Preprocessing of Aspect-based English Telugu Code Mixed Sentiment Analysis by Arun Kodirekka, Ayyagari Srinagesh

“…The second step is the data normalization task, and the final step is classification, which can be achieved using three different methods: lexicon, machine learning, and deep learning. …”
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Model adaptation via credible local context representation by Song Tang, Wenxin Su, Yan Yang, Lijuan Chen, Mao Ye

“…To capture the context, a discovery method is developed that performs fixed steps walking in deep space and takes the credible features in this path as the context. …”
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Impact of COVID-19 on continuing professional development: Perspectives of audiologists by Suvishka Barath, Andrew J. Ross

“…These include (1) the adoption of online learning, (2) improved flexibility, (3) cost-effectiveness, (4) diverse learning opportunities, (5) keeping current, (6) isolation and networking, (7) limited interactivity and (8) uncertain quality assurance. …”
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Universal Design for Learning (UDL): a framework for re-design of an Environmental Education (EE) outreach program for a more inclusive and impactful Science Festival event by Caroline Gilleran Stephens, Sarpong Hammond Antwi, Suzanne Linnane

“…Findings indicate that hands-on, experiential learning significantly improved both teacher and student engagement. …”
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Machine learning for predicting antimicrobial resistance in critical and high-priority pathogens: A systematic review considering antimicrobial susceptibility tests in real-world h... by Carlos M Ardila, Daniel González-Arroyave, Sergio Tobón

“…<h4>Results</h4>After completing the final step of the eligibility assessment, the systematic review ultimately included 21 papers. …”
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Deep Learning for Atmospheric Modeling: A Proof of Concept Using a Fourier Neural Operator on WRF Data to Accelerate Transient Wind Forecasting at Multiple Altitudes by Paulo Alexandre Costa Rocha, Jesse Van Griensven Thé, Victor Oliveira Santos, Bahram Gharabaghi

“…This study addresses the problem of the computational cost of transient CFD simulations, which rely on iterative time-step calculations, by employing deep learning to generate optimized initial conditions for accelerating the Weather Research and Forecasting (WRF) model. …”
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LiveDrive AI: A Pilot Study of a Machine Learning-Powered Diagnostic System for Real-Time, Non-Invasive Detection of Mild Cognitive Impairment by Firas Al-Hindawi, Peter Serhan, Yonas E. Geda, Francis Tsow, Teresa Wu, Erica Forzani

“…The implications of this work suggest a transformative step towards scalable, real-world solutions for early AD diagnosis, with the potential to improve patient outcomes and disease management.…”
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A Multi-Variable Coupled Control Strategy Based on a Deep Deterministic Policy Gradient Reinforcement Learning Algorithm for a Small Pressurized Water Reactor by Jie Chen, Kai Xiao, Ke Huang, Zhen Yang, Qing Chu, Guanfu Jiang

“…Simulation results show that the reinforcement learning control effect is better than the PID control effect under a ±10% FP step variable load condition, a linear variable load condition, and a load dumping condition, and that the reactor power overshooting amount and regulation time, the maximum deviation of the coolant average temperature, the steam pressure, the pressure of pressurizer and relative liquid level, and the regulation time are improved by at least 15.5% compared with the traditional control method. …”
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A Machine Learning Free Energy Functional for the 1D Reference Interaction Site Model: Towards Prediction of Solvation Free Energy for All Solvent Systems by Jonathan G. M. Conn, Abdullah Ahmad, David S. Palmer

“…This represents a significant step towards a universal machine learning SFE functional for RISM.…”
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18F-FDG PET/CT-based habitat radiomics combining stacking ensemble learning for predicting prognosis in hepatocellular carcinoma: a multi-center study by Chunxiao Sui, Qian Su, Kun Chen, Rui Tan, Ziyang Wang, Zifan Liu, Wengui Xu, Xiaofeng Li

“…First, intratumoral habitats were achieved by a two-step unsupervised clustering process based on k-means clustering. …”
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Meta-analysis of multi-center transcriptomic profiles and machine learning reveal phospholipase Cβ4 as a Wnt/Ca²+ signaling mediator in glioblastoma immunotherapy by Zhaoming Song, Fei Wang, Chen Yang, Yanao Guo, Jinfeng Li, Run Huang, Hongyi Ling, Guosheng Cheng, Zhouqing Chen, Zhanchi Zhu, Zhong Wang

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Unified Analysis of Decentralized Gradient Descent: A Contraction Mapping Framework by Erik G. Larsson, Nicolo Michelusi

“…The decentralized gradient descent (DGD) algorithm, and its sibling, diffusion, are workhorses in decentralized machine learning, distributed inference and estimation, and multi-agent coordination. …”
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The Application of Machine Learning Algorithms to Predict HIV Testing in Repeated Adult Population–Based Surveys in South Africa: Protocol for a Multiwave Cross-Sectional Analysis... by Musa Jaiteh, Edith Phalane, Yegnanew A Shiferaw, Refilwe Nancy Phaswana-Mafuya

“… BackgroundHIV testing is the cornerstone of HIV prevention and a pivotal step in realizing the Joint United Nations Program on HIV/AIDS (UNAIDS) goal of ending AIDS by 2030. …”
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A Systematic Review of AI-Based Techniques for Automated Waste Classification by Farnaz Fotovvatikhah, Ismail Ahmedy, Rafidah Md Noor, Muhammad Umair Munir

“…Waste classification is a critical step in waste management that is time-consuming and necessitates automation to replace traditional approaches. …”
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Identification of sepsis biomarkers through glutamine metabolism-mediated immune regulation: a comprehensive analysis employing mendelian randomization, multi-omics integration, an... by Zhuang’e Shi, Fuping Wang, Lishun Yang, Lishun Yang, Couwen Li, Bing Gong, Ruanxian Dai, Guobing Chen

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Data-Efficient Training of Gaussian Process Regression Models for Indoor Visible Light Positioning by Jie Wu, Rui Xu, Runhui Huang, Xuezhi Hong

“…The impacts of the parameter of active learning on the performance of the GPR-VLP are also presented via experimental investigation, which shows that (1) the proposed training method outperforms the conventional one regardless of the number of final effective training data (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="double-struck">E</mi></mrow></semantics></math></inline-formula>), especially for a small/moderate effective training dataset, (2) a moderate step size (<i>k</i>) should be chosen for updating the effective training dataset to balance the positioning accuracy and computational complexity, and (3) due to the interplay of the reliability of the initialized GPR model and the flexibility in reshaping such a model via active learning, the number of initial effective training data (<i>m</i>) should be optimized. …”
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Multimedia Interaktif Sebagai Media Pembelajaran Pada Masa Pandemi Covid 19 by Purbatua Manurung

“… This study aims to analyze the function of alternative learning media (interactive multimedia) during the Covid 19 pandemic. …”
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A high-precision displacement prediction model for landslide geological hazards based on APSO-SVR-LSTM combination by Nianhong Wang, Jun Zhang, Yunfei Xiang, Saipeng Huang

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Transforming Automotive Workforce: Contemporary Pillars of The Employee Development by Vasilii OSTIN

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Quantized Auto Encoder-Based Anomaly Detection for Multivariate Time Series Data in 5G Networks by Giovanni Trappolini, Antonio Purificato, Federico Siciliano, Luigi D'Addona, Anna Maria Spagnolo, Domenico Dato, Fabrizio Silvestri

“…This paper introduces <monospace>QAED</monospace> (Quantized Auto Encoder Detector), a novel deep learning approach for anomaly detection in 5G networks with three key innovations: 1) a vector quantization mechanism that effectively captures discrete network states, 2) a kernel density estimation preprocessing step that enables detection of both outliers and distribution shifts, and 3) an integrated architecture that processes multivariate time series data in a unified framework. …”
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