Projection of hydrological drought based on SSP scenarios using surface water supply index and SWAT model in mountainous watershed by Omid Babamiri, Yagob Dinpashoh, Alireza Samavati, Faeze Shoja

“…The calibration and validation of the SWAT model were conducted using the Sequential Uncertainty Fitting version 2 (SUFI-2) algorithm, covering the calibration period from 2004 to 2017 and the validation period from 2018 to 2020. …”
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Generalized distribution Sech^k by A. V. Ausiannikau

“…Base attributes and statisticians of distribution are given. Algorithms of point estimation of parameter of shift for known and unknown parameters of scale, and also algorithms of a sequential interval estimation of parameter of shift are resulted.…”
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Constrained Bayesian Optimization: A Review by Sasan Amini, Inneke Vannieuwenhuyse, Alejandro Morales-Hernandez

“…Bayesian optimization is a sequential optimization method that is particularly well suited for problems with limited computational budgets involving expensive and non-convex black-box functions. …”
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The Intelligent Recognition of Speech Emotions: Survey Study by Ali Abdulwahhab Yehya al_saffar, Fawziya Ramo

“…Speech emotion recognition (SER) is a challenging task in the field of artificial intelligence and machine learning. Over the years, researchers have proposed various approaches to recognize emotions from speech signals. …”
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The Role of Time in Facial Dynamics and Challenges in Automatic Emotion Recognition (2019–2024) by Williams Contreras-Higuera, Lucrezia Crescenzi-Lanna

“…The study provides valuable insights for selecting appropriate algorithms that are tailored to specific research objectives and contexts.…”
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Analyzing Fairness of Computer Vision and Natural Language Processing Models by Ahmed Rashed, Abdelkrim Kallich, Mohamed Eltayeb

“…The results reveal that some sequential applications improve the performance of mitigation algorithms by effectively reducing bias while maintaining the model’s performance. …”
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Enhancing wheat yellow rust detection through modified deep learning approach by Shant Kumar, Rohit Singh, Sudheer Kumar, Sandeep Gupta

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Reinforcement learning applications in water resource management: a systematic literature review by Linus Kåge, Vlatko Milić, Vlatko Milić, Maria Andersson, Magnus Wallén

“…Among the algorithms, deep Q-networks are the most commonly employed. …”
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Applications of Machine Learning Technologies for Feedstock Yield Estimation of Ethanol Production by Hyeongjun Lim, Sojung Kim

“…Given that it is becoming increasingly difficult to stably produce biofuel feedstocks as climate change worsens, research on developing predictive modeling for raw material supply using the latest ML techniques is very important. …”
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CALCULATION OF WEIGHT COEFFICIENTS IN CONTINUOUS PARTICLE FILTER by K. A. Rybakov

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Vision-based approach to knee osteoarthritis and Parkinson’s disease detection utilizing human gait patterns by Zeeshan Ali, Jihoon Moon, Saira Gillani, Sitara Afzal, Muazzam Maqsood, Seungmin Rho

“…Numerous clinical methods have been proposed in research to diagnose these disorders; however, a current trend in diagnosis is through human gait patterns. …”
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Global Feature Focusing and Information Enhancement Network for Occluded Pedestrian Detection by ZHENG Kaikui, JI Kangyou, LI Jun, LI Qiming

“…This mechanism first uses the bilinear interpolation algorithm to adjust the spatial resolution of different scale feature maps to be consistent. …”
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Extraction of exact symbolic stationary probability formulas for Markov chains with finite space with application to production lines. Part I: description of methodology by Konstantinos S. Boulas, Georgios D. Dounias, Chrissoleon T. Papadopoulos

“…This algorithm is applied sequentially, resulting in the formation of monomials, polynomials for each vertex, and, ultimately, the set of polynomials of the graph. …”
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On the Satisfiability and Model Checking for one Parameterized Extension of Linear-time Temporal Logic by Anton Romanovich Gnatenko, Vladimir Anatolyevich Zakharov

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Analyzing Random Forest’s Predictive Capability for Type 1 Diabetes Progression by Niels F. Cleymans, Mark Van De Casteele, Julie Vandewalle, Aster K. Desouter, Frans K. Gorus, Kurt Barbe

“…This explorative study aims to uncover the potential of random forest machine learning algorithms as survival models within the biomedical context of T1D. …”
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Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU by Pascal Riedel, Kaouther Belkilani, Manfred Reichert, Gerd Heilscher, Reinhold von Schwerin

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DGM-TOP: automatic identification of the critical boundaries in atrial tachycardia by Robin Van Den Abeele, Sander Hendrickx, Niels Carlier, Eike M. Wülfers, Arthur Santos Bezerra, Bjorn Verstraeten, Sebastiaan Lootens, Karel Desplenter, Arstanbek Okenov, Timur Nezlobinsky, Annika Haas, Armin Luik, Sebastien Knecht, Mattias Duytschaever, Nele Vandersickel

“…Interconnecting both critical boundaries with an ablation line terminates the tachycardia.MethodsThis research focuses on the specific algorithms for calculating the index/topological charge of each anatomical boundary, called DGM-TOP. …”
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Computational Strategy for Analyzing Effective Properties of Random Composites—Part I: Conductivity by Vladimir Mityushev, Piotr Drygaś, Simon Gluzman, Nurlan Nauryzbayev, Karlygash Nurtazina

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Surrogate modeling of passive microwave circuits using recurrent neural networks and domain confinement by Kaustab C. Sahu, Slawomir Koziel, Anna Pietrenko-Dabrowska

“…However, building accurate surrogates is a daunting task beyond simple cases (low dimensionality, narrow geometry parameter and frequency ranges). This research suggests a new technique for dependable modeling of microwave circuits. …”
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Selective Reviews of Bandit Problems in AI via a Statistical View by Pengjie Zhou, Haoyu Wei, Huiming Zhang

“…A key subset includes multi-armed bandit (MAB) and stochastic continuum-armed bandit (SCAB) problems, which model sequential decision-making under uncertainty. This review outlines the foundational models and assumptions of bandit problems, explores non-asymptotic theoretical tools like concentration inequalities and minimax regret bounds, and compares frequentist and Bayesian algorithms for managing exploration–exploitation trade-offs. …”
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