A validated multivariable machine learning model to predict cardio-kidney risk in diabetic kidney disease by James L. Jr. Januzzi, Naveed Sattar, Muthiah Vaduganathan, Craig A. Magaret, Rhonda F. Rhyne, Yuxi Liu, Serge Masson, Javed Butler, Michael K. Hansen

“…Using datafrom the CREDENCE trial of patients with type 2 diabetes and DKD,machine learning techniques were applied to create a highly accuratealgorithm to predict progressive DKD and adverse CV outcomes. …”
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Fermentation modeling and machine learning for flavor prediction in low-sodium radish paocai with potassium chloride substitution by Yaxin Li, Yunjing Gu, Weiye Cheng, Zifan Li, Xiru Zhang, Yaran Zhao, Kanghee Ko, Wenli Liu, Xiaoping Liu, Huamin Li

“…The methodology integrated microbial growth modeling with comprehensive flavor analysis (HS-SPME-GC-MS, HS-GC-IMS, E-tongue) and Random Forest (RF) machine learning. …”
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Predicting the risk of postoperative avascular necrosis in patients with talar fractures based on an interpretable machine learning model by Jian Zhang, Jian Zhang, Jian Zhang, Jihai Xu, Jihai Xu, Jiapei Yu, Jiapei Yu, Jiapei Yu, Hong Chen, Hong Chen, Xin Hong, Songou Zhang, Xin Wang, Xin Wang, Chengchun Shen, Chengchun Shen, Chengchun Shen

“…Potential risk factors for postoperative AVN were screened using univariate and multivariate logistic regression analyses. Six machine learning algorithms were employed to construct the prediction models. …”
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Machine learning model for reproducing subjective sensations and alleviating sound-induced stress in individuals with developmental disorders by Itsuki Ichikawa, Yukie Nagai, Yukie Nagai, Yasuo Kuniyoshi, Yasuo Kuniyoshi, Yasuo Kuniyoshi, Makoto Wada

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Timeseries Fault Classification in Power Transmission Lines by Non-Intrusive Feature Extraction and Selection Using Supervised Machine Learning by Rab Nawaz, Hani A. Albalawi, Syed Basit Ali Bukhari, Khawaja Khalid Mehmood, Muhammad Sajid

“…This paper presents a supervised machine learning approach using eight popular classifiers for fault classification in power transmission lines. …”
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Adaptable Reduced-Complexity Approach Based on State Vector Machine for Identification of Criminal Activists on Social Media by Imran Shafi, Sadia Din, Zahid Hussain, Imran Ashraf, Gyu Sang Choi

“…Additionally, change in criminal content require the learning models to identify altered malicious textual contents which poses extra challenge. …”
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Enhanced anomaly network intrusion detection using an improved snow ablation optimizer with dimensionality reduction and hybrid deep learning model by Fatimah Alhayan, Asma Alshuhail, Ahmed Omer Ahmed Ismail, Othman Alrusaini, Sultan Alahmari, Abdulsamad Ebrahim Yahya, Sami Saad Albouq, Mutasim Al Sadig

“…Machine learning (ML) and deep learning (DL) models are currently leveraged for anomaly intrusion detection in cybersecurity. …”
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Comparative analysis of machine learning models for predicting river water quality: a case study of the Zayandeh Rood River by Elham Fazel Najafabadi, Paria Shojaei, Mojgan Askarizadeh

“…This study evaluated five machine learning models, i.e., Lasso Regression, Random Forest (RF), Gradient Boosting (GB), XGBoost, and Support Vector Machine (SVM) for predicting four water quality parameters—EC (Electrical Conductivity), TDS (Total Dissolved Solids), Sodium Adsorption Ratio (SAR), and TH (Total Hardness)—using data collected over a 31-year period from eight monitoring stations along the Zayandeh Rood River, a vital water source for drinking, agriculture, and industry in the arid region of central Iran. …”
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Big data-driven corporate financial forecasting and decision support: a study of CNN-LSTM machine learning models by Aixiang Yang

“…With the rapid advancement of information technology, particularly the widespread adoption of big data and machine learning, corporate financial management is undergoing unprecedented transformation. …”
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Operational Performance Assessment of PV-Powered Street Lighting: A Comparative Study of Different Machine Learning Prediction Models by Safwan Nadweh, Nabil Mohammed, Charalambos Konstantinou, Shehab Ahmed

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Implementing partial least squares and machine learning regressive models for prediction of drug release in targeted drug delivery application by Anupam Yadav, B. Jayaprakash, Laith Hussein Jasim, Mayank Kundlas, Maan Younis Anad, Ankur Srivastava, M. Janaki Ramudu, B. Bharathi, Prabhat Kumar Sahu

“…Abstract A combined methodology was performed based on chemometrics and machine learning regressive models in estimation of polysaccharide-coated colonic drug delivery. …”
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CECT-Based Radiomic Nomogram of Different Machine Learning Models for Differentiating Malignant and Benign Solid-Containing Renal Masses by Qian L, Fu B, He H, Liu S, Lu R

“…Radiomic features were extracted from the arterial, venous and delayed phases and further analysed by dimensionality reduction and selection. Four mainstream machine learning algorithm training models, namely, support vector machine (SVM), k-nearest neighbour (kNN), light gradient boosting (LightGBM) and logistic regression (LR), were constructed to determine the best classifier model. …”
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Machine learning in Alzheimer’s disease genetics by Matthew Bracher-Smith, Federico Melograna, Brittany Ulm, Céline Bellenguez, Benjamin Grenier-Boley, Diane Duroux, Alejo J. Nevado, Peter Holmans, Betty M. Tijms, Marc Hulsman, Itziar de Rojas, Rafael Campos-Martin, Sven van der Lee, Atahualpa Castillo, Fahri Küçükali, Oliver Peters, Anja Schneider, Martin Dichgans, Dan Rujescu, Norbert Scherbaum, Jürgen Deckert, Steffi Riedel-Heller, Lucrezia Hausner, Laura Molina-Porcel, Emrah Düzel, Timo Grimmer, Jens Wiltfang, Stefanie Heilmann-Heimbach, Susanne Moebus, Thomas Tegos, Nikolaos Scarmeas, Oriol Dols-Icardo, Fermin Moreno, Jordi Pérez-Tur, María J. Bullido, Pau Pastor, Raquel Sánchez-Valle, Victoria Álvarez, Mercè Boada, Pablo García-González, Raquel Puerta, Pablo Mir, Luis M. Real, Gerard Piñol-Ripoll, Jose María García-Alberca, Eloy Rodriguez-Rodriguez, Hilkka Soininen, Sami Heikkinen, Alexandre de Mendonça, Shima Mehrabian, Latchezar Traykov, Jakub Hort, Martin Vyhnalek, Nicolai Sandau, Jesper Qvist Thomassen, Yolande A. L. Pijnenburg, Henne Holstege, John van Swieten, Inez Ramakers, Frans Verhey, Philip Scheltens, Caroline Graff, Goran Papenberg, Vilmantas Giedraitis, Julie Williams, Philippe Amouyel, Anne Boland, Jean-François Deleuze, Gael Nicolas, Carole Dufouil, Florence Pasquier, Olivier Hanon, Stéphanie Debette, Edna Grünblatt, Julius Popp, Roberta Ghidoni, Daniela Galimberti, Beatrice Arosio, Patrizia Mecocci, Vincenzo Solfrizzi, Lucilla Parnetti, Alessio Squassina, Lucio Tremolizzo, Barbara Borroni, Michael Wagner, Benedetta Nacmias, Marco Spallazzi, Davide Seripa, Innocenzo Rainero, Antonio Daniele, Fabrizio Piras, Carlo Masullo, Giacomina Rossi, Frank Jessen, Patrick Kehoe, Tsolaki Magda, Pascual Sánchez-Juan, Kristel Sleegers, Martin Ingelsson, Mikko Hiltunen, Rebecca Sims, Wiesje van der Flier, Ole A. Andreassen, Agustín Ruiz, Alfredo Ramirez, EADB, Ruth Frikke-Schmidt, Najaf Amin, Gennady Roshchupkin, Jean-Charles Lambert, Kristel Van Steen, Cornelia van Duijn, Valentina Escott-Price

“…We utilised Gradient Boosting Machines (GBMs), biological pathway-informed Neural Networks (NNs), and Model-based Multifactor Dimensionality Reduction (MB-MDR) models. …”
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Integrating Machine Learning and Multi-Objective Optimization in Biofuel Systems: A Review by Ivan P. Malashin, Dmitry A. Martysyuk, Vadim S. Tynchenko, Andrei P. Gantimurov, Vladimir A. Nelyub, Aleksei S. Borodulin

“…The optimization of biofuel production involves balancing multiple conflicting objectives such as yield maximization, cost minimization, and environmental impact reduction. Recent studies have explored various multi-objective optimization (MOO) techniques integrated with machine learning (ML) models to enhance process efficiency. …”
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Machine-Learning-Driven Approaches for Assessment, Delegation, and Optimization of Multi-Floor Building by Abtin Baghdadi, Harald Kloft

“…This study presents a novel integrated framework for the structural analysis and optimization of multi-floor buildings by combining validated theoretical models with machine learning and evolutionary algorithms. …”
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Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses by Hossein Gholizadeh, T. Prabhakar Clement, Christopher T. Green, Geoffrey R. Tick, Alain M. Plattner, Yong Zhang

“…Results revealed that a 50% increase in groundwater withdrawals caused seawater to advance ~ 320 m inland, whereas a 50% reduction led to a ~ 270-meter retreat. This study highlights the vulnerability of Alabama’s shallow coastal aquifers to seawater intrusion due to storm surges and human activities, and demonstrates that combining physics-based models with machine learning approaches can improve groundwater predictions, though its accuracy depends on the availability of site-specific data.…”
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Elucidating Early Radiation-Induced Cardiotoxicity Markers in Preclinical Genetic Models Through Advanced Machine Learning and Cardiac MRI by Dayeong An, El-Sayed Ibrahim

“…This study aimed to detect early markers of RIHD using machine learning (ML) techniques and cardiac MRI in a rat model. …”
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Dynamic Aggregation and Augmentation for Low-Resource Machine Translation Using Federated Fine-Tuning of Pretrained Transformer Models by Emmanuel Agyei, Xiaoling Zhang, Ama Bonuah Quaye, Victor Adeyi Odeh, Joseph Roger Arhin

“…The suggested method shows notable benefits, according to experimental results. The fine-tuned model achieves a remarkable increase in SPBLEU from 2.16% to 71.30%, a rise in ROUGE-1 from 15.23% to 65.24%, and a notable reduction in WER from 183.16% to 68.32%. …”
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Mitigating Climate Risk to China's Renewable Energy Generation by Power Plants Redeployment Using a Machine‐Learning Model by Lin Lv, Miaomiao Liu, Yuli Shan, Jinghang Xu, Jianxun Yang, Wen Fang, Zongwei Ma, Jun Bi

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Investigation of Micro-Scale Damage and Weakening Mechanisms in Rocks Induced by Microwave Radiation and Their Associated Strength Reduction Patterns: Employing Meta-Heuristic Opti... by Zhongyuan Gu, Xin Xiong, Chengye Yang, Miaocong Cao

“…This model was benchmarked against other prevalent machine learning frameworks, with Shapley additive explanatory methods employed to assess each parameter’s influence on UCSA. …”
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