Innovative Solutions for Oil Well Monitoring: Data-Driven Multiphase Virtual Flow Metering Using Ensemble Machine Learning and Historical Field Data by Wael A. Farag, Hussein H. Ismail, Muhammad Nadeem

“…By combining data-driven ensemble machine-learning algorithms and historical oil field portable test reports, this paper proposes a Data-Drive Multiphase Virtual Flow Meter (DD-MVFM) that estimates oil, gas, and water flow rates and provides real-time monitoring, and predicts future production with appropriate accuracy. …”
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The analysis of strategic management decisions and corporate competitiveness based on artificial intelligence by Yang Pu, Hao Li, Wenjia Hou, Xingchen Pan

“…Finally, real-world data are applied to evaluate model performance across indicators such as training time, convergence speed, and prediction effectiveness. The results demonstrate that the hybrid model successfully converges within 150 iterations and exhibits substantial advantages over traditional algorithms, particularly in prediction accuracy for market share (92%), profit growth rate (91%), and customer satisfaction (89%). …”
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Learning the Value of Place: Machine Learning Models for Real Estate Appraisal in Istanbul’s Diverse Urban Landscape by Ahmet Hilmi Erciyes, Toygun Atasoy, Abdurrahman Tursun, Sibel Canaz Sevgen

“…The prediction of real estate values is vital for taxation, transactions, mortgages, and urban policy development. …”
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Brown bear optimized random forest model for short term solar power forecasting by Rathika Senthil Kumar, P.S. Meera, V. Lavanya, S. Hemamalini

“…Most of the ensemble ML algorithms outperform the traditional ML algorithms in terms of prediction accuracy. …”
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Combating electricity fraud: Employing hybrid learning and computer vision for sustainable energy management by Jui-Sheng Chou, Nader Anwar Charaf, Dani Nugraha Limantono, Hoang-Minh Nguyen

“…Additionally, the study explores hybrid and ensemble AI models that combine the best-performing algorithms to improve prediction accuracy. The performance of these models is rigorously evaluated using various performance metrics, with results showing that the machine learning model with hyperparameters optimized by a metaheuristic optimization algorithm achieved the highest prediction accuracy. …”
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Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables by Sergio Cofre-Martel, Enrique Lopez Droguett, Mohammad Modarres

“…For the past decade, researchers have explored the application of deep learning (DL) regression algorithms to predict the system’s health state behavior based on sensor readings from the monitoring system. …”
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Evaluation of the elastic modulus of pavement layers using different types of neural networks models by M. M.M. Elshamy, A. N. Tiraturyan, E. V. Uglova

“…This paper studies the capability of different types of artificial neural networks (ANN) to predict the modulus of elasticity of pavement layers for flexible asphalt pavement under operating conditions. …”
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Effortless Student Attendance: A Smart Human-Computer Interactive System Using Real Time Facial Recognition by Ahmad S. Lateef, Mohammed Y. Kamil

“…Result: The system demonstrated accuracy of up to 100%, with deep learning algorithms outperforming machine learning methods. Conclusion: These promising results suggest that face recognition technology can effectively streamline and automate attendance tracking, offering a viable solution for educational institutions seeking to improve operational efficiency and accuracy…”
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Estimation of the High-Frequency Feature Slope in Gravitational Wave Signals from Core Collapse Supernovae Using Machine Learning by Alejandro Casallas-Lagos, Javier M. Antelis, Claudia Moreno, Ramiro Franco-Hernández

“…We evaluated the efficiency and performance of the ML algorithms using a set of metrics to measure their ability to accurately predict the HFF slope within the data set. …”
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Classification of mammograms: Comparing a graphical to a geometrical approach by Anirban Ghosh, Priya Ranjan, Kumar Dron Shrivastav, Rajiv Janardhanan

“…In this scenario, the large-scale deployment of computer-aided diagnosis using well-trained algorithms could significantly reduce the morbidity and mortality associated with this carcinoma. …”
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Recent Results on the Use of Artificial Intelligence Techniques Applied to Wireless Power Transfer Systems by Federico Amadei, Michele Quercio, Francesco Riganti Fulginei

“…We explore how ML can optimize the geometric and structural design of WPT coils, predict the optimal parameters for inductive couplers, and enhance coupling efficiency under varying conditions. …”
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Role of artificial intelligence in earlier diagnosis of bronchopulmonary dysplasia by Lam Q. Bui, Sandhya J. Kadam

“…A deep learning component can be used for lung segmentation of preterm chest radiographs to build a BPD prediction model by focusing on lung anatomy. This article explains the criteria used for BPD diagnosis and compares them with AI-based algorithms, especially for accuracy, applicability, and efficiency. …”
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Improved Key Microbial Biomarker Discovery Using Ensemble Statistical Methods by Walter Pirovano, Yashjit Gangopadhyay, Mirna Lilian Baak, Christiaan Arie de Leeuw, Radhika Bongoni, Eline Suzanne Klaassens

“…However, advanced biostatistical analysis is required to properly combine microbiome taxonomic abundance data with phenotypical metadata and reliably predict disease states. While differential abundance analysis and machine-learning techniques are widely used to perform such analyses, selecting the best method is not trivial due to the complexity and specific characteristics of both the data and the algorithms. …”
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Mathematical Modelling and Optimisation of Operating Parameters for Enhanced Energy Generation in Gas Turbine Power Plant with Intercooler by Anthony O. Onokwai, Udochukwu B. Akuru, Dawood A. Desai

“…The study demonstrated the potential of extending metaheuristic algorithms to provide sustainable solutions to energy system problems and reduced emissions through gas turbine power plant (GTPP) optimisation.…”
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Two General Data Protection Regulation (GDPR) Compliant Approaches to Scoring Firm Financial Frailty in Business Litigation by A. E Rodriguez, Gazi Murat Duman, Ron Kuntze

“…We then map the scores onto bivariate classes using Fisher-Jenks clustering. Each algorithm’s accuracy is obtained by comparing its predictions of either failure of viability to those of the labeled data in O'Haver (1993). …”
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Cardiac disease diagnosis based on GAN in case of missing data. by Xing Chen, Na Zhang, Xiaohui Yang, Chunyan Wang, Qi Na, Tianyun Luan, Wendi Zhu, Chenjie Zhang, Chao Yang

“…The experimental results demonstrate that compared to other filling algorithms, the data matrix filled by the proposed algorithm in this paper has more evident practical significance, fewer errors, and higher accuracy in downstream classification prediction.…”
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Service Function Chain Migration: A Survey by Zhiping Zhang, Changda Wang

“…Existing approaches have demonstrated promising results in both passive and active migration strategies, leveraging techniques such as reinforcement learning for dynamic scheduling and digital twins for resource prediction. Nonetheless, critical issues remain—particularly regarding service interruption control, state consistency, algorithmic complexity, and security and privacy concerns. …”
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Web Application for Mathematical Modeling of Unsteady Oil Flow in Porous Medium by A. A. Mazitov

“…The analysis and interpretation of hydrodynamic research data are based on theoretical models and computational algorithms. Despite the demand for this topic, numerous issues related to unsteady fluid flows in oil reservoirs still require solutions. …”
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An Analysis of Novel Money Laundering Data Using Heterogeneous Graph Isomorphism Networks. FinCEN Files Case Study by Filip Wójcik

“…The proposed model outperformed other algorithms in terms of F1 score, precision, and ROC AUC in both training and testing phases. …”
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Tuning of PID Controller for Speed Control of DC-Motor by using Generalized Regression Neural Network and Invasive Weed Optimization by Muhammad Hilal, Haider TH. Salim ALRikabi, Ibtisam A. Aljazaery

“…To optimize motor drive speed using GRNN and IWO algorithms, data on motor performance over time must be collected and used to train a GRNN model that can predict future motor performance based on past performance. …”
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