A GPT-Based Approach for Cyber Threat Assessment by Fahim Sufi

“…This research proposes a novel GPT-based framework for assessing cyber threats, leveraging artificial intelligence to process and analyze large-scale cyber event data. …”
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Task Offloading with LLM-Enhanced Multi-Agent Reinforcement Learning in UAV-Assisted Edge Computing by Feifan Zhu, Fei Huang, Yantao Yu, Guojin Liu, Tiancong Huang

“…This framework integrates the QTRAN algorithm with a large language model (LLM) for efficient region decomposition and employs graph convolutional networks (GCNs) combined with self-attention mechanisms to adeptly manage inter-subregion relationships. …”
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Evaluating the impact of hyperparameters on the performance of 1D CNN model for nutritional profiling of underutilized crops using NIRS data by Simardeep Kaur, Naseeb Singh, Amit Kumar, Philanim W S, Veerendra Kumar Verma, Amit Kumar Patil, Rakesh Bhardwaj, Amritbir Riar

“…The application of 1D Convolutional Neural Networks (CNNs) for nutritional profiling using Near-Infrared Spectroscopy (NIRS) data has increased significantly in recent times. …”
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Inference of Gene Regulatory Networks for Breast Cancer Based on Genetic Modules by Yihao Chen, Ling Guo, Yue Pan, Hui Cai, Zhitong Bing

“…The gene regulatory associations in gene modules were then transformed into 2-dimensional histogram types. A convolutional neural network was chosen as the main framework to fit the gene regulatory types and infer the GRN. …”
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Dynamic Agricultural Pest Classification Using Enhanced SAO-CNN and Swarm Intelligence Optimization for UAVs by Shiwei Chu, Wenxia Bao

“…Key contributions include: (1) A hybrid SAO-CNN architecture that dynamically adjusts convolution kernels and leverages unlabeled data through self-supervised learning, improving adaptability to lighting and background variations. (2) A UAV swarm intelligence framework optimized via bio-inspired algorithms, reducing flight time by 29.2% and energy consumption by 32% compared to non-optimized systems. (3) Superior performance with 91.2% classification accuracy, 0.89 recall, and 32 FPS processing speed, outperforming state-of-the-art models (e.g., YOLO variants, ResNet, and ConvLSTM) in both static and dynamic scenarios. …”
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Attention-driven echo cancellation: A novel transformer-based approach for robust acoustic echo and noise cancellation by Soni Ishwarya V, Mohanaprasad K

“…This paper introduces a novel end-to-end deep learning framework called the fully transformer-based neural network (FTNN). …”
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A digital intelligence visualization health monitoring device for Alzheimer’s disease patients based on WBAN technology by Minghong Zheng, Yi Wu, Changqing Weng

“…In the WBAN monitoring visualization group, we innovated a digital kit called AD-Cloud, which consists of a flexible wearable data collection and transmission device with a star topology, a progressive convolutional neural framework for remote data visualization with high arithmetic power, and an interactive mobile application. …”
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Lower Limb Movement Recognition Based on a Hybrid Deep Learning Model Using Surface Electromyography by Yonglin Han, Qing Tao

“…By integrating the advantages of three advanced architectures—Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM), and Transformer—the model constructs an efficient framework for lower limb motion recognition. …”
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End-to-End CNN conceptual model for a biometric authentication mechanism for ATM machines by Karthikeyan Velayuthapandian, Natchiyar Murugan, Saranya Paramasivan

“…This article presents a novel end-to-end multiscale attention-based light-weighted deep convolutional neural network (MA-LW-DCNN) framework for enhancing the accuracy of recognizing faces. …”
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Explore human parsing modality for action recognition by Jinfu Liu, Runwei Ding, Yuhang Wen, Nan Dai, Fanyang Meng, Fang‐Lue Zhang, Shen Zhao, Mengyuan Liu

“…The first human pose branch feeds robust skeletons in the graph convolutional network to model pose features, while the second human parsing branch also leverages depictive parsing feature maps to model parsing features via convolutional backbones. …”
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Optimal features assisted multi-attention fusion for robust fire recognition in adverse conditions by Inam Ullah, Nada Alzaben, Yousef Ibrahim Daradkeh, Mi Young Lee

“…This novel progressive attention-over-attention framework achieves state-of-the-art (SOTA) performance while maintaining computational efficiency. …”
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Linear-nonlinear cascades capture synaptic dynamics. by Julian Rossbroich, Daniel Trotter, John Beninger, Katalin Tóth, Richard Naud

“…These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks.…”
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DGANS:robustness image steganography model based on double GAN by Leqing ZHU, Yu GUO, Lingqiang MO, Daxing ZHANG

“…Deep convolutional neural networks can be effectively applied to large-capacity image steganography,but the research on their robustness is rarely reported.The DGANS (double-GAN-based steganography) applies the deep learning framework in image steganography,which is optimized to resist small geometric distortions so as to improve the model’s robustness.DGANS is made up of two consecutive generative adversarial networks that can hide a grayscale image into another color or grayscale image of the same size and can restore it later.The generated stego-images are augmented and used to further train and strengthen the reveal network so as to make it adaptive to small geometric distortion of input images.Experimental results suggest that DGANS can not only realize high-capacity image steganography,but also can resist geometric attacks within certain range,which demonstrates better robustness than similar models.…”
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Enhanced detection of Mpox using federated learning with hybrid ResNet-ViT and adaptive attention mechanisms by V. Maheskumar, R. Vijayarajeswari

“…Results demonstrate a detection accuracy of 98.78%, significantly outperforming traditional and existing methods in terms of precision, recall, and robustness. The new framework of ResViT-FLBoost incorporates ResNet convolutional features together with ViT contextual representations which are boosted by dynamic attention components. …”
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FlowMRI-Net: A generalizable self-supervised 4D flow MRI reconstruction network by Luuk Jacobs, Marco Piccirelli, Valery Vishnevskiy, Sebastian Kozerke

“…Methods: The proposed deep-learning-based framework, called FlowMRI-Net, employs physics-driven unrolled optimization using a complex-valued convolutional recurrent neural network and is trained in a self-supervised manner. …”
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A method for short-term wind power forecasting under extreme weather conditions based on meteorological factor interpretability and hybrid deep learning algorithms by Bo Wang, Shu Wang, Zheng Wang, Yingying Zheng, Xin Li

“…Here, this paper proposes a wind power forecasting framework that considers small sample expansion and hybrid deep learning algorithms. …”
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Predicting wheat yield using deep learning and multi-source environmental data by Muhammad Ashfaq, Imran Khan, Dilawar Shah, Shujaat Ali, Muhammad Tahir

“…The framework employs three leading deep learning models—convolutional neural networks (CNN), recurrent neural networks (RNN), and artificial neural networks (ANN)—trained on detrended yield data from 2017 to 2022. …”
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An enhanced integrated fuzzy logic-based deep learning techniques (EIFL-DL) for the recommendation system on industrial applications by Yasir Rafique, Jue Wu, Abdul Wahab Muzaffar, Bilal Rafique

“…The EIFL-DL framework leverages fuzzy logic to handle uncertainty and vagueness in industrial data. …”
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Dynamic Spatial–Temporal Graph Neural Network for Cooling Capacity Prediction in HVDC Systems by Hao Sun, Shaosen Li, Jianxiang Huang, Hao Li, Guanxin Jing, Ye Tao, Xincui Tian

“…Temporal dependencies are modeled using temporal convolutional layers and recurrent neural networks (RNNs), enabling the framework to learn both short-term variations and long-term trends. …”
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Explainable Artificial Intelligence for Diagnosis and Staging of Liver Cirrhosis Using Stacked Ensemble and Multi-Task Learning by Serkan Savaş

“…This study proposes a comprehensive deep-learning framework for the automatic diagnosis and staging of liver cirrhosis using T2-weighted MRI images. …”
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