Towards Transparent Deep Learning in Medicine: Feature Contribution and Attention Mechanism-Based Explainability by Thanveer Shaik, Xiaohui Tao, Haoran Xie, Lin Li, Niall Higgins, Juan D. Velásquez

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CT radiomics from intratumor and peritumor regions for predicting poorly differentiated invasive nonmucinous pulmonary adenocarcinoma by Lijun Duan, Wenyun Liu, Mingyang Li, Liang Guo, Mengran Ren, Xin Dong, Xiaoqian Lu, Dianbo Cao

“…A total of 451 patients with INMA were collected from three hospitals. They were divided into the train cohort (173 grade 1/2; 116 grade 3), internal test cohort (89 grade 1/2; 35 grade 3) and external test cohort (26 grade 1/2; 12 grade 3). …”
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Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches by Mohsin Akram, Muhammad Adnan, Syed Farooq Ali, Jameel Ahmad, Amr Yousef, Tagrid Abdullah N. Alshalali, Zaffar Ahmed Shaikh

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CT radiomics to assess severity of explosion-induced primary blast lung injury in goats by Bo Yang, Hanwei Wang, Ling Feng, Ming Li, Linlan Zeng, Ping Xiang, Yishan Yao, Kuijun Chen, Zhaoxia Duan, Jianmin Wang, Kunlin Xiong, Shunan Wang

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Developing Contextual Ontology for Chronic Diseases: AI-Enhanced Extension and Prediction in an Asthma Case Study by Batoul Msheik, Mehdi Adda, Hamid Mcheick, Youmna Nasser, Mohamed Dbouk

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Profiling the AI speaker user: Machine learning insights into consumer adoption patterns. by Yunwoo Choi, Changjun Lee

“…To do so, our analysis employs decision trees, random forests, support vector machines, artificial neural networks, and XGboost, which are typical machine learning techniques for classification and leverages the 2019 Media & Consumer Research survey data from the Korea Broadcasting and Advertising Corporation (N = 3,922). …”
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Predictive Analysis of Stress and Burnout Among Medical Students: Exploring Personal and Environmental Contributors by Qurratulain Mehfooz, Nadia Tufail, Sehar Shamshad Ali, Raheela Subhani, Sehar Amjad, Zertaj Kashif

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Research on the Construction of Crossborder e-Commerce Logistics Service System Based on Machine Learning Algorithms by Jinbo Xu, Shibiao Mu

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Hemodynamic-Based Evaluation on Thrombosis Risk of Fusiform Coronary Artery Aneurysms Using Computational Fluid Dynamic Simulation Method by Haoran Wang, Hitomi Anzai, Youjun Liu, Aike Qiao, Jinsheng Xie, Makoto Ohta

“…Computational fluid dynamics (CFD) provides a noninvasive means of hemodynamic research. Four three-dimensional models were constructed, representing coronary arteries with a normal diameter (1x) and CAAs with diameters two (2x), three (3x), and five times (5x) that of the normal diameter. …”
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Predicting Diabetes Mellitus with Machine Learning Techniques by Heba Ahmed Jassim, Omar R. Kadhim, Zahraa Khduair Taha, Johnny Koh Siaw Paw, Yaw Chong Tak, Tiong Sieh Kiong

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Risk Prediction Score for Thermal Mapping of Pharmaceutical Transport Routes in Brazil by Clayton Gerber Mangini, Nilsa Duarte da Silva Lima, Irenilza de Alencar Nääs

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ARTIFICIAL INTELLIGENCE IN SCOLIOSIS DIAGNOSIS: A COMPARATIVE STUDY BETWEEN CHATGPT AND SURGEONS by LUCAS SILVEIRA RABELLO DE OLIVEIRA, RAFAEL CARBONI DE SOUZA, ANDRÉ EVARISTO MARCONDES CESAR, BRUNO VIEIRA MOTTER, WILKER HERKSON DE ALMEIDA OLIVEIRA, GUILHERME FOIZER, GABRIELLE DO AMARAL VIRGINIO PEREIRA, LUCIANO MILLER REIS RODRIGUES

“…The model’s accuracy in the Lenke classification was consistent across all cases, demonstrating its ability to apply standardized criteria. …”
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Comprehensive Investigation of Machine Learning and Deep Learning Networks for Identifying Multispecies Tomato Insect Images by Chittathuru Himala Praharsha, Alwin Poulose, Chetan Badgujar

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Enhancing Pneumonia Diagnosis Through AI Interpretability: Comparative Analysis of Pixel-Level Interpretability and Grad-CAM on X-ray Imaging With VGG19 by Mohammad Ennab, Hamid Mcheick

“…Interpretability in AI models is vital for fostering trust among healthcare professionals by providing transparency in decision-making processes. …”
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A Machine Learning-Based Guide for Repeated Laboratory Testing in Pediatric Emergency Departments by Adi Shuchami, Teddy Lazebnik, Shai Ashkenazi, Avner Herman Cohen, Yael Reichenberg, Vered Shkalim Zemer

“…The aim of this study was to develop a decision tree (DT) model to guide physicians in minimizing unnecessary repeat blood tests in PEDs. …”
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A Machine Learning Approach to Evaluate the Performance of Rural Bank by Jun Wei, Tao Ye, Zhe Zhang

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Resolution-Aware Deep Learning with Feature Space Optimization for Reliable Identity Verification in Electronic Know Your Customer Processes by Mahasak Ketcham, Pongsarun Boonyopakorn, Thittaporn Ganokratanaa

“…By incorporating Monte Carlo dropout, the system estimates predictive uncertainty, addressing critical limitations of conventional black-box deep learning models. Experimental evaluations confirmed the effectiveness of the framework, achieving a classification accuracy of 99.7%, precision of 99.2%, recall of 99.3%, and an AUC score of 99.5% under standard testing conditions. …”
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Research on digital matching methods integrating user intent and patent technology characteristics by Jianwei Yang, Yi Wang, Bonan Zang, Min Peng, George Torrens

“…The method consists of four main steps: First, based on the Kano model, this research proposes a G-HOQ method for requirement mining, classification, and function mapping, integrating Grey Relational Analysis (GRA) and the House of Quality (HOQ). …”
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Automatic collateral quantification in acute ischemic stroke using U2-net by Qingqing Lu, Qingqing Lu, Hongyi Chen, Junyan Fu, Xiaodong Zheng, Yiren Xu, Yuning Pan

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Predicting recurrence risk in endometrial cancer: a multisequence MRI intratumoral and peritumoral radiomics nomogram approach by Jie Li, Jie Li, Dianpei Ma, Dianpei Ma, Xiuting Chen, Xiuting Chen, Junting Wei, Junting Wei, Jiali Xu, Yingming Zhao, Zhizhen Gao

“…Nine machine learning classifiers were employed to construct the intratumoral model (RM1). The best-performing classifiers were then used to develop the intratumoral and peritumoral 2 mm radiomics model (RM2) and the intratumoral and peritumoral 4 mm radiomics model (RM3). …”
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