Breast lesion classification via colorized mammograms and transfer learning in a novel CAD framework by Abbas Ali Hussein, Morteza Valizadeh, Mehdi Chehel Amirani, Sedighe Mirbolouk

“…In a subsequent step, Machine Learning (ML) algorithms are employed to classify these tumors as malign or benign cases. …”
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Comparison of 7 artificial intelligence models in predicting venous thromboembolism in COVID-19 patients by Indika Rajakaruna, Mohammad Hossein Amirhosseini, Mike Makris, Mike Laffan, Yang Li, Deepa J. Arachchillage

“…Methods: We used feature ranking through recursive feature elimination with AI algorithms (logistic regression and random forest classifier) and standard statistical methods to identify the significant factors that contribute to developing VTE in COVID-19 patients using a large dataset from “Coagulopathy associated with COVID-19,” a multicenter observational study. …”
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Predicting cardiotoxicity in drug development: A deep learning approach by Kaifeng Liu, Huizi Cui, Xiangyu Yu, Wannan Li, Weiwei Han

“…This study not only improved the predictive accuracy of cardiotoxicity models but also promoted a more reliable and scientifically interpretable method for drug safety assessment. …”
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Predicting carotid atherosclerosis in latent autoimmune diabetes in adult patients using machine learning models: a retrospective study by Xiaoqin Chen, Zhitong Li, Xiaoying Fan, Yuanyuan Yan, Shiwei Liu

“…Among the various machine learning models evaluated, the LR model exhibited the highest performance, achieving an area under the curve (AUC) of 0.936, alongside an accuracy of 86%. NNET and SVM models also demonstrated robust predictive capacities with AUC values of 0.919 and 0.918, respectively. …”
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The systemic oxidative stress index predicts clinical outcomes of esophageal squamous cell carcinoma receiving neoadjuvant immunochemotherapy by Jifeng Feng, Jifeng Feng, Liang Wang, Xun Yang, Qixun Chen, Qixun Chen

“…For prognostic prediction, a risk categorization method based on recursive partitioning analysis (RPA) was also created.ResultsFour SOS-related indicators, including albumin, creatinine, blood urea nitrogen, and direct bilirubin, were used to establish the SOSI. …”
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In-Memory Versus Disk-Based Computing with Random Forest for Stock Analysis: A Comparative Study by Chitra Joshi, Chitrakant Banchorr, Omkaresh Kulkarni, Kirti Wanjale

“…Mean squared error (MSE) and root mean square error (RMSE) were employed to assess the primary performance indicators of the models, while mean absolute error (MAE) and the R-squared value were used to evaluate the goodness of fit of the models.Results: The RMSE, MAE and MSE obtained for the Spark-based implementation were lower, compared to the MapReduce-based implementation, although these low values indicate high prediction accuracy. It also had a big impact on the time it took to train and run models because of its optimized in-memory processing. …”
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Ontological approach to modeling innovation processes on the example of a distributed educational network of the University by D. G. Korneev, M. S. Gasparian, A. A. Mikryukov

“…In this regard, the problem of modeling and algorithmization of the formation of educational programs as a chain of interrelated training modules and objects, taking into account the implementation of a competence-based model of learning in a distributed educational environment of the University, seems to be interesting.The modern education system is to be aimed primarily at adaptive generation of educational content, taking into account the needs of students and in accordance with constantly changing conditions and needs of the labor market. …”
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Investigating Drug-Induced Thyroid Dysfunction Adverse Events Associated With Non-Selective RET Multi-Kinase Inhibitors: A Pharmacovigilance Analysis Utilizing FDA Adverse Event Re... by Meng Z, Song L, Wang S, Duan G

“…Disproportionality analysis using ROR, PRR, BCPNN, and EBGM algorithms consistently demonstrated a positive association between Sunitinib, Cabozantinib, and Lenvatinib with TD adverse events. …”
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Prognosis and immune landscape of bladder cancer can be predicted using a novel miRNA signature associated with cuproptosis by Zhilei Zhang, Fang Liu, Yongbo Yu, Fei Xie, Tao Zhu

“…We evaluated the tumor microenvironment (TME) of every patient using immune ESTIMATE, CIBERSORT, and ssGSEA algorithms. We also investigated the differences in tumor mutation burden (TMB) and drug sensitivity between two groups. …”
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A comprehensive investigation of morphological features responsible for cerebral aneurysm rupture using machine learning by Mostafa Zakeri, Amirhossein Atef, Mohammad Aziznia, Azadeh Jafari

“…Neck circumference and outlet numbers from the new parameters were also deemed significant contributors.…”
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Computational methods and artificial intelligence-based modeling of magnesium alloys: a systematic review of machine learning, deep learning, and data-driven design and optimizatio... by Hanxuan Wang, Raman Kumar, Raman Kumar, Ashutosh Pattanaik, Rajender Kumar, Rajender Kumar, Ali Saeed Owayez Khawaf Aljaberi, Mayada Ahmed Abass

“…The study also discusses cross-cutting themes such as simulation speed-up metrics, model interpretability across domains, and limitations in dataset coverage. …”
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School-Based Online Surveillance of Youth: Systematic Search and Content Analysis of Surveillance Company Websites by Alison O'Daffer, Wendy Liu, Cinnamon S Bloss

“…Specifically, almost all companies reported conducting monitoring of students at school, but 86% (12/14) of companies reported also conducting monitoring 24/7 outside of school and 7% (1/14) reported conducting monitoring outside of school at school administrator-specified locations. …”
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AI-aided short-term decision making of rockburst damage scale in underground engineering by Chukwuemeka Daniel, Shouye Cheng, Xin Yin, Zakaria Mohamed Barrie, Yucong Pan, Quansheng Liu, Feng Gao, Minsheng Li, Xing Huang

“…Among the models evaluated, BO-RF model demonstrated the highest predictive accuracy and generalization capability, achieving 92% testing accuracy. BO-RF model also ranked top in a multi-criteria evaluation framework. …”
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Coronary Heart Disease Risk Prediction Model Based on Machine Learning by YUE Haitao, HE Chanchan, CHENG Yuyou, ZHANG Sencheng, WU You, MA Jing

“…However, the issue of data imbalance in these studies is often overlooked, despite its crucial role in enhancing the accuracy of CHD risk identification within classification algorithms. Objective To investigate the factors influencing CHD and to establish predictive models for CHD risk using two data balancing methods based on five algorithms, comparing the predictive value of these models for CHD risk. …”
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Deep reinforcement learning applications and prospects in industrial scenarios by JING TAN, Ligang YANG, Xiaorui LI, Zhaolin YUAN, Yunduan CUI, Chao YAO, Zongjie WANG, Xiaojuan BAN

“…Central to these systems are control algorithms, which enable the automation of operations, optimization of process parameters, and reduction of operational costs. …”
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Pretreatment CT-Based Machine Learning Radiomics Model Predicts Response in Inoperable Stage III NSCLC Treated with Concurrent Radiochemotherapy Plus PD-1 Inhibitors by Ya Li Bachelor, Min Zhang Bachelor, Yong Hu MM, Bo Du Bachelor, Youlong Mo Bachelor, Tianchu He MM, Mingdan Zhao Bachelor, Benlan Li Bachelor, Ji Xia Bachelor, Zhongjun Huang Bachelor, Fangyang Lu MD, Zhen Huang Bachelor, Bing Lu MD, Jie Peng MD

“…Results Based on the performance of radiomics models constructed by various machine learning algorithms in the prospective validation set, the LR with the highest AUC value (AUC: 90.00%) was finally selected, which also performed well in the independent test set (AUC: 84.96%). …”
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A real-world disproportionality analysis of FDA adverse event reporting system (FAERS) events for lecanemab by Linlin Yan, Linhai Zhang, Zucai Xu, Zhong Luo

“…This necessitates serial brain MRI surveillance for all patients during treatment, aimed not only at early ARIA detection but also vigilant monitoring of IMEs including cerebral haemorrhage, cerebral microhaemorrhages, subdural haematoma, cerebral edema, ischaemic stroke, and cerebral infarction. …”
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La Inteligencia Artificial en la educación: Big data, cajas negras y solucionismo tecnológico / Artificial Intelligence in Education: Big Data, Black Boxes, and Technological Solut... by Xavier Giró-Gracia, Juana María Sancho-Gil

“…Educators, educational researchers, and policymakers, in general, lack the knowledge and expertise to understand the underlying logic of these new systems, and there is insufficient research based evidence to fully understand the consequences for learners’ development of both the extensive use of screens and the increasing reliance on algorithms in educational settings. This article, geared towards educators, academics in the field of Education, and policymakers, first introduces the concepts of ‘Big Data’, Artificial Intelligence, Machine Learning algorithms and how they are presented and deployed as ‘black boxes’, and the possible impact on education these new software solutions can have. …”
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Free-space terabit/s coherent optical links via platicon frequency microcombs by Wenting Wang, Hao Liu, Jiagui Wu, James F. McMillan, Dong IL Lee, Futai Hu, Wenzheng Liu, Jinghui Yang, Hangbo Yang, Abhinav Kumar Vinod, Yahya H. Ezzeldin, Christina Fragouli, Mingbin Yu, Patrick Guo-Qiang Lo, Dim-Lee Kwong, Devin S. Kahrs, Ninghua Zhu, Chee Wei Wong

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Incorporating Deep Learning Into Hydrogeological Modeling: Advancements, Challenges, and Future Directions by Zhenxue Dai, Chuanjun Zhan, Huichao Yin, Junjun Chen, Lulu Xu, Yuzhou Xia, Songlin Yang, Wei Chen, Mingxu Cao, Zhengyang Du, Xiaoying Zhang, Bicheng Yan, Yue Ma, Hao Wang, Farzad Moeini, Mohamad Reza Soltanian, Hung Vo Thanh, Kenneth C. Carroll

“…Establishing standardized benchmarks will also be key for assessing the practical utility of DL models and facilitating their generalization in real‐world scenarios. …”
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