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Jianliang Min,1– 3,* Qihao Li,1,3,* Weijie Lai,1,3,* Zi Liu,4 Guodong Chen1,3 1Department of Organ Transplantation Center, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People’s Republic of China; 2School of Medicine, Jiaying University, Meizhou, People’s Republic of China; 3Guangdong Provincial Key Laboratory of Organ Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People’s Republic of China; 4School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, People’s Republic of China*These authors contributed equally to this workCorrespondence: Guodong Chen, Email chguod@mail.sysu.edu.cnBackground: Tacrolimus (TAC) dosing presents a persistent challenge in postoperative care owing to its narrow therapeutic window and high inter‑patient variability, which often leads to suboptimal exposure with increasing risks of nephrotoxicity or graft rejection. Algorithm‑based personalized dosing strategies offer a promising approach to support clinical decision and improve long‑term outcomes.Methods: Unlike approaches relying on a wide range of variables and local clinical scopes, this study proposed a novel and versatile algorithm-driven strategy to predict TAC doses. A hybrid optimization method was first employed to identify a minimal set of key clinical factors. These factors were then used to construct a cascaded deep forest model capable of predicting both follow‑up and initial TAC doses in adult kidney transplant recipients.Results: When validated on 615 patients using leave-one-subject-out cross-validation, it achieved predictions within ± 20% of actual values, with an accuracy of 89.8% for follow-up doses and 83.2% for initial doses. Independent external validation confirmed its robustness. A Shapley additive explanation analysis revealed significant correlations between input features and predictive doses. To support real-time clinical use, an open‑access web platform was provided (http://www.jcu-qiulab.com/tacp/).Conclusion: This approach offers a practical, effective, and algorithm-driven pipeline for automated drug dose analysis and prediction in clinical practice.Keywords: renal transplant, tacrolimus, cascaded deep forest, personalized dosing, artificial intelligence, AI

With the rapid development of wireless communication network, M-Commerce has achieved great success. Users leave a lot of historical behavior data when shopping on the M-Commerce platform. Using these data to predict future purchasing behaviors of the users will be of great significance for improving user experience and realizing mutual benefit and win-win result between merchant and user. Therefore, a sample balance-based multi-perspective feature ensemble learning was proposed in this study as the solution to predicting user purchasing behaviors, so as to acquire user’s historical purchasing behavioral data with sample balance. Influence feature of user purchasing behaviors was extracted from three perspectives—user, commodity and interaction, in order to further enrich the feature dimensions. Meanwhile, feature selection was carried out using XGBSFS algorithm. Large-scale real datasets were experimented on Alibaba M-Commerce platform. The experimental results show that the proposed method has achieved better prediction effect in various evaluation indexes such as precision and recall rate.

High-resolution remote sensing images can not only help forestry administrative departments achieve high-precision forest resource surveys, wood yield estimations and forest mapping but also provide decision-making support for urban greening projects. Many scholars have studied ways to detect single trees from remote sensing images and proposed many detection methods. However, the existing single tree detection methods have many errors of commission and omission in complex scenes, close values on the digital data of the image for background and trees, unclear canopy contour and abnormal shape caused by illumination shadows. To solve these problems, this paper presents progressive cascaded convolutional neural networks for single tree detection with Google Earth imagery and adopts three progressive classification branches to train and detect tree samples with different classification difficulties. In this method, the feature extraction modules of three CNN networks are progressively cascaded, and the network layer in the branches determined whether to filter the samples and feed back to the feature extraction module to improve the precision of single tree detection. In addition, the mechanism of two-phase training is used to improve the efficiency of model training. To verify the validity and practicability of our method, three forest plots located in Hangzhou City, China, Phang Nga Province, Thailand and Florida, USA were selected as test areas, and the tree detection results of different methods, including the region-growing, template-matching, convolutional neural network and our progressive cascaded convolutional neural network, are presented. The results indicate that our method has the best detection performance. Our method not only has higher precision and recall but also has good robustness to forest scenes with different complexity levels. The F1 measure analysis in the three plots was 81.0%, which is improved by 14.5%, 18.9% and 5.0%, respectively, compared with other existing methods.