Research on test strategy for randomness based on deep learning by Dongyu CHEN, Hua CHEN, Limin FAN, Yifang FU, Jian WANG

“…In order to achieve better test performance, researches on the randomness test strategies based on deep learning were conducted, including the batch average strategy proposed by EUROCRYPT 2021 and the selection strategy for data unit size.By introducing the randomness statistical test model based on deep learning methods, the statistical distribution and test power expression of two test strategies were theoretically derived, and it was pointed out that: (i) the batch average strategy could amplify the prediction accuracy of the model, but it was prone to an increase in the probability of the second type of error in statistics, instead reducing the statistical test power; (ii) the smaller data units of the deep model generally obtained the more powerful statistical tests.Based on the above understanding, a new bit-level deep learning model was proposed for randomness statistical tests, which gained the advantage of prediction with 80 times fewer parameters and 50% samples, compared with the previous work on linear congruent generator (LCG) algorithm, and achieved significant prediction advantages with 10～20 times fewer parameters by extending the model to apply to 5～7 rounds of Speck, compared with the model proposed by Gohr.…”
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Integrated bioinformatics identifies ferroptosis biomarkers and therapeutic targets in idiopathic pulmonary arterial hypertension by Yuhao Zhang, Tao Qian, Wei Jiang, Haoyong Yuan, Ting Lu, Ni Yin, Zhongshi Wu, Can Huang

“…The CIBESORT software was employed to predict immune genes and functions. Of 237 ferroptosis-related genes (FRGs), 27 differentially expressed FRGs (DE-FRGs) showed significant differences between IPAH and normal samples in GSE48149, with 15 downregulated and 12 upregulated genes. …”
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Immunogenic cell death-related genes as prognostic biomarkers and therapeutic insights in uterine corpus endometrial carcinoma: an integrative bioinformatics analysis by Tianfei Yi, Zhenglun Yang, Peng Shen, Yan Huang

“…The immune landscape was characterized through multiple bioinformatics approaches, and immunotherapy response was predicted using the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. …”
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Development of a warning model for drug-induced liver injury in the older patients by Qiaozhi Hu, Qiaozhi Hu, Xiaoqi Li, Xiaoqi Li, Dan Zou, Zhiyao He, Zhiyao He, Ting Xu

“…This study aimed to develop and compare eight machine learning (ML) models using routine clinical, pharmacological, and laboratory data to predict DILI in older hospitalized patients.MethodsWe conducted a retrospective analysis of older patients hospitalized in 2022 who exhibited abnormal liver function tests. …”
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%diag_test: a generic SAS macro for evaluating diagnostic accuracy measures for multiple diagnostic tests by Jacques K. Muthusi, Peter W. Young, Frankline O. Mboya, Samuel M. Mwalili

“…We also used the macro to reproduce results of published work on evaluating performance of multiple classification machine learning algorithms for predicting coronary artery disease. Conclusion The SAS macro presented here is a powerful analytic tool for analyzing data from multiple diagnostic tests. …”
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SECONDGRAM: Self-conditioned diffusion with gradient manipulation for longitudinal MRI imputation by Brandon Theodorou, Anant Dadu, Mike Nalls, Faraz Faghri, Jimeng Sun

“…We address this gap by proposing self-conditioned diffusion with gradient manipulation (SECONDGRAM) to generate absent follow-up imaging features, enabling predictions of MRI developments over time and enriching limited datasets through imputation. …”
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Factors influencing the response to periodontal therapy in patients with diabetes: post hoc analysis of a randomized clinical trial using machine learning by Nidia CASTRO DOS SANTOS, Arthur MANGUSSI, Tiago RIBEIRO, Rafael Nascimento de Brito SILVA, Mauro Pedrine SANTAMARIA, Magda FERES, Thomas VAN DYKE, Ana Carolina LORENA

“…Our findings demonstrate that PD and CAL were the most important variables contributing to the predictive performance of the Random Forest model. …”
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Machine learning approaches for grain seed quality assessment: a comparative study of maize seed samples in Malawi by Wisdom Richard Mgomezulu, Moses M. N. Chitete, Beston B. Maonga, Mthakati A. R. Phiri

“…Abstract The study assessed machine and deep learning algorithms’ ability to predict and classify the quality of maize grain seed for increased agricultural output. …”
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AAMS-YOLO: enhanced farmland parcel detection for high-resolution remote sensing images by Binyao Wang, Ya’nan Zhou, Weiwei Zhu, Li Feng, Jinke He, Tianjun Wu, Jiancheng Luo, Xin Zhang

“…During feature enhancement, to effectively detect targets of different scales, the Attentional Scale Sequence Fusion with P2 network (ASFP2Net) integrates the Triple Feature Encoder (TFE) module and Scale Sequence Feature Fusion (SSFF) module. In the prediction stage, a Multi-Scale Attention Head (MSAHead) enhances adaptability through multi-scale attention mechanisms. …”
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Resampling-driven machine learning models for enhanced high streamflow forecasting by Nureehan Salaeh, Sirimon Pinthong, Warit Wipulanusat, Uruya Weesakul, Jakkarin Weekaew, Quoc Bao Pham, Pakorn Ditthakit

“…These results present a promising framework for high streamflow prediction that can be adapted and applied to other river basins.…”
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Use of machine learning in osteoarthritis research: a systematic literature review by Francis Berenbaum, Jérémie Sellam, David Klatzmann, Atul J Butte, Karine Louati, Encarnita Mariotti-Ferrandiz, Marie Binvignat, Valentina Pedoia

“…Twelve articles were related to diagnosis, 7 to prediction, 4 to phenotyping, 12 to severity and 11 to progression. …”
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Method for Knowledge Transfer via Multi-Task Semi-Supervised Self-Paced by Yao Zhao, Hongying Liu, Huaxian Pan, Zhen Song, Chunting Liu, Anni Wei, Baoshuang Zhang, Wei Lu

“…We adopt Alternating convex search (ACS) method to solve MSSP, that is, each iteration sequentially trains the prediction model with a fixed set of labeled instances and then updates the labeled training set by adding more complex instances. …”
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New Insights into the Role of the Immune Microenvironment in Breast Carcinoma by Luis de la Cruz-Merino, Antonio Barco-Sánchez, Fernando Henao Carrasco, Esteban Nogales Fernández, Ana Vallejo Benítez, Javier Brugal Molina, Antonio Martínez Peinado, Ana Grueso López, Manuel Ruiz Borrego, Manuel Codes Manuel de Villena, Víctor Sánchez-Margalet, Adoración Nieto-García, Emilio Alba Conejo, Noelia Casares Lagar, José Ibáñez Martínez

“…Infiltration by tumor infiltrating lymphocytes (TIL) and their subtypes, tumor-associated macrophages (TAM) and myeloid-derived suppressive cells (MDSC) seem bona fide prognostic and even predictive biomarkers, that will eventually be incorporated into diagnostic and therapeutic algorithms of breast cancer. …”
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Determining time since deposition of latent fingerprints on forensic adhesive tape using ultrafast DESI-MS and machine learning by Nora Rajs, Yinon Harush-Brosh, Ron Raisch, Ravit Yakobi Arancibia, Amani Zoabi, Guy Nevet Golan, Moshe Shpitzen, Sarena Wiesner, Michal Levin-Elad, Tommy Kaplan, Katherine Margulis

“…Data analysis using the XGBoost and SMOTE algorithms achieved a correlation of 0.54 (p-value < 1e−5) between TSD prediction and true TSD, achieving 83.3% accuracy in distinguishing between 0-4 days and 10–15 days old prints. …”
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A machine learning-based recommendation framework for material extrusion fabricated triply periodic minimal surface lattice structures by Sajjad Hussain, Carman Ka Man Lee, Yung Po Tsang, Saad Waqar

“…This dataset was used to train both ML and DL algorithms. ML algorithms included Bayesian regression (BR), K-nearest neighbors (KNN), Random Forest (RF), Decision Tree (DT), and DL algorithm convolutional neural network (CNN). …”
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Budget impact analysis of using procalcitonin to optimize antimicrobial treatment for patients with suspected sepsis in the intensive care unit and hospitalized lower respiratory t... by Osvaldo Ulises Garay, Gonzalo Guiñazú, Wanda Cornistein, Javier Farina, Ricardo Valentini, Gabriel Levy Hara

“…<h4>Results</h4>The model predicted that using procalcitonin-guided algorithms would result in 734.5 [95% confidence interval (CI): 1,105.2;438.8] thousand fewer antibiotic treatment days, 7.9 [95% CI: 18.5;8.5] thousand antibiotic-resistant cases avoided, and 5.1 [95% CI: 6.7;4.2] thousand fewer Clostridioides difficile cases. …”
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A Deep Learning Method for Pneumoconiosis Staging on Chest X-Ray Under Label Noise by Wenjian Sun, Dongsheng Wu, Jiang Shen, Yang Luo, Hao Wang, Li Min, Chunbo Luo

“…In the coarse screening procedure, the proposed sample selection strategy divides the pneumoconiosis dataset into &#x2018;confident&#x2019; and &#x2018;less-confident&#x2019; subsets based on the logical relationship between the prediction correctness and confidence of multiple expert networks. …”
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Automatic Segmentation of Abdominal Aortic Aneurysm From Computed Tomography Angiography Using a Patch-Based Dilated UNet Model by Merjulah Roby, Juan C. Restrepo, Haehwan Park, Satish C. Muluk, Mark K. Eskandari, Seungik Baek, Ender A. Finol

“…Hence, there is a growing need for automated segmentation algorithms, particularly when these influence treatment planning. …”
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Toward causal artificial intelligence approach for PM2.5 interpretation: A discovery of structural causal models by Mallika Kliangkhlao, Apaporn Tipsavak, Thanathip Limna, Racha Dejchanchaiwong, Perapong Tekasakul, Kirttayoth Yeranee, Thanyabun Phutson, Bukhoree Sahoh

“…Understanding the causal mechanisms underlying PM2.5 generation is critical for developing effective prevention strategies, necessitating an approach that goes beyond prediction and seeks deeper causal explanations to support decision-making. …”
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Leaf area index-based phenotypic assessment of sweet potato varieties using UAV multispectral imagery and a hybrid retrieval approach by Philemon Tsele, Abel Ramoelo, Lucy Moleleki, Sunette Laurie, Whelma Mphela, Natasha Tshuma

“…However, the BRT performance in-comparison to KRR, captured more spatial variability of observed LAI with a better prediction accuracy across the 20 sweet potato varieties. …”
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