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161
Intelligent screening of narrow anterior chamber angle based on portable slit lamp
Published 2025-07-01“…Despite generalization challenges, portable slit lamps equipped with advanced algorithms show promise for NACA screening.…”
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162
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163
Using machine learning algorithms to predict colorectal cancer
Published 2025-02-01Get full text
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164
Using machine learning algorithms to predict colorectal polyps
Published 2025-02-01“…Interpretation: Using non-invasive factors and machine learning algorithms can accurately predict the occurrence of colorectal polyps in individuals with positive initial screening results. …”
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165
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166
A Seasonal Fresh Tea Yield Estimation Method with Machine Learning Algorithms at Field Scale Integrating UAV RGB and Sentinel-2 Imagery
Published 2025-01-01“…Subsequently, these 26 features were screened using the random forest algorithm and Pearson correlation analysis. …”
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167
Targeted Detection of 76 Carnitine Indicators Combined with a Machine Learning Algorithm Based on HPLC-MS/MS in the Diagnosis of Rheumatoid Arthritis
Published 2025-03-01“…The diagnostic model derived from the screened markers was validated using three machine learning algorithms. …”
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168
Short-Term Photovoltaic Power Combined Prediction Based on Feature Screening and Weight Optimization
Published 2025-01-01“…Aiming at the problem of low prediction accuracy caused by the intermittent and fluctuating characteristics of photovoltaic power, a short-term photovoltaic power combined prediction method based on feature screening and weight optimization is proposed. Firstly, K-means is used to cluster the photovoltaic power; Secondly, CEEMDAN is used to decompose photovoltaic power and wavelet decomposition is used to decompose irradiance, and sample entropy and K-means are used to reconstruct each component of photovoltaic power into high, intermediate, and low frequency terms; Then, Spearman’s correlation coefficient is used to calculate the correlation between each meteorological factor and the decomposed irradiance component and the high, intermediate, and low frequency terms of photovoltaic power, and the feature selection is carried out; Then, CNN-BiLSTM-Attention is used to predict the high frequency term, LSTM is used to predict the intermediate frequency and low frequency terms, and the results are superimposed to obtain the preliminary prediction value; Finally, the dung beetle algorithm is used to optimize the weights of the initial prediction values of the training set of high, intermediate, and low frequency terms, and the optimal weight is substituted into the test set to obtain the final prediction result. …”
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169
Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning.
Published 2025-01-01“…Three machine learning models, logistic regression, random forest, and support vector machine, showed that screened biomarkers had better classification ability and effect. …”
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170
Development of Electronic Nose as a Complementary Screening Tool for Breath Testing in Colorectal Cancer
Published 2025-02-01“…We then used machine learning algorithms to develop predictive models and provided the estimated accuracy and reliability of the breath testing. (3) Results: We enrolled 77 patients, with 40 cases and 37 controls. …”
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171
Domain name generation algorithm based on improved Markov chain
Published 2024-11-01“…Then, the improved Markov model algorithm was used to analyze the filtered data, and new subdomain names were generated and added to the result set. …”
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172
Reflective Distributed Denial of Service Detection: A Novel Model Utilizing Binary Particle Swarm Optimization—Simulated Annealing for Feature Selection and Gray Wolf Optimization-...
Published 2024-09-01“…The BPSO-SA algorithm enhances the global search capability of Particle Swarm Optimization (PSO) using the SA mechanism and effectively screens out the optimal feature subset; the GWO algorithm optimizes the hyperparameters of LightGBM by simulating the group hunting behavior of gray wolves to enhance the detection performance of the model. …”
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173
Laryngeal cancer diagnosis based on improved YOLOv8 algorithm
Published 2025-01-01“…A novel multiscale enhanced convolution module has been introduced to improve the model’s feature extraction capabilities for small-sized targets. …”
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174
Lightweight Detection Algorithm for Breast-Mass Features in Ultrasound Images
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175
Application of Artificial Intelligence Algorithms in the Field of Antimicrobial Peptide Prediction
Published 2025-06-01“…This article reviews the principles and applicability of various current artificial intelligence algorithmic models for predicting antimicrobial peptides, and explores prediction models specifically designed to address the dilemma facing the application of antimicrobial peptides. …”
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176
Blinded Validation of Breath Biomarkers of Lung Cancer, a Potential Ancillary to Chest CT Screening.
Published 2015-01-01“…Outcome modeling: We modeled the expected effects of combining breath biomarkers with chest CT on the sensitivity and specificity of lung cancer screening.…”
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177
Electricity Load Forecasting Method Based on the GRA-FEDformer Algorithm
Published 2025-07-01Get full text
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178
Analysis of E-Commerce Marketing Strategy Based on Xgboost Algorithm
Published 2023-01-01“…This paper reviews the current literature on e-commerce marketing and then analyzes the feasibility of precision marketing in e-commerce market in the new media era. In order to screen potential consumers and improve the success rate of precision marketing, this paper establishes a prediction model for precision marketing of bank credit cards. …”
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179
Artificial intelligence for screening and early diagnosis of pancreatic neoplasms in the context of centralization of the laboratory service in the region
Published 2024-07-01“…Determination of the optimal machine learning model for the creation of software for screening and early diagnosis of pancreatic neoplasms in the context of centralization of the laboratory service in the region. …”
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180
A Novel Method for Screening the PMU Phase Angle Difference Data Based on Hyperplane Clustering
Published 2019-01-01“…First, we develop the hyperplane cluster method to cluster the phase angle difference data. Second, in order to screen out the right data type, this paper compares the virtual reactance parameters of each data type obtained by voltage mean to the line reactance parameter given by the system model. …”
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