%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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Marine soundscape forecasting: A deep learning-based approach by Shashidhar Siddagangaiah

“…Despite the rapid development of anomaly detection algorithms and deep-learning models for forecasting, their application to marine soundscapes remains unexplored. …”
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Analysis and Validation of Autophagy-Related Gene Biomarkers and Immune Cell Infiltration Characteristic in Bronchopulmonary Dysplasia by Integrating Bioinformatics and Machine Lea... by Xiao S, Ding Y, Du C, Lv Y, Yang S, Zheng Q, Wang Z, Zheng Q, Huang M, Xiao Q, Ren Z, Bi G, Yang J

“…Subsequently, the hub genes were identified by Lasso and Cytoscape with three machine-learning algorithms (MCC, Degree and MCODE). In addition, hub genes were validated with ROC, single-cell sequence and IHC in hyperoxia mice. …”
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Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patients by Ran Du, Lijun Jing, Denggang Fu

“…The signature showed strong predictive power with AUC values of 0.68, 0.73, and 0.76 for 2-, 3-, and 5-year survival, respectively. …”
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Formation of a heterogeneous group of UAVS with a reasonable number of false and real drones by Volodymyr Prymirenko, Andrii Demianiuk, Roman Shevtsov, Serhii Bazilo, Andrey Pilipenko, Mykola Vovchanskyi

“…The availability of the developed mathematical model, algorithm, and program code makes it possible to predict the possible results of the combat use of heterogeneous groups of UAVs based on the initial parameters and to substantiate recommendations for a possible composition of such groups.…”
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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

“…These models are computer algorithms that simulate how information changes. SECONDGRAM addresses data scarcity by generating realistic follow-up MRI imaging features, thereby enriching limited datasets. …”
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A Mitochondria‐Related Signature in Diffuse Large B‐Cell Lymphoma: Prognosis, Immune and Therapeutic Features by Zhen‐Zhong Zhou, Jia‐Chen Lu, Song‐Bin Guo, Xiao‐Peng Tian, Hai‐Long Li, Hui Zhou, Wei‐Juan Huang

“…Conclusions We established a novel prognostic mitochondria‐related signature by machine learning algorithm, which also demonstrated outstanding predictive value in tumor microenvironment and responses to therapies.…”
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Estimating Winter Canola Aboveground Biomass from Hyperspectral Images Using Narrowband Spectra-Texture Features and Machine Learning by Xia Liu, Ruiqi Du, Youzhen Xiang, Junying Chen, Fucang Zhang, Hongzhao Shi, Zijun Tang, Xin Wang

“…Subsequently, machine learning algorithms were applied to develop estimation models for winter canola biomass. …”
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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

“…We tested seven different algorithms: K-Nearest Neighbors, Decision Tree, Support Vector Machine, Random Forest, Extreme Gradient Boosting, and Logistic Regression. …”
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Developing a Sleep Algxorithm to Support a Digital Medicine System: Noninterventional, Observational Sleep Study by Jeffrey M Cochran

“…ACC and ECG features were derived for each 5-minute window. The algorithm that most accurately predicted sleep parameters against PSG data was compared to commercially available wearable devices to further benchmark model performance. …”
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Classification and Comparison of Eight Different Growth Curve Methods for Pekin Duck by Hasan Önder, Mine Yılmaz, Mustafa Şahin, Uğur Şen, Sibel Bozkurt, Kadir Erensoy, İsmail Gök, Tolga Tolun, Ahmet Uçar

“…These models can be used for predicting body weight for a specific age from a dimensional perspective. …”
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Covariate Model Selection Approaches for Population Pharmacokinetics: A Systematic Review of Existing Methods, From SCM to AI by Mélanie Karlsen, Sonia Khier, David Fabre, David Marchionni, Jérôme Azé, Sandra Bringay, Pascal Poncelet, Elisa Calvier

“…AALASSO, a hybrid genetic algorithm, FREM with a clinical significance criterion and SCM+ with stagewise filtering were the best covariate model selection techniques—AALASSO being the very best one. …”
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Eagle-YOLOv8: UAV Object Detection Inspired by the Eagle-Eye Vision System by Dianwei Wang, Zehao Gao, Jie Fang, Yuanqing Li, Zhijie Xu

“…Confronting such challenges, we propose Eagle-YOLOv8, an object detection algorithm for UAV imagery inspired by the eagle-eye vision system. …”
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Spatiotemporal Analysis of Soil Moisture Variability and Precipitation Response Across Soil Texture Classes in East Kazakhstan by Dmitry Chernykh, Roman Biryukov, Andrey Bondarovich, Lilia Lubenets, Anatoly Pavlenko, Kamilla Rakhymbek, Denis Revenko, Zheniskul Zhantassova

“…The study of the hydrological regimes of rivers in different regions of the globe has revealed the need to include the soil moisture content in flood prediction models. This paper investigates the nature of the dependence of soil moisture content on soil texture in the East Kazakhstan region. …”
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Pilot Protection of New Energy Transmission Line in Active Distribution Network Based on 5G Communication by Tiecheng LI, Hui FAN, Weiming ZHANG, Xianzhi WANG, Yihong ZHANG, Zhihui DAI

“…Traditional pilot differential protection will have the problem of reliability reduction or even failure to operate after the access of new energy stations. …”
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Online variational Gaussian process for time series data by Weidong Wang, Mian Muhammad Yasir Khalil, Leta Yobsan Bayisa

“…In this paper, we propose the OnLine Variational Gaussian Process (OLVGP) algorithm, which introduces a novel approach for dynamically managing the number of inducing points based on the concept of eigenfunction inducing features. …”
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Improved simulation of thunderstorm characteristics and polarimetric signatures with LIMA two-moment microphysics in AROME by C. David, C. Augros, B. Vié, F. Bouttier, T. Le Bastard

“…<p>Thunderstorm forecasting remains challenging despite advances in numerical weather prediction (NWP) systems. The microphysics scheme that represents clouds in the model partly contributes to the introduction of uncertainties in the simulations. …”
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AI-Assisted Detection for Early Screening of Acute Myeloid Leukemia Using Infrared Spectra and Clinical Biochemical Reports of Blood by Chuan Zhang, Jialun Li, Wenda Luo, Sailing He

“…Subsequently, these vectors were assessed using a linear discriminant analysis (LDA) algorithm to estimate the likelihood of acute myeloid leukemia. …”
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Uncertainty Impact on Aggregator Performance for Peak Shaving by Ismail Arafat, Eduardo Castillo Guerra, Julian Meng

“…Accurate peak period forecasting and load consumption profile prediction are critical for utility resource planning and emergency response preparedness. …”
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A Two-Stage Deep Learning Method for Marine Oil Spill Localization and Segmentation From Synthetic Aperture Radar Images by Xudong Huang, Biao Zhang, William Perrie

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