Efficient neural network training method for unsteady flow field prediction based on data pool by Qiuchi Min, Tianyu Li, Guanxiong Li, Shuyuan Liu, Laiping Zhang, Xiaogang Deng

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Reproduction of reservoir pressure in oil field development: prospects and problems of using methods machine learning by I. N. Ponomarevа, D. A. Martyushev

“…In the absence of even a single reservoir pressure measurement for the wells, the model reliably reproduces its value using the kriging procedure used in the algorithms.…”
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Data-driven modeling of the Yld2000 yield criterion and its efficient application in numerical simulation by Xiaomin Zhang, Jianzhong Mao, Zhi Cheng

“…In contrast, other algorithmic models exhibited certain deficiencies in either convergence behavior or computational efficiency.…”
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Community detection in empirical kinase networks identifies new potential members of signalling pathways. by Celia De Los Angeles Colomina Basanta, Marya Bazzi, Maruan Hijazi, Conrad Bessant, Pedro R Cutillas

“…Here, we assess whether the application of community detection algorithms to such networks can identify new components linked to canonical signalling pathways. …”
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Effectiveness of machine learning models in diagnosis of heart disease: a comparative study by Waleed Alsabhan, Abdullah Alfadhly

“…Our study employs a wide range of ML algorithms, such as Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neibors (KNN), AdaBoost (AB), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LGBM), CatBoost (CB), Linear Discriminant Analysis (LDA), and Artificial Neural Network (ANN) to assess the predictive performance of these algorithms in the context of heart disease detection. …”
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Integrating machine learning for the sustainable development of smart cities by Manel Mrabet, Maha Sliti

“…This paper discusses a variety of real-world initiatives that have successfully employed machine learning to develop sustainable smart cities, as well as in-depth studies of the ML algorithms used and the obtained results. The paper also covers the challenges of implementing machine learning into smart city projects, such as data quality, model interpretability, scalability, and ethical considerations. …”
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A Review of Reinforcement Learning for Fixed-Wing Aircraft Control Tasks by David J. Richter, Ricardo A. Calix, Kyungbaek Kim

“…A lot of that can be attributed to the recent advancements in machine learning (ML) and deep learning (DL) as a whole, the power of deep neural networks and the incorporation of them into reinforcement learning algorithms and techniques. This marriage of deep neural networks and traditional reinforcement learning methods has enabled reinforcement learning algorithms to achieve incredible results in use-cases that were previously deemed to be too complex. …”
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Informed Consent for the Use of AI in the Process of Providing Medical Services by Katarzyna Wałdoch

“…The use of medical devices equipped with AI creates new types of risk, including the risk of algorithmic error, the risk of cyber-attack, and the risk of algorithmic mismatch (false-positive or false-negative results). …”
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The quantum circuit implementation and feasibility analysis of quantum public-key cryptosystem based on the $$QSCD_{ff}$$ Q S C D ff problem by Anyi Li, Qiqing Xia, Qianru Zhu, Li Yang

“…Our contribution provides a theoretical foundation for the physical feasibility analysis of quantum cryptographic algorithms, offering insights into the challenges and prospects of implementing these algorithms in quantum computation environments.…”
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High-Performance Modeling and Optimization of Wireless Networks Using Bottleneck Structures by Aleix Torres-Camps, Alex Batlle Casellas, Yuyang Wang, Mauro Filomeno Rivero, Naga Bhushan, Jordi Ros-Giralt

“…Leveraging fast computational graph algorithms from the field of automatic differentiation such as backpropagation, these structures can solve certain wired network problems two to three orders of magnitude faster than traditional network simulators, without losing significant precision. …”
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CytoLNCpred-a computational method for predicting cytoplasm associated long non-coding RNAs in 15 cell-lines by Shubham Choudhury, Naman Kumar Mehta, Gajendra P. S. Raghava

“…Using composition and correlation based features, machine learning algorithms achieved an average AUC of 0.7049 and 0.7089, respectively for 15 cell-lines. …”
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A SYNTHESIS METHOD OF BASIC TERNARY BENT-SQUARES BASED ON THE TRIAD SHIFT OPERATOR by O. N. Zhdanov, A. V. Sokolov

“…The paper results are valuable for practical use: the development of new constant amplitude codes for MC-CDMA technology, cryptographic primitives, data compression algorithms, signal structures, algorithms of block and stream encryption, based on advanced principles of many-valued logic. …”
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Identification of metabolic biomarkers in idiopathic pulmonary arterial hypertension using targeted metabolomics and bioinformatics analysis by Chuang Yang, Yi-Hang Liu, Hai-Kuo Zheng

“…This study used metabolomics, machine learning algorithms and bioinformatics to screen for potential metabolic biomarkers associated with the diagnosis of PAH. …”
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Empowering machine learning for robust cyber-attack prevention in online retail: an integrative analysis by Kamran Razzaq, Mahmood Shah, Mohammad Fattahi, Jing Tang

“…Machine learning (ML) algorithms play a significant role in preventing cyber-attacks. …”
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Modification of Adomian decomposition technique in multiplicative calculus and application for nonlinear equations by Farooq Ahmed Shah, Muhammad Waseem, Alexey Mikhaylov, Gabor Pinter

“…Graphical comparison is also provided by plotting log of residuals. The purpose in constructing new algorithms is to show the implementation and effectiveness of multiplicative calculus.…”
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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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Digital Pathology for Analyzing Endomyocardial Biopsy Specimens by A. A. Slavinskiy, A. A. Verevkin, L. M. Chuprinenko, V. L. Drushevskaya, M. V. Kristesiashvili

“…We created and field-tested algorithms for digital morphometric analysis to assess the intensity of IHC reactions and to identify the relative area of pathological changes in the EBM specimens.   …”
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Efficient Resource Allocation for Blockchain-Enabled Mobile Edge Computing: A Joint Optimization Approach by Moein Valitabar, Mohammad Fathi, Keivan Navaie

“…The approach also contributes to reducing network outage probability. …”
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Learning from Octopuses: Cutting-Edge Developments and Future Directions by Jinjie Duan, Yuning Lei, Jie Fang, Qi Qi, Zhiming Zhan, Yuxiang Wu

“…In addition, the distributed nervous system of octopuses inspires multi-processor architecture and intelligent optimization algorithms. This paper also introduces the concept of expected functional safety for the first time to explore the safe design of soft robots in failure or unknown situations. …”
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Diel and Annual Patterns of Vocal Activity of Three Neotropical Wetland Birds Revealed via BirdNET by Cristian Pérez-Granados, Karl-L. Schuchmann

“…The use of machine learning algorithms such as BirdNET may improve bird monitoring in tropical areas but also facilitate research that improves our knowledge of birds’ natural history, which remains unknown for many tropical species.…”
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