Evolutionary search algorithm for learning activation function of an artificial neural network by Yurshin Viacheslav

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Mathematical Analysis and Performance Evaluation of the GELU Activation Function in Deep Learning by Minhyeok Lee

“…Selecting the most suitable activation function is a critical factor in the effectiveness of deep learning models, as it influences their learning capacity, stability, and computational efficiency. …”
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Learning and memory function in young people with and without perinatal HIV in England. by Alejandro Arenas-Pinto, Ali Judd, Diane Melvin, Marthe Le Prevost, Caroline Foster, Kate Sturgeon, Alan Winston, Lindsay C Thompson, Diana M Gibb, Hannah Castro, Adolescents and Adults Living with Perinatal HIV (AALPHI) Steering Committee

“…We assess these functions in young people (YP) with perinatal HIV (PHIV) (with or without a previous AIDS-defining illness) and a comparable group of HIV-negative YP. 234 PHIV and 68 HIV-negative YP completed 9 tests; 5 National Institutes of Health (NIH) Toolbox tests (2 executive function, 1 speed of information processing, 2 memory); 2 Hopkins Verbal Learning Test Revised (HVLT-R) (learning (L), delayed recall (R)), and 2 verbal application measures. …”
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Quantification of Motor Learning in Hand Adjustability Movements: An Evaluation Variable for Discriminant Cognitive Decline by Kazuya Toshima, Yu Chokki, Toshiaki Wasaka, Tsukasa Tamaru, Yoshifumi Morita

Subjects: “…Motor learning…”
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FUNCTIONAL ANALYSIS AND FUNCTIONAL MAPS OF QUALIFICATIONS IN ECVET CONTEXT by V. A. Kopnov, O. V. Shmurygina, D. E. Shchipanova, M. A. Dremina, L. Papaloizou, Y. Orphanidou, P. Morevs

Subjects: “…functional analysis…”
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Language learning as a factor of social cohesion in multilingual society: rection on the functional change of teaching and learning foreign languages by Aída Fernández Bueno

“…Teaching / learning foreign languages in Spain has improved a signi cant functional change in recent years, motivated directly by the social changes, and especially, by immigration. …”
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Data normalization in machine learning by V. V. Starovoitov, Yu. I. Golub

“…In machine learning, the input data is often given in different dimensions. …”
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A malware detection method with function parameters encoding and function dependency modeling by Ronghao Hou, Dongjie Liu, Xiaobo Jin, Jian Weng, Guanggang Geng

“…Although researchers have introduced traditional machine learning and deep learning methods to conduct extensive research on functions in malware detection, these methods have largely ignored the analysis of function parameters and functional dependencies. …”
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Integrated Model Selection and Scalability in Functional Data Analysis Through Bayesian Learning by Wenzheng Tao, Sarang Joshi, Ross Whitaker

Subjects: “…functional data analysis…”
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Associations between Executive Functions and Sensorimotor Performance in Children at Risk for Learning Disabilities by Cecília Tószegi, Andras N. Zsido, Beatrix Lábadi

“…The aim of the current study is to provide new insights into the relationship between executive functions and sensorimotor development by considering the risks associated with learning difficulties. …”
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An Extension of the Quadratic Error Function for Learning Imprecise Data in Multivariate Nonlinear Regression by Castro Gbêmêmali Hounmenou, Kossi Essona Gneyou, Romain Glélé Kakaï

“…We give an extension of the generalized least squares error function in a context of multivariate nonlinear regression to learn imprecise data. …”
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Compensatory Relation Between Executive Function and Fluid Intelligence in Predicting Math Learning by Marina Vasilyeva, Linxi Lu, Kennedy Damoah, Elida V. Laski

Subjects: “…math learning…”
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Functional connectivity and GABAergic signaling modulate the enhancement effect of neurostimulation on mathematical learning. by George Zacharopoulos, Masoumeh Dehghani, Beatrix Krause-Sorio, Jamie Near, Roi Cohen Kadosh

“…Our multimodal approach elucidates the causal role of the dlPFC and frontoparietal network in a critical academic learning skill, shedding light on the interplay between functional connectivity and GABAergic modulation in the efficacy of brain-based interventions to augment learning outcomes, particularly benefiting individuals who would learn less optimally based on their neurobiological profile.…”
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Sb-PiPLU: A Novel Parametric Activation Function for Deep Learning by Ayan Mondal, Vimal K. Shrivastava, Ayan Chatterjee, Raghavendra Ramachandra

“…In recent years, a variety of non-linear activation functions have been proposed. However, many of these suffer from drawbacks that limit the effectiveness of deep learning models. …”
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Multi-Function Working Mode Recognition Based on Multi-Feature Joint Learning by Lei Liu, Minghua Wu, Dongyang Cheng, Wei Wang

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Options for Strengthening the Ensemble of Hypotheses under Uncertainty of the Objective Learning Function Formation by A. F. Chernyavsky, A. I. Kazlova

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Effects of aminooxyacetic acid on learning and memory function and neurochemical changes in chronic alcoholism by Hongbo Jiang, Xunling Wang, Yingwei Liang, Yinghan Hou, Xinping Yue, Zhiyi Zhang, Dan Chen, Xinyi Fan, Ailin Du

“…Objective: This study aimed to investigate the effect of aminooxyacetic acid (AOAA) on cognitive function, particularly learning and memory, in a rat model of chronic alcoholism. …”
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Deployment method for vEPC virtualized network function via Q-learning by Quan YUAN, Hong-bo TANG, Kai-zhi HUANG, Xiao-lei WANG, Yu ZHAO

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Understanding Occlusion and Temporomandibular Joint Function Using Deep Learning and Predictive Modeling by Taseef Hasan Farook, James Dudley

“…This narrative review explores the application of predictive modeling and deep learning to identify clinical trends and associations related to occlusion and TMJ function. …”
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Multi-scale machine learning model predicts muscle and functional disease progression by Silvia S. Blemker, Lara Riem, Olivia DuCharme, Megan Pinette, Kathryn Eve Costanzo, Emma Weatherley, Jeff Statland, Stephen J. Tapscott, Leo H. Wang, Dennis W. W. Shaw, Xing Song, Doris Leung, Seth D. Friedman

“…This study introduces a multi-scale machine learning framework leveraging whole-body magnetic resonance imaging (MRI) and clinical data to predict regional, muscle, joint, and functional progression in FSHD. …”
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