Factors influencing student’s transition from student to workforce in intensive care units by Leonie Olivier, Charlene Downing

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Artificial intelligence based hybrid solar energy systems with smart materials and adaptive photovoltaics for sustainable power generation by Udit Mamodiya, Indra Kishor, Ramakrishna Garine, Priyam Ganguly, Nithesh Naik

“…The system comprises a CNN-LSTM model for accurate solar irradiance forecasting, reinforcement learning for real-time dual-axis tracking, and Edge AI for low-latency control decisions. …”
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Multi-Scale Differentiated Network with Spatial–Spectral Co-Operative Attention for Hyperspectral Image Denoising by Xueli Chang, Xiaodong Wang, Xiaoyu Huang, Meng Yan, Luxiao Cheng

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Denoising of Heart Sounds Using Lightweight FCNs and Spectrograms With and Without Context by Declan Duggan, Andriy Temko, Volodymyr Sarana, Andreea Factor, Emanuel Popovici

“…We also implement the denoising inference on an edge device to show the feasibility of running this scheme on an embedded system. This work is a step towards a real-time deep learning-based denoiser for use with a digital stethoscope.…”
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A Hybrid Approach of DenseNet121 with Attention and Bi-LSTM for Yoga Pose Estimation by Aarthy K., Alice Nithya

“…Feature extraction is conducted using OpenPose, a widely recognized tool for obtaining key human body points. This step is crucial for capturing detailed information about the postures. …”
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Three-Dimensional Automated Breast Ultrasound (ABUS) Tumor Classification Using a 2D-Input Network: Soft Voting or Hard Voting? by Shaode Yu, Xiaoyu Liang, Songnan Zhao, Yaoqin Xie, Qiurui Sun

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Spatial and Temporal Patterns of Grassland Species Diversity and Their Driving Factors in the Three Rivers Headwater Region of China from 2000 to 2021 by Mingxin Yang, Ang Chen, Wenqiang Cao, Shouxin Wang, Mingyuan Xu, Qiang Gu, Yanhe Wang, Xiuchun Yang

“…Among models based on diverse variable selection and machine learning methods, the random forest (RF) combined stepwise regression (STEP) model was found to be the optimal model for estimating grassland species diversity in this study, which had an R² of 0.44 and an RMSE of 2.56 n/m² on the test set. …”
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Health Modeling — An Innovative Educational Program for the General Medicine Specialty by M. M. Litvinova, M. S. Khamidulina, T. M. Litvinova, Yu. A. Lutokhina, E. N. Dudnik, N. V. Kireeva, K. V. Ivashkin, B. A. Volel

“…The “Health Modeling” educational program represents a significant step forward in modernizing medical education with an emphasis on preventive care. …”
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A PSO-CNN-LSTM Model for Seismic Facies Analysis: Methodology and Applications by Luyao Liao, Huailai Zhou, Junping Liu, Jie Zhou, Donghang Zhang, Jian Wang

“…Seismic facies analysis, as a crucial step in the study of depositional facies, effectively delineates the distribution patterns of depositional facies between wells. …”
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A seismic random noise suppression method based on CNN-Mamba by Xiujuan WEI, Xingye LIU, Huailai ZHOU

“…BackgroundSeismic random noise suppression is recognized as a key step to improve the quality of seismic data. Data-driven deep learning provides an intelligent solution for the noise suppression. …”
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Protocol for detection and monitoring of post-stroke cognitive impairment through AI-powered speech analysis: a mixed methods pilot study by Ravi Shankar, Effie Chew, Effie Chew, Effie Chew, Anjali Bundele, Anjali Bundele, Amartya Mukhopadhyay, Amartya Mukhopadhyay

“…Statistical analysis will include correlation analysis between speech features and MoCA scores, as well as machine learning classification and regression models to predict cognitive impairment. …”
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Optimized AI and IoT-Driven Framework for Intelligent Water Resource Management by Mahmoud Badee Rokaya Mahmoud, Dalia Ismaeil Ibrahim Hemdan, Samah Hazzaa Alajmani, Raneem Yousif Alyami, Ghada Elmarhomy, Hassan Hashim, El-Sayed Atlam

“…The architecture combines the ensemble-learning algorithms (XGBoost, LightGBM), hybrid AIs (XGBoost + Autoencoder), and metaheuristic feature selection (GA, PSO, SA) for making intelligent decisions. …”
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Automated sample annotation for diabetes mellitus in healthcare integrated biobanking by Johannes Stolp, Christoph Weber, Danny Ammon, André Scherag, Claudia Fischer, Christof Kloos, Gunter Wolf, P. Christian Schulze, Utz Settmacher, Michael Bauer, Andreas Stallmach, Michael Kiehntopf, Boris Betz

“…The study evaluates a machine learning (ML) and natural language processing (NLP) based two-step procedure for timely and precise sample annotation for diabetes mellitus. …”
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IDNet: A Diffusion Model-Enhanced Framework for Accurate Cranio-Maxillofacial Bone Defect Repair by Xueqin Ji, Wensheng Wang, Xiaobiao Zhang, Xinrong Chen

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The role of artificial intelligence in language teacher education: EFL learners’ views on the concept of “ChatGPT” through metaphorical analysis by Serpil Azap

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Target recognition in diverse synthetic aperture radar image datasets with low size weight and power processing hardware by Richard O. Lane, Wendy J. Holmes, Timothy Lamont‐Smith

“…Further qualitative analysis of algorithm performance on additional data with different characteristics highlighted the importance of gathering relevant training data and carrying out suitable pre‐processing steps.…”
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Elimination-based reasoning with LLM for multiple-choice educational question answering by Qianli Zhao, Mei Zhang

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Structural strength analysis and optimization of converter lug based on Kriging model by HE Guanqiang, LIU Yongjiang, LI Hua, CHEN Liming

“…In this paper, machine learning methods were considered to approximate the functional relationships between design variables and responses. …”
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Sentiment Analysis of Public Comments on X Social Media Related to Israeli Product Boycotts Using The Long Short-Term Memory (LSTM) Method by Pitra Rahmadani Panggabean, Asrianda Asrianda, Hafizh Al-Kausar Aidilof

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Continuous Action Air Combat Maneuver Decision-Making Based on T-MGMM by Junzhe Jiang, Hongming Wang, Zhixing Huang, Zhuangfeng Zhou, Xiang Wu, Wenqin Deng, Xueyun Chen

“…Traditional reinforcement learning (RL) algorithms often rely on discretization or independent Gaussian assumptions, which fail to capture correlations between control variables, limiting the expressiveness of strategies. …”
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