Spatio-temporal variability of San Francisco Bay Plume from space by Piero L. F. Mazzini, Cassia Pianca, Cassia Pianca, L. Fernando Pareja-Roman, Kelly L. Cole, Ryan K. Walter, Renato M. Castelao, Elias J. Hunter, Robert J. Chant

“…West Coast, they form the San Francisco Bay Plume (SFBP), which spreads offshore and influences the Gulf of the Farallones (GoF), an ecologically significant region in the California Current System that is also home to three National Marine Sanctuaries. This paper provides the first observationally based investigation of the spatio-temporal variability of the SFBP, using a plume tracking algorithm applied to more than two decades (2002-2023) of ocean color data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard satellites Aqua and Terra. …”
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A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein by Bahareh Behkamal, Fatemeh Asgharian Rezae, Amin Mansoori, Rana Kolahi Ahari, Sobhan Mahmoudi Shamsabad, Mohammad Reza Esmaeilian, Gordon Ferns, Mohammad Reza Saberi, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…The framework integrated diverse ML algorithms, including Linear Regression (LR), Decision Trees (DT), Support Vector Machine (SVM), Random Forest (RF), Balanced Bagging (BG), Gradient Boosting (GB), and Convolutional Neural Networks (CNNs). …”
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Integrating CEUS Imaging Features and LI-RADS Classification for Postoperative Early Recurrence Prediction in Solitary Hepatocellular Carcinoma: A Machine Learning-Based Prognostic... by Liang L, Pang J, Zhang B, Que Q, Gao R, Wu Y, Peng J, Zhang W, Bai X, Wen R, He Y, Yang H

“…Patients were randomly assigned to training (n = 196) and validation (n = 83) cohorts in a 7:3 ratio. Feature selection was performed using univariate Cox regression (p ≤ 0.05), and four ML algorithms—Random Survival Forest (RSF), Gradient Boosting Machine (GBM), CoxBoost, and XGBoost—were applied to develop recurrence prediction models. …”
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Toward Closing the Loop in Image-to-Image Conversion in Radiotherapy: A Quality Control Tool to Predict Synthetic Computed Tomography Hounsfield Unit Accuracy by Paolo Zaffino, Ciro Benito Raggio, Adrian Thummerer, Gabriel Guterres Marmitt, Johannes Albertus Langendijk, Anna Procopio, Carlo Cosentino, Joao Seco, Antje Christin Knopf, Stefan Both, Maria Francesca Spadea

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Neural Networks Method in modeling of the financial company’s performance by I. P. Kurochkina, I. I. Kalinin, L. A. Mamatova, E. B. Shuvalova

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A novel nomogram for survival prediction in renal cell carcinoma patients with brain metastases: an analysis of the SEER database by Fei Wang, Xihao Wang, Zhigang Feng, Jun Li, Hailiang Xu, Hengming Lu, Lianqu Wang, Zhihui Li

“…In addition, the SHAP values indicated that surgical treatment was the most important prognostic risk factor for OS at 6-months, 1-year, 2-years, and 3-years. After further balancing the baseline characteristics between the surgical and non-surgical groups using PSM, we observed that patients with BM who underwent surgical intervention showed significantly better survival outcomes across all subgroups compared to non-surgical patients, though unmeasured confounders may contribute to this association.ConclusionWe developed a novel nomogram for predicting prognostic factors in RCC patients with BM, offering a valuable tool to support accurate clinical decision-making. …”
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Predicting Pineapple Quality from Hyperspectral Data of Plant Parts Applied to Machine Learning by Vitória Carolina Dantas Alves, Sebastião Ferreira de Lima, Dthenifer Cordeiro Santana, Rafael Ferreira Barreto, Roger Augusto da Cunha, Ana Carina da Silva Cândido Seron, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo Teodoro, Rita de Cássia Félix Alvarez, Cid Naudi Silva Campos, Carlos Antonio da Silva Junior, Fábio Luíz Checchio Mingotte

“…The aim of this study was to verify accurate ML models for predicting pineapple fruit quality and the best inputs for algorithms: Artificial Neural Networks (ANNs), M5P (model tree), REPTree decision trees, Random Forest (RF), Support Vector Machine (SMV) and Zero R. …”
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Application of machine learning in predicting fruit waste in a South African fresh produce wholesale market by Ikechukwu Kingsley Opara, Douglas Chinenye Divine, Yardjouma Silue, Umezuruike Linus Opara, Jude A. Okolie, Olaniyi Amos Fawole

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Exploring Artificial Intelligence for Enhanced Endodontic Practice: Applications, Challenges, and Future Directions by Santosh R. Patil, Mohmed Isaqali Karobari

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Real-world evidence market: key players and key segments by A. R. Kasimova, A. S. Kolbin

“…They may be necessary for administrators (to create and improve algorithms for treating patients with various nosologies), physicians (to make a better clinical decision in favor of the patient) and directly to patients and their relatives (to better understand the treatment process). …”
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Leveraging Spectral Neighborhood Information for Corn Yield Prediction with Spatial-Lagged Machine Learning Modeling: Can Neighborhood Information Outperform Vegetation Indices? by Efrain Noa-Yarasca, Javier M. Osorio Leyton, Chad B. Hajda, Kabindra Adhikari, Douglas R. Smith

“…Set 4 showed slight gains over Sets 2 and 3, with XGB and RF achieving the highest R² values. …”
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Accurate estimation of permeability reduction resulted from low salinity water flooding in clay-rich sandstones by Xiaojuan Zhang, Muntadher Abed Hussein, Tarak Vora, Anupam Yadav, Asha Rajiv, Aman Shankhyan, Sachin Jaidka, Mehul Manu, Farzona Alimova, Issa Mohammed Kadhim, Zainab Jamal Hamoodah, Fadhil Faez, Ahmad Khalid

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Integrated artificial intelligence approach for well-log fluid identification in dual-medium tight sandstone gas reservoirs by Wurong Wang, Wurong Wang, Linbo Qu, Linbo Qu, Dali Yue, Dali Yue, Wei Li, Wei Li, Junlong Liu, Wujun Jin, Jialin Fu, Jialin Fu, Jiarui Zhang, Jiarui Zhang, Dongxia Chen, Dongxia Chen, Qiaochu Wang, Qiaochu Wang, Sha Li, Sha Li

“…To address the limitations of conventional machine learning algorithms, which have low accuracy due to data inhomogeneity and weak fluid logging responses, this study introduces a novel method for fluid logging evaluation in dual-medium tight sandstone gas reservoirs.MethodsThe method integrates core, thin section, and scanning electron microscope observations, taking into account the effect of fractures.ResultsReservoirs are divided into three types: fractured reservoirs (FR), porous reservoirs (PR), and microfracture-pore composite reservoirs (MPCR), highlighting the distinct fluid logging responses of each type. …”
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The Impact of AI Software on Financial Transactions by Li Boyang

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Defining Heritage Science: A Consilience Pathway to Treasuring the Complexity of Inheritable Human Experiences through Historical Method, AI, and ML by Andrea Nanetti

“…Artificial intelligence and machine learning algorithms can assist the next generation of historians, heritage stakeholders, and decision-makers in (1) decoding unstructured knowledge and wisdom embedded in selected cultural artefacts and social rituals, (2) encoding data in machine-readable systems, (3) aggregating information according to the user’s needs in real time, and (4) simulating the consequences of either erasing, neglecting, putting in latency, or preserving and sharing specific human experiences. …”
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Statistical models for urban growth forecasting: With application to the Baltimore–Washington area by Carlo Grillenzoni

“…The corresponding datasets are in the form of big 3D arrays and require fast algorithms of parameter estimation and forecasting. …”
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An interpretable machine learning model for predicting early liver metastasis after pancreatic cancer surgery by Hao Zhu, Yiyan Zhou, Danyang Shen, Kejia Wu, Xiaojie Gan, Xiaofeng Xue, Weigang Zhang, Xiaohua Yang, Junyi Qiu, Ding Sun

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Artificial Intelligence for Predicting Delivery Modes: A Systematic Review of Applications, Challenges, and Future Directions by Yu Zhou, Jing Li, Xiaomei Hou, Zhen Li, Yanan Xu, Yan Wang, Mingze Sun, Fumin Zheng, Enhui Guo, Jun Zhou

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Prognostic value of multi-PLD ASL radiomics in acute ischemic stroke by Zhenyu Wang, Yuan Shen, Xianxian Zhang, Qingqing Li, Congsong Dong, Shu Wang, Haihua Sun, Mingzhu Chen, Xiaolu Xu, Pinglei Pan, Pinglei Pan, Zhenyu Dai, Fei Chen, Fei Chen

“…Features were selected using least absolute shrinkage and selection operator regression, and three models were developed: a clinical model, a CBF radiomics model, and a combined model, employing eight ML algorithms. …”
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Decentralized multi-agent federated and reinforcement learning for smart water management and disaster response by H. Mancy, Naglaa E. Ghannam, Amr Abozeid, Ahmed I. Taloba

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