Process optimisation: spinal versus general anaesthesia for endourological surgery. A randomised, controlled trial and machine-learning approach by Kornel Skitek, Gregor A. Schittek, Jens Soukup

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Analysis and Predictions of Spread, Recovery, and Death Caused by COVID-19 in India by Rajani Kumari, Sandeep Kumar, Ramesh Chandra Poonia, Vijander Singh, Linesh Raja, Vaibhav Bhatnagar, Pankaj Agarwal

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A novel method for assessing cycling movement status: an exploratory study integrating deep learning and signal processing technologies by Yingchun He, Yi-haw Jan, Fan Yang, Yunru Ma, Chun Pei

“…Spearman’s rank correlation analysis, intraclass correlation coefficient (ICC), error analysis, and t-test were conducted to compare the consistency of data obtained from the two movement capture systems, including the peak frequency of acceleration, transition time point between movement statuses, and the complexity index average (CIA) of the movement status based on multiscale entropy analysis.The KR algorithm showed excellent consistency (ICC1,3=0.988) between the two methods when estimating the peak acceleration frequency. …”
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Instantaneous Metabolic Energetics: Data-Driven Modeling Using Function-Based Surrogates and Gradient Boosting by Christopher Buglino, William Z. Peng, Stacy Ashlyn, Hyunjong Song, Howard J. Hillstrom, Joo H. Kim

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Computational Thinking and Academic Performance Across Different Instructional Modalities in Pre‐University Courses: A Data‐Driven Study by Jorge Parraga‐Alava, Jorge Rodas‐Silva

“…ABSTRACT In preuniversity education, educators and decision‐makers need to understand how teaching methods affect student learning in computational thinking (CT). This helps identify factors influencing student outcomes and inform the development of personalized learning programs. …”
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Upskilling consumers for the digital health era: a content analysis of resources for consumer representative training by Jing Hern Kevin Poh, Clare Mullen, Sonali Munot, Pip Brennan, Clara Chow, Edel O’Hagan

“…It is unclear what resources are freely available to consumer representatives and advocates to learn how digital health tools and platforms are developed, evaluated, and implemented.Aim To examine what freely available resources are available for consumers to learn how digital health tools and platforms are developed, evaluated, and implemented.Methods This was a content analysis study. …”
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Development and evaluation of statistical and artificial intelligence approaches with microbial shotgun metagenomics data as an untargeted screening tool for use in food production by Kristen L. Beck, Niina Haiminen, Akshay Agarwal, Anna Paola Carrieri, Matthew Madgwick, Jennifer Kelly, Victor Pylro, Ban Kawas, Martin Wiedmann, Erika Ganda

“…We also show through analysis of publicly available fluid milk microbial data that our artificial intelligence approach is able to successfully predict milk in different stages of processing. …”
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Signal Enhancement for Downhole Microseismic Data Using Improved Attention Mechanism Based on Autoencoder Network by Wenxuan Ge, Qinghui Mao, Wei Zhou, Zhixian Gui, Peng Wang

“…Simulation experiments are conducted using waveform analysis, time-frequency analysis, first arrival picking, and polarization analysis methods to validate the effectiveness of the model. …”
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Student Modeling and Analysis in Adaptive Instructional Systems by Jing Liang, Ryan Hare, Tianyu Chang, Fangli Xu, Ying Tang, Fei-Yue Wang, Shimeng Peng, Mingyu Lei

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Binary Particle Swarm Optimization with Manta Ray Foraging Learning Strategies for High-Dimensional Feature Selection by Jianhua Liu, Yuxiang Chen, Shanglong Li

“…High-dimensional feature selection is one of the key problems of big data analysis. The binary particle swarm optimization (BPSO) method, when used to achieve feature selection for high-dimensional data problems, can get stuck in local optima, leading to reduced search efficiency and inferior feature selection results. …”
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Tree Species Classification at the Pixel Level Using Deep Learning and Multispectral Time Series in an Imbalanced Context by Florian Mouret, David Morin, Milena Planells, Cécile Vincent-Barbaroux

“…Validation on independent in situ data shows that all models struggle to predict in areas not well covered by training data, but even in this situation, the RF algorithm is largely outperformed by deep learning models for minority classes. …”
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Client grouping and time-sharing scheduling for asynchronous federated learning in heterogeneous edge computing environment by Qianpiao MA, Qingmin JIA, Jianchun LIU, Hongli XU, Renchao XIE, Tao HUANG

“…To overcome the three key challenges of federated learning in heterogeneous edge computing, i.e., edge heterogeneity, data Non-IID, and communication resource constraints, a grouping asynchronous federated learning (FedGA) mechanism was proposed.Edge nodes were divided into multiple groups, each of which performed global updated asynchronously with the global model, while edge nodes within a group communicate with the parameter server through time-sharing communication.Theoretical analysis established a quantitative relationship between the convergence bound of FedGA and the data distribution among the groups.A time-sharing scheduling magic mirror method (MMM) was proposed to optimize the completion time of a single round of model updating within a group.Based on both the theoretical analysis for FedGA and MMM, an effective grouping algorithm was designed for minimizing the overall training completion time.Experimental results demonstrate that the proposed FedGA and MMM can reduce model training time by 30.1%～87.4% compared to the existing state-of-the-art methods.…”
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Improving Audio Recognition With Randomized Area Ratio Patch Masking: A Data Augmentation Perspective by Weichun Wong, Yachun Li, Shihan Li

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ADVANCED NEURAL NETWORKS AND DEEP LEARNING TECHNIQUES IN FINANCIAL MARKET PREDICTION by ENE CEZAR CATALIN

“…Mimicking the computational structure of the human brain, ANN processes interconnected data points enabling efficient analysis and forecasting. …”
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Deep Learning-Based Real-Time Driver Cognitive Distraction Detection by Matteo Fresta, Francesco Bellotti, Igor Bochenko, Luca Lazzaroni, Gaetan Merlhiot, Fabio Tango, Riccardo Berta

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Smart Coffee: Machine Learning Techniques for Estimating Arabica Coffee Yield by Cleverson Henrique de Freitas, Rubens Duarte Coelho, Jéfferson de Oliveira Costa, Paulo Cesar Sentelhas

“…This study applied machine learning to predict the Arabica coffee yield in the region, analyzing two groups of cultivars (G1 and G2) using data from 1993 to 2020. …”
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Inequalities in Mild Cognitive Impairment Risk Among Chinese Middle-Aged and Older Adults: Insights from an Integrated Learning Model by Bi S, Guo D, Tan H, Chen Y, Li G

“…The model’s robust performance and interpretability highlight its potential to inform public health strategies and interventions aimed at addressing inequalities in dementia risk.Keywords: mild cognitive impairment, inequality, integrated learning, CNN-BiLSTM-Attention, SHAP analysis, Mediation analysis…”
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Bankruptcy Prediction Using First-Order Autonomous Learning Multi-Model Classifier by Amine Sabek, Jakub Horák, Hussam Musa, Amélia Ferreira da Silva

“…The traditional approaches for prediction, including logistic regression and discriminant analysis, are constrained by their inability to deal with complex and high-dimensional data (Odom and Sharda, 1990; Min and Lee, 2005). …”
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Modeling Political Discourse in Indonesia’s 2024 Election Using Unsupervised Machine Learning by Malikhatul Ibriza, Maya Rini Handayani, Wenty Dwi Yuniarti, Khothibul Umam

“…The 2024 General Election in Indonesia has generated a large volume of diverse and unstructured digital political discourse, necessitating a machine learning-based analytical approach for efficient, objective, and scalable data processing. …”
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Machine learning derivation of two cardiac arrest subphenotypes with distinct responses to treatment by Weidong Zhang, Chenxi Wu, Peifeng Ni, Sheng Zhang, Hongwei Zhang, Ying Zhu, Wei Hu, Mengyuan Diao

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