Towards sustainable architecture: Enhancing green building energy consumption prediction with integrated variational autoencoders and self-attentive gated recurrent units from mult... by Qing Zeng, Fang Peng, Xiaojuan Han

“…This study addresses these challenges by proposing an advanced deep learning framework that integrates Time-Dependent Variational Autoencoder (TD-VAE) with Adaptive Gated Self-Attention GRU (AGSA-GRU). The framework incorporates self-attention mechanisms and Multi-Task Learning (MTL) strategies to capture long-term dependencies and complex patterns in energy consumption time series data, while simultaneously optimizing prediction accuracy and anomaly detection. …”
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FPGA Implementation for 24.576-Gbit/s Optical PAM4 Signal Transmission with MLP-Based Digital Pre-Distortion by Sheng Hu, Tianqi Zheng, Chengzhen Bian, Xiongwei Yang, Xinda Sun, Zonghui Zhu, Yumeng Gou, Yuanxiao Meng, Jie Zhang, Jingtao Ge, Yichen Li, Kaihui Wang

“…At the receiver, the parallel constant modulus algorithm (PCMA) was applied for signal processing. The bit error rate (BER) achieved met the 2.4 × 10⁻² threshold for soft-decision forward error correction (SD-FEC), enabling a net transmission bit rate of 24.576 Gbit/s. …”
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High-speed threat detection in 5G SDN with particle swarm optimizer integrated GRU-driven generative adversarial network by R. Shameli, Sujatha Rajkumar

“…Moreover, the performance of this model is evaluated using the InSDN dataset and compared with existing DL model-based intrusion detection approaches and the results demonstrate a significantly higher accuracy rate of 98.4%, precision rate of 98%, recall rate of 98.5%, less detection time of 2.464 s, lesser Log loss rate of 1.0 and more metrics instilling confidence in the effectiveness of the proposed method.…”
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Multi-heat keypoint incorporation in deep learning model to tropical cyclone centering and intensity classifying from geostationary satellite images by Thanh-Ha Do, Son-The Phan, Duc-Tien Du, Dinh-Quan Dang, Khanh-Hung Mai, Lars R. Hole

“…This paper proposes a new multitask deep learning model with attention gate mechanisms to work with satellite images and construct heatmaps for TC’s centering and classification. …”
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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 time domain, Bidirectional Gated Recurrent Units (BiGRU) capture both forward and backward dependencies within the time series, ensuring a comprehensive understanding of local sequence patterns. …”
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A Frequency-Aware Transformer for Multiscale Fault Diagnosis in Electrical Machines by Yurim Choi, Inwhee Joe

“…Experimental results demonstrate that the proposed model achieves 99.9% diagnostic accuracy by maintaining an exceptionally low false alarm rate and missed detection rate, thereby ensuring high reliability. …”
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Enhancing AI-driven forecasting of diabetes burden: a comparative analysis of deep learning and statistical models by Rasool Esmaeilyfard, Mohsen Bayati

“…LSTM effectively captured short-term patterns but struggled with long-term dependencies, while GRU, though computationally efficient, exhibited higher error rates. …”
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Towards a theory of urban fragmentation: A cross-cultural analysis of fear, privatization, and the state by Setha Low

“…This paper employs a cross-cultural analysis to explore regional and national variations in residential gating and enclosure as a first step in developing an integrated theory of urban fragmentation. …”
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Finger drawing on smartphone screens enables early Parkinson's disease detection through hybrid 1D-CNN and BiGRU deep learning architecture. by Zhaohui Zhu, E Wu, Pengfei Leng, Jiajun Sun, Mingming Ma, Zhigeng Pan

“…Our hybrid model combined multi-scale convolutional feature extraction (using parallel 1D-Convolutional branches) with bidirectional temporal pattern recognition (via gated recurrent unit [GRU] networks) to analyze movement abnormalities and detect the disease.…”
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