Pruning methods for RBF networks by Tarannum Shakir.

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Discrete differential geometry driven methods for architectural geometry by Yao, Sidan

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Data-driven methods to predict the stability metrics of catalytic nanoparticles by Prabhu, Asmee M., Choksi, Tej S.

“…In this review we discuss the recent advances in data-driven methods to predict stability metrics of nanoparticles.…”
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A data-driven method for automated data superposition with applications in soft matter science by Lennon, Kyle R., McKinley, Gareth H., Swan, James W.

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A transparent data-driven method for stability-constrained load restoration considering multi-phase load dynamics by Xie, Dunjian, Xu, Yan, Nadarajan, Sivakumar, Viswanathan, Vaiyapuri, Gupta, Amit Kumar

“…A transparent data-driven method based on optimal decision trees is developed to address all the short-term stability criteria (including frequency, voltage, and transient stability) considering the inrush phase of load dynamics. …”
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Psychotropic taxonomies: constructing a therapeutic framework for psychiatry by McCutcheon, RA, Cowen, P, Nour, MM, Pillinger, T

“…Finally, we consider how data-driven methods might address the issue of classification at an intermediate level, based around transdiagnostic neurobiological and psychopathological markers.…”

Transfer learning on UR robots by Yu, Xiwei

“…As neural networks and deep learning develop, researchers are continually exploring the capabilities and potential of using data-driven methods to control robots. As a data-driven approach, training neural networks requires substantial data support, making the collection of sufficient data to create datasets crucial. …”
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AI Analytics for Carbon-Neutral City Planning: A Systematic Review of Applications by Cong, Cong, Page, Jessica, Kwak, Yoonshin, Deal, Brian, Kalantari, Zahra

“…We also consider the importance of interdisciplinary collaboration, responsible AI governance, and community engagement in guiding data-driven methods and suggest how AI can contribute to supporting carbon-neutrality goals.…”
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Weakly supervised learning on road area extraction via classification labels by Liu, Xuanyi

“…Since annotating roads at pixel level is labor intensive, several data-driven methods have been proposed for this task also, including both unsupervised methods and fully supervised methods. …”
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A Data Mining-Based Model for Assessing Guangzhou's Higher Vocational Colleges 'New Energy Automobile Majors' Vocational Skills by He, Ling, Hamid, Hashima

“…The findings show the positive aspects of using modern data-driven methods to more closely match educational institutions with the constantly evolving needs of the AS. …”
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Investigating heterogeneity across autism, ADHD, and typical development using measures of cortical thickness, surface area, cortical/subcortical volume, and structural covariance by Sadat-Nejad, Y, Vandewouw, MM, Cardy, R, Lerch, J, Taylor, MJ, Iaboni, A, Hammill, C, Syed, B, Brian, JA, Kelley, E, Ayub, M, Crosbie, J, Schachar, R, Georgiades, S, Nicolson, R, Anagnostou, E, Kushki, A

“…To address this gap, data-driven methods can be used to discover groups of individuals with shared biological patterns. …”

Advanced data analytics for future smart grid stability assessment by Ren, Chao

“…The main contributions through this Ph.D. research are as follows: Firstly, the temporal-adaptive data-driven methods are proposed based on optimized hybrid ensemble learning mechanism for online STVS assessment. …”
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Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel by Zhao, Hongbo, Deng, Haitao Dean, Cohen, Alexander E, Lim, Jongwoo, Li, Yiyang, Fraggedakis, Dimitrios, Jiang, Benben, Storey, Brian D, Chueh, William C, Braatz, Richard D, Bazant, Martin Z

“…Experimental characterizations of such materials by operando microscopy produce rich image datasets3,4,5,6, but data-driven methods to learn physics from these images are still lacking because of the complex coupling of reaction kinetics, surface chemistry and phase separation7. …”
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Knowledge-guided fuzzy logic modeling to infer cellular signaling networks from proteomic data by Liu, Hui, Zhang, Fan, Mishra, Shital Kumar, Zhou, Shuigeng, Zheng, Jie

“…However, canonical signaling pathways curated from literature are seldom context-specific and thus can hardly predict cell type-specific response to external perturbations; purely data-driven methods also have drawbacks such as limited biological interpretability. …”
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AI techniques in CFD: leveraging physical priors with deep learning to improve on numerical solvers by Loh, Jessica Sher En

“…Computational fluid dynamics (CFD) simulation is an irreplaceable modelling step in many engineering designs, but it is often computationally expensive to execute numerically. Data-driven methods such as using neural networks to simulate the solving of partial differential equations such as Navier-Stokes solving have increasingly emerged. …”
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0-1 Knapsack in Nearly Quadratic Time by Jin, Ce

“…To extend this approach to our 0-1 setting, we use a novel pruning method, as well as the two-level color-coding of Bringmann (2017) and the SMAWK algorithm on tall matrices.…”
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A multimodal approach to analysis of steady state visually evoked potentials by Vij, Nikhil, Zuobin, Wu, Bjornsdotter, Malin, Dauwels, Justin, Vialatte, François-Benoît

“…We demonstrate that the proposed approach is a practical data-driven method to achieve spatio-frequency fusion of EEG and fMRI SSVEP responses.…”
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Artificial intelligence-based lifetime estimation for battery in electric vehicles by Manya, Miglani

“…This report presents a data-driven method to estimate the lifetime of Electric Vehicles (EVs) batteries using simulations based on a Li-Ion battery model. …”
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Multi-view CNNS for hand gesture recognition by Dou, Yudi

“…Di↵erent from the traditional model-driven method, we do not need colored markers to extract features or skin color models to represent hand region. …”
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Search for single-production of vector-like quarks decaying into Wb in the fully hadronic final state in pp collisions at s = 13 TeV with the ATLAS detector by Aad, G, Aakvaag, E, Abbott, B, Abdelhameed, S, Abeling, K, Abicht, NJ, Abidi, SH, Aboelela, M, Aboulhorma, A, Abramowicz, H, Abreu, H, Abulaiti, Y, Acharya, BS, Ackermann, A, Adam Bourdarios, C, Adamczyk, L, Addepalli, SV, Addison, MJ, Adelman, J, Adiguzel, A, Adye, T, Affolder, AA, Afik, Y, Agaras, MN, Barr, AJ, Bortoletto, D, Bruckler, TL, Celli, F, Chen, M, Conroy, EI, Cooper-Sarkar, AM, Dingley, T, Draguet, MA, Eberwein, GH, Frost, JA, Gallas, EJ, Grabarczyk, RP, Gwenlan, C, Haslbeck, F, Hays, CP, Hodkinson, BH, Huffman, TB, Iizawa, T, Karava, K, Koch, SF, Li, Z, Moser, B, Nagai, K, Nedic, L, Nickerson, RB, Pacey, HA, Rossi, E, Ruggiero, A, Schopf, E, Shipsey, IPJ, Sultan, DMS, Viehhauser, GHA, Weidberg, AR, Yildirim, TP

“…The main background is QCD multijet production, which is estimated using a data-driven method. Upon finding no significant excess in data, mass limits at 95% confidence level are obtained as a function of the global coupling parameter, κ. …”