Evaluation and use of in-silico structure-based epitope prediction with foot-and-mouth disease virus. by Daryl W Borley, Mana Mahapatra, David J Paton, Robert M Esnouf, David I Stuart, Elizabeth E Fry

“…Therefore we have extended several existing structural prediction algorithms to build a method for identifying epitopes on the appropriate outer surface of intact virus capsids (which are structurally different from globular proteins in both shape and arrangement of multiple repeated elements) and applied it here as a proof of principle concept to the capsid of foot-and-mouth disease virus (FMDV). …”
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Enhancing Software Defect Prediction Using Ensemble Techniques and Diverse Machine Learning Paradigms by Ayesha Siddika, Momotaz Begum, Fahmid Al Farid, Jia Uddin, Hezerul Abdul Karim

“…This research addresses this need by combining advanced techniques (ensemble techniques) with seventeen machine learning algorithms for predicting software defects, categorised into three types: semi-supervised, self-supervised, and supervised. …”
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Spatial differences in predicted Phalaris arundinacea (reed canarygrass) occurrence in floodplain forest understories by John T. Delaney, M. Van Appledorn, N. R. De Jager, K. L. Bouska, J. J. Rohweder

“…We used an ensemble of species distribution models including Bayesian additive regression trees, boosted trees, and random forest algorithms to predict habitat suitability for reed canarygrass in forest understories across the Upper Mississippi River floodplain (~41,000 ha). …”
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Elastic net with Bayesian Density Estimation model for feature selection for photovoltaic energy prediction by Venkatachalam Mohanasundaram, Balamurugan Rangaswamy

“…This research investigation optimizes Feature Selection (FS) and prediction results for PV energy prediction by applying Bayesian Density Estimation (BDE) with Elastic Net (ELNET) regression analysis. …”
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Interpretable machine learning model for predicting post-hepatectomy liver failure in hepatocellular carcinoma by Tianzhi Tang, Tianyu Guo, Bo Zhu, Qihui Tian, Yang Wu, Yefu Liu

“…Variable selection was performed using the least absolute shrinkage and selection operator regression in conjunction with random forest and recursive feature elimination (RF-RFE) algorithms. Subsequently, 12 distinct ML algorithms were employed to identify the optimal prediction model. …”
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Development and clinical application of an automated machine learning-based delirium risk prediction model for emergency polytrauma patients by Zhenyi Liu, Yihao Huang, Long Li, Yisha Xu, Peng Wu, Zhigang Zhang, Tingyong Han, Liangjie Zhang, Ming Zhang

“…ObjectiveTo address the limitations of conventional delirium prediction models in emergency polytrauma care, this study developed an interpretable machine learning (ML) framework incorporating trauma-specific biomarkers and advanced optimization algorithms for risk stratification of delirium in emergency polytrauma patients.MethodsThis multi-center retrospective observational cohort study was conducted across six hospitals in the Ya’an region. …”
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Impact of atmospheric corrections on satellite imagery for corn yield prediction using machine learning by Octávio Pereira da Costa, Franklin Daniel Inácio, Jéssica Elaine da Silva, Thiago Orlando Costa Barboza, Wender Henrique Batista da Silva, Lorena Nunes Lacerda, Adão Felipe dos Santos

“…However, the performance of the models differed for corn yield estimation. The SVM algorithm showed the lowest performance during the main crop season (R² = 0.36), while both RF and kNN yielded prediction results with an accuracy of over 55 %, with RF providing the highest R² values and the lowest errors (RMSE = 0.3 t ha−1). …”
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Predicting the interfacial tension of CO2 and NaCl aqueous solution with machine learning by Kashif Liaqat, Daniel J. Preston, Laura Schaefer

“…Our findings indicate a notable enhancement in prediction accuracy over previous ML studies in this area. …”
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Predicting mother and newborn skin-to-skin contact using a machine learning approach by Sanaz Safarzadeh, Nastaran Safavi Ardabili, Mohammadsadegh Vahidi Farashah, Nasibeh Roozbeh, Fatemeh Darsareh

“…Results Of 8031 eligible mothers, 3759 (46.8%) experienced SSC. The algorithms created by deep learning (AUROC: 0.81, accuracy: 0.75, precision: 0.67, recall: 0.77, and F_1 Score: 0.73) and linear regression (AUROC: 0.80, accuracy: 0.75, precision: 0.66, recall: 0.75, and F_1 Score: 0.71) had the highest performance in predicting SSC. …”
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Application of Ensemble Learning and VISSIM in Intersection Traffic Flow Prediction and Signal Timing Optimization by Yutong Rou, Chao Liang, Zhizhan Lu

“…By integrating the predictions with the SARSA-A2C algorithm, a hybrid strategy for predictive signal timing optimization is implemented. …”
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Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems by Samir A. Hamad, Mohamed A. Ghalib, Amr Munshi, Majid Alotaibi, Mostafa A. Ebied

“…To achieve this, the research explores various ML algorithms, such as Linear Regression (LR), Ridge Regression (RR), Lasso Regression (Lasso R), Bayesian Regression (BR), Decision Tree Regression (DTR), Gradient Boosting Regression (GBR), and Artificial Neural Networks (ANN), to predict the MPP of PV systems. …”
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A Deep Learning Approach Based on Interpretable Feature Importance for Predicting Sports Results by Bendiaf Messaoud, Khelifi Hakima, Mohdeb Djamila, Belazzoug Mouhoub, Saifi Abdelhamid

“…These algorithms can learn from historical data to identify complex relationships between different variables, and then make predictions about the outcome of future matches. …”
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Predicting Mesothelioma Using Artificial Intelligence: A Scoping Review of Common Models and Applications by Malihe Ram MS, Mohammad Reza Afrash PhD, Khadijeh Moulaei PhD, Erfan Esmaeeli, Mohadeseh Sadat Khorashadizadeh, Ali Garavand PhD, Parastoo Amiri PhD, Azam Sabahi PhD

“…This study aims to review the latest research conducted in artificial intelligence applications to predict mesothelioma. Methods Until April 24, 2023, PubMed, Scopus, and Web of Science databases were searched comprehensively for articles on artificial intelligence in mesothelioma management. …”
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Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysis by Martina Votto, Annalisa De Silvestri, Lorenzo Postiglione, Maria De Filippo, Sara Manti, Stefania La Grutta, Gian Luigi Marseglia, Amelia Licari

“…Conclusion This study provides the most comprehensive assessment of AI-based algorithms in predicting paediatric asthma exacerbations to date. …”
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Deep Learning in Glaucoma Detection and Progression Prediction: A Systematic Review and Meta-Analysis by Xiao Chun Ling, Henry Shen-Lih Chen, Po-Han Yeh, Yu-Chun Cheng, Chu-Yen Huang, Su-Chin Shen, Yung-Sung Lee

“…<b>Purpose:</b> To evaluate the performance of deep learning (DL) in diagnosing glaucoma and predicting its progression using fundus photography and retinal optical coherence tomography (OCT) images. …”
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Modified PISAPED Criteria in Combination with Ventilation Scintigraphic Finding for Predicting Acute Pulmonary Embolism by Naoyuki Watanabe, Jure Fettich, Nurie Küçük, Otakar Kraft, Fernando Mut, Partha Choudhury, Surendra Sharma, Keigo Endo, Maurizio Dondi

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Grey Wolf Optimizer-Based ANNs to Predict the Compressive Strength of Self-Compacting Concrete by Amir Andalib, Babak Aminnejad, Alireza Lork

“…Nonetheless, their nonlinear behavior has made the prediction of their mix properties more demanding. Furthermore, the complex relationship between mixed proportions and rheological and mechanical properties of SCC renders their behavior prediction challenging. …”
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Global and Local Interpretable Machine Learning Allow Early Prediction of Unscheduled Hospital Readmission by Rafael Ruiz de San Martín, Catalina Morales-Hernández, Carmen Barberá, Carlos Martínez-Cortés, Antonio Jesús Banegas-Luna, Francisco José Segura-Méndez, Horacio Pérez-Sánchez, Isabel Morales-Moreno, Juan José Hernández-Morante

“…Individualized prediction models also revealed a high sensitivity. …”
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Continuous Speech-Based Fatigue Detection and Transition State Prediction for Air Traffic Controllers by Susmitha Vekkot, Surya Teja Chavali, Charan Tej Kandavalli, Rama Sai Abhishek Podila, Deepa Gupta, Mohammed Zakariah, Yousef Ajami Alotaibi

“…This paper presents a study that investigates speech features responsible for detecting ATC fatigue and proposes an approach to predict the timestamp at which an ATC transitions into a fatigue state from a continuous speech sample. …”
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Evaluation of machine learning methods for prediction of heart failure mortality and readmission: meta-analysis by Hamed Hajishah, Danial Kazemi, Ehsan Safaee, Mohammad Javad Amini, Maral Peisepar, Mohammad Mahdi Tanhapour, Arian Tavasol

“…Conclusion In conclusion, this review emphasizes the strong potential of ML models in predicting HF readmission and mortality. ML algorithms show promise in improving prognostic accuracy and enabling personalized patient care. …”
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