Ensemble machine learning model for forecasting wind farm generation by A. G. Rusina, Osgonbaatar Tuvshin, P. V. Matrenin, N. N. Sergeev

“…This study is carried out by ensemble algorithms, such as Random Forest, AdaBoost and XGBoost, which are one of the machine learning approaches. …”
Get full text

Urban sentinel: advancing structural health monitoring for building damage measurement in districts through IoT integration and self-optimizing machine learning by Parsa Parsafar

“…These sensors transmit data using LoRaWAN wireless technology to a centralized management system, where a regression AI model harnesses the power of machine learning algorithms to analyze the data and predict the health status of the buildings. …”
Get full text

Multi-omics analysis untangles the crosstalk between intratumor microbiome, lactic acid metabolism and immune status in lung squamous cell carcinoma by Xun Qiu, Dan Li

“…Multiple machine learning algorithms were used to generate the LUSC classification. …”
Get full text

Between Two Worlds: Investigating the Intersection of Human Expertise and Machine Learning in the Case of Coronary Artery Disease Diagnosis by Ioannis D. Apostolopoulos, Nikolaos I. Papandrianos, Dimitrios J. Apostolopoulos, Elpiniki Papageorgiou

“…These results highlight a potential synergistic relationship between human expertise and advanced algorithmic predictions, suggesting a hybrid approach as a promising direction for enhancing CAD diagnostics.…”
Get full text

Evaluating the strength properties of high-performance concrete in the form of ensemble and hybrid models using deep learning techniques by Zhe Wang, Tao Sun, Yan Sun, Na Liu

“…Deep learning techniques, including hybrid and ensemble methods, were developed to predict these properties with high accuracy. This paper focuses on forecasting models using T-SFIS, GBMBoost, and Decision Tree, combined with metaheuristic algorithms (GWO, QPSO) in hybrid and ensemble frameworks. …”
Get full text

Dataset on the long-term monitoring of foundation vertical deformations on medium-expansive soilMendeley Data by Hawkar Hashim Ibrahim, Rizgar Ali Hummadi

“…It is particularly useful in developing machine learning algorithms that can be used to predict foundation behavior in response to different environmental conditions, optimize foundation designs on expansive soils, and specifically predict foundation heave. …”
Get full text

Genetic analysis of a serologically weak D phenotype caused by the p. R191G variant of the RHAG gene by ZHANG Xu, LI Xiaofeng, LI Jianping

“…R191Q) mutation was predicted to be “probably damaging”, “deleterious” and “affected” by PolyPhen2, PROVEAN and Mutation Taster algorithms, respectively. …”
Get full text

Application of machine learning in forensic geochemistry using presalt oil samples from the Santos basin by Gil Marcio Avelino Silva, Fernando Pellon de Miranda, Jarbas Vicente Poley Guzzo, Wagner Leonel Bastos, Ygor Rocha, Igor Viegas Alves Fernandes de Souza, Italo Oliveira Matias, Sarah Barron Torres, Francisco Fabio de Araujo Ponte

“…A dataset comprising 2200 presalt oil samples and 75 attributes from the Santos Basin underwent preprocessing and exploratory analysis, resulting in 2137 samples and 62 predictive attributes. Seven machine learning algorithms were evaluated, with the random forest model achieving the highest classification accuracy of 91%. …”
Get full text

The influence of pH and temperature on benthic chlorophyll-a: Insights from SHAP-XGBoost and random forest models by Sangar Khan, Noël P.D. Juvigny-Khenafou, Tatenda Dalu, Paul J. Milham, Yasir Hamid, Kamel Mohamed Eltohamy, Habib Ullah, Bahman Jabbarian Amiri, Hao Chen, Naicheng Wu

“…There is little information on machine learning predictive models of benthic chl–a and input parameters in lotic ecosystems, and to fill this gap, we predict benthic chl–a levels in China's Thousand Islands Lake (TIL) watershed using machine learning algorithms. …”
Get full text

Determination of Colles fracture risk index by X-ray images with the computer vision application by Wisam A. Hussein, Hussain J. AlKhatteib, Jawad K. Abbas

“…Background. Modelling a predictive risk index for Colles fractures using X-ray image analysis is a crucial application in orthopaedics since these fractures have essential health and economic burdens, particularly among the elderly. …”
Get full text

Survival analysis using machine learning in transplantation: a practical introduction by Andrea Garcia-Lopez, Maritza Jiménez-Gómez, Andrea Gomez-Montero, Juan Camilo Gonzalez-Sierra, Santiago Cabas, Fernando Giron-Luque

“…The integration of machine learning techniques, particularly the Random Survival Forest (RSF) model, offers potential enhancements to predictive modeling and decision-making. This study aims to provide an introduction to the application of the RSF model in survival analysis in kidney transplantation alongside a practical guide to develop and evaluate predictive algorithms. …”
Get full text

Diagnostic value of leukocytosis in patients presenting to the emergency department with abdominal pain: A retrospective observational study by Erkan Boğa

“…Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of leukocytosis in predicting surgical needs were calculated. …”
Get full text

Efficient Resource Allocation for Blockchain-Enabled Mobile Edge Computing: A Joint Optimization Approach by Moein Valitabar, Mohammad Fathi, Keivan Navaie

“…Performance evaluation results demonstrate the effectiveness of these algorithms, achieving significant reductions in total energy consumption while maximizing the efficiency of communication and computational resources. …”
Get full text

Preface to Special Issue on AI-Based Future Intelligent Networks and Communication Security by Sunil Kumar, Glenford Mapp, Abhay Bansal, Korhan Cengiz

“… Recent advancements in science focus on the study and development of algorithms that can learn from and make predictions and decisions based on data collected through intelligent devices. …”
Get full text

Identification of biomarkers associated with inflammatory response in Parkinson's disease by bioinformatics and machine learning. by Yatan Li, Wei Jia, Chen Chen, Cheng Chen, Jinchao Chen, Xinling Yang, Pei Liu

“…LASSO, SVM-RFE and Random Forest algorithms were used to screen biomarker genes. Then, ROC curves were drawn and PD risk predicting models were constructed on the basis of the biomarker genes. …”
Get full text

Efficient topology control for time-varying spacecraft networks with unreliable links by Wei Zhang, Hong Ma, Tao Wu, Xueshu Shi, Yiwen Jiao

“…Simulation results demonstrate the efficiency of our model and topology control algorithms.…”
Get full text

A novel deep learning approach to identify embryo morphokinetics in multiple time lapse systems by Guillaume Canat, Antonin Duval, Nina Gidel-Dissler, Alexandra Boussommier-Calleja

“…Today, most of the literature has characterized algorithms that predict pregnancy, ploidy or blastocyst quality, leaving to the side the task of identifying key morphokinetic events. …”
Get full text

A Comprehensive Survey on AI in Counter-Terrorism and Cybersecurity: Challenges and Ethical Dimensions by Ioannis Syllaidopoulos, Klimis S. Ntalianis, Ioannis Salmon

“…This paper provides a comprehensive overview of AI methodologies, such as predictive analytics, Natural Language Processing (NLP), and machine learning architectures (e.g., Support Vector Machines – SVM and Long Short-Term Memory – LSTM), and optimization algorithms (e.g., Particle Swarm Optimization – PSO), assessing their effectiveness in security applications. …”
Get full text

Sensor Image, Anomaly Detection Method for Hydroelectric Dam Structure Using Sensors Measurements and Deep Learning by Van-Phuong Ha, Dinh-Van Nguyen, Trong-Chuong Trinh, Duc-Cuong Quach, Van HuyBui

“…To better prevent future disasters, machine-learning algorithms have been employed. Often, these algorithms are trained on historical sensor data to predict future events. …”
Get full text

Integrating deep learning in public health: a novel approach to PICC-RVT risk assessment by Yue Li, Yue Li, Shengxiao Nie, Lei Wang, Dongsheng Li, Shengmiao Ma, Ting Li, Hong Sun

“…Stability varied with the number of predictive factors, with Cox-Time showing the highest ICC (0.974) with 16 predictive factors, and DeepSurv the most stable with 26 predictive factors (ICC: 0.983). …”
Get full text