Deep learning-based object detection for environmental monitoring using big data by Wenbo Lin, Tingting Li, Xiao Li

“…EGAN constructs a spatiotemporal graph representation that integrates physical proximity, ecological similarity, and temporal dynamics, and applies graph convolutional encoders to learn expressive spatial features. …”
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The Use of Hybrid CNN-RNN Deep Learning Models to Discriminate Tumor Tissue in Dynamic Breast Thermography by Andrés Munguía-Siu, Irene Vergara, Juan Horacio Espinoza-Rodríguez

“…In this study, we evaluated hybrid convolutional-recurrent neural network (CNN-RNN) models based on five state-of-the-art pre-trained CNN architectures coupled with three RNNs to discern tumor abnormalities in dynamic breast thermographic images. …”
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Modeling eye gaze velocity trajectories using GANs with spectral loss for enhanced fidelity by Shailendra Bhandari, Pedro Lencastre, Rujeena Mathema, Alexander Szorkovszky, Anis Yazidi, Pedro G. Lind

“…This study introduces a Generative Adversarial Network (GAN) framework employing Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) generators and discriminators to generate high-fidelity synthetic eye gaze velocity trajectories. …”
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A Machine Learning Model for Procurement of Secondary Reserve Capacity in Power Systems with Significant vRES Penetrations by João Passagem dos Santos, Hugo Algarvio

“…Specifically, the methodology incorporates neural networks, such as Long Short-Term Memory (LSTM) or Convolutional neural network (CNN) models, to improve forecasting accuracy by capturing temporal dependencies and nonlinear patterns in the data. …”
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A Data-Driven Approach for Generating Synthetic Load Profiles with GANs by Tsvetelina Kaneva, Irena Valova, Katerina Gabrovska-Evstatieva, Boris Evstatiev

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The best angle correction of basketball shooting based on the fusion of time series features and dual CNN by Meicai Xiao

“…Segmenting the shooting video, taking the video frame as the input of the key node extraction network of the shooting action, obtaining the video frame with the sequence information of the bone points, extracting the continuous T-frame video stack from it, and inputting it into the spatial context feature extraction network in the shooting posture prediction model based on dual stream CNN (MobileNet V3 network with multi-channel attention mechanism fusion module), extract the space context features of shooting posture; The superimposed optical flow graph of continuous video frames containing sequence information of bone points is input into the time convolution network (combined with Bi-LSTM network of multi-channel attention mechanism fusion module), extract the skeleton temporal sequence features during the shooting movement, using the spatial context features and skeleton temporal sequence features extracted from the feature fusion module, and realizing the prediction of shooting posture through Softmax according to the fusion results, calculate the shooting release speed under this attitude, solve the shooting release angle, and complete the correction of the best shooting release angle by comparing with the set conditions. …”
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Spatiotemporal information conversion machine for time-series forecasting by Hao Peng, Pei Chen, Rui Liu, Luonan Chen

“…STICM combines the advantages of both the STI equation and the temporal convolutional network, which maps the high-dimensional/spatial data to the future temporal values of a target variable, thus naturally providing the forecasting of the target variable. …”
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MA-DenseUNet: A Skin Lesion Segmentation Method Based on Multi-Scale Attention and Bidirectional LSTM by Wenbo Huang, Xudong Cai, Yang Yan, Yufeng Kang

“…Built upon the U-Net architecture, the proposed model enhances the encoder with dense convolutions and an adaptive feature fusion module to strengthen feature extraction and multi-scale information integration. …”
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Attention-Enhanced Hybrid Automatic Modulation Classification for Advanced Wireless Communication Systems: A Deep Learning-Transformer Framework by Sam Ansari, Khawla A. Alnajjar, Sohaib Majzoub, Eqab Almajali, Anwar Jarndal, Talal Bonny, Abir Hussain, Soliman Mahmoud

“…The proposed framework is rigorously compared against six representative models—recurrent neural networks (RNN), long short-term memory (LSTM), gated recurrent units (GRU), convolutional neural networks-transformer graph neural network (CTGNet), MobileViT, and DeepsigNet—across multiple evaluation criteria. …”
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Energy-Efficient on-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing by Flor Ortiz, Nicolas Skatchkovsky, Eva Lagunas, Wallace A. Martins, Geoffrey Eappen, Saed Daoud, Osvaldo Simeone, Bipin Rajendran, Symeon Chatzinotas

“…To benchmark the performance of the proposed model, we implement conventional Convolutional Neural Networks (CNN) on a Xilinx Versal VCK5000, and provide a detailed comparison of accuracy, precision, recall, and energy efficiency for different traffic demands. …”
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Federated Deep Learning for Scalable and Explainable Load Forecasting in Privacy-Conscious Smart Cities by Ibrahim Alzamil

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Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population by Lydia Aslanidou, Georgios Rovas, Ramin Mohammadi, Sokratis Anagnostopoulos, Cemre Çelikbudak Orhon, Nikolaos Stergiopulos

“…A large previously generated virtual cohort of n = 3818 subjects with varied hemodynamic profiles served as data bank for arterial pulse waves and reference CO values. Two-layered convolutional neural networks (CNN) yielded CO estimates based on entire pressure traces from the radial, superficial temporal and common carotid arteries. …”
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Optimizing 5G resource allocation with attention-based CNN-BiLSTM and squeeze-and-excitation architecture by Anfal Musadaq Rayyis, Mohammad Maftoun, Maryam Khademi, Emrah Arslan, Silvia Gaftandzhieva

“…Traditional machine learning models struggle to capture intricate temporal dependencies and handle imbalanced data distributions, limiting their effectiveness in real-world applications.MethodsTo overcome these limitations, this study presents an innovative deep learning-based framework that combines a convolutional layer with squeeze-and-excitation block, bidirectional long short-term memory, and a self-attention mechanism for resource allocation prediction. …”
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Regional Short‐Term Wind Power Prediction Based on CEEMDAN‐FTC Feature Mapping and EC‐TCN‐BiLSTM Deep Learning by Guoyuan Qin, Xiaosheng Peng, Zimin Yang

“…To improve the accuracy of regional short‐term WPP, a method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), fine‐to‐coarse (FTC) feature mapping, and error compensation‐temporal convolutional network‐bidirectional Long short‐term memory network (EC‐TCN‐BiLSTM) is proposed in this paper. …”
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Short-Term Wind Power Forecast Based on CNN&LSTM-GRU Model Integrated with CEEMD-SE Algorithm by Guohua YANG, Xin QI, Rui JIA, Yifeng LIU, Fei MENG, Xin MA, Xiaowen XING

“…First, the original wind power output series are decomposed into several intrinsic mode function components and one residual component by complementary set empirical mode decomposition, and those components of similar mode are reconstructed by sample entropy algorithm. Next, the parallel network structure of convolutional neural network and long short term memory network is set up, and the local and temporal features of the data are extracted. …”
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A Multi-Spatial Scale Ocean Sound Speed Prediction Method Based on Deep Learning by Yu Liu, Benjun Ma, Zhiliang Qin, Cheng Wang, Chao Guo, Siyu Yang, Jixiang Zhao, Yimeng Cai, Mingzhe Li

“…To investigate the interactions across multiple spatial scales and to achieve accurate predictions, we propose the STA-ConvLSTM framework that integrates spatiotemporal attention mechanisms with convolutional long short-term memory neural networks (ConvLSTM). …”
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Reconfigurable versatile integrated photonic computing chip by Yufei Wang, Kun Liao, Kuo Zhang, Zhuochen Du, Ze Wang, Bo Ni, Tianyu Xu, Shuai Feng, Yan Yang, Qi-Fan Yang, Quan Sun, Xiaoyong Hu, Qihuang Gong

“…We unlock the intrinsic capabilities of a compact cross-waveguide coupled microring component to natively support both static and dynamic temporal tasks. As a proof of concept, we experimentally integrated a turnkey soliton microcomb as the light source on the photonic computing platform, demonstrating the realization of fully connected, convolutional, and recurrent neural network models within a unified structure. …”
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Design and development of an efficient RLNet prediction model for deepfake video detection by Varad Bhandarkawthekar, T. M. Navamani, Rishabh Sharma, K. Shyamala

“…While existing methods often focus on spatial features, they may overlook crucial temporal information distinguishing real from fake content and need to investigate several other Convolutional Neural Network architectures on video-based deep fake datasets.MethodsThis study introduces an RLNet deep learning framework that utilizes ResNet and Long Short Term Memory (LSTM) networks for high-precision deepfake video detection. …”
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I-AIR: intention-aware travel itinerary recommendation via multi-signal fusion and spatiotemporal constraints by Xiao Cui, Zhihua Wang, Ping Li, Qiang Xu

“…The model combines a multi-head self-attention transformer to capture the sequential and temporal dynamics of user behavior, with a graph convolutional network (GCN) that models complex co-visitation patterns among POIs. …”
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Enhancing microgrid forecasting accuracy with SAQ-MTCLSTM: A self-adjusting quantized multi-task ConvLSTM for optimized solar power and load demand predictions by Ehtisham Lodhi, Nadia Dahmani, Syed Muhammad Salman Bukhari, Sujan Gyawali, Sanjog Thapa, Lin Qiu, Muhammad Hamza Zafar, Naureen Akhtar

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