Learning model combined with data clustering and dimensionality reduction for short-term electricity load forecasting by Hyun-Jung Bae, Jong-Seong Park, Ji-hyeok Choi, Hyuk-Yoon Kwon

“…Here, we adapt k-means clustering for data clustering, kernel principal component analysis (kernel PCA), universal manifold approximation and projection (UMAP), and t-stochastic nearest neighbor (t-SNE) for dimensionality reduction. …”
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Genetic Algorithms in Antennas and Smart Antennas Design Overview: Two Novel Antenna Systems for Triband GNSS Applications and a Circular Switched Parasitic Array for WiMax Applic... by Stylianos C. Panagiotou, Stelios C. A. Thomopoulos, Christos N. Capsalis

“…Genetic algorithms belong to a stochastic class of evolutionary techniques, whose robustness and global search of the solutions space have made them extremely popular among researchers. …”
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Analysis and Simulation of Intervention Strategies against Bus Bunching by means of an Empirical Agent-Based Model by Wei Liang Quek, Ning Ning Chung, Vee-Liem Saw, Lock Yue Chew

“…Monte Carlo sampling is then performed on these two derived probability distributions to yield the stochastic dynamics of both the buses’ motion and passengers’ arrival. …”
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From TORTORA to MegaTORTORA—Results and Prospects of Search for Fast Optical Transients by Grigory Beskin, Sergey Bondar, Sergey Karpov, Vladimir Plokhotnichenko, Adriano Guarnieri, Corrado Bartolini, Giuseppe Greco, Adalberto Piccioni, Andrew Shearer

“…To study short stochastic optical flares of different objects (GRBs, SNs, etc.) of unknown localizations as well as NEOs it is necessary to monitor large regions of sky with high-time resolution. …”
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Financial institutions efficiency: a systematic literature review by Danijel Petrović, Goran Karanović

“…The results reveal that both parametric (Stochastic Frontier Approach) and non-parametric (Data Envelopment Analysis) models are equally utilized in estimating the efficiency of financial institutions. …”
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A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States. by John M Drake, Andreas Handel, Éric Marty, Eamon B O'Dea, Tierney O'Sullivan, Giovanni Righi, Andrew T Tredennick

“…To support decision-making and policy for managing epidemics of emerging pathogens, we present a model for inference and scenario analysis of SARS-CoV-2 transmission in the USA. The stochastic SEIR-type model includes compartments for latent, asymptomatic, detected and undetected symptomatic individuals, and hospitalized cases, and features realistic interval distributions for presymptomatic and symptomatic periods, time varying rates of case detection, diagnosis, and mortality. …”
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Learning a Quantum Computer's Capability by Daniel Hothem, Kevin Young, Tommie Catanach, Timothy Proctor

“…Our CNN capability models obtain approximately a 1% average absolute prediction error when modeling processors experiencing both Markovian and non-Markovian stochastic Pauli errors. We also apply our CNNs to model the capabilities of cloud-access quantum computing systems, obtaining moderate prediction accuracy (average absolute error around 2–5%), and we highlight the challenges to building better neural network capability models.…”
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A Time Variant Outdoor-to-Indoor Channel Model for Mobile Radio Based Navigation Applications by Wei Wang, Thomas Jost, Uwe-Carsten Fiebig, Wolfgang Koch

“…In this paper, an outdoor-to-indoor channel model is proposed based on an extension of the geometry-based stochastic modeling approach to fulfill the requirements. …”
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Impact of Irrigation Ecology on Rice Production Efficiency in Ghana by John Kanburi Bidzakin, Simon C. Fialor, Dadson Awunyo-Vitor, Iddrisu Yahaya

“…Cross-sectional data was obtained from 350 rice farmers across rain fed and irrigation ecologies. Stochastic frontier analyses are used to estimate the production efficiency and endogenous treatment effect regression model is used to estimate the impact of irrigation ecology on rice production efficiency. …”
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PSI Methodologies for Nuclear Data Uncertainty Propagation with CASMO-5M and MCNPX: Results for OECD/NEA UAM Benchmark Phase I by W. Wieselquist, T. Zhu, A. Vasiliev, H. Ferroukhi

“…Two complimentary UQ techniques have been developed thus far: (i) direct perturbation (DP) and (ii) stochastic sampling (SS). The DP technique is, first and foremost, a robust and versatile sensitivity coefficient calculation, applicable to all types of input and output. …”
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Implementation of flexible DEA structure in the management of business processes by Šegrt Slobodan, Tomić Radoljub, Anđelković Maja

“…In tests, the deviation from the DEA borderline can be viewed as a stochastic variable. The DEA estimate is certainly biased in finite samples (debatable statistics), while the expected value of the DEA efficiency is almost certainly the true value of the parameter in large samples (complete statistics). …”
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An examination of changes in autumn Eurasian snow cover and its relationship with the winter Arctic Oscillation using 20th Century Reanalysis version 3 by G. J. Marshall

“…Novel aspects are (i) analysis back to 1836, (ii) adjusting the reanalysis SC through comparison with observations, and (iii) analysing the statistical significance of the frequency of periods of significant SC–AO relationships to determine whether these connections can be distinguished from stochastic processes. Across the full span of 20CRv3, there is a small increase in mean September Eurasian SC. …”
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A Trial-and-Error Method with Autonomous Vehicle-to-Infrastructure Traffic Counts for Cordon-Based Congestion Pricing by Zhiyuan Liu, Yong Zhang, Shuaian Wang, Zhibin Li

“…Two practical properties of the cordon-based pricing are further considered in this article: the toll charge on each entry of one pricing cordon is identical; the total inbound flow to one cordon should be restricted in order to maintain the traffic conditions within the cordon area. Then, the stochastic user equilibrium (SUE) with asymmetric link travel time functions is used to assess each feasible toll pattern. …”
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Innovative Framework for Historical Architectural Recognition in China: Integrating Swin Transformer and Global Channel–Spatial Attention Mechanism by Jiade Wu, Yang Ying, Yigao Tan, Zhuliang Liu

“…To gain deeper insights into the model’s decision-making process, we employed comprehensive interpretability methods including t-SNE (t-distributed Stochastic Neighbor Embedding), Grad-CAM (gradient-weighted class activation mapping), and multi-layer feature map analysis, revealing the model’s systematic feature extraction process from structural elements to material textures. …”
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Transformer-Based Optimization for Text-to-Gloss in Low-Resource Neural Machine Translation by Younes Ouargani, Noussaim El Khattabi

“…The trials involve optimizing a minimal model, and our complex model with different optimizers; The findings from these trials show that both Adaptive Gradient (AdaGrad) and Adaptive Momentum (Adam) offer significantly better performance than Stochastic Gradient Descent (SGD) and Adaptive Delta (AdaDelta) in the minimal model scenario, however, Adam offers significantly better performance in the complex model optimization task. …”
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A Reliable Method for Identification of Antibiotics by Terahertz Spectroscopy and SVM by Jin Guo, Hu Deng, Quancheng Liu, Linyu Chen, Zhonggang Xiong, Liping Shang

“…For dimensionality reduction, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) were implemented, respectively. …”
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Heavy-tailed flood peak distributions: what is the effect of the spatial variability of rainfall and runoff generation? by E. Macdonald, B. Merz, B. Merz, V. D. Nguyen, S. Vorogushyn

“…This is done using a model chain consisting of a stochastic weather generator, a conceptual rainfall-runoff model, and a river routing routine. …”
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Bi-level coordinated restoration for the distribution system and multi-microgrids by Hao Zhu, Xiaotian Sun, Haipeng Xie, Lingfeng Tang, Zhaohong Bie

“…The proposed framework is capable of finding the optimal restoration scheme provided that the autonomy of MGs is retained. The stochastic programming is leveraged to model the uncertainty of renewable energy, and the coordinated restoration framework is formulated as a bi-level mixed integer linear programming (BMILP). …”
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Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting by Hai Tao, Ali Omran Al-Sulttani, Ameen Mohammed Salih Ameen, Zainab Hasan Ali, Nadhir Al-Ansari, Sinan Q. Salih, Reham R. Mostafa

“…This has made it more efficient in forecasting stochastic river flow behaviour compared to the other developed hybrid models.…”
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A systematic literature review of aggregate production planning (APP): Social and economic perspectives by Wan Yee Leong, Kuan Yew Wong, Ali Anjomshoae

“…Findings: The outcome shows that most of the previous studies applied mixed-integer linear programming (MILP) methods in developing the APP models while stochastic and fuzzy methods are the most common approaches to deal with uncertainties. …”
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