netDx: interpretable patient classification using integrated patient similarity networks by Shraddha Pai, Shirley Hui, Ruth Isserlin, Muhammad A Shah, Hussam Kaka, Gary D Bader

“…Abstract Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis, and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data types, and handle sparse data. …”
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Artificial intelligence technology in ophthalmology public health: current applications and future directions by ShuYuan Chen, Wen Bai, Wen Bai

“…AI offers significant improvements in ophthalmic data management, disease screening and monitoring, risk prediction and early warning systems, medical resource allocation, and health education and patient management. …”
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Remote Sensing Time Series Analysis: A Review of Data and Applications by Yingchun Fu, Zhe Zhu, Liangyun Liu, Wenfeng Zhan, Tao He, Huanfeng Shen, Jun Zhao, Yongxue Liu, Hongsheng Zhang, Zihan Liu, Yufei Xue, Zurui Ao

“…., a constellation strategy, increasing computing power and advancing deep learning algorithms, are driving a paradigm shift from traditional point-in-time mapping to near-real-time monitoring tasks, and even to modeling integration of parameter inversion and prediction in land, water, and air science. …”
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A Novel Method of Self-Healing Concrete to Improve Durability and Extend the Service Life of Civil Infrastructure by Yan Xue, Weiliang Gao, Yanming Zhao

“…Building upon this, the enhanced concrete durability prediction model based on the NSGA-II algorithm proves to be highly effective in predicting the optimal concrete mix proportion scheme. …”
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Case-control study combined with machine learning techniques to identify key genetic variations in GSK3B that affect susceptibility to diabetic kidney diseases by Jinfang Song, Yi Xu, Liu Xu, Tingting Yang, Ya Chen, Changjiang Ying, Qian Lu, Tao Wang, Xiaoxing Yin

“…A case-control study was conducted to evaluate the predictive effect of GSK3B genetic polymorphisms on the susceptibility to DKD, with the aim of providing a theoretical basis and laboratory rationale for the prediction of the risk of developing DKD in patients with type 2 diabetes mellitus (T2DM). …”
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Increased risk of COVID-19-related admissions in patients with active solid organ cancer in the West Midlands region of the UK: a retrospective cohort study by Peter Nightingale, Fahad Mahmood, Akinfemi Akingboye, Nabeel Amiruddin, Olorunseun O Ogunwobi

“…We aim to quantify the risk of hospitalisation in patients with active cancer and use a machine learning algorithm (MLA) and traditional statistics to predict clinical outcomes and mortality.Design Retrospective cohort study.Setting A single UK district general hospital.Participants Data on total hospital admissions between March 2018 and June 2020, all active cancer diagnoses between March 2019 and June 2020 and clinical parameters of COVID-19-positive admissions between March 2020 and June 2020 were collected. 526 COVID-19 admissions without an active cancer diagnosis were compared with 87 COVID-19 admissions with an active cancer diagnosis.Primary and secondary outcome measures 30-day and 90-day post-COVID-19 survival.Results In total, 613 patients were enrolled with male to female ratio of 1:6 and median age of 77 years. …”
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Integrated single-cell and bulk RNA dequencing to identify and validate prognostic genes related to T Cell senescence in acute myeloid leukemia by Mengyao Sha, Jun Chen, Haifeng Hou, Huaihui Dou, Yan Zhang

“…RT-qPCR verified that prognostic gene expression was consistent with the data prediction results.ConclusionCALR, CDK6, HOXA9, and PARP1 predicted disease progression and prognosis in patients with AML. …”
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An Operational Status Assessment Model for SF₆ High-Voltage Circuit Breakers Based on IAR-BTR by Ningfang Wang, Yujia Wang, Yifei Zhang, Ci Tang, Chenhao Sun

“…In recent years, the frequency of extreme weather events has been increasing, necessitating a comprehensive consideration of both internal and external factors in the operational status prediction of SF₆ high-voltage circuit breakers. …”
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Does Artificial Intelligence Bring New Insights in Diagnosing Phlebological Diseases?—A Systematic Review by Sergiu-Ciprian Matei, Sorin Olariu, Ana-Maria Ungureanu, Daniel Malita, Flavia Medana Petrașcu

“…The future of AI in venous diagnostics is promising, and several areas of development were noted, including AI algorithms embedding directly into ultrasound devices to provide instantaneous diagnostic insights during patient evaluations; combining AI-processed Doppler data with other imaging modalities, such as computed tomography or MRI, for comprehensive assessments; AI usage in order to predict disease progression and tailor treatment strategies based on individual patient profiles; and constructing large-scale, multicenter datasets to improve the robustness and generalizability of AI algorithms.…”
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ROBUST NUMERICAL METHODS FOR SOLVING THE poorly determined taskS OF ELECTRODYNAMICS AND NONLINEAR DYNAMICS by A. A. Kuraev, T. L. Popkova, V. V. Matveenko

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Harnessing Novel Data‐Driven Techniques for Precise Rainfall–Runoff Modeling by Saad Sh. Sammen, Reza Mohammadpour, Karam AlSafadi, Ali Mokhtar, Shamsuddin Shahid

“…Although the GMDH predicts runoff with higher accuracy, ELM provides reliable performance in simulating both low and high values. …”
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Study on privacy preserving encrypted traffic detection by Xinyu ZHANG, Bingsheng ZHANG, Quanrun MENG, Kui REN

“…Existing encrypted traffic detection technologies lack privacy protection for data and models, which will violate the privacy preserving regulations and increase the security risk of privacy leakage.A privacy-preserving encrypted traffic detection system was proposed.It promoted the privacy of the encrypted traffic detection model by combining the gradient boosting decision tree (GBDT) algorithm with differential privacy.The privacy-protected encrypted traffic detection system was designed and implemented.The performance and the efficiency of proposed system using the CICIDS2017 dataset were evaluated, which contained the malicious traffic of the DDoS attack and the port scan.The results show that when the privacy budget value is set to 1, the system accuracy rates are 91.7% and 92.4% respectively.The training and the prediction of our model is efficient.The training time of proposed model is 5.16 s and 5.59 s, that is only 2-3 times of GBDT algorithm.The prediction time is close to the GBDT algorithm.…”
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From smoking cessation to physical activity: Can ontology-based methods for automated evidence synthesis generalise across behaviour change domains? [version 2; peer review: 2 appr... by Candice Moore, Emily Hayes, Oscar Castro, Ella Howes, Alison J Wright, Emma Norris, Susan Michie, Robert West

“…The Human Behaviour-Change Project (HBCP) aims to improve evidence synthesis in behavioural science by compiling intervention reports and annotating them with an ontology to train information extraction and prediction algorithms. The HBCP used smoking cessation as the first ‘proof of concept’ domain but intends to extend its methodology to other behaviours. …”
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Monitoring Canopy Height in the Hainan Tropical Rainforest Using Machine Learning and Multi-Modal Data Fusion by Qingping Ling, Yingtan Chen, Zhongke Feng, Huiqing Pei, Cai Wang, Zhaode Yin, Zixuan Qiu

“…The results showed that RH80 was the optimal choice for the prediction model regarding percentile selection, and the RF algorithm exhibited the optimal performance in terms of accuracy and stability, with R² values of 0.71 and 0.60 for the training and testing sets, respectively, and a relative root mean square error of 21.36%. …”
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Hidden diversity in anthropophilic sand flies of the Monticola Series (Diptera, Psychodidae) by Bruno Leite Rodrigues, Alessandra Gutierrez de Oliveira, Leonardo Estevam Honorato da Silva, Thiago Vasconcelos dos Santos, Lidiane de Nazaré Carvalho de Oliveira, Felipe Dutra Rêgo, Andrey José de Andrade, Glédson Bandeira Maia, Israel de Souza Pinto, José Dilermando Andrade Filho, Eunice Aparecida Bianchi Galati

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AI-driven healthcare: Fairness in AI healthcare: A survey. by Sribala Vidyadhari Chinta, Zichong Wang, Avash Palikhe, Xingyu Zhang, Ayesha Kashif, Monique Antoinette Smith, Jun Liu, Wenbin Zhang

“…AI applications have significantly improved diagnostic accuracy, treatment personalization, and patient outcome predictions by leveraging technologies such as machine learning, neural networks, and natural language processing. …”
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The Effect of Derived Features on Art Genre Classification with Machine Learning by Didem Abidin

“…Although this process was used to be done by art experts before, now artificial intelligence techniques may help people manage this classification task. The algorithms used for classification are already improved, and now they can make classifications and predictions for any kind of genre classification. …”
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Day-Ahead Scheduling of IES Containing Solar Thermal Power Generation Based on CNN-MI-BILSTM Considering Source-Load Uncertainty by Kun Ding, Yalu Sun, Boyang Chen, Jing Chen, Lixia Sun, Yingjun Wu, Yusheng Xia

“…The validity of the proposed model is verified by algorithm prediction simulation and day-ahead scheduling experiments under different configurations.…”
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Utilising artificial intelligence in prehospital emergency care systems in low- and middle-income countries: a scoping review by Odhran Mallon, Odhran Mallon, Odhran Mallon, Freddy Lippert, Freddy Lippert, Willem Stassen, Marcus Eng Hock Ong, Caitlin Dolkart, Caitlin Dolkart, Thomas Krafft, Eva Pilot

“…Articles assessing dispatch forecasting were the most common, although artificial intelligence tools are also utilised in classification and disease prediction. There was significant variation in sample sizes throughout the selected studies. …”
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Evaluating sowing uniformity in hybrid rice using image processing and the OEW-YOLOv8n network by Zehua Li, Zehua Li, Yihui Pan, Xu Ma, Yongjun Lin, Xicheng Wang, Hongwei Li

“…Compared to the advanced object detection algorithms such as Faster-RCNN, SSD, YOLOv4, YOLOv5s YOLOv7-tiny, and YOLOv10s, the mAP of the new network increased by 5.2%, 7.8%, 4.9%, 2.8% 2.9%, and 3.3%, respectively. …”
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