Optimizing DNA Sequence Classification via a Deep Learning Hybrid of LSTM and CNN Architecture by Elias Tabane, Ernest Mnkandla, Zenghui Wang

“…It contrasts traditional machine learning with advanced deep learning approaches to ascertain performance with respect to genomic data complexity. A hybrid network combining long short-term memory (LSTM) and convolutional neural networks (CNN) was developed to extract long-distance dependencies as well as local patterns from DNA sequences. …”
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A Study on the STGCN-LSTM Sign Language Recognition Model Based on Phonological Features of Sign Language by Yuxin Han, Yong Han, Qi Jiang

“…To deal with these challenges, this paper proposes a dual-stream deep learning model built on Spatio-Temporal Graph Convolutional Network-Long Short-Term Memory(STGCN-LSTM), which aims to capture both the local features of sign language and the global spatio-temporal characteristics of sign words. …”
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Targeting Trefoil Factor Family 3 in Obstructive Airway Diseases: A Computational Approach to Novel Therapeutics by Alireza Shahriary, Mohsen Sisakht, Masoud Arabfard, Esmaeil Behmard, Ali Najafi

“…Toxicity evaluation leveraged a Graph Convolutional Network (GCN). Statistical significance was set at P<0.05.Results: Eight of the compounds assessed significantly reduced TFF3 expression, with binding affinities (ΔG) ranging from -7 to -9.4 kcal/mol. …”
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A novel method for soil organic carbon prediction using integrated ‘ground-air-space’ multimodal remote sensing data by Yilin Bao, Xiangtian Meng, Huanjun Liu, Mengyuan Xu, Mingchang Wang

“…We also evaluated the performance of various algorithms (e.g., Random Forest (RF), Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), and Multi-Layer Perceptron (MLP)) across these models. …”
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STHFD: Spatial–Temporal Hypergraph-Based Model for Aero-Engine Bearing Fault Diagnosis by Panfeng Bao, Wenjun Yi, Yue Zhu, Yufeng Shen, Boon Xian Chai

“…However, current approaches relying on Convolutional Neural Networks (CNNs) for Euclidean data and Graph Convolutional Networks (GCNs) for non-Euclidean structures struggle to simultaneously capture heterogeneous data properties and complex spatio-temporal dependencies. …”
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Multi-Channel Speech Enhancement Using Labelled Random Finite Sets and a Neural Beamformer in Cocktail Party Scenario by Jayanta Datta, Ali Dehghan Firoozabadi, David Zabala-Blanco, Francisco R. Castillo-Soria

“…In this research, a multi-channel target speech enhancement scheme is proposed that is based on deep learning (DL) architecture and assisted by multi-source tracking using a labeled random finite set (RFS) framework. A neural network based on minimum variance distortionless response (MVDR) beamformer is considered as the beamformer of choice, where a residual dense convolutional graph-U-Net is applied in a generative adversarial network (GAN) setting to model the beamformer for target speech enhancement under reverberant conditions involving multiple moving speech sources. …”
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Accelerating spin Hall conductivity predictions via machine learning by Jinbin Zhao, Junwen Lai, Jiantao Wang, Yi‐Chi Zhang, Junlin Li, Xing‐Qiu Chen, Peitao Liu

“…Here, we have developed a residual crystal graph convolutional neural network (Res‐CGCNN) deep learning model to classify and predict SHCs solely based on the structural and compositional information. …”
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Utilizing deep learning for intelligent monitoring and early warning of slope disasters in public space design by Wang Ting, Ying Wang

“…The Adaptive Spatial Design Model (ASDM) incorporates real-time geospatial data, user behavior analytics, and environmental sensing to dynamically assess risk. It employs convolutional and recurrent neural networks for geo-hazard prediction, graph-theoretic optimization for decision-making, and adaptive spatial strategies to enhance model accuracy and responsiveness in changing environments.ResultsExperimental validation on real-world datasets shows that the proposed system effectively reduces false alarms and improves response times by 35% compared to traditional methods. …”
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Improving the accuracy of prediction models for small datasets of Cytochrome P450 inhibition with deep learning by Elpri Eka Permadi, Reiko Watanabe, Kenji Mizuguchi

“…This study underscores the significant potential of multitask deep learning, particularly when utilising a graph convolutional network with data imputation, to enhance the accuracy of CYP inhibition predictions under the conditions of limited data availability. …”
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An Ontology-Based Framework for Complex Urban Object Recognition through Integrating Visual Features and Interpretable Semantics by Xiao Xie, Xiran Zhou, Jingzhong Li, Weijiang Dai

“…Although previous works have proposed sophisticatedly probabilistic models that has strong capability of extracting features from remote sensing data (e.g., convolutional neural networks, CNN), the efforts that focus on exploring the human’s semantics on the object to be recognized are required more explorations. …”
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Lightweight hybrid transformers-based dyslexia detection using cross-modality data by Abdul Rahaman Wahab Sait, Yazeed Alkhurayyif

“…DL architectures, including convolutional neural networks (CNNs) and vision transformers (ViTs), encounter challenges in extracting meaningful patterns from cross-modality data. …”
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Intention Recognition of AAV Swarm Based on GAT-EPool-BiGRU Model by Jiajun Yuan, Xiang Jia, Yu An, Liang Geng, Lei Shu

“…Addressing the limitations of existing methods&#x2014;such as the low feature transfer efficiency of stacked autoencoders (SAE) and the tendency of panoramic convolutional long short-term memory networks (PC-LSTM) to lose tactical details&#x2014;this paper proposes a novel deep learning model called GAT-EPool-BiGRU, which integrates Graph Attention Networks (GAT), Edge Pooling (EPool), and Bidirectional Gated Recurrent Units (BiGRU). …”
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A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and Applications by Ibomoiye Domor Mienye, Theo G. Swart

“…Therefore, this paper provides a comprehensive review of recent DL advances, covering the evolution and applications of foundational models like convolutional neural networks (CNNs) and Recurrent Neural Networks (RNNs), as well as recent architectures such as transformers, generative adversarial networks (GANs), capsule networks, and graph neural networks (GNNs). …”
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Adaptive Cut Selection in Mixed-Integer Linear Programming by Turner, Mark, Koch, Thorsten, Serrano, Felipe, Winkler, Michael

“…We propose a variation on the design of existing graph convolutional neural networks, adapting them to learn cut selection rule parameters. …”
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Cost-Efficient Fall Risk Assessment With Attention Augmented Vision Machine Learning on Sit-to-Stand Test Videos by Chunhua Pan, Boting Qu, Rui Miao, Xin Wang

“…Furthermore, a novel Attention-augmented Spatial-Temporal Graph Convolutional Network (AST-GCN) is developed for reliably identifying the action in each frame, enabling accurate computation of key kinematic features for fall risk prediction. …”
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Identification of spodumene using a remote-sensing index cube from SDGSAT-1 and other satellites by Siyuan Li, Nannan Zhang, Yong Li, Li Chen, Hao Zhang, Jinyu Chang, Jintao Tao, Jianpeng Jing

“…The model combines a convolute onal neural network (CNN) and a graph convolutional network (GCN), integrating spatial and spectral features to enhance identification accuracy. …”
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Comparative analysis of machine learning approaches for heatwave event prediction in India by Ritesh Choudary V, Anita Christaline Johnvictor, Prem Sankar N

“…The study evaluates the performance of models including Random Forest, Convolutional Neural Networks, LightGBM, Long Short-Term Memory Networks, Transformer Networks, Support Vector Machines, Graph Neural Networks, Extreme Gradient Boosting and Autoencoders for Anomaly Detection in heatwave. …”
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Unlocking chickpea flour potential: AI-powered prediction for quality assessment and compositional characterisation by Ali Zia, Muhammad Husnain, Sally Buck, Jonathan Richetti, Elizabeth Hulm, Jean-Philippe Ral, Vivien Rolland, Xavier Sirault

“…Using a dataset comprising 136 chickpea varieties, the research compares the performance of several state-of-the-art deep learning models, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Graph Convolutional Networks (GCNs), and compares the most effective model, CNN, against the traditional Partial Least Squares Regression (PLSR) method. …”
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Deep learning for single-site solar irradiance forecasting using multi-station data by Daniel Guatibonza, Gabriel Narvaez, Luis Felipe Giraldo

“…This study examines the integration of data from multiple stations for solar irradiance forecasting at a single site using advanced deep learning models, such as long-term memory (LSTM), deep modular attention (DeepMap), and graph convolutional networks (GC-LSTM). The research addresses an important gap: the statistical evaluation of the contribution of neighboring data to improving forecast accuracy in solar PV applications. …”
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Ultra-short-term Probabilistic Forecasting of Distributed Photovoltaic Power Generation Based on Hierarchical Correlation Modeling by Can CHEN, Zinuo SU, Yuan MA, Jialin LIU, Yuqing WANG, Fei WANG

“…Then, a probabilistic forecasting model based on hierarchical graph convolutional neural networks (GCNs) is proposed to mine deep spatio-temporal correlation features between PV power stations, thereby enhancing the accuracy of ultra-short-term probabilistic forecasting of regional distributed PV power. …”
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