The Improved-EFI Score: A Multi-Omics-Based Novel Efficacy Predictive Tool for Predicting the Natural Fertility of Endometriosis Patients by He Q, Zhang C, Hu Y, Deng J, Zhang S

“…An improved endometriosis fertility index (EFI) predictive model was created based on ultrasound radiomics and urinary proteomics gathered during the patient’s initial admission, using two machine learning algorithms. …”
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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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Artificial intelligence assisted risk prediction in organ transplantation: a UK Live-Donor Kidney Transplant Outcome Prediction tool by Hatem Ali, Arun Shroff, Tibor Fülöp, Miklos Z. Molnar, Adnan Sharif, Bernard Burke, Sunil Shroff, David Briggs, Nithya Krishnan

“…We set out to apply artificial intelligence (AI) algorithms to create a highly predictive risk stratification indicator, applicable to the UK’s transplant selection process.Methodology: Pre-transplant characteristics from 12,661 live-donor kidney transplants (performed between 2007 and 2022) from the United Kingdom Transplant Registry database were analyzed. …”
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Research on kd-tree Cache Optimization Based on Particle Index Sorting Algorithm by 张挺, 林震寰, 杨丁颖, 王宗锴, 陈轶凡

“…Future work might investigate the integration of MAD-index-sort with other advanced cache management techniques, such as predictive caching algorithms or hybrid data structures, to further elevate performance. …”
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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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Predictive performance of risk prediction models for lung cancer incidence in Western and Asian countries: a systematic review and meta-analysis by Yah Ru Juang, Lina Ang, Wei Jie Seow

“…Abstract Numerous prediction models have been developed to identify high-risk individuals for lung cancer screening, with the aim of improving early detection and survival rates. …”
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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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Development and Analysis of a Methodology for Selecting Infrastructure Metrics for Predictive Incident Monitoring by Andrew Egorkin

“…This paper proposes a formalized two-stage metric selection procedure designed to improve the accuracy and efficiency of predictive monitoring: (1) a multicriteria correlation filter using Pearson coefficients (|r| > 0.60), Kendall’s τ (> 0.50), and Maximal Information Coefficient (MICe > 0.35) to eliminate redundant and non-linearly related features; (2) verification of causal relationships using the Granger test (lag = 5, p < 0.01), the PCMCI algorithm (FDR = 10%), and the Directed Information metric (DI > 0.1 bits/step) to identify true drivers of the target metric. …”
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Analysis of Traffic Conflicts at Roundabout Entrances and Exits – A Machine Learning Approach for Enhanced Safety by Yuzhou DUAN, Zhipeng LIN, Yulong WANG, Qiaowen BAI

“…It achieved high accuracy in predicting traffic conflict areas at the entrances and exits of a roundabout, with a prediction accuracy of 0.86 and an AUC (area under the receiver operating characteristic curve) of 0.88. …”
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Predictive Control for Earthquake Response Mitigation of Buildings Using Semiactive Fluid Dampers by F. Oliveira, P. Morais, A. Suleman

“…A predictive control strategy in conjunction with semiactive control algorithms is proposed for damping control of base-isolated structures employing semiactive fluid dampers when subjected to earthquake loads. …”
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Data-Driven Optimization for Low-Carbon Prefabricated Components Production Based on Ant Colony Algorithms by Chun-Ling Ho, Chien-Chih Wang, Shenjun Qi, Zichen Zhang

“…Utilizing ant colony algorithms, the model aims to minimize costs while adhering to low-carbon principles and fostering the sustainable development of prefabricated buildings. …”
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Studying the impact of the Green Deal on the EU economy using Gradient Boosting Algorithm by Frahi Fadila

“…  This study aims to assess the impact of the European Green Deal on the EU economy by employing a Gradient Boosting Algorithm to analyze the influence of various environmental policies. …”
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State-of-Charge Estimation of Medium- and High-Voltage Batteries Using LSTM Neural Networks Optimized with Genetic Algorithms by Romel Carrera, Leonidas Quiroz, Cesar Guevara, Patricia Acosta-Vargas

“…This study presents a hybrid method for state-of-charge (SOC) estimation of lithium-ion batteries using LSTM neural networks optimized with genetic algorithms (GA), combined with Coulomb Counting (CC) as an initial estimator. …”
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The ZTF Source Classification Project. III. A Catalog of Variable Sources by Brian F. Healy, Michael W. Coughlin, Ashish A. Mahabal, Theophile Jegou du Laz, Andrew Drake, Matthew J. Graham, Lynne A. Hillenbrand, Jan van Roestel, Paula Szkody, LeighAnna Zielske, Mohammed Guiga, Muhammad Yusuf Hassan, Jill L. Hughes, Guy Nir, Saagar Parikh, Sungmin Park, Palak Purohit, Umaa Rebbapragada, Draco Reed, Daniel Warshofsky, Avery Wold, Joshua S. Bloom, Frank J. Masci, Reed Riddle, Roger Smith

“…We find that several classifiers achieve high precision and recall scores, suggesting the reliability of their predictions for 209,991,147 light curves across 77 ZTF fields. …”
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Implementation of Machine Learning in Flat Die Extrusion of Polymers by Nickolas D. Polychronopoulos, Ioannis Sarris, John Vlachopoulos

“…The ML models were trained to predict thickness variations, pressure drops, and the lowest wall shear rate (targets). …”
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Combination ATR-FTIR with Multiple Classification Algorithms for Authentication of the Four Medicinal Plants from <i>Curcuma</i> L. in Rhizomes and Tuberous Roots by Qiuyi Wen, Wenlong Wei, Yun Li, Dan Chen, Jianqing Zhang, Zhenwei Li, De-an Guo

“…The results showed that support vector machine (SVM) modeling was superior to other models and the accuracy of validation and prediction was preferable, with a modeling time of 127.76 s. …”
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Investigating the performance of random oversampling and genetic algorithm integration in meteorological drought forecasting with machine learning by Tahsin Baykal, Özlem Terzi, Gülsün Yıldırım, Emine Dilek Taylan

“…Abstract Drought is one of the most significant natural hazards worldwide, affecting water resources, agriculture, and socio-economic stability. Accurate drought prediction is essential for sustainable water management. …”
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A New Self-Tuning Nonlinear Model Predictive Controller for Autonomous Vehicles by Yasin Abdolahi, Sajad Yousefi, Jafar Tavoosi

“…The model predictive controller (MPC) is one of the efficient approaches by which the speed and direction of the near future of an automobile could be predicted and controlled. …”
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Systematic review railway infrastructure monitoring: From classic techniques to predictive maintenance by Giovanni Bianchi, Chiara Fanelli, Francesco Freddi, Felice Giuliani, Aldo La Placa

“…Recent Artificial Intelligence (AI) algorithms, which enable the use of digital tools such as Data-Driven models that can automatically adapt system operation, make decisions and suggest strategies based on collected data, form the basis of modern Predictive Maintenance (PdM). …”
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Parametric Optimization Approach to Evaluate Dynamic Shading Within Double-Skin Insulated Glazed Units for Multi-Criteria Daylighting Performance in Tropics by Ali Ahmed Bahdad, Nooriati Taib, Fahad Saud Allahaim, Ali Mohammed Ajlan

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