LCFANet: A Novel Lightweight Cross-Level Feature Aggregation Network for Small Agricultural Pest Detection by Shijian Huang, Yunong Tian, Yong Tan, Zize Liang

“…Within the feature extraction and fusion networks, we introduce the Dual Temporal Feature Aggregation C3k2 (DTFA-C3k2) module, leveraging a spatiotemporal fusion mechanism to integrate multi-receptive field features while preserving fine-grained texture and structural details across scales. …”
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Enhanced SOC estimation method for lithium-ion batteries using Bayesian-optimized TCN–LSTM neural networks by Taotao Hu, Xiting Zhu, Maokai Tian

“…To address this, a novel Bayesian-optimized temporal convolution network (TCN)–long short-term memory (LSTM) network is proposed, combining temporal convolution network (TCN) and long short-term memory (LSTM) to enhance SOC estimation accuracy. …”
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Deep learning models for enhanced forest-fire prediction at Mount Kilimanjaro, Tanzania: Integrating satellite images, weather data and human activities data by Cesilia Mambile, Shubi Kaijage, Judith Leo

“…Specifically, Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), and Convolutional Long Short-Term Memory (ConvLSTM) models were employed to analyze Sentinel-2 satellite imagery and weather data, along with anthropogenic factors such as beekeeping, tourism, agriculture, and deforestation rates. …”
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A Hybrid Deep Learning–Based Approach for Visual Field Test Forecasting by Ashkan Abbasi, PhD, Sowjanya Gowrisankaran, PhD, Wei-Chun Lin, MD, PhD, Xubo Song, PhD, Bhavna Josephine Antony, PhD, Gadi Wollstein, MD, Joel S. Schuman, MD, Hiroshi Ishikawa, MD

“…Methods: Three deep learning models were trained for pointwise forecasting of VF test data: (1) a recurrent neural network (RNN), (2) CascadeNet-5, a convolutional neural network (CNN), and (3) Hybrid-VF-Net, our proposed method that combines an RNN with a CNN equipped with depthwise transformers for both spatial and temporal modeling. …”
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SIG-ShapeFormer: A Multi-Scale Spatiotemporal Feature Fusion Network for Satellite Cloud Image Classification by Xuan Liu, Zhenyu Lu, Bingjian Lu, Zhuang Li, Zhongfeng Chen, Yongjie Ma

“…However, most existing models—such as those based on convolutional neural networks (CNNs), Transformer architectures, and their variants like Swin Transformer—primarily focus on spatial modeling of static images and do not explicitly incorporate temporal information, thereby limiting their ability to effectively integrate spatiotemporal features. …”
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Spatiotemporal DeepWalk Gated Recurrent Neural Network: A Deep Learning Framework for Traffic Learning and Forecasting by Jian Yang, Jinhong Li, Lu Wei, Lei Gao, Fuqi Mao

“…In the framework, the spatial dependency between nodes of an entire road network is extracted by graph convolutional network (GCN), whereas the temporal dependency between speeds is captured by a gated recurrent unit network (GRU). …”
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Novel deep neural network architecture fusion to simultaneously predict short-term and long-term energy consumption. by Abrar Ahmed, Safdar Ali, Ali Raza, Ibrar Hussain, Ahmad Bilal, Norma Latif Fitriyani, Yeonghyeon Gu, Muhammad Syafrudin

“…Therefore, this research proposes a novel hybrid model employing Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) to simultaneously predict both short-term and long-term residential energy consumption with enhanced accuracy measures. …”
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Energy-efficient human-like trajectory planning for wheeled robots in unstructured environments based on the RCSM-PL network by Hao Xu, Guanyu Zhang, Huanyu Zhao

“…This study proposes a human-like trajectory planning method based on deep learning to address energy inefficiency. A convolutional neural network (CNN) with multi-dimensional attention extracts spatial features from driving scenes and radar maps of hazardous areas. …”
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Identification of Subtypes of Post-Stroke and Neurotypical Gait Behaviors Using Neural Network Analysis of Gait Cycle Kinematics by Andrian Kuch, Nicolas Schweighofer, James M. Finley, Alison McKenzie, Yuxin Wen, Natalia Sanchez

“…We first trained a Convolutional Neural Network and a Temporal Convolutional Network to extract features that distinguish impaired from neurotypical gait. …”
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Construction of a traffic flow prediction model based on neural ordinary differential equations and Spatiotemporal adaptive networks by Li Ma, Yunshun Wang, Xiaoshi Lv, Lijun Guo

“…In the long-term spatiotemporal branch, the Transformer structure is employed, and a self-supervised masking mechanism is utilized to pretrain the heterogeneity in long-term temporal and spatial dimensions separately. Additionally, a spatiotemporal adaptive module is designed, which adapts to and guides short-term traffic flow prediction across time series and traffic flow networks. …”
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Enhanced Multiple Sound Event Detection and Classification Using Physical Signal Properties in Recurrent Spiking Neural Networks by Zahra Roozbehi, Ajit Narayanan, Mahsa Mohaghegh, Samaneh-Alsadat Saeedinia

“…Our simulations reveal substantial performance improvements, achieving the highest precision of 73% in classification tasks, including multilayer perceptrons (MLP), convolutional recurrent neural networks (CRNN), and recurrent neural networks (RNN). …”
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The prediction method for ground surface settlement of pipe jacking tunnels based on a spatiotemporal transfer learning network by Hairong Huang, Lian Yuan, Jian Chen, Shixia Zhang

“…The Long Short-Term Memory-Convolutional Neural Network model with Transfer Learning (LSTM-CNN-TL) is proposed to achieve settlement prediction under data-scarce conditions. …”
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RUL Prediction of Rolling Bearings Based on Fruit Fly Optimization Algorithm Optimized CNN-LSTM Neural Network by Jiaping Shen, Haiting Zhou, Muda Jin, Zhongping Jin, Qiang Wang, Yanchun Mu, Zhiming Hong

“…This method utilizes the deep feature mining capabilities of convolutional neural networks (CNN) and long short-term memory networks (LSTM) to effectively extract spatial features and temporal information sequences from the dataset. …”
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Rolling bearing fault diagnosis under small sample conditions based on WDCNN-BiLSTM Siamese network by Chenxu Bian, Chunni Jia, Jibo Li, Xiangjun Chen, Pei Wang

“…To address this problem, a novel Siamese Neural Network (SNN) model, integrating Deep Convolutional Neural Networks with Wide First-layer Kernel (WDCNN) and Bidirectional Long Short-Term Memory (BiLSTM) network is proposed. …”
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Non-Contact Blood Pressure Monitoring Using Radar Signals: A Dual-Stage Deep Learning Network by Pengfei Wang, Minghao Yang, Xiaoxue Zhang, Jianqi Wang, Cong Wang, Hongbo Jia

“…By employing advanced preprocessing techniques, the system captures subtle chest wall vibrations and their second-order derivatives, feeding dual-channel inputs into a hierarchical neural network. Specifically, Stage 1 deploys convolutional depth-adjustable lightweight residual blocks to extract spatial features from micro-motion characteristics, while Stage 2 employs a transformer architecture to establish correlations between these spatial features and BP periodic dynamic variations. …”
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Multi-modal denoised data-driven milling chatter detection using an optimized hybrid neural network architecture by Haining Gao, Haoyu Wang, Hongdan Shen, Shule Xing, Yong Yang, Yinlin Wang, Wenfu Liu, Lei Yu, Mazhar Ali, Imran Ali Khan

“…Sensitivity analysis of time–frequency domain features is conducted using Pearson correlation coefficient analysis. A hybrid neural network model (DBMA) for chatter detection is constructed by integrating dual-scale parallel convolutional neural networks, bidirectional gated recurrent units, and multi-head attention mechanisms. …”
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A Hybrid CNN-LSTM Model With Attention Mechanism for Improved Intrusion Detection in Wireless IoT Sensor Networks by Pendukeni Phalaagae, Adamu Murtala Zungeru, Abid Yahya, Boyce Sigweni, Selvaraj Rajalakshmi

“…Existing intrusion detection systems (c) often struggle with scalability and efficiency under the unique demands of IoT networks. This work introduces an Intrusion Detection System (IDS) framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks in a hybrid architecture, enhanced by an attention mechanism to improve feature extraction and classification accuracy. …”
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A Human-Centric, Uncertainty-Aware Event-Fused AI Network for Robust Face Recognition in Adverse Conditions by Akmalbek Abdusalomov, Sabina Umirzakova, Elbek Boymatov, Dilnoza Zaripova, Shukhrat Kamalov, Zavqiddin Temirov, Wonjun Jeong, Hyoungsun Choi, Taeg Keun Whangbo

“…A custom hybrid backbone that couples convolutional networks with transformers keeps the model nimble enough for edge devices. …”
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A deep Reinforcement learning-based robust Intrusion Detection System for securing IoMT Healthcare Networks by Jamshed Ali Shaikh, Chengliang Wang, Muhammad Wajeeh Us Sima, Muhammad Arshad, Muhammad Owais, Dina S. M. Hassan, Reem Alkanhel, Mohammed Saleh Ali Muthanna

“…The CNN captures spatial patterns in network traffic, while the LSTM identifies temporal patterns. …”
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FEN-MRMGCN: A Frontend-Enhanced Network Based on Multi-Relational Modeling GCN for Bus Arrival Time Prediction by Ting Qiu, Chan-Tong Lam, Bowie Liu, Benjamin K. Ng, Xiaochen Yuan, Sio Kei Im

“…The network then uses a conventional time-series model to capture temporal dynamics. …”
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