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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Investigation and application of data balancing and combined discriminant model in rock burst severity prediction by Shaohong Yan, Runze Liu, Yanbo Zhang, Xulong Yao, Yueqi Yang, Qi Wang, Bin Guo, Shuai Wang

“…To accurately predict rock burst disasters and mitigate or eliminate related threats, this paper proposes a composite prediction model that integrates Density-Based Nonlinear Resampling (DBNR)-Tomek Link data balancing algorithms with Bayesian Optimization (BO)-Multilayer Perceptron (MLP)-Random Forest (RF). …”
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Link Prediction Model for Weighted Networks Based on Evidence Theory and the Influence of Common Neighbours by Miaomiao Liu, Yang Wang, Jing Chen, Yongsheng Zhang

“…Experiments are performed on 9 real and 40 simulation-weighted datasets, and these findings are compared with several classic algorithms. Results show that the proposed method has higher precision than other methods, which can achieve good performance in link prediction in weighted networks.…”
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Development of data-driven machine learning models and their potential role in predicting dengue outbreak by Bushra Mazhar, Nazish Mazhar Ali, Farkhanda Manzoor, Muhammad Kamran Khan, Muhammad Nasir, Muhammad Ramzan

“…This artificial intelligence model uses real world data such as dengue surveillance, climatic variables, and epidemiological data and combines big data with machine learning algorithms to forecast dengue. Monitoring and predicting dengue incidences has been significantly enhanced through innovative approaches. …”
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Wheat yield prediction of Rajasthan using climatic and satellite data and machine learning techniques by KAVITA JHAJHARIA

“…The solar induced chlorophyll fluorescence is more sensitive to photosynthesis than any other vegetation indices, so it is crucial to uncover its potential for accurately predicting wheat yields. In the present study, we implemented three machine learning algorithms, support vector regression, Random Forest and XGBoost, one linear regression method, Least Absolute Shrinkage and Selection Operator regression, and one deep learning method, long short-term memory, to predict the wheat yield prediction from 2008 to 2019 using satellite data (SIF) and vegetation indices. …”
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TradeWise: Towards Context-Aware Stock Market Predictions with Sentiment and Political Insights by Andreas Marpaung, David Masterson

“…Our novel approach suggests that sentiment and political insights, when processed and integrated effectively, offer substantial predictive value that could refine the accuracy of financial prediction models. …”
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Research on multimodal social media information popularity prediction based on large language model by WANG Jie, WANG Zitong, PENG Yan, HAO Bowen

“…To address the limitations of strong feature dependency, insufficient generalization, and inadequate performance in few-shot/cold-start settings in existing multimodal social media popularity prediction algorithms, a MultiSmpLLM model based on large language model with instruction fine-tuning and human alignment was proposed. …”
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Machine learning applications for chloride ingress prediction in concrete: insights from recent literature by Quynh-Chau Truong, Anh-Thu Nguyen Vu

“…Various algorithms, such as Artificial Neural Networks (ANNs), Gene Expression Programming (GEP), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Ensemble Learning, have shown potential in estimating corrosion processes, predicting material properties, and evaluating structural durability. …”
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Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features by You Wu, Ke Tang, Chunzheng Wang, Hao Song, Fanfan Zhou, Ying Guo

“…In this study, by integrating cellular transcriptome and cell viability data using four machine learning algorithms (support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)) and two ensemble algorithms (voting and stacking), highly accurate prediction models of 50% and 80% cell viability were developed with area under the receiver operating characteristic curve (AUROC) of 0.90 and 0.84, respectively; these models also showed good performance when utilized for diverse cell lines. …”
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Position Weight Matrix, Gibbs Sampler, and the Associated Significance Tests in Motif Characterization and Prediction by Xuhua Xia

“…Here I review PWM-based methods used in motif characterization and prediction (including a detailed illustration of the Gibbs sampler for de novo motif discovery), present statistical and probabilistic rationales behind statistical significance tests relevant to PWM, and illustrate their application with real data. …”
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Mounting Angle Prediction for Automotive Radar Using Complex-Valued Convolutional Neural Network by Sunghoon Moon, Younglok Kim

“…The predicted offsets can then be used for physical radar alignment or integrated into compensation algorithms to enhance data interpretation accuracy in ADAS applications. …”
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Reversible data hiding in encrypted image based on bit-plane compression of prediction error by Youqing WU, Wenjing MA, Zhaoxia YIN, Yinyin PENG, Xinpeng ZHANG

“…To further improve the performance of reversible data hiding in encrypted image, an algorithm for lossless compression of the prediction error bit-plane using joint encoding was proposed, which could make full use of image redundancy and reserve more embedding room.Firstly, the image owner calculated the prediction error of the image and divided the prediction error bit-plane into non-overlapping blocks of the same size.Then, the prediction error bit-plane was rearranged according to blocks and the rearranged bitstream was compressed by run-length encoding and Huffman encoding to reserve room.The data hider embedded information in the reserved room of the encrypted image.At the receiving end, the legitimate receiver extracted information and recovered images losslessly and separately.Experimental results show that the proposed algorithm makes full use of the bit-plane distribution characteristics and achieves higher embedding performance.The average embedding rates in BOSSbase and BOWS-2 datasets reach 3.763 bpp and 3.642 bpp, which are at least 0.081 bpp and 0.058 bpp higher than the state-of-the-art algorithms.…”
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Bearing remaining useful life prediction based on optimized VMD and BiLSTM-CBAM. by Wei Liu, Sen Liu

“…To address the issue of low accuracy in existing remaining useful life (RUL) prediction algorithms for rolling bearings, this paper proposes a novel RUL prediction method based on the Beluga Whale Optimization (BWO) algorithm, Variational Mode Decomposition (VMD), an improved Convolutional Block Attention Module (CBAM*), and a Bidirectional Long Short-Term Memory (BiLSTM) network. …”
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EvoWeaver: large-scale prediction of gene functional associations from coevolutionary signals by Aidan H. Lakshman, Erik S. Wright

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