Gut microbiota and plasma metabolites in pregnant mothers and infant atopic dermatitis: A multi-omics studyKey Message by Bingqian Du, MA.Sc, Aga Shama, BA.Sc, Yi Zhang, BA.Sc, Baolan Chen, MA.Sc, Yongqi Bu, BA.Sc, Pei-an Chen, MA.Sc, Chuzhi Lin, BA.Sc, Jie Liu, MA.Sc, Juan Zheng, MA.Sc, Zhenjun Li, MD, PhD, Qingsong Chen, PhD, Yu Sun, PhD, Xi Fu, PhD

“…Several other linoleic acids and flavonoids were negatively associated with AD (FDR<0.05). Neural network analysis revealed that microorganisms enriched in health group may produce these protective fatty acids. …”
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Comparisons of adverse events associated with immune checkpoint inhibitors in the treatment of non-small cell lung cancer: a real-world disproportionality analysis based on the FDA... by Ruichen Gao, Wenjun Liang, Jintao Chen, Mingxia Yang, Xiaowei Yu, Xiaohua Wang

“…Methods Disproportionality analysis and Bayesian confidence propagation neural network (BCPNN) were utilized to identify pharmacovigilance signals from the FDA Adverse Event Reporting System (FAERS). …”
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Aging Alters Olfactory Bulb Network Oscillations and Connectivity: Relevance for Aging-Related Neurodegeneration Studies by A. Ahnaou, D. Rodriguez-Manrique, S. Embrechts, R. Biermans, N. V. Manyakov, S. A. Youssef, W. H. I. M. Drinkenburg

“…However, age-dependent alterations in neural network appeared spontaneously in the OB circuit, suggesting the neurophysiological basis of synaptic deficits underlying olfactory processing. …”
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Enhancing semantical text understanding with fine-tuned large language models: A case study on Quora Question Pair duplicate identification. by Sifei Han, Lingyun Shi, Fuchiang Rich Tsui

“…In our previous study, we developed a Siamese Convolutional Neural Network (S-CNN) that achieved an F1 score of 82.02% (95% C.I.: 81.83%-82.20%). …”
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Correlation-guided decoding strategy for low-resource Uyghur scene text recognition by Miaomiao Xu, Jiang Zhang, Lianghui Xu, Wushour Silamu, Yanbing Li

“…Specifically, (1) CGDS employs a hybrid encoding strategy that combines Convolutional Neural Network (CNN) and Transformer. This hybrid encoding effectively leverages the advantages of both methods: On one hand, the convolutional properties and shared weight mechanism of CNN allow for efficient extraction of local features, reducing dependency on large datasets and minimizing errors caused by similar characters. …”
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Corrosion inhibition effects of eco-friendly clarithromycin molecules on aluminium in hydrochloric acid solution via experimental, theoretical and optimization approach by O.D. Onukwuli, I.A. Nnanwube, F.O. Ochili, M. Omotioma, J.I. Obibuenyi

“…Optimization by RSM gave an optimum IE of 85.43 %, from which artificial neural network (ANN) predicted improved inhibition efficiency. …”
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Evaluation of Rainfall-Induced Accumulation Landslide Susceptibility Based on Remote Sensing Interpretation by Zhen Wu, Runqing Ye, Jue Huang, Xiaolin Fu, Yao Chen

“…Various machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), and BP Neural Network models, were employed to assess the susceptibility of rainfall-induced accumulation landslides in the study area. …”
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The Use of Machine Learning to Create a Risk Score to Predict Survival in Patients with Hepatocellular Carcinoma: A TCGA Cohort Analysis by Samer Tohme, Hamza O Yazdani, Amaan Rahman, Sanah Handu, Sidrah Khan, Tanner Wilson, David A Geller, Richard L Simmons, Michele Molinari, Christof Kaltenmeier

“…The current study uses Artificial Neural Network (ANN) and Classification Tree Analysis (CTA) to create a gene signature score that can help predict survival in patients with HCC. …”
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A novel perspective on survival prediction for AML patients: Integration of machine learning in SEER database applications by Zheng-yi Jia, Maierbiya Abulimiti, Yun Wu, Li-na Ma, Xiao-yu Li, Jie Wang

“…In addition, both the XGBoost classifier and the neural network classifier showed high accuracy and reliability at each prediction stage. …”
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Application of Dragonnet and Conformal Inference for Estimating Individualized Treatment Effects for Personalized Stroke Prevention: Retrospective Cohort Study by Sermkiat Lolak, John Attia, Gareth J McKay, Ammarin Thakkinstian

“…ObjectiveThis study aimed to estimate the individualized treatment effects (ITEs) for stroke prevention using a novel combination of Dragonnet, a causal neural network, and conformal inference. The study also aimed to determine and validate the causal effects of known stroke risk factors—hypertension (HT), diabetes mellitus (DM), dyslipidemia (DLP), and atrial fibrillation (AF)—using both a conventional causal model and machine learning models. …”
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Enhanced prediction of energy dissipation rate in hydrofoil-crested stepped spillways using novel advanced hybrid machine learning models by Ehsan Afaridegan, Nosratollah Amanian

“…This study investigates the prediction of EDR using advanced hybrid Machine Learning (ML) models, including the Tabular Neural Network with Moth Flame Optimization (TabNet-MFO), Long Short-Term Memory with Ant Lion Optimizer (LSTM-ALO), Extreme Learning Machine with Jaya and Firefly Optimization (ELM-JFO), and Support Vector Regression with Improved Whale Optimization (SVR-IWOA). …”
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A novel hybrid methodology for wind speed and solar irradiance forecasting based on improved whale optimized regularized extreme learning machine by S. Syama, J. Ramprabhakar, R Anand, V. P. Meena, Josep M. Guerrero

“…The evaluation criteria demonstrate that the suggested method outperforms the existing methods in terms of prediction accuracy and stability, thus confirming that a hybrid forecasting model approach combining an efficient decomposition method with a simplified but efficient parameter-optimized neural network can enhance its accuracy and stability.…”
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Analysis of Sparse Trajectory Features Based on Mobile Device Location for User Group Classification Using Gaussian Mixture Model by Yohei Kakimoto, Yuto Omae, Hirotaka Takahashi

“…We then construct three machine learning (ML) models—support vector classifier (SVC), random forest (RF), and deep neural network (DNN)—using the GMM-based features and compare their performance with that of the improved DNN (IDNN), which is an existing feature extraction approach. …”
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A novel lightweight model for tea disease classification based on feature reuse and channel focus attention mechanism by Junjie Liang, Renjie Liang, Dongxia Wang

“…To improve the recognition accuracy, the traditional classic convolutional neural network (CNN) models require higher model complexity. …”
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Enhancing Drought Forecast Accuracy Through Informer Model Optimization by Jieru Wei, Wensheng Tang, Pakorn Ditthakit, Jiandong Shang, Hengliang Guo, Bei Zhao, Gang Wu, Yang Guo

“…This study employed the Informer model to forecast drought and conducted a comparative analysis with Autoregressive Integrated Moving Average (ARIMA), long short-term memory (LSTM), and Convolutional Neural Network (CNN) models. The findings indicate that the Informer model outperforms the other three models in terms of drought forecasting accuracy across all time scales. …”
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Safety assessment of tolvaptan: real-world adverse event analysis using the FAERS database by Peiyang Cao, Qian Wang, Yan Wang, Qing Qiao, Liyuan Yan

“…ObjectiveThis study aims to analyze the adverse drug events (ADEs) associated with tolvaptan in the Food and Drug Administration Adverse Event Reporting System database from the fourth quarter of 2009 to the second quarter of 2024.MethodsAfter standardizing the data, various signal detection techniques, including Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network, and Multi-Item Gamma Poisson Shrinker, were employed for analysis.ResultsAmong the 7,486 ADE reports where tolvaptan was the primary suspected drug, a total of 196 preferred terms were identified, spanning 24 different system organ classes. …”
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Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipel... by Nalinda D. Liyanagedera, Corinne A. Bareham, Heather Kempton, Hans W. Guesgen

“…While testing different feature extraction algorithms, a common neural network structure was used as the classification algorithm to compare the performance of the feature extraction algorithms. …”
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Liver fibrosis stage classification in stacked microvascular images based on deep learning by Daisuke Miura, Hiromi Suenaga, Rino Hiwatashi, Shingo Mabu

“…Methods This single-center, cross-sectional study included 517 patients with CLD who underwent ultrasonography and liver stiffness testing between August 2019 and October 2022. A convolutional neural network model was constructed to evaluate the degree of liver fibrosis from stacked microvascular images generated by accumulating high-sensitivity Doppler (i.e., high-definition color) images from these patients. …”
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Advancing Horticultural Crop Loss Reduction Through Robotic and AI Technologies: Innovations, Applications, and Practical Implications by H. W. Gammanpila, M. A. Nethmini Sashika, S. V. G. N. Priyadarshani

“…For instance, Ji et al. in 2007 developed an artificial neural network (ANN)-based system for rice yield prediction in Fujian, China, improving accuracy over traditional models. …”
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New probabilistic methods for quantitative climate reconstructions applied to palynological data from Lake Kinneret by T. Netzel, A. Miebach, T. Litt, A. Hense

“…For the models and biome distributions used, a simple feedforward neural network provides the optimal choice of the classification problem.…”
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