Prediction of GNSS Velocity Accuracies Using Machine Learning Algorithms for Active Fault Slip Rate Determination and Earthquake Hazard Assessment by Halil İbrahim Solak

“…This study employs machine learning (ML) algorithms to predict GNSS velocity accuracies for fault slip rate estimation and earthquake hazard analysis. …”
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Harnessing Data-mining Algorithms to Model and Evaluate Factors Influencing Distortion Product Otoacoustic Emission Variations in a Mining Industry by Sajad Zare, Reza Esmaeili, Mojtaba Nakhaei pour

“…The results also showed that the DL algorithm had higher accuracy compared to the SVM algorithm. …”
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Assessment of Machine Learning Algorithms in Short-term Forecasting of PM10 and PM2.5 Concentrations in Selected Polish Agglomerations by Bartosz Czernecki, Michał Marosz, Joanna Jędruszkiewicz

“…We tested four ML models: AIC-based stepwise regression, two tree-based algorithms (random forests and XGBoost), and neural networks. …”
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Hub-and-spoke arrangements and tacit collusion: recent national decisions and the competition market authority paper on algorithms, competition, and consumer harm by Sofia Oliveira Pais

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Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms by D. Ciani, C. Fanelli, B. Buongiorno Nardelli

“…Retrieving consistent high-resolution ADT and SST information from space is challenging, due to instrument limitations, sampling constraints, and degradations introduced by the interpolation algorithms used to obtain gap-free (L4) analyses. To address these issues, we developed and tested different deep learning methodologies, specifically convolutional neural network (CNN) models that were originally proposed for single-image super resolution. …”
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Rehabilitation monitoring and assessment: a comparative analysis of feature engineering and machine learning algorithms on the UI-PRMD and KIMORE benchmark datasets by Moamen Zaher, Amr S. Ghoneim, Laila Abdelhamid, Ayman Atia

“…The performances of feature ranking algorithms–X2, ReliefF, Gini Decrease, FCBF, Information Gain, and Information Gain Ratio–were examined alongside ML algorithms such as SVMs, RFs, KNN, LDA, and lightGBM, amongst others. …”
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Optimizing Autonomous Multi-UAV Path Planning for Inspection Missions: A Comparative Study of Genetic and Stochastic Hill Climbing Algorithms by Faten Aljalaud, Yousef Alohali

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Integration of ground-based and remote sensing data with deep learning algorithms for mapping habitats in Natura 2000 protected oak forests by Lucia Čahojová, Ivan Jarolímek, Barbora Klímová, Michal Kollár, Michaela Michalková, Karol Mikula, Aneta A. Ožvat, Denisa Slabejová, Mária Šibíková

“…A dataset was selected for the training of a deep learning algorithm called the Natural Numerical Network on the basis of the analysis results. …”
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PiLiMoT: A Modified Combination of LoLiMoT and PLN Learning Algorithms for Local Linear Neurofuzzy Modeling by Atiye Sarabi-Jamab, Babak N. Araabi

“…Locally linear model tree (LoLiMoT) and piecewise linear network (PLN) learning algorithms are two approaches in local linear neurofuzzy modeling. …”
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Optimization of the Mechanical Property of Friction Stir Welded Heat Treatable Aluminum Alloy by using Bio-Inspired Artificial Intelligence Algorithms by Akshansh Mishra, Anish Dasgupta

“…The results showed that the Differential Evolution algorithm resulted in a slightly higher value of the Ultimate Tensile Strength in comparison to the Max LIPO algorithm. …”
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Solving the Problem of Time, Cost, and Quality Trade-Off in Project Scheduling under Fuzzy Conditions Using Meta-Heuristic Algorithms by Hojjatullah Ghadir, Seyed Ahmad Shayannia, Mehdi Amir Miandargh

“…Since different quality combinations can be obtained with different combinations of parameters involved in the implementation of an algorithm, the Taguchi experimental design method has been used to adjust the parameters of the proposed algorithm. …”
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Predicting hydropower generation: A comparative analysis of Machine learning models and optimization algorithms for enhanced forecasting accuracy and operational efficiency by Chunyang Wang, Chao Li, Yudong Feng, Shoufeng Wang

“…Machine Learning techniques such as integrated Gradient Boosting and Categorical Gradient Boosting, optimized with Hunger Games search, Chaos game optimization, and Archimedes Optimization Algorithm algorithms, are used to forecast and optimize hydropower generation. …”
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A Comparison of Classification Algorithms for Predicting Dis-tinctive Characteristics in Fine Aroma Cocoa Flowers Using WE-KA Modeler by Daniel Tineo, Yuriko S. Murillo, Mercedes Marín, Darwin Gomez, Victor H. Taboada, Malluri Goñas, Lenin Quiñones Huatangari

“…Three attribute evaluators (InfoGainAttributeEval, CorrelationAttributeEval and GainRatioAttributeEval), and six algorithms (Naive Bayes, Multinomial Logistic Regression, J48, Random Forest, LTM and Simple Logistic) were employed in this study. …”
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Evaluation of Machine Learning Algorithms for Classification of Visual Stimulation-Induced EEG Signals in 2D and 3D VR Videos by Mingliang Zuo, Xiaoyu Chen, Li Sui

“…The combination of CSP features with RF, KNN, and SVM consistently showed superior performance compared to other feature-algorithm combinations, underscoring the effectiveness of CSP and these algorithms in distinguishing EEG responses to different VR experiences. …”
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Multiple Attribute Group Decision-Making Models Using Single-Valued Neutrosophic and Linguistic Neutrosophic Hybrid Element Aggregation Algorithms by Sumin Zhang, Jun Ye

“…By comparison with the existing techniques, our new techniques reveal obvious advantages in the mixed information denotation, aggregation algorithms, and decision-making methods in handling MAGDM issues with the quantitative and qualitative attributes in the setting of SVNLNHSs.…”
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Iterative Algorithms for New General Systems of Set-Valued Variational Inclusions Involving (A,η)-Maximal Relaxed Monotone Operators by Ting-jian Xiong, Heng-you Lan

“…By using the general resolvent operator technique associated with (A,η)-maximal relaxed monotone operators, we construct some new iterative algorithms for finding approximation solutions to the general system of set-valued variational inclusion problem and prove the convergence of this algorithm. …”
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Machine and deep learning algorithms for sentiment analysis during COVID-19: A vision to create fake news resistant society. by Muhammad Tayyab Zamir, Fida Ullah, Rasikh Tariq, Waqas Haider Bangyal, Muhammad Arif, Alexander Gelbukh

“…The results indicate that the BiGRU deep learning classifier demonstrates high accuracy and efficiency, with the following indicators: accuracy of 0.91, precision of 0.90, recall of 0.93, and F1-score of 0.92. For the same algorithm, the true negatives, and true positives came out to be 555 and 580, respectively, whereas, the false negatives and false positives came out to be 81, and 68, respectively. …”
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A Practical Approach for Predicting Power in a Small-Scale Off-Grid Photovoltaic System using Machine Learning Algorithms by Aadyasha Patel, O. V. Gnana Swathika, Umashankar Subramaniam, T. Sudhakar Babu, Alok Tripathi, Samriddha Nag, Alagar Karthick, M. Muhibbullah

“…The collected data from the datalogger is used as a training set for machine learning algorithms. The estimation of power generation is done by a linear regression algorithm. …”
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An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems by Sankaramoorthy Muthubalaji, Naresh Kumar Muniyaraj, Sarvade Pedda Venkata Subba Rao, Kavitha Thandapani, Pasupuleti Rama Mohan, Thangam Somasundaram, Yousef Farhaoui

“…The Polar Bear Optimization (PBO) algorithm is used to efficiently determine the parameters for the estimate of radial basis function. …”
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Developing heuristic and meta-heuristic algorithms for the problem of joint order batching and collector routing in single and multiple-cross-aisle warehouses by Zahra Shamlou, Taha Keshavarz

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