Artificial intelligence for children with attention deficit/hyperactivity disorder: a scoping review by Bo Sun, Bo Sun, Fei Cai, Huiman Huang, Bo Li, Bing Wei

“…Artificial intelligence provides advanced models and algorithms for better diagnosis, prediction and classification of attention deficit/hyperactivity disorder. …”
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Deep learning-based prediction of individualized Real-time FSH doses in GnRH agonist long protocols by Na Kong, Yu Xia, Zhilong Wang, Hui Zhang, Liyan Duan, Yingchun Zhu, Chenyang Huang, Guijun Yan, Jie Mei, Wujun Li, Haixiang Sun

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Detection of strawberry bloom phenology based on YOLO-RCMC and flower opening scale by Junquan Zhen, Yuke Wang, Xunhui Liu, Linfan Zhou, Shuqin Yang, Dong Wang

“…In addition, the RCM variants were integrated into the head of the model to reduce classification errors and improve detection accuracy. …”
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Optimizing timing and cost-effective use of plasma biomarkers in Alzheimer’s disease by Hsin-I Chang, Mi-Chia Ma, Kuo-Lun Huang, Chung-Gue Huang, Shu-Hua Huang, Chi-Wei Huang, Kun-Ju Lin, Chiung-Chih Chang

“…Discussion The use of multiclass LR model enhances amyloid classification, particularly at earlier clinical stages. …”
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Ensemble machine learning algorithm for anti-VEGF treatment efficacy prediction in diabetic macular edema by Yu Fang, Jianwei Lin, Peiwen Xie, Huishan Zhu, Tsz Kin Ng, Guihua Zhang

“…The top 3 performing models were selected to build an ensemble model, and evaluated in the test dataset. …”
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Identification of Maize Kernel Varieties Using LF-NMR Combined with Image Data: An Explainable Approach Based on Machine Learning by Chunguang Bi, Xinhua Bi, Jinjing Liu, He Chen, Mohan Wang, Helong Yu, Shaozhong Song

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OPTISTACK: A Hybrid Ensemble Learning and XAI-Based Approach for Malware Detection in Compressed Files by Khaled Mahmud Sujon, Rohayanti Binti Hassan, M. Abdullah-Al-Wadud, Jia Uddin

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An Ensemble Learning Framework with Explainable AI for interpretable leaf disease detection by Mohammad Rifat Ahmmad Rashid, Md. AL Ehtesum Korim, Mahamudul Hasan, Md Sawkat Ali, Mohammad Manzurul Islam, Taskeed Jabid, Raihan Ul Islam, Maheen Islam

“…The findings confirm that transfer learning, model ensembling, and interpretability methods significantly enhance classification performance, especially in cases of limited data, offering a scalable solution for improved disease management in agriculture. …”
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Real-Time Intelligent Recognition and Precise Drilling in Strongly Heterogeneous Formations Based on Multi-Parameter Logging While Drilling and Drilling Engineering by Aosai Zhao, Yang Yu, Bin Wang, Yewen Liu, Jingyue Liu, Xubiao Fu, Wenhao Zheng, Fei Tian

“…In the Hugin Formation of the Volve field in the Norwegian North Sea, the application of UMAP demonstrates superior data separability compared with PCA and t-SNE, effectively distinguishing thin reservoirs with strong heterogeneity. The CatBoost model achieves a balanced accuracy of 92.7% and an F1-score of 89.3% in six lithology classifications. …”
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A nomogram to predict postoperative overall and cancer specific survival in patients with primary parotid squamous cell carcinoma: a population based study by Xudong Liu, Jincai Xue, Fang Dong, Xingyue Wang, Youxin Tian, Qinjiang Liu, Jun Wang, Yunsheng Wang

“…The Kaplan-Meier curve demonstrated significant differences in survival among the three groups. In this study, new nomograms and risk classification systems were successfully developed to predict the 1-, 3-, and 5-year OS and CSS of pPSCC patients, which has good accuracy and superiority and can help doctors and patients make clinical decisions.…”
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Antimicrobial Susceptibility Profiles of <i>Escherichia coli</i> Isolates from Clinical Cases of Ducks in Hungary Between 2022 and 2023 by Ádám Kerek, Ábel Szabó, Ákos Jerzsele

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ARTERIAL HYPERTENSION PREDICTORS AND OCCUPATION by G. V. Artamonova, S. A. Maximov, M. Yu. Ogarkov, S. A. Makarov, A. E. Skripchenko, E. V. Indukaeva, E. B. Shapovalova, M. Yu. Yankin, T. A. Mulerova, N. N. Kozyreva

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Development and validation of an artificial intelligence-based pipeline for predicting oral epithelial dysplasia malignant transformation by Adam J. Shephard, Hanya Mahmood, Shan E. Ahmed Raza, Anna Luíza Damaceno Araújo, Alan Roger Santos-Silva, Marcio Ajudarte Lopes, Pablo Agustin Vargas, Kris D. McCombe, Stephanie G. Craig, Jacqueline James, Jill Brooks, Paul Nankivell, Hisham Mehanna, Syed Ali Khurram, Nasir M. Rajpoot

“…Results Model testing yielded an F1-score of 0.96 for classification of dysplastic vs non-dysplastic slides, and an AUROC of 0.73 for malignancy prediction, gaining comparable results to clinical grading systems. …”
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Unveiling the Impact of Socioeconomic and Demographic Factors on Graduate Salaries: A Machine Learning Explanatory Analytical Approach Using Higher Education Statistical Agency Dat... by Bassey Henshaw, Bhupesh Kumar Mishra, William Sayers, Zeeshan Pervez

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Leveraging federated learning and edge computing for pandemic-resilient healthcare by Atlanta Choudhury, Kandarpa Kumar Sarma, Vyacheslav Gulvanskii, Dmitrii Kaplun, Lachit Dutta

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A GPT-4o-powered framework for identifying cognitive impairment stages in electronic health records by Yu Leng, Yingnan He, Samad Amini, Colin Magdamo, Ioannis Paschalidis, Shibani S. Mukerji, Lidia M. V. R. Moura, M. Brandon Westover, Ana-Maria Vranceanu, Christine S. Ritchie, Deborah Blacker, John R. Dickson, Sudeshna Das

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Emotion Recognition in the Eye Region Using Textural Features, IBP and HOG by Laura Jalili, Josue Espejel, Jair Cervantes, Farid Lamont

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Macro and micronutrient based soil fertility zonation using fuzzy logic and geospatial techniques by Meeniga Venkateswarlu, Srinivas Rallapalli, Amit Singh, G. Sai Sesha Chalapathi, Suresh Kumar, Yashwant Bhaskar Katpatal, Gouligari Sujatha

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Research on Aircraft Control System Fault Risk Assessment Based on Composite Framework by Tongyu Shi, Yi Gao, Long Xu, Yantao Wang

“…According to the classification standard of the risk level, the failure risk level of the aircraft belongs to a controlled risk, and emergency measures need to be taken, which is consistent with the actual disposal decision in this case. …”
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Convolution of the physical point cloud for predicting the self-assembly of colloidal particles by Seunghoon Kang, Young Jin Lee, Kyung Hyun Ahn

“…Therefore, this study is expected to serve as a highly useful decision-support method for engineering soft matter with desired morphologies.…”
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