Ensemble machine learning algorithm for anti-VEGF treatment efficacy prediction in diabetic macular edema by Yu Fang, Jianwei Lin, Peiwen Xie, Huishan Zhu, Tsz Kin Ng, Guihua Zhang

“…This study aims to integrate 3D-OCT features and clinical variables to develop machine learning (ML) models for predicting anti-VEGF treatment outcomes. …”
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Predicting Flight Delays with Machine Learning: A Case Study from Saudi Arabian Airlines by Meshal Alfarhood, Rakan Alotaibi, Bassam Abdulrahim, Ahmad Einieh, Mohammed Almousa, Abdulrhman Alkhanifer

“…To achieve this, we collected flight information from September 2017 to April 2023, along with weather data, and performed extensive feature engineering to extract informative features to train our model. We conduct a comparative analysis of various popular machine learning architectures with distinctive characteristics, aiming to determine their efficacy in achieving optimal accuracy on our newly proposed dataset. …”
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A high-precision segmentation method based on UNet for disc cutter holder of shield machine by Dandan Peng, Guoli Zhu, Zhe Xie

“…By integrating attention mechanisms, we develop the Res-UNet-CA architecture, which achieves state-of-the-art metrics on independent test sets: accuracy (99.45%), precision (98.9%), recall (99.11%), F1-score (99%), and mIoU (98.63%). The Res-UNet-CA model significantly outperforms other semantic segmentation models in prediction quality, offering an innovative solution for shield machine disc cutter holder detection.…”
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Predicting postoperative complications after pneumonectomy using machine learning: a 10-year study by Yaxuan Wang, Shiyang Xie, Jiayun Liu, He Wang, Jiangang Yu, Wenya Li, Aika Guan, Shun Xu, Yong Cui, Wenfei Tan

“…The optimal model was analyzed and filtered using multiple machine-learning models (Logistic regression, eXtreme Gradient Boosting, Random forest, Light Gradient Boosting Machine and Naïve Bayes). …”
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Machine-learning-driven feature importance analysis for guiding the protonic ceramic fuel cell manufacturing by Jinwoo Kim, Jaewan Baek, Mingi Choi

“…To address these issues, this study proposes a method by which to analyze the effects of certain materials and manufacturing processes on the fabrication of PCFCs, assisted by machine learning (ML). Based on data from earlier work, we first evaluate the performance-predicting capabilities of 6 ML models, showing the best-predicting performance with XGBoost model. …”
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Significance of Machine Learning-Driven Algorithms for Effective Discrimination of DDoS Traffic Within IoT Systems by Mohammed N. Alenezi

“…Findings revealed that the RF model outperformed other models by delivering optimal detection speed and remarkable performance across all evaluation metrics, while KNN (K = 7) emerged as the most efficient model in terms of training time.…”
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Differentiating Emphysema From Emphysema-Dominated COPD Patients with CT Imaging Feature and Machine Learning by Guo W, Li M, Li Y, Fan X, Wu L

“…Quantitative computed tomography (QCT) offers potential for improved characterization, yet its optimal application in conjunction with machine learning for this differentiation is not fully established.Methods: This prospective study enrolled 476 participants (99 with emphysema, 377 with emphysema-dominant COPD) aged 34– 88 years. …”
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Maize and soybean yield prediction using machine learning methods: a systematic literature review by Ramandeep Kumar Sharma, Jasleen Kaur, Gary Feng, Yanbo Huang, Chandan Kumar, Yi Wang, Sandhir Sharma, Johnie Jenkins, Jagmandeep Dhillon

“…Abstract Today’s agronomy is data-rich, and machine learning (ML) provides the ability to efficiently predict crop yields, utilizing high-volume data to optimize agricultural decision-making. …”
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Optimal Strategy of Unreliable Flexible Production System Using Information System by Sadok Rezig, Sadok Turki, Ayoub Chakroun, Nidhal Rezg

“…<i>Background</i>: Optimization approaches and a models can be applied for critical production systems that experience equipment failure, repair delays and product quality control in order to maximize the total profit. …”
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Classification prediction of load losses in power stations using machine learning multilayer stack ensemble by Bathandekile M. Boshoma, Oluwole S. Akinola, Peter Olukanmi, Peter Olukanmi

“…To support the decision-making of improving plant reliability, we experimented with six machine learning classifiers to find the model combination that produces the best prediction performance, called the Explainable Multilayer Stack Ensemble. …”
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Spatiotemporal Risk Mapping of Statewide Weather-related Traffic Crashes: A Machine Learning Approach by Abimbola Ogungbire, Srinivas S. Pulugurtha

“…Space-time cubes were created using an optimized 5 mi x 5mi grid size and 1-month time aggregation. …”
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Assessing excavatability in varied rockmass conditions using real-time data and machine learning technique by Shafi Muhammad Pathan, Abdul Ghani Pathan, Muhammad Saad Memon

“…This research underscores the importance of the key rock properties in evaluating the excavation performance predictions and support optimized operational strategies in mining. Future work could expand on these findings by using additional machine learning techniques and exploring non-linear models to capture complex relationships.…”
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Machine learning-based pattern recognition of Bender element signals for predicting sand particle-size by Yong-Hoon Byun, Juik Son, Jungmin Yun, Hyunwook Choo, Jongmuk Won

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Machine learning for high-risk hospitalization prediction in outpatient individuals with diabetes at a tertiary hospital by Carolina Deina, Flavio S. Fogliatto, Mateus Augusto dos Reis, Beatriz D. Schaan

“…Within this group, 82.98% (512 patients) did not require hospitalization, while 17.02% (105 patients) were hospitalized at least once. Multiple machine learning algorithms were tested, and the combination of XGBoost and Instance Hardness Threshold models displayed the best predictive performance. …”
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Machine learning-based prediction of antimicrobial resistance and identification of AMR-related SNPs in Mycobacterium tuberculosis by Yi Xu, Ying Mao, Xiaoting Hua, Yan Jiang, Yi Zou, Zhichao Wang, Zubi Liu, Hongrui Zhang, Lingling Lu, Yunsong Yu

“…Then, we compared the performances of the various ML models and used the SHapley Additive exPlanations (SHAP) framework to decipher why and how decisions are made within the optimal algorithm. …”
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A Comprehensive Investigation of Anomaly Detection Methods in Deep Learning and Machine Learning: 2019–2023 by Shalini Kumari, Chander Prabha, Asif Karim, Md. Mehedi Hassan, Sami Azam

“…Future research directions include improving model performance, leveraging multiple validation techniques, optimizing resource utilization, generating high-quality datasets, and focusing on real-world applications. …”
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A Compact Dual-Band Antenna for Brain–Machine Interface and Skin-Implantable Biotelemetry Applications by Emtiaz Ahmed Mainul, Md. Faruque Hossain

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Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis by Fernando Pedro Silva Almeida, Mauro Castelli, Nadine Côrte-Real

“…This study addresses the gap in existing research by comprehensively analyzing the performance of various machine learning algorithms, including ensemble learning and deep learning models, to improve prediction accuracy. …”
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Hybrid strategy enhanced crayfish optimization algorithm for breast cancer prediction by Yu-Jiong Li

“…When combined with Extreme Learning Machine (ELM) and applied to the Wisconsin breast cancer dataset, the MSCOA-ELM model achieved 100% accuracy and F1 score, a 28.9% improvement over the baseline ELM, demonstrating the algorithm’s efficiency and generalization ability in solving practical optimization problems.…”
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Optimizing Feature Selection for IOT Intrusion Detection Using RFE and PSO by zahraa mehssen agheeb Alhamdawee

“…Four observed classifier algorithms have been applied: k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT). …”
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