A novel system applying artificial intelligence in the identification of air leak sitesCentral MessagePerspective by Yuka Kadomatsu, MD, PhD, Megumi Nakao, PhD, Harushi Ueno, MD, Shota Nakamura, MD, PhD, Toyofumi Fengshi Chen-Yoshikawa, MD, PhD

“…In the training phase, deep learning-based air leak detection software was developed using leak-positive endoscopic images. …”
Get full text

A robust automated segmentation method for white matter hyperintensity of vascular-origin by Haoying He, Jiu Jiang, Sisi Peng, Chu He, Tianqi Sun, Fan Fan, Hao Song, Dong Sun, Zhipeng Xu, Shenjia Wu, Dongwei Lu, Junjian Zhang

“…The aims of this study are to develop and validate a robust deep learning segmentation method for WMH of vascular-origin. …”
Get full text

LRMAHpan: a novel tool for multi-allelic HLA presentation prediction using Resnet-based and LSTM-based neural networks by Xue Mi, Shaohao Li, Zheng Ye, Zhu Dai, Bo Ding, Bo Sun, Yang Shen, Yang Shen, Zhongdang Xiao, Zhongdang Xiao

“…We trained and tested the LRMAHpan BA (binding affinity) and the LRMAHpan AP (antigen processing) models using mass spectrometry data, subsequently combined them into the LRMAHpan PS (presentation score) model. Our approach is based on a novel pHLA encoding method that enables the integration of neoantigen prediction tasks into computer vision methods. …”
Get full text

Exploring a learning-to-rank approach to enhance the Retrieval Augmented Generation (RAG)-based electronic medical records search engines by Cheng Ye

“…Background: This study addresses the challenge of enhancing Retrieval Augmented Generation (RAG) search engines for electronic medical records (EMR) by learning users' distinct search semantics. The specific aim is to develop a learning-to-rank system that improves the accuracy and relevance of search results to support RAG-based search engines. …”
Get full text

Composition Design and Property Prediction for AlCoCrCuFeNi High-Entropy Alloy Based on Machine Learning by Cuixia Liu, Meng Meng, Xian Luo

Get full text

Automatic assessment of root numbers of vertical mandibular third molar using a deep learning model based on attention mechanism by SUN Chunsheng, DAI Xiubin, ZHOU Manting, JING Qiuping, ZHANG Chi, YANG Shengjun, WANG Dongmiao

“…Objective To develop a deep learning network based on attention mechanism to identify the number of the vertical mandibular third molar(MTM) roots(single or double) on panoramic radiographs in an automatic way. …”
Get full text

Secure internet of medical things based electronic health records scheme in trust decentralized loop federated learning consensus blockchain by Megha Kuliha, Sunita Verma

Get full text

Novel Deepfake Image Detection with PV-ISM: Patch-Based Vision Transformer for Identifying Synthetic Media by Orkun Çınar, Yunus Doğan

“…Following extensive hyperparameter tuning, an accuracy of 96.60% was achieved, surpassing the performance of ResNet50 transfer learning approaches (93.32%) and other comparable methods reported in the literature. …”
Get full text

Uncovering subtype-specific metabolic signatures in breast cancer through multimodal integration, attention-based deep learning, and self-organizing maps by Parisa Shahnazari, Kaveh Kavousi, Hamid Reza Khorram Khorshid, Zarrin Minuchehr, Bahram Goliaei, Reza M. Salek

“…A feedforward attention-based deep learning model effectively selected 99 significant metabolites, outperforming traditional static methods in classification performance and biomarker consistency. …”
Get full text

The influence of perceived social loafing on knowledge sharing intentions among college students by N. T. Duong, T. D. Pham Thi

“…Based on the findings, the authors suggest that teachers should not only enhance students’ learning goal orientation, decrease perceived social loafing to promote the intention to share knowledge in teams, but also make students have positive attitudes towards KS.…”
Get full text

A few-shot learning-based dual-input neural network for complex spectrogram recognition system with millimeter-wave radar by Kaiyu Chen, Shaoxi Wang, Wei Li, Yucheng Wang, Cunqian Feng, Yannian Zhou, Jian Cao, Binfeng Zong, Minming Gu

Get full text

Joint image reconstruction and segmentation of real-time cardiovascular magnetic resonance imaging in free-breathing using a model based on disentangled representation learning by Tobias Wech, Oliver Schad, Simon Sauer, Jonas Kleineisel, Nils Petri, Peter Nordbeck, Thorsten A. Bley, Bettina Baeßler, Bernhard Petritsch, Julius F. Heidenreich

“…Methods: A multi-tasking neural network architecture, incorporating disentangled representation learning, was trained using simulated examinations based on data from a public repository along with cardiovascular magnetic resonance (CMR) scans specifically acquired for model development. …”
Get full text

Identifying emphysema risk using brominated flame retardants exposure: a machine learning predictive model based on the SHAP methodology by Qihang Xie, Haoran Qu, Jianfeng Li, Rui Zeng, Wenhao Li, Rui Ouyang, Chengxiang Zhang, Siyu Xie, Siyu Xie, Ming Du

Get full text

Design of an improved graph-based model for real-time anomaly detection in healthcare using hybrid CNN-LSTM and federated learning by G Muni Nagamani, Chanumolu Kiran Kumar

“…This model improves the anomaly detection to an F1-score of 0.92, which means a performance increase of 15 % over all unimodal approaches. The proposed methods offer multifaceted solutions that provide advanced machine learning techniques, second-by-second real-time processing, and strict privacy measures all at once. …”
Get full text

Multilingual competency and academic performance: a machine learning-based analysis of the 2022/2023 Somaliland national primary exam data by Jibril Abdikadir Ali, Mustafe Khadar Abdi, Tawakal Abdi Ali, Abdisalan Hassan Muse, Mukhtar Abdi Omar, Mukhtaar Axmed Cumar

Get full text

Machine learning-based source apportionment and source-oriented probabilistic ecological risk assessment of heavy metals in urban green spaces by Jun Li, Jia-Yi Lu, Xin-Ying Tuo, Chao Wang, Jun-Zhuo Liu, Zhan-Dong Gao, Cun-Hao Yu, Fei Zang

Get full text

Correlation between computational thinking skills and cognitive learning outcomes: Insights from genetic trait inheritance based on Mendel's laws by Ade Suryanda, Yulilina Retno Dewahrani, Siti Nur Afifah

“…The tests that have been carried out show that there is a significant positive and linear relationship between computational thinking skills and cognitive learning outcomes of genetic trait inheritance based on Mendel’s law. …”
Get full text

Option Pricing Based on Modular Neural Network by Moslem Peymany Foroushani, mohamad ali dehghan dehnavi, Milad Kouhkan

Get full text

Large Language Models Outperform Traditional Natural Language Processing Methods in Extracting Patient-Reported Outcomes in Inflammatory Bowel Disease by Perseus V. Patel, Conner Davis, Amariel Ralbovsky, Daniel Tinoco, Christopher Y.K. Williams, Shadera Slatter, Behzad Naderalvojoud, Michael J. Rosen, Tina Hernandez-Boussard, Vivek Rudrapatna

Get full text

Performance and hypothetical clinical impact of an mNGS-based machine learning model for antimicrobial susceptibility prediction of five ESKAPEE bacteria by Yaoguang Li, Sizhen Liu, Peng Han, Jun Lei, Huifen Wang, Weiwei Zhu, Zihui Dong, Yize Zhang, Zhi Jiang, Beiwen Zheng, Guanhua Rao, Zujiang Yu, Ang Li

“…The secondary outcomes included the proportion of patients who could benefit from mNGS-based AST. It could allow earlier and suitable antibacterial adjustments in 32.05% of culture-positive patients (25/78) and offer actionable antimicrobial susceptibility results in 16.67% of culture-negative cases (6/36). mNGS-based AST offers a promising approach for individualized antibacterial therapy.IMPORTANCEMetagenomic next-generation sequencing (mNGS)-based antimicrobial susceptibility prediction (AST) is a novel method for predicting the antimicrobial susceptibility of ESKAPEE bacteria using a machine learning approach and short-read sequencing data. …”
Get full text