Use of artificial intelligence to assess genetic predisposition to develop critical COVID-19 disease: a comparative study of machine learning models by Martín Pérez Salomón, Sanchez Jimenez Flora, Fuentes Cantero Sandra, Jímenez Barragan Marta, Sanchez Mora Catalina, Borreguero Leon Juan M., Teresa Arrobas Velilla, Valido Morales Agustín, Delgado Torralbo Juan A., León Justel Antonio

“…The objective of this study was to optimize predictive models for critical COVID-19 disease. Clinical data, laboratory data and genetic polymorphisms were integrated into AI models to compare the performance of different machine learning algorithms.…”
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Optimization of Rituximab Therapy in Adult Patients With PLA2R1-Associated Membranous Nephropathy With Artificial Intelligence by Alexandre Destere, Maxime Teisseyre, Diane Merino, Marion Cremoni, Alexandre O Gérard, Thomas Crepin, Noémie Jourde-Chiche, Daisy Graça, Kévin Zorzi, Céline Fernandez, Vesna Brglez, Sylvia Benzaken, Vincent L.M. Esnault, Sylvain Benito, Milou-Daniel Drici, Barbara Seitz-Polski

“…We developed a machine learning algorithm to predict the risk of underdosing based on patients’ characteristics at rituximab infusion. …”
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Two Symbol Expansion Methods and Their Application to Reversible Data Hiding by Hyoung Joong Kim, Changhee Kang, Sang-Ug Kang

“…The performance of the proposed simple method is comparable to or better than that of more advanced methods that rely on computationally complex prediction techniques.…”
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Optimization of sports injury treatment through artificial intelligence: Methods for effective prevention, diagnosis and rehabilitation by Kimi Milić Marko, Sinanović Šćepan, Jestrović Vladimir

“…The research methodology includes big data analysis, image processing, machine learning, and customized algorithms for prediction and rehabilitation monitoring. …”
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Performance-Enhancing Market Risk Calculation Through Gaussian Process Regression and Multi-Fidelity Modeling by N. Lehdili, P. Oswald, H. D. Nguyen

“…More precisely, multi-fidelity modeling combines models of different fidelity levels, defined as the degree of detail and precision offered by a predictive model or simulation, to achieve rapid yet precise prediction. …”
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Transformer-based long-term predictor of subthalamic beta activity in Parkinson’s disease by Salvatore Falciglia, Laura Caffi, Claudio Baiata, Chiara Palmisano, Ioannis Ugo Isaias, Alberto Mazzoni

“…Our study paves the way for remote monitoring strategies and the implementation of new algorithms for personalized auto-tuning aDBS devices.…”
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Applicability of machine learning technique in the screening of patients with mild traumatic brain injury. by Miriam Leiko Terabe, Miyoko Massago, Pedro Henrique Iora, Thiago Augusto Hernandes Rocha, João Vitor Perez de Souza, Lily Huo, Mamoru Massago, Dalton Makoto Senda, Elisabete Mitiko Kobayashi, João Ricardo Vissoci, Catherine Ann Staton, Luciano de Andrade

“…Among the tested models, he linear extreme gradient boosting was the best algorithm, with the highest sensitivity (0.70 ± 0.06). …”
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Investigation of Vessel Manoeuvring Abilities in Shallow Depths by Applying Neural Networks by Lúcia Moreira, C. Guedes Soares

“…The system is developed using the Levenberg–Marquardt backpropagation training algorithm and the resilient backpropagation scheme to demonstrate the correlation between the vessel forces and the respective trajectories and velocities. …”
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Used economy market insight: Sailboat industry pricing mechanism and regional effects. by Zhanni Huang, Hansheng Hu, Di Wu

“…Therefore, this article uses the random forest model and XGBoost algorithm to identify core price indicators, and uses an innovative rolling NAR dynamic neural network model to simulate and predict second-hand sailboat price data. …”
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A Classification Method Related to Respiratory Disorder Events Based on Acoustical Analysis of Snoring by Can WANG, Jianxin PENG, Xiaowen ZHANG

“…A classification method is presented based on respiratory disorder events to predict the apnea-hypopnea index (AHI) of OSAHS patients. …”
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D2D cooperative caching strategy based on graph collaborative filtering model by Ningjiang CHEN, Linming LIAN, Pingjie OU, Xuemei YUAN

“…A D2D cooperative caching strategy based on graph collaborative filtering model was proposed for the problem of difficulty in obtaining sufficient data to predict user preferences in device-to-device (D2D) caching due to the limited signal coverage of base stations.Firstly, a graph collaborative filtering model was constructed, which captured the higher-order connectivity information in the user-content interaction graph through a multilayer graph convolutional neural network, and a multilayer perceptron was used to learn the nonlinear relationship between users and content to predict user preferences.Secondly, in order to minimize the average access delay, considering user preference and cache delay benefit, the cache content placement problem was modeled as a Markov decision process model, and a cooperative cache algorithm based on deep reinforcement learning was designed to solve it.Simulation experiments show that the proposed caching strategy achieves optimal performance compared with existing caching strategies for different content types, user densities, and D2D communication distance parameters.…”
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Ambulance Siren Detection using Artificial Intelligence in Urban Scenarios by Muhammad Usaid, Muhammad Asif, Tabarka Rajab, Munaf Rashid, Syeda Iqra Hassan

“…With this validated algorithm, researchers can develop a real-time hardware-based model to detect emergency vehicles and make them arrive at the hospital as soon as possible. …”
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A mode of action protein based approach that characterizes the relationships among most major diseases by Hongyi Zhou, Brice Edelman, Jeffrey Skolnick

“…Abstract Disease classification is important for understanding disease commonalities on both the phenotypical and molecular levels. Based on predicted disease mode of action (MOA) proteins, our algorithm PICMOA (Pan-disease Classification in Mode of Action Protein Space) classifies 3526 diseases across 20 clinically classified classifications (ICD10-CM major classifications). …”
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Artificial intelligence in colorectal cancer: a review by G. Singh

“…Challenges in AI development are addressed, such as data standardization and the interpretability of machine learning algorithms. The potential of AI in treatment decision support, precision medicine, and prognosis prediction is discussed, emphasizing the role of AI in selecting optimal treatments and improving surgical precision. …”
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Breast Cancer Identification from Patients’ Tweet Streaming Using Machine Learning Solution on Spark by Nahla F. Omran, Sara F. Abd-el Ghany, Hager Saleh, Ayman Nabil

“…Two feature selection algorithms are recursive feature elimination and univariate feature selection algorithms which are applied to features after correlation to select the essential features. …”
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Incorporating Wave-ViT for Breast Cancer Diagnosis Using MRI Imaging by Sahil Mahey, Hamid Usefi

“…Machine learning (ML) algorithms offer a transformative solution by automating this process, improving efficiency, and enhancing diagnostic accuracy. …”
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Comparison of Machine Learning and Statistical Approaches of Detecting Anomalies Using a Simulation Study by Klaudia Lenart

“…Results: While the accuracy of the statistical methods was dependent on the precise prediction of the percentage of the anomalies that would occur in the data, the machine learning algorithms’ recall was significantly lower when the change in the marginal distribution of the value parameters was smaller. …”
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Sex estimation with ensemble learning: an analysis using anthropometric measurements of piriform aperture by Muhammed Emin Parlak, Yasin Etli, Murat Beyhan, Kubilay Kanat, Hüseyin Alper Kızıloğlu

“…Results Sex prediction results were obtained with a maximum accuracy of 76.5% with discriminant analysis, 84.2% with machine learning algorithms, and 85.7% with the ensemble learning method. …”
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Building occupancy type classification and uncertainty estimation using machine learning and open data by Tom Narock, J. Michael Johnson, Justin Singh-Mohudpur, Arash Modaresi Rad

“…Multiple ML algorithms are compared. We address strategies to handle significant class imbalance and introduce Bayesian neural networks to handle prediction uncertainty. …”
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Dynamic analysis of malicious behavior propagation based on feature selection in software network by Huajian Xue, Huajian Xue, Yali Wang, Qiguang Tang

“…The method is divided into three stages: First, variable-length N-gram algorithms are utilized to extract subsequences of varying lengths from the sample APl call sequences as continuous dynamic features. …”
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