An efficient parametric modeling and path planning method for 3D printing of curved surface corrugated sandwich structures by Tan Gui, Zhihong Li, Yongjun Cao, Jianghong Yang, Yingjun Wang

“…Additionally, a comparison of the printing time between preprocessed models and standard models reveals a significant reduction in nozzle idle time. Moreover, as the infill density increases, the reduction in printing time becomes more pronounced. …”
Get full text

Analysis and Identification of Factors Influencing the Survival of Burn Injury Patients with an Artificial Intelligence Approach by Sara Hashemi, Shahla Faramarzi, Laya Rahmani Pirouz, Azita Yazdani

“…Conclusion: The use of machine learning algorithms in predicting the survival of burn patients is promising. …”
Get full text

Pix2Pix-Based Modelling of Urban Morphogenesis and Its Linkage to Local Climate Zones and Urban Heat Islands in Chinese Megacities by Mo Wang, Ziheng Xiong, Jiayu Zhao, Shiqi Zhou, Qingchan Wang

“…This study employed the Conditional Generative Adversarial Network (cGAN) of the Pix2Pix algorithm as a predictive model to simulate 3D urban morphologies aligned with Local Climate Zone (LCZ) classifications. …”
Get full text

Intersections of Big Data and IoT in Academic Publications: A Topic Modeling Approach by Diana-Andreea Căuniac, Andreea-Alexandra Cîrnaru, Simona-Vasilica Oprea, Adela Bâra

“…Topic 6 focuses on technical aspects such as <i>modeling, system performance and prediction algorithms</i>. It delves into the efficiency of IoT networks with terms like “accuracy”, “power” and “performance” standing out.…”
Get full text

ERBB3-related gene PBX1 is associated with prognosis in patients with HER2-positive breast cancer by Shufen Mo, Haiming Zhong, Weiping Dai, Yuanyuan Li, Bin Qi, Taidong Li, Yongguang Cai

“…ERBB3 expression-related differentially expressed genes (DEGs) were identified and intersected with survival status-related DEGs to obtain intersected genes. Three algorithms, LASSO, RandomForest and XGBoost were combined to identify the signature genes. we construct risk models and generate ROC curves for prediction. …”
Get full text

Antiviral therapy can effectively suppress irAEs in HBV positive hepatocellular carcinoma treated with ICIs: validation based on multi machine learning by Shuxian Pan, Zibing Wang

“…The accuracy of the model is verified in the DCA curve.ResultsA total of 274 HBV-related liver cancer patients were enrolled in the study. Predictive models were constructed using three machine learning algorithms to analyze and statistically evaluate clinical characteristics, including immune cell data. …”
Get full text

The unwell patient with advanced chronic liver disease: when to use each score? by Oliver Moore, Wai-See Ma, Scott Read, Jacob George, Golo Ahlenstiel

“…Incorporating artificial intelligence to personalise predictive algorithms may provide the most effective prognostication for all clinical phenotypes. …”
Get full text

Advances in the application of nomograms for patients with gastric cancer associated with peritoneal metastasis by Shiyang Jin, Zeshen Wang, Qiancheng Wang, Zhenglong Li, Xirui Liu, Kuan Wang

“…Abstract This review elucidates advancements in nomogram applications for predicting peritoneal metastasis (PM) and prognostication in gastric cancer (GC). …”
Get full text

How spatial resolution mediates canopy spectral diversity as a proxy for marsh plant diversity by Yi Fu, Yunlong Yao, Lei Wang, Huaihu Yi, Yuanqi Shan

“…The optimal spatial resolution for predicting plant diversity varies among different VIs, but VIs calculated from the same spectral bands consistently show similar predictive trends. …”
Get full text

Effectiveness of machine learning models in diagnosis of heart disease: a comparative study by Waleed Alsabhan, Abdullah Alfadhly

“…An extensive array of preprocessing techniques is thoroughly examined in order to optimize the predictive models’ quality and performance. Our study employs a wide range of ML algorithms, such as Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neibors (KNN), AdaBoost (AB), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LGBM), CatBoost (CB), Linear Discriminant Analysis (LDA), and Artificial Neural Network (ANN) to assess the predictive performance of these algorithms in the context of heart disease detection. …”
Get full text

"Proof-of-concept" evaluation of an automated sputum smear microscopy system for tuberculosis diagnosis. by James J Lewis, Violet N Chihota, Minty van der Meulen, P Bernard Fourie, Katherine L Fielding, Alison D Grant, Susan E Dorman, Gavin J Churchyard

“…<h4>Discussion</h4>Compared to a research microscopist, the hybrid software/human approach had similar specificity and positive predictive value, but sensitivity requires further improvement. …”
Get full text

Leveraging mixed-effects regression trees for the analysis of high-dimensional longitudinal data to identify the low and high-risk subgroups: simulation study with application to g... by Mina Jahangiri, Anoshirvan Kazemnejad, Keith S. Goldfeld, Maryam S. Daneshpour, Mehdi Momen, Shayan Mostafaei, Davood Khalili, Mahdi Akbarzadeh

“…Previous studies have shown that this model can be sensitive to parametric assumptions and provides less predictive performance than non-parametric methods such as random effects-expectation maximization (RE-EM) and unbiased RE-EM regression tree algorithms. …”
Get full text

Improved estimation of two-phase capillary pressure with nuclear magnetic resonance measurements via machine learning by Oriyomi Raheem, Misael M. Morales, Wen Pan, Carlos Torres-Verdín

“…In contrast, nuclear magnetic resonance (NMR) measurements, which provide information on pore body size distribution, are faster and can be leveraged to estimate capillary pressure using machine learning algorithms. Recently, artificial intelligence methods have also been applied to capillary pressure prediction (Qi et al., 2024).Currently, no readily applicable predictive model exists for estimating an entire capillary pressure curve directly from standard petrophysical logs and core data. …”
Get full text

An Advanced Hybrid Forecasting System for Wind Speed Point Forecasting and Interval Forecasting by Haipeng Zhang, Hua Luo

“…Therefore, in this paper, we developed a prediction system integrating an advanced data preprocessing strategy, a novel optimization model, and multiple prediction algorithms. …”
Get full text

Improving accuracy of self-reported diagnoses of rheumatoid arthritis in the French prospective E3N-EPIC cohort: a validation study by Marie-Christine Boutron-Ruault, Yann Nguyen, Carine Salliot, Gaëlle Gusto, Elise Descamps

“…Medical records were independently reviewed.Primary and secondary outcome measures Positive predictive values (PPV) of self-reported RA alone, then coupled with the IRD questionnaire, and with a medication reimbursement database were assessed. …”
Get full text

Mapping Polyclonal HIV-1 Antibody Responses via Next-Generation Neutralization Fingerprinting. by Nicole A Doria-Rose, Han R Altae-Tran, Ryan S Roark, Stephen D Schmidt, Matthew S Sutton, Mark K Louder, Gwo-Yu Chuang, Robert T Bailer, Valerie Cortez, Rui Kong, Krisha McKee, Sijy O'Dell, Felicia Wang, Salim S Abdool Karim, James M Binley, Mark Connors, Barton F Haynes, Malcolm A Martin, David C Montefiori, Lynn Morris, Julie Overbaugh, Peter D Kwong, John R Mascola, Ivelin S Georgiev

“…Here, we present next-generation NFP algorithms that substantially improve prediction accuracy for individual donors and enable serologic analysis for entire cohorts. …”
Get full text

Application of inertial navigation high precision positioning system based on SVM optimization by Ruiqun Han

“…In addition, support vector machines were used to optimize pedestrian trajectory prediction algorithms, and a pedestrian motion state recognition algorithm was designed based on this. …”
Get full text

Decoding Subjective Understanding: Using Biometric Signals to Classify Phases of Understanding by Milan Lazic, Earl Woodruff, Jenny Jun

“…AU patterns associated with each phase were then identified through the application of six supervised machine learning algorithms. Distinct AU patterns were found for all five phases, with gradient boosting machine and random forest models achieving the highest predictive accuracy. …”
Get full text

Can artificial intelligence and contrast-enhanced mammography be of value in the assessment and characterization of breast lesions? by Lamiaa Mohamed Bassam Hashem, Heba Monir Azzam, Ghadeer Saad Abd El-Shakour El-Gamal, MennatAllah Mohamed Hanafy

“…The resulting mammographic images were processed using AI algorithm. In our study, CEM demonstrated a sensitivity of 98.33%, specificity of 92.86%, positive predictive value (PPV) of 98.34%, negative predictive value (NPP) of 92.85%, and accuracy of 97.3%. …”
Get full text

Standardized conversion model for retinal thickness measurements between spectral-domain and swept-source optical coherence tomography based on machine learning by Zhongping Tian, Yinning Guo, Xi Chen, Qifeng Zhou, Yuan Liu, Zhizhu Yi, Li Zhang, Li Zhang

“…Machine learning models exhibited superior performance in central subfield thickness (CST) prediction, achieving test set R2 values of 0.930 (LR), 0.926 (LASSO), 0.936 (SVR), and 0.892 (RF). …”
Get full text