Development of an AI Model for Predicting Methacholine Bronchial Provocation Test Results Using Spirometry by SangJee Park, Yehyeon Yi, Seon-Sook Han, Tae-Hoon Kim, So Jeong Kim, Young Soon Yoon, Suhyun Kim, Hyo Jin Lee, Yeonjeong Heo

“…Feature importance analysis of the MLP model identified key contributing features, including Pre-FEF_25–75 (%), Pre-FVC (L), Post FEV₁/FVC, Change-FEV₁ (L), and Change-FEF_25–75 (%), providing insight into the interpretability and clinical applicability of the model. …”
Get full text

AI-based approach to dissect the variability of mouse stem cell-derived embryo models by Paolo Caldarelli, Luca Deininger, Shi Zhao, Pallavi Panda, Changhuei Yang, Ralf Mikut, Magdalena Zernicka-Goetz

“…Our best-performing model achieves 88% accuracy at 90 h post-cell seeding and 65% accuracy at the initial cell-seeding stage, forecasting developmental trajectories. …”
Get full text

Urban Greening Analysis: A Multimodal Large Language Model for Pinpointing Vegetation Areas in Adverse Weather Conditions by Hanzhang Liu, Shijie Yang, Chengwu Long, Jiateng Yuan, Qirui Yang, Jiahua Fan, Bingnan Meng, Zhibo Chen, Fu Xu, Chao Mou

“…To address these challenges, we propose the UGSAM method that utilizes the high-performance multimodal large language model, the Segment Anything Model (i.e., SAM). …”
Get full text

A DeBERTa-Based Semantic Conversion Model for Spatiotemporal Questions in Natural Language by Wenjuan Lu, Dongping Ming, Xi Mao, Jizhou Wang, Zhanjie Zhao, Yao Cheng

“…To address current issues in natural language spatiotemporal queries, including insufficient question semantic understanding, incomplete semantic information extraction, and inaccurate intent recognition, this paper proposes NL2Cypher, a DeBERTa (Decoding-enhanced BERT with disentangled attention)-based natural language spatiotemporal question semantic conversion model. The model first performs semantic encoding on natural language spatiotemporal questions, extracts pre-trained features based on the DeBERTa model, inputs feature vector sequences into BiGRU (Bidirectional Gated Recurrent Unit) to learn text features, and finally obtains globally optimal label sequences through a CRF (Conditional Random Field) layer. …”
Get full text

Research on building energy consumption prediction algorithm based on customized deep learning model by Zheng Liang, Junjie Chen

“…This paper proposes a customized convolutional neural network with Q-Learning (CCNN-QL) based reinforcement learning algorithm for predicting energy consumption in building.The suggested CCNN-QL model offers an auto-learning feature that predicts building energy consumption through an automated method, continually improving its predictive accuracy.To assess its performance, various building types were selected to study the factors influencing excessive energy consumption, and data were collected from multiple Chinese cities. …”
Get full text

CMRNet: An Automatic Rapeseed Counting and Localization Method Based on the CNN-Mamba Hybrid Model by Jie Li, Chenbo Yang, Chengyong Zhu, Tao Qin, Jingmin Tu, Binhui Wang, Jian Yao, Jiangwei Qiao

“…The model synergizes local feature extraction via CNN with the global modeling strengths of the Mamba state space model, yielding semantically rich features while significantly enhancing computational efficiency and inference speed. …”
Get full text

Research on subway settlement prediction based on the WTD-PSR combination and GSM-SVR model by Miren Rong, Chao Feng, Yinping Pang, Hailong Wang, Ying Yuan, Wensong Zhang, Lanxin Luo

“…The validity of the embedding dimension determined by the phase space reconstruction is verified, providing rich multi-dimensional features for the subsequent prediction models. Based on the reconstructed data, traditional Support Vector Regression (SVR) models and SVR models optimized by the Grid Search Method (GSM) are constructed. …”
Get full text

Enhancing diabetes risk prediction through focal active learning and machine learning models. by Wangyouchen Zhang, Zhenhua Xia, Guoqing Cai, Junhao Wang, Xutao Dong

“…The method integrates SHAP (SHapley Additive Explanations) to quantify feature importance and applies attention mechanisms to dynamically adjust feature weights, enhancing model interpretability and performance in predicting diabetes risk. …”
Get full text

Optimizing deep learning models to combat amyotrophic lateral sclerosis (ALS) disease progression by Haoshen Qin, Lal Hussain, Ziang Liu, Xu Yan, Fuad A. Awwad, Faisal Mehmood Butt, Umair Ahmad Salaria, Emad A.A. Ismail

“…Initially, machine learning models (XGBoost, LightGBM) and a deep learning sequential model were evaluated with default parameters, using R-squared (R2) and Root Mean Squared Error (RMSE) as performance metrics. …”
Get full text

Cooperative Hybrid Modelling and Dimensionality Reduction for a Failure Monitoring Application in Industrial Systems by Morgane Suhas, Emmanuelle Abisset-Chavanne, Pierre-André Rey

“…However, the CHMC model exhibits superior performance in detecting true negatives (90% vs. 89%) and differentiating between healthy and failure states. …”
Get full text

Low-cost quantum mechanical descriptors for data efficient skin sensitization QSAR models by Davy Guan, Raymond Lui, Slade T. Matthews

“…Quantitative Structure Activity Relationship modelling methodologies need to incorporate relevant mechanistic information to have high predictive performance and validity. …”
Get full text

Leveraging large language models for preoperative prevention of cardiopulmonary bypass-associated acute kidney injury by Kai Wang, Ling Lin, Rui Zheng, Shan Nan, Xudong Lu, Huilong Duan

“…Our model performed better with an area under the receiver operating characteristic curve of 0.9201 compared with the baseline models. …”
Get full text

Exploring the Causes of Multicentury Hydroclimate Anomalies in the South American Altiplano with an Idealized Climate Modeling Experiment by Ignacio Alonso Jara, Orlando Astudillo, Pablo Salinas, Limbert Torrez-Rodríguez, Nicolás Lampe-Huenul, Antonio Maldonado

“…We performed a series of 100-year-long idealized simulations using the Weather Research and Forecasting (WRF) model, configured to repeat annually the oceanic and atmospheric forcing leading to the exceptionally humid austral summers of 1983/1984 and 2011/2012. …”
Get full text

Corticostriatal glutamate‐mediated dynamic therapeutic efficacy of electroacupuncture in a parkinsonian rat model by Xinxin Jiang, Min Sun, Yitong Yan, Yanhua Wang, Xinyu Fan, Jing Wei, Ke Wang, Peirong Liang, Zirui Wang, Jihan Wang, Xiaomin Wang, Jun Jia

“…Abstract Background Motor impairments are the defining cardinal features of Parkinson's disease (PD), resulting from malfunction of the cortico‐basal ganglia circuit. …”
Get full text

Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis by Ji Soo Park, Sa-Yoon Park, Jae Won Moon, Kwangsoo Kim, Dong In Suh

“…ObjectiveThis systematic review and meta-analysis assess the performance of ML models in pediatric lung sound analysis. …”
Get full text

Machine learning model for predicting corneal stiffness and identifying keratoconus based on ocular structures by Longhui Li, Yifan Xiang, Xi Chen, Duoru Lin, Lanqin Zhao, Jun Xiao, Zhenzhe Lin, Jianyu Pang, Xiaotong Han, Lixue Liu, Yuxuan Wu, Zhenzhen Liu, Jingjing Chen, Jing Zhuang, Keming Yu, Haotian Lin

“…The addition of the predicted SP-A1 and cCBI significantly improved model performance in diagnosing keratoconus, with NRI of 0.607 (95% CI 0.367-0.812) and 0.188 (95% CI −0.022-0.398), and IDI of 0.028 (95% CI 0.006-0.048) and 0.045 (95% CI 0.018-0.072), respectively. …”
Get full text

A numerical model and comparative investigation of a thermoelectric generator with novel profile designs by Tan Nguyen Tien, Duc Tran Duy, Vinh Nguyen Duy, Dien Vu Minh, Hoa Binh Pham, Quang Khong Vu

“…This paper offers three alternatives to the cooling exchanger based on a commercial model with different channel profiles. All are simulated and evaluated as thermodynamic and mass factors for TEG performance, such as temperature, pressure, and occupied solid volume. …”
Get full text

Explicit Model for Chiller Fault Diagnosis Based on Multi-objective Regression with Different Weights by Wu Kongrui, Han Hua, Yang Yuting, Lu Hailong, Ling Minbin

“…The weighted regression model was slightly more complex than the pure linear regression model; however, the fault diagnosis performance was clearly better, and the minimum performance was improved by 40.50% under different feature sets. …”
Get full text

Theoretical and experimental investigations of the CMOS compatible Pirani gauges with a temperature compensation model by Shizhen Xu, Gai Yang, Junfu Chen, Rui Jiao, Ruoqin Wang, Hongyu Yu, Huikai Xie, Xiaoyi Wang

Get full text

TLDDM: An Enhanced Tea Leaf Pest and Disease Detection Model Based on YOLOv8 by Jun Song, Youcheng Zhang, Shuo Lin, Huijie Han, Xinjian Yu

“…Initially, the C2f-Faster-EMA module is employed to reduce the number of parameters and model complexity while enhancing image feature extraction capabilities. …”
Get full text