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3861
An In-Depth Study of Personalized Anesthesia Management Models in Gastrointestinal Endoscopy Based on Multimodal Deep Learning
Published 2025-01-01“…Compared with LSTM networks integrated with convolutional neural networks (CNN) and support vector machines (SVM), the LSTM model combined with GMO and sparse matrix classifiers, along with personalized physiological data, achieved a recall rate of 83% and an F1-score of 0.711 in drug usage prediction. …”
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3862
Ensemble machine learning models for lung cancer incidence risk prediction in the elderly: a retrospective longitudinal study
Published 2025-01-01“…For each subgroup, random forest, extreme gradient boosting, deep neural networks, support vector machine, multiple logistic regression and deep Q network (DQN) models were developed and validated. …”
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3863
Advances in tissue engineering of peripheral nerve and tissue innervation – a systematic review
Published 2025-02-01“…Auto- and allografts are the first choice of treatment, but tissue survival or functionality is not guaranteed due to often limited vascular and neural networks. In response, tissue-engineered solutions have been developed, yet clinical translations is rare. …”
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3864
TCN-Based DDoS Detection and Mitigation in 5G Healthcare-IoT: A Frequency Monitoring and Dynamic Threshold Approach
Published 2025-01-01“…This research evaluates the proposed model against prior methods, including Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNNs), demonstrating improved accuracy. …”
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3865
Information Geometry and Manifold Learning: A Novel Framework for Analyzing Alzheimer’s Disease MRI Data
Published 2025-01-01“…Geodesic distances, computed with the Fisher Information metric, quantified class differences. Graph Neural Networks, including Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and GraphSAGE, were utilized to categorize impairment levels using graph-based representations of the MRI data. …”
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3866
Forecasting Foreign Exchange Volatility Using Deep Learning Autoencoder-LSTM Techniques
Published 2021-01-01“…Recently, various deep learning models based on artificial neural networks (ANNs) have been widely employed in finance and economics, particularly for forecasting volatility. …”
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3867
Balancing Privacy and Utility in Split Learning: An Adversarial Channel Pruning-Based Approach
Published 2025-01-01“…Split Learning (SL) has emerged as a promising technique to mitigate these risks through partitioning neural networks into the client and the server subnets. …”
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3868
MFCEN: A lightweight multi-scale feature cooperative enhancement network for single-image super-resolution
Published 2024-10-01“…In recent years, significant progress has been made in single-image super-resolution with the advancements of deep convolutional neural networks (CNNs) and transformer-based architectures. …”
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3869
Deteksi Cepat Kadar Alkohol Pada Minuman Kopi dengan Metode Dielektrik dan Jaringan Syaraf Tiruan
Published 2022-02-01“…The purpose of this study was to estimate the alcohol content and pH of coffee drinks based on the bioelectric of material and Artificial Neural Networks (ANN). The back propagation algorithm was used to connect the input of bioelectric properties and output of prediction of alcohol content and pH in liqueur coffee. …”
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3870
Solar Power Generation in Smart Cities Using an Integrated Machine Learning and Statistical Analysis Methods
Published 2022-01-01“…The present idea in this research uses linear regression techniques to forecast utilising artificial neural networks (ANN). The most important factor in sizing the installation of solar power producing units is the daily mean sun irradiation. …”
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3871
Lightning-induced vulnerability assessment in Bangladesh using machine learning and GIS-based approach
Published 2025-01-01“…By analyzing spatiotemporal patterns of lightning and casualties, and incorporating meteorological, geographical, and socio-economic factors into ML models (Random Forest, Multinomial Logistic Regression, Support Vector Machine, and Artificial Neural Networks), this research provides a nuanced understanding of lightning impacts. …”
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3872
Predicting nonequilibrium Green’s function dynamics and photoemission spectra via nonlinear integral operator learning
Published 2025-01-01“…In this paper, we develop an operator-learning framework based on recurrent neural networks (RNNs) to address this challenge. We utilize RNNs to learn the nonlinear mapping between Green’s functions and convolution integrals in KBEs. …”
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3873
Neural Correlates of Social Touch Processing: An fMRI Study on Brain Functional Connectivity
Published 2025-01-01“…Results: The findings indicated the involvement of discrete neural networks in the processing of social touch, with notable discrepancies in functional connectivity observed between the experimental and control groups. …”
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3874
NeuTox 2.0: A hybrid deep learning architecture for screening potential neurotoxicity of chemicals based on multimodal feature fusion
Published 2025-01-01“…We incorporated transfer learning based on self-supervised learning, graph neural networks, and molecular fingerprints/descriptors. …”
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3875
Universal conditional networks (UniCoN) for multi-age embryonic cartilage segmentation with Sparsely annotated data
Published 2025-01-01“…To address these limitations, we propose novel DL methods that can be adopted by any DL architectures—including Convolutional Neural Networks (CNNs), Transformers, or hybrid models—which effectively leverage age and spatial information to enhance model performance. …”
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3876
Diagnostic accuracy of artificial intelligence algorithms to predict remove all macroscopic disease and survival rate after complete surgical cytoreduction in patients with ovarian...
Published 2025-01-01“…Most studies agree that Artificial Neural Networks (ANN) and Machine Learning (ML) models outperform conventional statistics in predicting postoperative outcomes.…”
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3877
Advancing terrestrial snow depth monitoring with machine learning and L-band InSAR data: a case study using NASA’s SnowEx 2017 data
Published 2025-01-01“…Using 3 m resolution L-band InSAR products over Grand Mesa, Colorado, we compared the performance of three machine learning approaches (XGBoost, ExtraTrees, and Neural Networks) across open, vegetated, and the combined (open + vegetated) datasets using Root Mean Square Error (RMSE), Mean Bias Error (MBE), and R2 metrics. …”
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3878
Accelerating the design and discovery of tribocorrosion-resistant metals by interfacing multiphysics modeling with machine learning and genetic algorithms
Published 2025-01-01“…The ML model employs an ensemble method of artificial neural networks (ANNs) to predict the tribocorroded surface profile and total material loss based on FEA simulation results, significantly reducing computational time compared to conventional FEA methods. …”
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3879
Automated Audit and Self-Correction Algorithm for Seg-Hallucination Using MeshCNN-Based On-Demand Generative AI
Published 2025-01-01“…The ASHSC algorithm utilizes a two-stage on-demand strategy with mesh-based convolutional neural networks and generative artificial intelligence. …”
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3880
Human Detection and Action Recognition for Search and Rescue in Disasters Using YOLOv3 Algorithm
Published 2023-01-01“…The existing methods (Balmukund et al. 2020) used were faster-region based convolutional neural networks (F-RCNNs), single shot detector (SSD), and region-based fully convolutional network (R-FCN) for the detection of human and recognition of action. …”
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