LSTM and TCN application for airport surface distress detection by Ludek Cicmanec, Adam Kubica, Jiri Maslan

“…Both algorithms demonstrated high accuracy on training and validation data and performed well on other independent test samples.…”
Get full text

Identifying Asthma-Related Symptoms From Electronic Health Records Using a Hybrid Natural Language Processing Approach Within a Large Integrated Health Care System: Retrospective S... by Fagen Xie, Robert S Zeiger, Mary Marycania Saparudin, Sahar Al-Salman, Eric Puttock, William Crawford, Michael Schatz, Stanley Xu, William M Vollmer, Wansu Chen

“…Subsequently, transformer-based deep learning algorithms were trained using the same manually annotated datasets. …”
Get full text

OSBPL3 modulates the immunosuppressive microenvironment and predicts therapeutic outcomes in pancreatic cancer by Qihui Sun, Xiaoqi Zhu, Qi Zou, Yang Chen, Tingting Wen, Tingting Jiang, Xiaojia Li, Fang Wei, Keping Xie, Jia Liu

“…Notably, the “rf” algorithm model showed an AUC of 1 in the training set and AUCs of 0.887 and 0.977 in two validation datasets. …”
Get full text

Analysis of shared pathogenic mechanisms and drug targets in myocardial infarction and gastric cancer based on transcriptomics and machine learning by Junyang Ma, Junyang Ma, Shufu Hou, Xinxin Gu, Peng Guo, Jiankang Zhu

Get full text

Integrative machine learning and bioinformatics analysis to identify cellular senescence-related genes and potential therapeutic targets in ulcerative colitis and colorectal cancer by Tianle Xue, Yunpeng Chen, Xiaomeng Li, Zhixiang Zhou, Qiyang Chen

“…The diagnostic performance of the candidate genes was evaluated using receiver operating characteristic (ROC) analyses in both training and validation cohorts. In addition, immune cell infiltration, protein–protein interaction (PPI) networks, and drug enrichment analyses—including molecular docking—were performed to further elucidate the biological functions and therapeutic potentials of the identified genes.ResultsOur analysis revealed significant transcriptomic alterations in UC and CRC tissues, with the turquoise module demonstrating the strongest association with disease traits. …”
Get full text

Inertial measurement unit technology for gait detection: a comprehensive evaluation of gait traits in two Italian horse breeds by Vittoria Asti, Michela Ablondi, Arnaud Molle, Andrea Zanotti, Matteo Vasini, Alberto Sabbioni

Get full text

EXPRESSION OF MATRIX METALLOPROTEINASES IN LACRIMAL FLUID AND GENE POLYMORPHISM OF COMPLEMENT FACTOR H (CFH) IN PATIENTS WITH PRIMARY OPEN-ANGLE GLAUCOMA by V. A. Sokolov, V. G. Likhvantseva, O. N. Levanova, A. A. Nikiforov, V. A. Vygodin

Get full text

Integrin factor (FAM27E3), as a metastatic marker of papillary thyroid carcinoma, through the p53 signaling pathway promoting lymph node metastasis by Dong Zhang, Kai-Fang Xiang, Chen-Chen Hong, Xiao-Ping Bao, Yan Wu, Rui Wu

“…FAM27E3 was identified as a hub gene associated with PTC lymph node metastasis and had a significantly positive correlation with EMT_score. …”
Get full text

Acute kidney disease in hospitalized pediatric patients: risk prediction based on an artificial intelligence approach by Lingyu Xu, Siqi Jiang, Chenyu Li, Xue Gao, Chen Guan, Tianyang Li, Ningxin Zhang, Shuang Gao, Xinyuan Wang, Yanfei Wang, Lin Che, Yan Xu

“…This study presents a machine learning-based risk prediction model for AKI and AKD in pediatric patients, enabling personalized risk predictions.Methods Data from 2,346 hospitalized pediatric patients, collected between January 2020 and January 2023, were divided into an 85% training set and a 15% test set. Predictive models were constructed using eight machine learning algorithms and two ensemble algorithms, with the optimal model identified through AUROC. …”
Get full text

Development and validation of an ensemble learning risk model for sepsis after abdominal surgery by Xin Shu, Yujie Li, Yiziting Zhu, Zhiyong Yang, Xiang Liu, Xiaoyan Hu, Chunyong Yang, Lei Zhao, Tao Zhu, Yuwen Chen, Bin Yi

“…Routine clinical variables were implemented for model development. The Boruta algorithm was applied for feature selection. Afterwards, ensemble learning and eight other conventional algorithms were used for model fitting and validation based on all features and selected features. …”
Get full text

Predicting postoperative neurological outcomes of degenerative cervical myelopathy based on machine learning by Shuai Zhou, Shuai Zhou, Shuai Zhou, Shuai Zhou, Zexiang Liu, Zexiang Liu, Zexiang Liu, Haoge Huang, Haoge Huang, Haoge Huang, Hanxu Xi, Xiao Fan, Xiao Fan, Xiao Fan, Yanbin Zhao, Yanbin Zhao, Yanbin Zhao, Xin Chen, Xin Chen, Xin Chen, Yinze Diao, Yinze Diao, Yinze Diao, Yu Sun, Yu Sun, Yu Sun, Hong Ji, Feifei Zhou, Feifei Zhou, Feifei Zhou

“…After training and optimizing multiple ML algorithms, we generated a model with the highest area under the receiver operating characteristic curve (AUROC) to predict short-term outcomes following DCM surgery. …”
Get full text

Spatial Prediction of Soil Continuous and Categorical Properties Using Deep Learning Approaches for Tamil Nadu, India by Thamizh Vendan Tarun Kshatriya, Ramalingam Kumaraperumal, Sellaperumal Pazhanivelan, Nivas Raj Moorthi, Dhanaraju Muthumanickam, Kaliaperumal Ragunath, Jagadeeswaran Ramasamy

“…The validation and test results obtained for each of the soil attributes for both the algorithms were most comparable with the DL-MLP algorithm depicting the attributes’ most intricate spatial organization details, compared to the 1D-CNN model. …”
Get full text

Direct Estimation of Forest Aboveground Biomass from UAV LiDAR and RGB Observations in Forest Stands with Various Tree Densities by Kangyu So, Jenny Chau, Sean Rudd, Derek T. Robinson, Jiaxin Chen, Dominic Cyr, Alemu Gonsamo

“…Our results show that using an unsupervised LiDAR-only algorithm for tree crown delineation alongside a self-supervised RGB deep learning model trained on LiDAR-derived annotations leads to an 18% improvement in AGB estimation accuracy. …”
Get full text

Contrast-enhanced mammography-based interpretable machine learning model for the prediction of the molecular subtype breast cancers by Mengwei Ma, Weimin Xu, Jun Yang, Bowen Zheng, Chanjuan Wen, Sina Wang, Zeyuan Xu, Genggeng Qin, Weiguo Chen

“…Conclusions The radiological characteristics of breast tumors extracted from CEM were found to be associated with breast cancer subtypes in our study. …”
Get full text

Intermittent hypoxemia during hemodialysis: AI-based identification of arterial oxygen saturation saw-tooth pattern by Hanjie Zhang, Andrea Nandorine Ban, Peter Kotanko

“…Conclusion Our 1D-CNN algorithm accurately classifies SaO2 saw-tooth pattern. …”
Get full text

Automating attendance management in human resources: A design science approach using computer vision and facial recognition by Bao-Thien Nguyen-Tat, Minh-Quoc Bui, Vuong M. Ngo

“…Haar Cascade is a cost-effective and user-friendly machine learning-based algorithm for detecting objects in images and videos. …”
Get full text

Machine learning models for reinjury risk prediction using cardiopulmonary exercise testing (CPET) data: optimizing athlete recovery by Arezoo Abasi, Ahmad Nazari, Azar Moezy, Seyed Ali Fatemi Aghda

“…Machine learning (ML) algorithms offer promising capabilities in uncovering complex patterns within this data, allowing for more accurate injury risk assessment. …”
Get full text

Identifying potential biomarkers and molecular mechanisms related to arachidonic acid metabolism in vitiligo by Xiaoqing Li, Xiaoqing Li, Li Yang, Longfei Zhu, Jingying Sun, Jingying Sun, Cuixiang Xu, Cuixiang Xu, Lijun Sun, Lijun Sun

“…Therefore, we aimed to identify the biomarkers and molecular mechanisms associated with AAM in vitiligo using bioinformatics methods.MethodsThe GSE75819 and GSE65127 datasets were used in this study as the training and validation sets, respectively, along with 58 AAM-related genes (AAM-RGs). …”
Get full text

Extraction of Alteration Information from Hyperspectral Data Base on Kernel Extreme Learning Machine by Shuhan Yang, Shufang Tian

“…To enhance the accuracy of remote-sensing-alteration mineral information, this study focuses on the extraction of alteration information from hyperspectral remote sensing data using the Kernel-Based Extreme Learning Machine (KELM) optimized with the Sparrow Search Algorithm (SSA). The ideal parameters of the Kernel Extreme Learning Machine model were successfully acquired by utilizing the sparrow optimization method for continuous search and iteration, avoiding the blindness and arbitrariness associated with parameter selection by humans. …”
Get full text

Optical quantum sensing for agnostic environments via deep learning by Zeqiao Zhou, Yuxuan Du, Xu-Fei Yin, Shanshan Zhao, Xinmei Tian, Dacheng Tao

“…This distilled knowledge facilitates the identification of optimal optical setups associated with maximal QFI. Subsequently, DQS employs a trigonometric interpolation algorithm to recover the unknown parameter estimates for the identified optical setups. …”
Get full text