Establishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study by Yubing Wang, Chao Qu, Jiange Zeng, Yumin Jiang, Ruitao Sun, Changlei Li, Jian Li, Chengzhi Xing, Bin Tan, Kui Liu, Qing Liu, Dianpeng Zhao, Jingyu Cao, Weiyu Hu

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The ethical legitimacy of autonomous Weapons systems: reconfiguring war accountability in the age of artificial Intelligence by Jie Guo

“…Proponents justify AWS on grounds of (1) enhanced military efficiency and reduced soldier casualties, (2) improved compliance with international humanitarian law (IHL) through algorithmic precision, and (3) operational necessity in high-threat environments. …”
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New concept for the value function of prospect theory by Kazuo Sano

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What happens between first symptoms and first acute exacerbation of COPD – observational study of routine data and patient survey by Alex Bottle, Alex Adamson, Xiubin Zhang, Benedict Hayhoe, Jennifer K Quint

“…Around three-quarters were diagnosed in primary care, with a slight fall in cohort 3. …”
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The Legal Challenges of Implementing AI in Emergency Departments by Shaghayegh Rahmani, Sahar Azamghasemzadeh

“…In emergencies, timely decisions are crucial for saving patients. When AI is involved in high-pressure decision support systems, concerns inevitably arise about liability when mistakes occur (3). …”
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Swedish regional population-based organised prostate cancer testing: why, what and how? by Ola Bratt, Salma Tunå Butt, Charlotte Carlsson, Lisa Jelf-Eneqvist, Olof Gunnarsson, Alma Ihre, Thomas Jiborn, Anna Lantz, Heide Larsson, Helena Strömqvist, Johan Styrke, Nils-Erik Svedberg, Rebecka Arnsrud Godtman

“… Objective: This study aimed to describe the regional, population-based, organised prostate cancer testing (OPT) programmes that are being introduced throughout Sweden: motives, structure, target population, diagnostic algorithm, quality control, outcomes, research, and future perspectives. …”
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Integrating machine learning models with multi-omics analysis to decipher the prognostic significance of mitotic catastrophe heterogeneity in bladder cancer by Haojie Dai, Zijie Yu, You Zhao, Ke Jiang, Zhenyu Hang, Xin Huang, Hongxiang Ma, Li Wang, Zihao Li, Ming Wu, Jun Fan, Weiping Luo, Chao Qin, Weiwen Zhou, Jun Nie

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Novel meta-ensemble modelling approach and comparison of machine-learning models for rebar price estimation by Sahin Tolga Guvel, Abdulkadir Budak, Ibrahim Karataş

“…Rebar price prediction by considering economic indicators significantly affects investment costs and decisions. Therefore, in this study, using historical data for rebar construction material and economic indicators, nine machine-learning algorithms were used to determine the estimated rebar price for 1-, 3-, 6-, 9-, and 12-month lags. …”
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A new signature associated with anoikis predicts the outcome and immune infiltration in nasopharyngeal carcinoma by Yonglin Luo, Wenyang Wei, Yaxuan Huang, Jun Li, Weiling Qin, Quanxiang Hao, Jiemei Ye, Zhe Zhang, Yushan Liang, Xue Xiao, Yonglin Cai

“…Key gene expression levels were validated through real-time PCR and immunohistochemical staining. Results Three differentially expressed ARGs (CDC25C, E2F1 and RBL2) with prognostic value were identified by the intersection of multiple machine learning algorithms. …”
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Data-driven insights into pre-slaughter mortality: Machine learning for predicting high dead on arrival in meat-type ducks by Chalita Jainonthee, Phutsadee Sanwisate, Panneepa Sivapirunthep, Chanporn Chaosap, Raktham Mektrirat, Sudarat Chadsuthi, Veerasak Punyapornwithaya

“…This classification was performed using machine learning (ML) algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), Decision Tree (DT), Random Forests (RF), and Extreme Gradient Boosting (XGBoost). …”
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Sensor-Based Yield Prediction in Durum Wheat Under Semi-Arid Conditions Using Machine Learning Across Zadoks Growth Stages by Süreyya Betül Rufaioğlu, Ali Volkan Bilgili, Erdinç Savaşlı, İrfan Özberk, Salih Aydemir, Amjad Mohamed Ismael, Yunus Kaya, João P. Matos-Carvalho

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Toward causal artificial intelligence approach for PM2.5 interpretation: A discovery of structural causal models by Mallika Kliangkhlao, Apaporn Tipsavak, Thanathip Limna, Racha Dejchanchaiwong, Perapong Tekasakul, Kirttayoth Yeranee, Thanyabun Phutson, Bukhoree Sahoh

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Time series forecasting of Valley fever infection in Maricopa County, AZ using LSTMResearch in context by Xueting Jin, Fangwu Wei, Srinivasa Srivatsav Kandala, Tejas Umesh, Kayleigh Steele, John N. Galgiani, Manfred D. Laubichler

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Next-Gen UAV-Satellite Communications: AI Innovations and Future Prospects by Sherief Hashima, Ahmad Gendia, Kohei Hatano, Osamu Muta, Mostafa S. Nada, Ehab Mahmoud Mohamed

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Enhancing Security in Industrial IoT Networks: Machine Learning Solutions for Feature Selection and Reduction by Ahmad Houkan, Ashwin Kumar Sahoo, Sarada Prasad Gochhayat, Prabodh Kumar Sahoo, Haipeng Liu, Syed Ghufran Khalid, Prince Jain

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Research on Traffic Accident Severity Level Prediction Model Based on Improved Machine Learning by Jiming Tang, Yao Huang, Dingli Liu, Liuyuan Xiong, Rongwei Bu

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Deep learning and hyperspectral features for seedling stage identification of barnyard grass in paddy field by Siqiao Tan, Qiang Xie, Wenshuai Zhu, Yangjun Deng, Lei Zhu, Xiaoqiao Yu, Zheming Yuan, Zheming Yuan, Yuan Chen, Yuan Chen

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Optimizing photocatalytic dye degradation: A machine learning and metaheuristic approach for predicting methylene blue in contaminated water by Yunus Ahmed, Keya Rani Dutta, Sharmin Nahar Chowdhury Nepu, Meherunnesa Prima, Hamad AlMohamadi, Parul Akhtar

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MMLT: Efficient object tracking through machine learning-based meta-learning by Bibek Das, Asfak Ali, Suvojit Acharjee, Jaroslav Frnda, Sheli Sinha Chaudhuri

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Comparing machine learning models for predicting preoperative DVT incidence in elderly hypertensive patients with hip fractures: a retrospective analysis by Xue Ge, Lan Yao, Yan Liu, Yi Wang, Fang Zhang

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