Triage-HF Validation in Heart Failure Clinical Practice: Importance of Episode Duration by Daniel García Iglesias, David Ledesma Oloriz, Diego Pérez Diez, David Calvo Cuervo, Rut Álvarez Velasco, Alejandro Junco-Vicente, José Manuel Rubín López

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Prospects for predicting and preventing the heart failure deterioration: an analytical review by V. N. Larina, I. K. Skiba

“…An integrated approach using scales, algorithms and relevant therapy strategies can significantly improve treatment outcomes and quality of life in patients with HF.…”
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Prediction and Impact Analysis of Soil Nitrogen and Salinity Under Reclaimed Water Irrigation: A Case Study by Zeyu Liu, Kai Fang, Xiaoqin Sun, Yandong Wang, Zhuo Tian, Jing Liu, Liying Bai, Qilin He

“…The models achieved high predictive accuracy, with NSE values of 0.918, 0.946, 0.936, 0.967, and 0.887 for NO₃⁻-N, NH₄⁺-N, TN, EC, and Cl⁻, respectively, demonstrating their robustness. …”
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Predictive Study on the Cutting Energy Efficiency of Dredgers Based on Specific Cutting Energy by Junlang Yuan, Ke Yang, Taiwei Yang, Haoran Xu, Ting Xiong, Shidong Fan

“…First, eigenvalue screening is carried out based on the dredging knowledge and mechanism, then outliers are removed, and finally data processing is performed using Spearman correlation coefficient and PCA dimensionality reduction techniques. Subsequently, five machine learning algorithms, such as RF and XGBoost, are used in combination with a grid search to find the optimal hyperparameters, and Lasso is used as the meta-learner to integrate the prediction results. …”
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Stacked ensemble model for NBA game outcome prediction analysis by Guangsen He, Hyun Soo Choi

“…Abstract This research presents a stacked ensemble approach that employs artificial intelligence (AI) techniques to predict the outcomes of NBA games. Several machine learning algorithms were utilized, including Naïve Bayes, AdaBoost, Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), XGBoost, Decision Tree, and Logistic Regression. …”
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Data-Driven Digital Twin Framework for Predictive Maintenance of Smart Manufacturing Systems by Tarana Khan, Urfi Khan, Adnan Khan, Calahan Mollan, Inga Morkvenaite-Vilkonciene, Vijitashwa Pandey

“…Various machine learning (ML) algorithms exist for analysis and prediction that can be used in this scenario. …”
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Development and Validation of an Interpretable Machine Learning Model for Prediction of the Risk of Clinically Ineffective Reperfusion in Patients Following Thrombectomy for Ischem... by Hu X, Qi D, Li S, Ye S, Chen Y, Cao W, Du M, Zheng T, Li P, Fang Y

“…Our CIR risk prediction platform enables early intervention and personalized treatment. …”
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Student Dropout Prediction Using Random Forest and XGBoost Method by Lalu Ganda Rady Putra, Didik Dwi Prasetya, Mayadi Mayadi

“…Objective: This study aims to evaluate the effectiveness of the Random Forest and XGBoost algorithms in predicting student attrition based on demographic, socioeconomic, and academic performance factors. …”
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MODEL PREDICTIVE CONTROL FOR PHOTOVOLTAIC STATION MAXIMUM POWER POINT TRACKING SYSTEM by I. Elzein, Yu. N. Petrenko

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Predicting cardiotoxicity in drug development: A deep learning approach by Kaifeng Liu, Huizi Cui, Xiangyu Yu, Wannan Li, Weiwei Han

“…We used four types of molecular fingerprints and descriptors combined with machine learning and deep learning algorithms, including Gaussian naive Bayes (NB), random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), eXtreme gradient boosting (XGBoost), and Transformer models, to build predictive models. …”
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Machine learning techniques for predictive modelling in geotechnical engineering: a succinct review by Shrikant M. Harle, Rajan L. Wankhade

“…Key areas of focus include the prediction of foundation settlement, where various ML algorithms—such as regression models, hybrid approaches, and numerical analysis techniques—are emphasized for their contributions to real-time monitoring and risk management. …”
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Predictive Analysis of Carbon Emissions in China’s Construction Industry Based on GIOWA Model by Tianyue Hu, Zhiheng Bao, Baiyang Zhang, Xinnan Gao

“…A case study is conducted based on historical data (1997–2021) from the construction industry, and the research findings indicate that: (1) the GIOWA combination forecasting model effectively integrates the algorithmic strengths of SVR and LSTM, achieving an average prediction accuracy of 98.16%, which signifies a remarkable improvement over both individual models; (2) the carbon emissions in China’s construction industry will maintain a downward trend during the period 2022–2030, although the decline rate is expected to decrease gradually; (3) by 2030, a reduction of nearly 35% in carbon emissions is anticipated relative to the historical peak. …”
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Predicting the Tensile Strength of Plant Leaves Based on GA-SVM by Wei Chang, Meihong Liu, Yayu Huang, Junjie Lei, Kai Wu

“…A genetic algorithm (GA) is then applied to optimize the structural parameters of the support vector machine (SVM), establishing a GA-SVM-based predictive model for the tensile strength of plant leaves. …”
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Prediction of Airline Ticket Price Using Machine Learning Method by Hüseyin Korkmaz

“…This paper aims to predict ticket prices based on airline flight data using ML algorithms and to compare the performance of ML algorithms. …”
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Protein structure prediction via deep learning: an in-depth review by Yajie Meng, Zhuang Zhang, Chang Zhou, Xianfang Tang, Xinrong Hu, Geng Tian, Jialiang Yang, Yuhua Yao, Yuhua Yao, Yuhua Yao

“…The application of deep learning algorithms in protein structure prediction has greatly influenced drug discovery and development. …”
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Enhancing Cloud Security: A Multi-Factor Authentication and Adaptive Cryptography Approach Using Machine Learning Techniques by K. Sasikumar, Sivakumar Nagarajan

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Evaluating the Performance and Potential Bias of Predictive Models for Detection of Transthyretin Cardiac Amyloidosis by Jonathan Hourmozdi, MD, MSAI, MA, Nicholas Easton, MSAI, Simon Benigeri, MSAI, James D. Thomas, MD, Akhil Narang, MD, David Ouyang, MD, Grant Duffy, BS, Ross Upton, PhD, Will Hawkes, PhD, Ashley Akerman, PhD, Ike Okwuosa, MD, Adrienne Kline, MD, PhD, Abel N. Kho, MD, Yuan Luo, PhD, Sanjiv J. Shah, MD, Faraz S. Ahmad, MD, MS

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Machine learning-based prediction of short- and long-term mortality for shared decision-making in older hip fracture patients: the Dutch Hip Fracture Audit algorithms in 74,396 cases by Hidde Dijkstra, Cathleen S Parsons, Hanne-Eva VAN Bremen, Hanna C Willems, Anne A H De Hond, Barbara C van Munster, Job N Doornberg, Jacobien H F Oosterhoff

“…Conclusion: We developed and validated ML algorithms, including logistic regression, for mortality prediction in older hip fracture patients with adequate performance. …”
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Interpretable machine learning for predicting isolated basal septal hypertrophy. by Lei Gao, Boyan Tian, Qiqi Jia, Xingyu He, Guannan Zhao, Yueheng Wang

“…However, no predictive models for BSH have been developed using machine learning algorithms.…”
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International Chinese Education Expert System Based on Artificial Intelligence and Machine Learning Algorithms by Lin Shen, Faiza Latif

“…In addition, this study constructs an intelligent system based on the improved algorithm. The research shows that the international Chinese education expert system based on artificial intelligence and machine learning algorithm proposed in this study has a very good effect.…”
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