Time series image coding classification theory based on Lagrange multiplier method by Wentao Jiang, Ming Zhao, Hongbo Li

“…This rigorous proof framework enhances the theoretical foundation for TSI classification based on image encoding. …”
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A New and Tested Ionospheric TEC Prediction Method Based on SegED-ConvLSTM by Yuanhang Liu, Yingkui Gong, Hao Zhang, Ziyue Hu, Guang Yang, Hong Yuan

“…We compared our model with traditional image-based models such as convolutional neural networks (CNNs), convolutional long short-term memory networks (ConvLSTMs), a self-attention mechanism-integrated ConvLSTM (SAM-ConvLSTM) model, and one-day predicted ionospheric products (C1PG) provided by the Center for Orbit Determination in Europe (CODE). …”
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Hybrid Deep Learning for Survival Prediction in Brain Metastases Using Multimodal MRI and Clinical Data by Cristian Constantin Volovăț, Călin Gheorghe Buzea, Diana-Ioana Boboc, Mădălina-Raluca Ostafe, Maricel Agop, Lăcrămioara Ochiuz, Ștefan Lucian Burlea, Dragoș Ioan Rusu, Laurențiu Bujor, Dragoș Teodor Iancu, Simona Ruxandra Volovăț

“…<b>Methods:</b> We propose a novel hybrid deep learning framework that integrates volumetric MRI-derived imaging biomarkers with structured clinical and demographic data to predict overall survival time. …”
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Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data by S. R. Mani Sekhar, D. M. Mushtaq Ahmed, G. M. Siddesh

“…It contributes to the growing field of Smart Cities by introducing a scalable and adaptable framework that harnesses the power of deep learning to improve urban living. …”
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A practical guide for nephrologist peer reviewers: evaluating artificial intelligence and machine learning research in nephrology by Yanni Wang, Wisit Cheungpasitporn, Hatem Ali, Jianbo Qing, Charat Thongprayoon, Wisit Kaewput, Karim M. Soliman, Zhengxing Huang, Min Yang, Zhongheng Zhang

“…Overfitting in AI is driven by small patient cohorts faced with thousands of candidate features; our framework spotlights this imbalance and offers concrete remedies. …”
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DualCMNet: a lightweight dual-branch network for maize variety identification based on multi-modal feature fusion by Xinhua Bi, Hao Xie, Ziyi Song, Jinge Li, Chang Liu, Xiaozhu Zhou, Helong Yu, Chunguang Bi, Ming Zhao

“…Additionally, existing multimodal methods face high computational complexity, making it difficult to balance accuracy and efficiency.MethodsBased on multi-modal data from 11 maize varieties, this paper presents DualCMNet, a novel dual-branch deep learning framework that utilizes a one-dimensional convolutional neural network (1D-CNN) for hyperspectral data processing and a MobileNetV3 network for spatial feature extraction from images. …”
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Fine-Grained Classification of Pressure Ulcers and Incontinence-Associated Dermatitis Using Multimodal Deep Learning: Algorithm Development and Validation Study by Alexander Brehmer, Constantin Seibold, Jan Egger, Khalid Majjouti, Michaela Tapp-Herrenbrück, Hannah Pinnekamp, Vanessa Priester, Michael Aleithe, Uli Fischer, Bernadette Hosters, Jens Kleesiek

“…ConclusionsOur multimodal deep learning framework effectively differentiates between PUs and IAD, achieving high accuracy and outperforming human wound care experts. …”
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Advanced Multi-Level Ensemble Learning Approaches for Comprehensive Sperm Morphology Assessment by Abdulsamet Aktas, Taha Cap, Gorkem Serbes, Hamza Osman Ilhan, Hakkı Uzun

“…<b>Results:</b> The proposed ensemble framework was evaluated using the Hi-LabSpermMorpho dataset, which contains 18 distinct sperm morphology classes. …”
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Wheat disease recognition method based on the SC-ConvNeXt network model by Tianliang Dong, Xiao Ma, Bin Huang, Wenyu Zhong, Qingan Han, Qinghai Wu, You Tang

“…Abstract When utilizing convolutional neural networks for wheat disease identification, the training phase typically requires a substantial amount of labeled data. …”
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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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Vision Foundation Model Guided Multimodal Fusion Network for Remote Sensing Semantic Segmentation by Chen Pan, Xijian Fan, Tardi Tjahjadi, Haiyan Guan, Liyong Fu, Qiaolin Ye, Ruili Wang

“…Specifically, the framework incorporates a cross-modal collaborative network for feature embedding that blends a convolutional neural network and vision transformer to simultaneously capture both local information and long-range dependencies from the input modalities. …”
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A deep learning model for predicting systemic lupus erythematosus-associated epitopes by Jiale He, Zixia Liu, Xiaopo Tang

“…This study proposes a hybrid deep learning architecture that synergistically integrates handcrafted biochemical features with data-driven deep sequence modeling to improve the identification of SLE-associated epitopes. Methods The framework comprises six interconnected components: (1) handcrafted feature extraction encoding biochemical and physicochemical attributes; (2) an embedding layer for dense sequence representation; (3) a Convolutional Neural Network (CNN) branch that captures local patterns from handcrafted features; (4) a Long Short-Term Memory branch for learning temporal dependencies in sequence data; (5) a scaled dot-product attention-based fusion module that integrates complementary information from both branches; and (6) a Multi-Layer Perceptron for final classification. …”
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An Optimized Transformer–GAN–AE for Intrusion Detection in Edge and IIoT Systems: Experimental Insights from WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT Datasets by Ahmad Salehiyan, Pardis Sadatian Moghaddam, Masoud Kaveh

“…Results demonstrate that the proposed Transformer–GAN–AE framework outperforms all baseline methods, achieving a best accuracy of 98.92%, along with superior recall and AUC values. …”
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Parallax-Tolerant Weakly-Supervised Pixel-Wise Deep Color Correction for Image Stitching of Pinhole Camera Arrays by Yanzheng Zhang, Kun Gao, Zhijia Yang, Chenrui Li, Mingfeng Cai, Yuexin Tian, Haobo Cheng, Zhenyu Zhu

“…The total framework consists of two stages. In the first stage, based on the differences between high-dimensional feature vectors extracted by a convolutional module, a parallax-tolerant color correction network with dynamic loss weights is utilized to adaptively compensate for color differences in overlapping regions. …”
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LSTM and ResNet18 for optimized ambulance routing and traffic signal control in emergency situations by Madallah Alruwaili, Ali Ali, Mohammed Almutairi, Abdulaziz Alsahyan, Mahmood Mohamed

“…Visual data is processed through a ResNet18 convolutional neural network, pre-trained on ImageNet using inductive transfer learning. …”
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Privacy–preserving dementia classification from EEG via hybrid–fusion EEGNetv4 and federated learning by Muhammad Umair, Muhammad Shahbaz Khan, Muhammad Hanif, Wad Ghaban, Ibtehal Nafea, Sultan Noman Qasem, Sultan Noman Qasem, Faisal Saeed

“…This study proposes lightweight and privacy-preserving EEG classification framework combining deep learning and Federated Learning (FL). …”
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A Data-Driven Approach for Automatic Aircraft Engine Borescope Inspection Defect Detection Using Computer Vision and Deep Learning by Thibaud Schaller, Jun Li, Karl W. Jenkins

“…This paper presents a data-driven deep learning framework capable of automatically detecting defects on reactor blades. …”
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Prediction of Temperature Distribution with Deep Learning Approaches for SM1 Flame Configuration by Gökhan Deveci, Özgün Yücel, Ali Bahadır Olcay

“…At the same time, file sizes and usability were examined. The second framework employs a U-Net-based convolutional neural network enhanced by an RGB Fusion preprocessing technique, which integrates multiple scalar fields from non-reacting (cold flow) conditions into composite images, significantly improving spatial feature extraction. …”
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Enhancement and evaluation for deep learning-based classification of volumetric neuroimaging with 3D-to-2D knowledge distillation by Hyemin Yoon, Do-Young Kang, Sangjin Kim

“…This framework is designed to employ volumetric prior knowledge in training 2D CNNs. …”
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Localization of Multiple GNSS Interference Sources Based on Target Detection in C/N₀ Distribution Maps by Qidong Chen, Rui Liu, Qiuzhen Yan, Yue Xu, Yang Liu, Xiao Huang, Ying Zhang

“…Subsequently, an Oriented Squeeze-and-Excitation-based Faster Region-based Convolutional Neural Network (OSF-RCNN) framework is proposed to process the C/N₀ distribution maps. …”
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