A deep neural network framework for estimating coastal salinity from SMAP brightness temperature data by Yidi Wei, Qing Xu, Qing Xu, Xiaobin Yin, Xiaobin Yin, Yan Li, Yan Li, Kaiguo Fan

“…The framework leverages machine learning interpretability tools (Shapley Additive Explanations, SHAP) to optimize input feature selection and employs a grid search strategy for hyperparameter tuning.Results and discussionSystematic validation against independent in-situ measurements demonstrates that the baseline DNN model constructed for the entire region and time period outperforms conventional algorithms including K-Nearest Neighbors, Random Forest, and XGBoost and the standard SMAP SSS product, achieving a reduction of 36.0%, 33.4%, 40.1%, and 23.2%, respectively in root mean square error (RMSE). …”
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Current Signature-Based Bearing Fault Severity Classification Using a Robust Multilevel Cascaded Framework by Korawege N. C. Jayasena, Battur Batkhishig, Babak Nahid-Mobarakeh, Ali Emadi

“…These features are fed into an ANN-based level I algorithm using various fusion techniques, offering a more interpretable algorithmic framework. …”
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Federated Learning for privacy-Friendly Health Apps: A Case Study on Ovulation Tracking by Nikolaos Pavlidis, Andreas Sendros, Theodoros Tsiolakis, Periklis Kostamis, Christos Karasoulas, Eleni Briola, Christos Chrysanthos Nikolaidis, Vasilis Perifanis, George Drosatos, Eleftheria Katsiri, Despoina Elisavet Filippidou, Anastasios Manos, Pavlos S. Efraimidis

“…Unlike conventional centralized systems, FLORA ensures that sensitive information remains on users’ devices, with predictive algorithms powered by local computations. …”
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A novel smart baby cradle system utilizing IoT sensors and machine learning for optimized parental care by Kunal Chandnani, Suryakant Tripathy, Ashutosh Krishna Parbhakar, Kshitij Takiar, Urvi Singhal, P. Sasikumar, S. Maheswari

“…Microcontrollers like Raspberry Pi and NodeMCU use intelligent machine-learning algorithms to process the collected data and trigger adaptive responses. …”
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Unveiling tumor-infiltrating immune cell-driven immune-mediated drug resistance in clear cell renal cell carcinoma: prognostic insights and therapeutic strategies by Jifeng Yang, Yixuan Xing, Jiusong Luan, Wenbo Yang, Xin Zhang, Yanhua Tian, Haisong Zhang

“…Results The TIIC-based model demonstrated superior predictive performance for patient outcomes compared to 53 published models. …”
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REVOLUTIONIZING LUXURY: THE ROLE OF AI AND MACHINE LEARNING IN ENHANCING MARKETING STRATEGIES WITHIN THE TOURISM AND HOSPITALITY LUXURY SECTORS by Maria Nascimento CUNHA, Manuel PEREIRA, António CARDOSO, Jorge FIGUEIREDO, Isabel OLIVEIRA

“…AI and ML applications, such as chatbots for 24/7 customer service and predictive analytics for tailoring travel recommendations, have greatly improved customer interaction and operational efficiencies. …”
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Integrating Machine Learning and Geospatial Data for Mapping Socioeconomic Vulnerability to Urban Natural Hazard by Esaie Dufitimana, Paterne Gahungu, Ernest Uwayezu, Emmy Mugisha, Jean Pierre Bizimana

“…Among these, MLP achieved the best predictive performance, with an AUC score of 0.902 and an F1-score of 0.86. …”
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Iron Ore Information Extraction Based on CNN-LSTM Composite Deep Learning Model by Haili Chen, Mengxiang Xia, Yaping Zhang, Ruonan Zhao, Bingran Song, Yang Bai

“…In contrast, with the use of data-driven and sophisticated algorithms, modern hyperspectral technology can quickly deliver high-precision iron ore information, increasing efficiency. …”
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MAST Kinases’ Function and Regulation: Insights from Structural Modeling and Disease Mutations by Michael C. Lemke, Nithin R. Avala, Michael T. Rader, Stefan R. Hargett, Daniel S. Lank, Brandon D. Seltzer, Thurl E. Harris

“…We also estimate the functional consequences of disease point mutations on protein stability by integrating predictive algorithms and AlphaFold. <b>Results</b>: Higher-order organisms often have multiple MASTs and a single MASTL kinase. …”
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Methodological aspects of inventory management in logistics under modern conditions by A. G. Sakharov, A. E. Trubin, O. P. Kultygin, A. Yu. Anisimov

“…The article aims to design optimal inventory management algorithms in logistics in the current environment. …”
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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

“…From this, the derived 12 predictive models were validated on internal- and external validation cohorts. …”
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Towards a digital twin: Digitization and model-based optimization of the innovative high-gradient magnetic separatorMendeley Data by Marko Tesanovic, Torben Bardel, Robin Karl, Sonja Berensmeier

“…Furthermore, process efficiency is often not fully realized due to the reliance on fixed operational recipes.This study presents a digital twin framework for a pilot-scale HGMS system, integrating real-time monitoring, automated control, advanced mechanistic models, and multi-objective optimization using Bayesian algorithms. The framework was validated for robustness, scalable data handling, and predictive control. …”
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Exploring Feature Selection with Deep Learning for Kidney Tissue Microarray Classification Using Infrared Spectral Imaging by Zachary Caterer, Jordan Langlois, Connor McKeown, Mikayla Hady, Samuel Stumo, Suman Setty, Michael Walsh, Rahul Gomes

“…Through the integration of scalable deep learning models coupled with feature selection, we have developed a classification pipeline with high predictive power, which could be integrated into a high-throughput real-time IR imaging system. …”
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A new method for internal urinary metabolite exposure and dietary exposure association assessment of 3-MCPD and glycidol and their esters based on machine learning by Yimei Tian, Sunan Gao, Fan Zhang, Xuzhi Wan, Wei Jia, Jingjing Jiao, Yilei Fan, Yu Zhang

“…The seven machine learning models demonstrated strong predictive capabilities for internal urinary metabolite exposure and dietary exposure associations (average R > 0.6). …”
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Efficient diagnosis of diabetes mellitus using an improved ensemble method by Blessing Oluwatobi Olorunfemi, Adewale Opeoluwa Ogunde, Ahmad Almogren, Abidemi Emmanuel Adeniyi, Sunday Adeola Ajagbe, Salil Bharany, Ayman Altameem, Ateeq Ur Rehman, Asif Mehmood, Habib Hamam

“…The second phase employed the same algorithms alongside sequential ensemble methods—XG Boost, AdaBoostM1, and Gradient Boosting—using an average voting algorithm for binary classification. …”
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A Novel Dataset for Early Cardiovascular Risk Detection in School Children Using Machine Learning by Rafael Alejandro Olivera Solís, Emilio Francisco González Rodríguez, Roberto Castañeda Sheissa, Juan Valentín Lorenzo-Ginori, José García

“…We conducted a rigorous performance evaluation of 10 machine learning (ML) algorithms to classify cardiovascular risk into two categories: at risk and not at risk. …”
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A Systematic Literature Review of Concept Drift Mitigation in Time-Series Applications by Mujaheed Abdullahi, Hitham Alhussian, Norshakirah Aziz, Said Jadid Abdulkadir, Yahia Baashar, Abdussalam Ahmed Alashhab, Afroza Afrin

“…Machine Learning (ML) plays a key role in time-series applications because it analyzes observed data and predicts future values. The effectiveness of ML models in time-series forecasting is reduced by the occurrence of Concept Drift (CD). …”
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Amismart an advanced metering infrastructure for power consumption monitoring and forecasting in smart buildings by Sarah Hadri, Mehdi Najib, Mohamed Bakhouya, Youssef Fakhri, Mohamed El aroussi, Zaradatcht Taifour, Jaafar Gaber

“…A single Machine learning algorithm using Long Short-Term Memory (LSTM) and hybrid Machine learning algorithms (CNN-LSTM), and ensembles machine learning approaches including eXtreme Gradient Boosting Machine (XGBoost) and Random Forest (RF). …”
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iMESc – an interactive machine learning app for environmental sciences by Danilo Cândido Vieira, Danilo Cândido Vieira, Fabiana S. Paula, Luciana Erika Yaginuma, Gustavo Fonseca

“…Finally, a hybrid model combining an unsupervised SOM and followed by the supervised Random Forest model returned an accuracy of 83.47% for the training and 80.77% for the test, with Bathymetry, Chlorophyll, and Coarse Sand as key predictive variables. IMESc permits the customization of plots and saving the workflows into “savepoints” guarantying reproducibility. iMESc bridges the gap between the complexity of machine learning algorithms and the need for user-friendly interfaces in environmental research. …”
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TPE-LCE-SHAP: A Hybrid Framework for Assessing Vehicle-Related PM2.5 Concentrations by Hamad Almujibah, Abdulrazak H. Almaliki, Caroline Mongina Matara, Adil Abdallah Mohammed Elhassan, Khalaf Alla Adam Mohamed, Mudthir Bakri, Afaq Khattak

“…This hybrid framework delivers robust predictive accuracy and actionable insights, making it a valuable tool for effective environmental management and policy making.…”
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