Machine learning and AVO class II workflow for hydrocarbon prospectivity in the Messinian offshore Nile Delta Egypt by Nadia Abd-Elfattah, Aia Dahroug, Manal El Kammar, Ramy Fahmy

“…This method is particularly useful for identifying low seismic amplitude anomalies, which are often challenging to detect with conventional seismic analysis. (1) This study developed a workflow to detect low seismic amplitude gas fields in near-field exploration. (2) It uses a machine learning algorithm to classify and explore low-seismic-amplitude gas sand reservoirs. (3) This approach helps estimate the likelihood of success and reduces the risk associated with hydrocarbon exploration wells.…”
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Prediction and Stage Classification of Pressure Ulcers in Intensive Care Patients by Machine Learning by Mürsel Kahveci, Levent Uğur

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The cadenza woodwind dataset: Synthesised quartets for music information retrieval and machine learningZenodo by Gerardo Roa Dabike, Trevor J. Cox, Alex J. Miller, Bruno M. Fazenda, Simone Graetzer, Rebecca R. Vos, Michael A. Akeroyd, Jennifer Firth, William M. Whitmer, Scott Bannister, Alinka Greasley, Jon P. Barker

“…The dataset consists of the audio and associated metadata.…”
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Machine learning insights into early mortality risks for small cell lung cancer patients post-chemotherapy by Min Liang, Min Liang, Fuyuan Luo

“…However, the treatment is associated with significant risks, including heightened toxicity and early mortality. …”
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The diagnostic value investigation of programmed cell death genes in heart failure by Qiuyue Chen, Su Tu

“…Abstract Background We aimed to identify the potential diagnostic markers and associated molecular mechanisms based on programmed cell death (PCD)-related genes in patients with heart failure (HF). …”
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On the Potential of Bayesian Neural Networks for Estimating Chlorophyll-a Concentration from Satellite Data by Mohamad Abed El Rahman Hammoud, Nikolaos Papagiannopoulos, George Krokos, Robert J. W. Brewin, Dionysios E. Raitsos, Omar Knio, Ibrahim Hoteit

“…BNNs are probabilistic models that associate a probability distribution to the neural network parameters and rely on Bayes’ rule for training. …”
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Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory by Sergei Manzhos, Johann Lüder, Pavlo Golub, Manabu Ihara

“…However, ML models are not as easily handled as analytic expressions; they need to be provided in the form of algorithms and associated data. Here, we bridge the two approaches and construct an analytic expression for a KEF guided by interpretative ML of crystal cell-averaged kinetic energy densities ( ${\bar{\tau}}$ ) of several hundred materials. …”
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Volcano activity classification from synergy of EO data and machine learning: an application to Mount Etna volcano (Italy) by C. Petrucci, G. Romoli, A. Pignatelli, E. Trasatti, F. Zuccarello, F. Greco, M. Dozzo, G. Bilotta, F. Spina, G. Ganci

“…Using satellite data, including ground deformation, radiance, land surface temperature, sulfur dioxide emissions, and gravity anomalies, five volcanic activity states were identified: Quiet, Preparatory, Unrest, Eruption, and Cooling. Supervised ML algorithms, such as random forest, support vector machines, decision trees, and k-nearest neighbors, were employed to classify these states. …”
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SHASI-ML: a machine learning-based approach for immunogenicity prediction in Salmonella vaccine development by Ottavia Spiga, Ottavia Spiga, Ottavia Spiga, Anna Visibelli, Francesco Pettini, Bianca Roncaglia, Annalisa Santucci, Annalisa Santucci, Annalisa Santucci

“…The Extreme Gradient Boosting (XGBoost) algorithm was employed for model development and optimization.ResultsSHASI-ML demonstrated robust performance in identifying bacterial immunogens, achieving 89.3% precision and 91.2% specificity. …”
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Investigation of TsGAN-based multimodal image fusion to augment image pre-processing abilities by Priyanka Bhatambarekar, Gayatri Phade

“…Empirical evaluations confirm the effectiveness of the proposed approach, highlighting its superiority over existing algorithms in both qualitative and quantitative analysis. …”
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Construction of machine learning-based prognostic model of centrosome amplification-related genes for esophageal squamous cell carcinoma by LI Chaoqun, ZHENG Hongliang, HUANG Ping

“…A prognostic model of CARGs was constructed by incorporating 12 machine learning algorithms, and univariate and multivariate Cox regression analyses were applied to evaluate whether the 12 models as an independent prognostic factor or not. …”
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BRIDGING THE GAP: OPPORTUNITIES, CHALLENGES AND STRATEGIES FOR AI DEPLOYMENT IN PUBLIC SERVICE DELIVERY by Richard Douglas Kamara

“…At the same time, it emphasises significant challenges, such as algorithmic bias, data privacy risks, public trust deficits, and resource disparities that may impede the equitable adoption of AI. …”
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Deep learning-based framework for Mycobacterium tuberculosis bacterial growth detection for antimicrobial susceptibility testing by Hoang-Anh T. Vo, Sang Nguyen, Ai-Quynh T. Tran, Han Nguyen, Hai Bich Ho, Philip W. Fowler, Timothy M. Walker, Thuy Thi Nguyen

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Machine learning-based prognostic modeling integrating PANoptosis in head and neck squamous cell carcinoma by Chen Li, Jiajing Lu, Jialin Zhu, Tao Zhou, Qijie Shen, Bikun Huang, Qingsong Li

“…A prognostic model was constructed using 101 machine learning algorithms and their combinations, with TCGA-HNSCC as the training set and GSE41613 and GSE65858 as validation sets. …”
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Мethods of Machine Learning in Ophthalmology: Review by D. D. Garri, S. V. Saakyan, I. P. Khoroshilova-Maslova, A. Yu. Tsygankov, O. I. Nikitin, G. Yu. Tarasov

“…Machine learning includes models and algorithms that mimic the architecture of biological neural networks. …”
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Synthetic Data-Enhanced Classification of Prevalent Osteoporotic Fractures Using Dual-Energy X-Ray Absorptiometry-Based Geometric and Material Parameters by Luca Quagliato, Jiin Seo, Jiheun Hong, Taeyong Lee, Yoon-Sok Chung

“…It included 9,260 patients, aged 55 to 99, comprising 242 femur fracture (FX) cases and 9,018 non-fracture (NFX) cases. To model the association of the bone’s current health status with prevalent FXs, three prediction algorithms—extreme gradient boosting (XGB), support vector machine, and multilayer perceptron—were trained using two-dimensional dual-energy X-ray absorptiometry (2D-DXA) analysis results and subsequently benchmarked. …”
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Integrative analysis of bulk and single-cell transcriptomic data reveals novel insights into lipid metabolism and prognostic factors in hepatocellular carcinoma by Feiyu Qi, Guiming Zha, Yanfang Zhang, Sihua Liu, Yuhang Yang, Wanliang Sun, Dongdong Wang, Zhong Liu, Zheng Lu, Dengyong Zhang

“…The single-cell sequencing data was subjected to dimensionality reduction, which facilitated the annotation of distinct cell subpopulations and marker gene expression analysis within each subpopulation. Genes associated with lipid metabolism in liver cells were identified, and a machine-learning model was developed using the bulk transcriptomic data randomly partitioned into training and validation sets. …”
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Nutritional markers of undiagnosed type 2 diabetes in adults: Findings of a machine learning analysis with external validation and benchmarking. by Kushan De Silva, Siew Lim, Aya Mousa, Helena Teede, Andrew Forbes, Ryan T Demmer, Daniel Jönsson, Joanne Enticott

“…We tested three machine learning algorithms on original and resampled training datasets built using three resampling methods. …”
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Age- and Post-Based Complex Analyses of Heart Rate Variability in Young Male Handball Players for Potential Prevention of Overload-induced Injuries by Zita Petrovszki, Olivér Czimbalmos, Vera Gal, Gabor Korosi, Edit Nagy, Rita Mikulan, Gyongyi Horvath

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Exploration of shared pathogenic factors and causative genes in early-stage endometrial cancer and osteoarthritis by Yiyun Bai, Sang Luo, Ruzhen Shuai, Xiaomei Zhang, Liwei Yuan, Dan Liu

“…The EC prediction model based on these four genes demonstrated high performance (AUC = 0.974 for the training set; AUC = 0.966 for the validation set), and these genes were significantly associated with immune cell infiltration (P < 0.05). …”
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