RUL Prediction Based on MBGD-WGAN-GRU for Lithium-Ion Batteries by Zhiguo Zhao, Ke Li, Yibo Dai, Biao Chen, Yeqin Wang, Qian Zhao

“…First, the IHF, which captures battery aging characteristics, is extracted from raw LIBs data, and the dataset is partitioned into training (70%) and testing (30%) subsets. Subsequently, the mini-batch stochastic gradient descent algorithm is employed to optimize a Wasserstein generative adversarial network, thereby augmenting the training set and enhancing data diversity. …”
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Holographic reconstruction of black hole spacetime: machine learning and entanglement entropy by Byoungjoon Ahn, Hyun-Sik Jeong, Keun-Young Kim, Kwan Yun

“…Our algorithm successfully extracts the corresponding bulk metrics from these data. …”
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Development of a 5-Year Risk Prediction Model for Transition From Prediabetes to Diabetes Using Machine Learning: Retrospective Cohort Study by Yongsheng Zhang, Hongyu Zhang, Dawei Wang, Na Li, Haoyue Lv, Guang Zhang

“…Significant predictors were selected on the training set using recursive feature elimination methods, followed by prediction model development using 7 machine learning algorithms (logistic regression, random forest, support vector machine, multilayer perceptron, extreme gradient boosting machine, light gradient boosting machine, and categorical boosting machine [CatBoost]), optimized through grid search and 5-fold cross-validation. …”
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Sparse representation for massive MIMO satellite channel based on joint dictionary learning by Qing yang Guan, Shuang Wu

“…Abstract A constrained joint dictionary learning (CJDL) algorithm for high‐precision channel representation in massive multiple input multiple output (MIMO) satellite systems is proposed. …”
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Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model. by Gregory L Watson, Di Xiong, Lu Zhang, Joseph A Zoller, John Shamshoian, Phillip Sundin, Teresa Bufford, Anne W Rimoin, Marc A Suchard, Christina M Ramirez

“…The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for cases and deaths in U.S. states. …”
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Research on adversarial attacks and defense performance of image classification models for automated driving systems by TANG Jun, HUANG Wenjing, LI Shuang, WU Zili

“…Then, sensitivity analyses were carried out across different regions and examples to identify attack mechanisms using three adversarial interpretation algorithms: LRP, Grad-CAM, and LIME. Based on these analysis results, optimization algorithms such as swarm intelligence defense and adversarial training were adopted to verify the classification performance of the model following adversarial training. …”
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A Radio Frequency Interference Screening Framework—From Quick-Look Detection Using Statistics-Assisted Network to Raw Echo Tracing by Jiayuan Shen, Bing Han, Yang Li, Zongxu Pan, Di Yin, Yugang Feng, Guangzuo Li

“…By combining the statistics-assisted network with the sliding-window algorithm and the error-tolerant training strategy, it is possible to accurately detect and locate RFI in the quick looks of an SLC product. …”
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Developing and validating a drug recommendation system based on tumor microenvironment and drug fingerprint by Yan Wang, Xiaoye Jin, Rui Qiu, Bo Ma, Sheng Zhang, Xuyang Song, Jinxi He

“…This study aimed to develop a personalized drug recommendation model leveraging genomic profiles to optimize therapeutic outcomes.MethodsA content-based filtering algorithm was implemented to predict drug sensitivity. …”
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Inflammation Biomarker-Driven Vertical Visualization Model for Predicting Long-Term Prognosis in Unstable Angina Pectoris Patients with Angiographically Intermediate Coronary Lesio... by Zhou B, Tan W, Duan S, Wang Y, Bian F, Zhao P, Wang J, Yao Z, Li H, Hu X, Wang J, Liu J

“…The assessed outcome was the occurrence of major adverse cardiac and cerebrovascular events (MACCEs). The Boruta algorithm was applied to identify potential risk factors and develop a prognostic multimodal model.Results: A total of 773 patients were enrolled and divided into a training cohort (n=463) and validation cohort (n=310). …”
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Major Adverse Kidney Events in Hospitalized Older Patients With Acute Kidney Injury: Machine Learning–Based Model Development and Validation Study by Xiao-Qin Luo, Ning-Ya Zhang, Ying-Hao Deng, Hong-Shen Wang, Yi-Xin Kang, Shao-Bin Duan

“…The eXtreme Gradient Boosting algorithm was applied to establish a prediction model for MAKE30. …”
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Computed tomography-based radiomics predicts prognostic and treatment-related levels of immune infiltration in the immune microenvironment of clear cell renal cell carcinoma by Shiyan Song, Wenfei Ge, Xiaochen Qi, Xiangyu Che, Qifei Wang, Guangzhen Wu

“…Radiomics features were screened using LASSO analysis. Eight ML algorithms were selected for diagnostic analysis of the test set. …”
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Real-time damage detection of bridges using adaptive time-frequency analysis and ANN by V. Ahmadian, S. B. Beheshti Aval, E. Darvishan

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A novel adenosine-to-inosine RNA editing-based nomogram for predicting prognosis of hepatocellular carcinoma by Shiqiong Huang, Ji Sun

“…Harrell’s C-index and calibration plot was utilized to evaluate the significant prognostic factors.Results:Nine ATIRE sites were screened to establish the ATIRE risk score, and it was found to be associated with prognosis of HCC. Survival analysis revealed that higher ATIRE-based risk scores were significantly associated with worse overall survival (OS) in both the training dataset (p < 0.001) and the validation dataset (p = 0.011), as well as in the combined dataset (p < 0.001). …”
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Artificial intelligence in academic research: Benefits, challenges, and ethical dimensions in doctoral studies by Zaki EL HANI, Jaouad KARROUM, Abdelmajid ELOUADI, Aniss MOUMEN

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Machine learning for predicting Chagas disease infection in rural areas of Brazil. by Fabio De Rose Ghilardi, Gabriel Silva, Thallyta Maria Vieira, Ariela Mota, Ana Luiza Bierrenbach, Renata Fiuza Damasceno, Lea Campos de Oliveira, Alexandre Dias Porto Chiavegatto Filho, Ester Sabino

“…We analyzed data from the Retrovirus Epidemiology Donor Study (REDS) to train five popular machine learning algorithms. The sample comprised 2,006 patients, divided into 75% for training and 25% for testing algorithm performance. …”
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The Future of the Psychiatrist by James B. Potash, Alex McClanahan, Jay Davidson, William Butler, Nathan Carroll, Anne Ruble, Mary Yaden, Darlene King, John Torous, Peter P. Zandi, Katherine G. Kennedy, Thomas E. Smith, Arpan Waghray, Robert Trestman, Marketa Wills

“…Objective The American Psychiatric Association (APA) issued a 2023 report on the future of psychiatry, focusing on how the organization should position itself in relation to coming developments over the next 10 years. …”
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Analysis of Influence of Different Relations Types on the Quality of Thesaurus Application to Text Classification Problems by Nadezhda S. Lagutina, Ksenia V. Lagutina, Ivan A. Shchitov, Ilya V. Paramonov

“…The basis of the study is an automatically generated thesaurus of a subject area, that contains three types of relations: synonymous, hierarchical and associative. To generate the thesaurus the authors use a hybrid method based on several linguistic and statistical algorithms for extraction of semantic relations. …”
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Development of a PANoptosis-related LncRNAs for prognosis predicting and immune infiltration characterization of gastric Cancer by Yangjian Hong, Cong Luo, Yanyang Liu, Zeng Wang, Huize Shen, Wenyuan Niu, Jiaming Ge, Jie Xuan, Gaofeng Hu, Bowen Li, Qinglin Li, Huangjie Zhang

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Hydrogen Leakage Location Prediction in a Fuel Cell System of Skid-Mounted Hydrogen Refueling Stations by Leiqi Zhang, Qiliang Wu, Min Liu, Hao Chen, Dianji Wang, Xuefang Li, Qingxin Ba

“…The Softmax classifier and Adam optimizer were used, with a training set for 1500 epochs. The results show that the algorithm can predict leakage locations not included in the training set. …”
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The predictive value of radiomics and deep learning for synchronous distant metastasis in clear cell renal cell carcinoma by Wan-Bin He, Chuan Zhou, Zhi-Jun Yang, Yun-Feng Zhang, Wen-Bo Zhang, Han He, Jia Wang, Feng-Hai Zhou

“…Results Of the 944 radiomic features identified, 15 were closely associated with SDM. With these 15 features, the support vector machine (SVM) model emerged as the most effective, demonstrating areas under the curve (AUC) of 0.860 and 0.813 in the training and validation cohort, respectively. …”
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