Forecasting the volatility of Financial Time Series by Tree Ensembles by O. S. Vidmant

“…The constant volatility of the world indices forces all financial market players to improve risk management models and, at the same time, to revise the policy of capital investment. …”
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TECHNOLOGICAL ADVANCES IN ELECTROPLATING: ARTIFICIAL INTELLIGENCE TO PREDICT ZINC COATING THICKNESS ON SAE 1008 LOW CARBON STEELS by Luciano M. L. de Oliveira, Fabiana L. da Silva, Paulo R. Janissek, Juliano C. Toniolo

“…Statistical analysis and supervised machine learning algorithms, including multivariate regression, random forest, and extreme gradient boosting (XGBoost), were employed to develop prediction models. …”
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A review on multi-fidelity hyperparameter optimization in machine learning by Jonghyeon Won, Hyun-Suk Lee, Jang-Won Lee

“…Tuning hyperparameters effectively is crucial for improving the performance of machine learning models. …”
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Joint Channel Assignment and Routing in Multiradio Multichannel Wireless Mesh Networks: Design Considerations and Approaches by Omar M. Zakaria, Aisha-Hassan A. Hashim, Wan H. Hassan, Othman O. Khalifa, M. Azram, Lalitha B. Jivanadham, Mistura L. Sanni, Mahdi Zareei

“…Multiradio wireless mesh network is a promising architecture that improves the network capacity by exploiting multiple radio channels concurrently. …”
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Advanced Wildfire Prediction in Morocco: Developing a Deep Learning Dataset From Multisource Observations by Ayoub Jadouli, Chaker El El Amrani

“…Our best-performing models achieve an accuracy of up to 90%, significantly improving upon traditional approaches. …”
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Convolutional neural networks and vision transformers for Plankton Classification by Loris Nanni, Alessandra Lumini, Leonardo Barcellona, Stefano Ghidoni

“…The study considers the creation of ensembles combining different Convolutional Neural Network (CNN) models and transformer architectures to understand whether different optimization algorithms can result in more robust and efficient classification across various plankton datasets. …”
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Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review by Rim Missaoui, Wided Hechkel, Wajdi Saadaoui, Abdelhamid Helali, Marco Leo

“…In this study, we focus on current advances in medical imaging, particularly magnetic resonance imaging (MRI), and how machine learning (ML) and deep learning (DL) algorithms might be combined with clinical assessments to improve brain tumor diagnosis. …”
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Does a Meta-Combining Method Lead to More Accurate forecasts in the decision-making process? by Emrah Gulay, Serkan Aras

“…When researchers deal with time series data by using dissimilar models, the combined forecasts of these models are expected to be superior. …”
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Criteria for predicting the initiation of rolling contact fatigue damage in the railway wheels and rails by V. I. Sakalo, A. V. Sakalo

“…The assumptions that are used in the development of the algorithms concerning the models of the contacting bodies, the shape of the contact patch, the distribution of the contact pressures and the tangential forces are considered. …”
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Application of big data method in forecasting potential sensitive customers of electric power by Xiaofeng CHEN, Yadi ZHAO, Lipeng ZHANG, Feng ZHU

“…With the continuous development and extension of 95598 business,the intensity of manual telephone traffic increases.In order to further deepen the consciousness and understanding of the hidden features and the demands of the customers,improve the customer service level of 95598,the typical application scenarios in the customer service such as the tendency of the complaint were refined.Based on the data of power service orders,the key index of modeling was selected.Through entropy weight method,principal component analysis and decision tree and other data mining algorithms,potential complaint propensity customers and planned blackout sensitive customers in order to carry out targeted service resource scheduling were identified,fully do a good job of response measures,effectively reduce complaint pressure and improve service accuracy.…”
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Research Progress on Process Optimization of Metal Materials in Wire Electrical Discharge Machining by Xinfeng Zhao, Binghui Dong, Shengwen Dong, Wuyi Ming

“…The review systematically compares the advantages and disadvantages of traditional methods and AI models in terms of nonlinear modeling capabilities, adaptability, and generalization. …”
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A systematic review of UAV and AI integration for targeted disease detection, weed management, and pest control in precision agriculture by Iftekhar Anam, Naiem Arafat, Md Sadman Hafiz, Jamin Rahman Jim, Md Mohsin Kabir, M.F. Mridha

“…The focus of this study is on the incorporation of machine learning and deep learning algorithms into these UAV systems. We have conducted a thorough analysis of recent studies, particularly 2022–24, to evaluate the effectiveness of different unmanned aerial vehicle models, sensor types, and computational methods to improve crop monitoring and disease control strategies. …”
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A comprehensive approach to predict a rocket's impact with stochastic estimators and artificial neural networks by Jose Abreu, Roberto L. Oliveira, Joao V. Fonseca Neto

“…This approach combines tracking algorithms that are based on stochastic estimators with artificial neural network (ANN) models. …”
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Soft computing approaches of direct torque control for DFIM Motor's by Zakariae Sakhri, El-Houssine Bekkour, Badre Bossoufi, Nicu Bizon, Mishari Metab Almalki, Thamer A.H. Alghamdi, Mohammed Alenezi

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Artificial Intelligence Applications in Pediatric Craniofacial Surgery by Lucas M. Harrison, Ragan L. Edison, Rami R. Hallac

“…Artificial intelligence is rapidly transforming pediatric craniofacial surgery by enhancing diagnostic accuracy, improving surgical precision, and optimizing postoperative care. …”
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Analysis of Blockchain-Technology by Danylo Dvorchuk, Iryna Shpinareva

“…The research also highlights emerging trends in blockchain development, particularly hybrid models and AI-driven optimization techniques, which can enhance blockchain efficiency and security. …”
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Application of artificial neural networks for saturation correction in current and voltage transformers by E. A. Temnikov, K. I. Nikitin

“…The paper describes methods for training neural networks using historical data, modeling transformer operation under various conditions, and developing algorithms for correcting distortions caused by saturation.…”
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A review of the search for AGB stars by Hai-Ling Lu, Hai-Ling Lu, Yin-Bi Li, A-Li Luo, A-Li Luo, A-Li Luo, Zhi-Qiang Zou, Zhi-Qiang Zou, Zhi-Qiang Zou, Xiao-Ming Kong, Zhen-Ping Yi, Hugh R. A. Jones, Jun-Chao Liang, Jun-Chao Liang, Shuo Li, Shuo Li

“…By integrating multi-wavelength data such as optical and infrared spectra, along with stellar evolution models, we analyze the existing methods and potential directions for improvement. …”
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The methodology for reactive power control to ensure voltage quality using fuzzy logic by Kholiddinov Ilkhombek, Sharobiddinov Mirzokhid, Kholiddinova Mashkhurakhon, Komolddinov Sokhib, Qodirov Afzal, Tukhtasinov Saidislomkhon

“…The article discusses the relevance of various models in addressing the challenges of solving electrical supply issues, particularly in the context of monitoring and optimizing power distribution networks. …”
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A Review of Enhancement Techniques for Cone Beam Computed Tomography Images by Hassn Mazin Al-alaaf, Mohammed Sabah Jarjees

“…These methods encompass various mathematical algorithms, machine learning approaches, and hybrid models, which aim to mitigate the imperfections present in CBCT data while preserving diagnostically relevant information. …”
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