Multimodal Fusion Multi-Task Learning Network Based on Federated Averaging for SDB Severity Diagnosis by Songlu Lin, Renzheng Tang, Yuzhe Wang, Zhihong Wang

“…The proposed framework is built upon a hybrid deep neural architecture that integrates convolutional layers (CNN) for spatial representation, bidirectional GRUs for temporal modeling, and multi-head self-attention for long-range dependency learning. …”
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Edge–Cloud Intelligence for Sustainable Wind Turbine Blade Transportation: Machine-Vision-Driven Safety Monitoring in Renewable Energy Systems by Yajun Wang, Xiaodan Wang, Yihai Wang, Shibiao Fang

“…The framework employs an optimized YOLOv7-Tiny model. It is enhanced with convolutional block attention modules (CBAMs) for feature refinement, CARAFE upsampling for better contextual detail, and bidirectional feature pyramid networks (BiFPNs) for multi-scale object detection. …”
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Hierarchical Transfer Learning with Transformers to Improve Semantic Segmentation in Remote Sensing Land Use by Miaomiao Chen, Lianfa Li

“…Our framework represents multilevel transfer learning, progressing from non-remote sensing classification to coarse classification, then to the refined classification of remote sensing. …”
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Cyberattack Detection Systems in Industrial Internet of Things (IIoT) Networks in Big Data Environments by Abdullah Orman

“…This study proposes an advanced IDS framework integrating machine learning, deep learning, and hybrid models to enhance cybersecurity in IIoT environments. …”
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Multimodal learning for enhanced SPECT/CT imaging in sports injury diagnosis by Zhengzheng Jiang, YaWen Shen

“…Our method introduces a hybrid model combining convolutional neural networks for spatial feature extraction and transformer-based temporal attention for sequential pattern recognition. …”
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Evaluation Model Based on the SGCNiFormer for the Influence of Different Storage Environments on Wheat Quality by Qingchuan Zhang, Zexi Song, Mingwen Bi

“…This integrated method provides a systematic theoretical framework for optimizing storage parameters and offers substantial support for intelligent grain storage management.…”
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FishermaskFormer: Lightweight Remote Sensing Scene Classification With Masked Transformer by Wei Wu, Xianbin Hu, Zhu Li, Xueliang Luo

“…This is aimed at offering a flexible and effective framework for preserving classification accuracy while significantly reducing the complexity. …”
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Forecasting Sales in Live-Streaming Cross-Border E-Commerce in the UK Using the Temporal Fusion Transformer Model by Qi Zhang, Xue Li, Pengbin Gao

“…To address these challenges, we propose a novel sales forecasting framework utilizing the Temporal Fusion Transformer (TFT) model. …”
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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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Leveraging Deep Learning for Fault Detection and Localization in Distributed Systems by Debolina Ghosh, Jay Prakash Singh

“…A deep learning-based fault localization framework that combines Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), LSTM+CNN, and Autoencoder+LSTM models is proposed in this study. …”
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Multi-Task Learning-Based Traffic Flow Prediction Through Highway Toll Stations During Holidays by Xiaowei Liu, Yunfan Zhang, Zhongyi Han, Hao Qiu, Shuxin Zhang, Jinlei Zhang

“…Under the multi-task learning framework, the model shares spatial–temporal features between inbound flow and outbound flow, enhancing their representations and improving multi-step prediction accuracy. …”
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Fusing multiplex heterogeneous networks using graph attention-aware fusion networks by Ziynet Nesibe Kesimoglu, Serdar Bozdag

“…Abstract Graph Neural Networks (GNN) emerged as a deep learning framework to generate node and graph embeddings for downstream machine learning tasks. …”
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Hybrid deep learning model for identifying the cancer type by Singamaneni Krishnapriya, Hyma Birudaraju, M. Madhulatha, S. Nagajyothi, K.S. Ranadheer Kumar

“…In this work, we introduce a hybrid deep learning-based framework for accurate cancer type and subtype identification by using pre-trained convolutional neural networks, custom deep learning networks, and traditional machine learning classifiers. …”
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Advanced 3D Face Reconstruction from Single 2D Images Using Enhanced Adversarial Neural Networks and Graph Neural Networks by Mohamed Fathallah, Sherif Eletriby, Maazen Alsabaan, Mohamed I. Ibrahem, Gamal Farok

“…This paper presents a novel framework for 3D face reconstruction from single 2D images and addresses critical limitations in existing methods. …”
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Deep Learning-Based Automatic Summarization of Chinese Maritime Judgment Documents by Lin Zhang, Yanan Li, Hongyu Zhang

“…Experimental results show that HybridSumm’s lexicon-guided two-stage framework significantly enhances the standardization of legal terminology and semantic coherence in long-text summaries, validating its practical value in advancing judicial intelligence development.…”
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Fusion of Recurrence Plots and Gramian Angular Fields with Bayesian Optimization for Enhanced Time-Series Classification by Maria Mariani, Prince Appiah, Osei Tweneboah

“…We introduce a novel framework that transforms time series into image representations by fusing recurrence plots (RPs) with both Gramian Angular Summation Fields (GASFs) and Gramian Angular Difference Fields (GADFs). …”
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Use of Xception Architecture for the Classification of Skin Lesions by Cledmir Tejada, Gustavo Espinoza, Daniel Subauste

“…The study utilizes the TensorFlow framework and the HAM10000 dataset, comprising a vast collection of benign and malignant skin lesion images, for training and evaluating the Xception model. …”
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Rail Transit Prediction Based on Multi-View Graph Attention Networks by Li Wang, Xin Wang, Jiao Wang

“…Thus, this paper proposes a framework with multi-view and multi-layer attention, which aims to solve the problem of node prediction involving multiple relationships. …”
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With eyes of a machine: A three-step guide for applying machine learning to visual content analysis in social research by Anna Helene Kvist Møller, Massimo Airoldi

“…Combining machine automation with human expertise and reflexivity, the proposed methodological framework bridges the gap between computer vision and social research. …”
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PatchDetect: Breast Cancer Detection combining Unet-ResNet-50 and Patch Embedding LSTM by Hadj Ahmed Bouarara, kadda benyahia

“… This study presents a novel framework for breast cancer detection, combining patch embedding, feature extraction using a pre-trained Convolutional Neural Network (CNN) model (ResNet50), Long Short-Term Memory (LSTM) networks for image sequence analysis, and Fully Connected Layers for final classification. …”
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