Design of intelligent English translation teaching system combined with bidirectional encoder representation by Shanshan Xu

“…In addition, the system integrates machine learning algorithms that analyze students' learning habits and error patterns to provide customized exercises and tutoring. …”
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REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised tria... by Lily G Bessette, Elad Yom-Tov, Niteesh K Choudhry, Julie C Lauffenburger, Nancy Haff, Marie E McDonnell, Constance P Fontanet, Ellen S Sears, Erin Kim, Kaitlin Hanken, Renee A Barlev, Punam A Keller, J Joseph Buckley

“…By contrast, reinforcement learning is a machine learning method that can be used to identify individuals’ patterns of responsiveness by observing their response to cues and then optimising them accordingly. …”
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Nitrogen content estimation of apple trees based on simulated satellite remote sensing data by Meixuan Li, Xicun Zhu, Xicun Zhu, Xinyang Yu, Cheng Li, Dongyun Xu, Ling Wang, Dong Lv, Yuyang Ma

“…Correlation coefficient method and partial least squares regression were used to screen sensitive bands for apple tree nitrogen content. Support Vector Machine (SVM) and Backpropagation Neural Network (BPNN) algorithms were used to construct and screen the optimal models for apple tree nitrogen content estimation.ResultsResults showed that visible light, red edge, near-infrared, and yellow edge bands were sensitive bands for estimating apple tree nitrogen content. …”
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Hyperspectral and LiDAR space-borne data for assessing mountain forest volume and biomass by Rodolfo Ceriani, Sebastian Brocco, Monica Pepe, Silvio Oggioni, Giorgio Vacchiano, Renzo Motta, Roberta Berretti, Davide Ascoli, Matteo Garbarino, Donato Morresi, Francesco Bassi, Francesco Fava

“…GEDI LiDAR proved to be a necessary input for accurate SV and AGB retrieval, and GPR was the best-performing ML algorithm. The resulting spatial maps were artifact-free and successfully delineated ecological gradients and management patterns. …”
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Consensus-Based Intelligent Distributed Secondary Control for Multiagent Islanded Microgrid by Ali M. Jasim, Basil H. Jasim, Flah Aymen, Hossam Kotb, Ahmed Althobaiti

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Bridging aging, immunity, and atherosclerosis: novel insights into senescence-related genes by Yan Lu, Rong Yuan, Rong Yuan, Qiqi Xin, Qiqi Xin, Keji Chen, Keji Chen, Weihong Cong, Weihong Cong

“…Subsequently, differential expression analysis, weighted gene co-expression network analysis, accompanied by 3 machine learning algorithms, LASSO, SVM and RF, were performed to identify diagnostic genes. …”
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Towards a computer-assisted assessment of imitation in children with autism spectrum disorder based on a fine-grained analysis by Rujing Zhang, Jingying Chen, Xiaodi Liu, Yanling Gan, Guangshuai Wang

“…In this process, several quantitative indicators were applied to quantify the children’s imitation ability based on a fine-grained analysis of their visual attention and motor execution patterns. Then, three classic machine-learning algorithms were employed to explore whether the indicators could efficiently classify children with imitation difficulties. …”
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Exploring the Relationship between Spatiotemporal Distribution of Urban Vibrancy and Neighborhood Attributes by Coupling Multi-Source Data: A Case of Nanshan District in Shenzhen by Liu Feng, Tang Zhong, Zhang Liang, Yu Lingmin, Liu Ke

“…This work focuses on the Nanshan District of Shenzhen City as the study area, and has three objectives: (i) one-week passenger flow data that characterized the spatiotemporal distribution of urban vibrancy were provided; (ii) broadly collected Street View Images (SVI) were incorporated as a visional environmental factor, together with functional and morphological factors, into understanding the influencing mechanism of urban vibrancy; and finally, (iii) machine learning tools were employed to apply the Random Forest Regression (RFR) algorithm in exploring the independent driving role of the characteristic factors behind the temporal distribution of urban vibrancy, and the Geographically Weighted Regression (GWR) model was used to probe the influence of the characteristic factors on the spatial distribution of this dynamic concept. …”
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Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments: Real-World Evaluation by Zongqi Xia, Prerna Chikersal, Shruthi Venkatesh, Elizabeth Walker, Anind K Dey, Mayank Goel

“…Smartphone sensors recorded call activity, location, and screen use, while fitness trackers captured heart rate, sleep patterns, and step count. We extracted patient-level behavioral features and categorized them into 2 feature sets: one from the prediction period (called action) and one from the preceding period (called context). …”
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Appliance-Specific Noise-Aware Hyperparameter Tuning for Enhancing Non-Intrusive Load Monitoring Systems by João Góis, Lucas Pereira

“…The results indicate that the noise metric provides valuable guidance for selecting the input sequence length, particularly for user-dependent appliances with more unpredictable usage patterns, such as washing machines and electric kettles.…”
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Application of artificial intelligence technologies in HR management by S. V. Okladnikova, A. S. Pankrashov

“…Based on the fact that in the field of AI, methods mean algorithms by which tasks are solved, the following number of methods related to AI theory were identified: neural networks, fuzzy logic, expert systems, evolutionary modeling, Machine Learning.Result. …”
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Classification of differentially activated groups of fibroblasts using morphodynamic and motile features by Minwoo Kang, Chanhong Min, Somayadineshraj Devarasou, Jennifer H. Shin

“…We extract these features from label-free live-cell imaging data of fibroblasts co-cultured with breast cancer cell lines using deep learning and machine learning algorithms. Our findings show that morphodynamic and motile features offer robust insights into fibroblast activation states, complementing molecular markers and overcoming their limitations. …”
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A comprehensive evaluation of oversampling techniques for enhancing text classification performance by Salimkan Fatma Taskiran, Bahaeddin Turkoglu, Ersin Kaya, Tunc Asuroglu

“…Each dataset was vectorized using the MiniLMv2 transformer model to obtain semantically rich representations, and classification was performed using six machine learning algorithms. The balanced and imbalanced scenarios were compared in terms of F1-Score and Balanced Accuracy. …”
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Equivariant learning leveraging geometric invariances in 3D molecular conformers for accurate prediction of quantum chemical properties by Jianhua Sun, Ye Cao, Huijing Hu, Baoqiao Qi

“…In this study, we present a computational framework termed 3D molecular structure enhanced (3DMSE) that seamlessly integrates the rich structural information inherent in 3D molecular geometries with state-of-the-art machine learning algorithms to enable highly precise and computationally efficient prediction of crucial quantum chemical properties. …”
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An explainable transformer model for Alzheimer’s disease detection using retinal imaging by Saeed Jamshidiha, Alireza Rezaee, Farshid Hajati, Mojtaba Golzan, Raymond Chiong

“…The Retformer model is trained on datasets of different modalities of retinal images from patients with AD and age-matched healthy controls, enabling it to learn complex patterns and relationships between image features and disease diagnosis. …”
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Exploration of Epigenetic Mechanisms and Biomarkers Among Patients with Very-Late-Onset Schizophrenia-Like Psychosis by Gan Y, Yue W, Sun J, Yang D, Fang C, Zhou Z, Yin J, Zhou H

“…Machine learning algorithms generated diagnostic models, with classification performance evaluated using Area Under the Curve (AUC) metrics.Results: Analysis revealed distinct DNA methylation signatures in VLOSLP patients compared to controls. …”
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Comprehensive Analysis Reveals the Molecular Features and Immune Infiltration of PANoptosis-Related Genes in Metabolic Dysfunction-Associated Steatotic Liver Disease by Yan Huang, Jingyu Qian, Zhengyun Luan, Junling Han, Limin Tang

“…Machine learning algorithms prioritized key PANoDEGs, while ROC curves assessed their diagnostic efficacy. …”
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Different oscillatory mechanisms of dementia-related diseases with cognitive impairment in closed-eye state by Talifu Zikereya, Yuchen Lin, Zhizhen Zhang, Ignacio Taguas, Kaixuan Shi, Chuanliang Han

“…We accurately and clearly identified three stable oscillation targets (theta, ∼5 Hz, alpha, ∼10 Hz, and beta, ∼18 Hz) that facilitate differentiation between AD, FTD, and HC both statistically and through classification using machine learning algorithms. Overall, the differences between AD and HC were the most pronounced, with FTD exhibiting intermediate characteristics. …”
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Wave propagation-based tests for concrete piles – an overview by Reham Samaan, Abdelsalaam Mokhtar, Mohamed Saafan, Ahmed Ebid

“…This investigation establishes the practical and theoretical basis for implementing advanced machine learning predictive models that combine previous records with pattern recognition algorithms, potentially converting traditional PIT interpretation from an uncertain process to a reliable and precise evaluation system. …”
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Self-Supervised Neural Networks for Precoding in MIMO Rate Splitting Multiple Access Systems by Dheeraj Raja Kumar, Carles Anton-Haro, Xavier Mestre

“…The intention is to explore several alternatives to conventional iterative precoding benchmarks like Weighted Minimum Mean Square Error (WMMSE) which are computationally intensive algorithms. We evaluate the different precoding policies learnt by the neural network architectures by closely studying the respective radiation patterns. …”
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