen Chen

Bi-Mamba-Load: A bidirectional structured state space model with sequence relationship adaptive perception for electricity load forecasting

Junwei Zhu, Lingzhi Xi, [ ... ], Lei Zhang

Long-term electricity load forecasting model based on multi-period and multi-scale analysis

Xiaolin Zhang, Zhi Liu, Chunyang Wang, Zhen Qian

An interpretable multimodal deep learning model for physiological stress detection

Ahmed Tibermacine, Imad Eddine Tibermacine, [ ... ], Mustapha Habib

Stock-level volatility forecasting using machine learning: A comparative study of GARCH, Random Forest, LSTM, and GRU on US Equities

R. V. Pragadeeshwaran, R. Tarun Narayanan, R. M. Bhavadharini

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