Deep Learning-Based Glaucoma Detection Using Clinical Notes: A Comparative Study of Long Short-Term Memory and Convolutional Neural Network Models by Ali Mohammadjafari, Maohua Lin, Min Shi

“…Different popular models are explored to predict the binary glaucomatous status defined from a comprehensive vision function assessment. <b>Methods:</b> We compared multiple deep learning architectures, including Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and transformer-based models BERT and BioBERT. …”
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Rheological behavior of MWCNT-SnO2/SAE50 hybrid nanolubricant: Experimental evaluation and viscosity prediction using optimized machine learning model by Mojtaba Sepehrnia, Hamid Maleki, Alireza Hamidi, Ali Davoodi, Hamidreza Golmohammadi

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CCR2 restricts IFN-γ production by hippocampal CD8 TRM cells that impair learning and memory during recovery from WNV encephalitis by Shenjian Ai, Artem Arutyunov, Joshua Liu, Jeremy D. Hill, Xiaoping Jiang, Robyn S. Klein

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Channel-Dependent Multilayer EEG Time-Frequency Representations Combined with Transfer Learning-Based Deep CNN Framework for Few-Channel MI EEG Classification by Ziang Liu, Kang Fan, Qin Gu, Yaduan Ruan

“…The study of electroencephalogram (EEG) signals is crucial for understanding brain function and has extensive applications in clinical diagnosis, neuroscience, and brain–computer interface technology. …”
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G2Grad-CAMRL: An Object Detection and Interpretation Model Based on Gradient-Weighted Class Activation Mapping and Reinforcement Learning in Remote Sensing Images by Shoulin Yin, Liguo Wang, Muhammad Shafiq, Lin Teng, Asif Ali Laghari, Muhammad Faizan Khan

“…At the same time, we improve the reward function of reinforcement learning to improve the effectiveness of the region generation network. …”
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Aprendizagem de procedimentos e efeitos ansiolíticos: medidas eletrencefalográficas, motora e atencional Procedural learning and anxiolytic effects: electroencephalographic, motor... by Claudio Elidio Portella, Julio Guilherme Silva, Victor Hugo Bastos, Dionis Machado, Marlo Cunha, Maurício Cagy, Luis Basile, Roberto Piedade, Pedro Ribeiro

“…The coherence measure was not a sensitive tool at demonstrating differences between cortical areas as a function of procedural learning.…”
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Alpha absolute power: motor learning of practical pistol shooting Potência absoluta de alfa: aprendizagem motora do tiro prático de pistola by Clayton Amaral Domingues, Sergio Machado, Emerson Garcia Cavaleiro, Vernon Furtado, Mauricio Cagy, Pedro Ribeiro, Roberto Piedade

“…It was observed a decrease in alpha power in all electrodes examined during task execution when compared with baseline and learning control blocks. The findings suggest that alpha power decreases as the function of the motor learning task when subjects are engaged in the motor execution.…”
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Intelligent Wireless Power Scheduling for Lunar Multienergy Systems: Deep Reinforcement Learning for Real-Time Adaptive Beam Steering and Vehicle-to-Grid Energy Optimization by Thomas Tongxin Li, Shuangqi Li, Cynthia Xin Ding, Zhaoyao Bao, Mohannad Alhazmi

“…The reward function penalizes energy misallocation and misalignment losses while maximizing charging efficiency and systemwide energy resilience. …”
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Detecting Conversation Topics in Recruitment Calls of African American Participants to the All of Us Research Program Using Machine Learning: Model Development and Validation Study by Priscilla Pemu, Michael Prude, Atuarra McCaslin, Elizabeth Ojemakinde, Christopher Awad, Kelechi Igwe, Anny Rodriguez, Jasmine Foriest, Muhammed Idris

“…MethodsOur study design was an observational, retrospective approach using machine learning for content analysis. Specifically, we used structural topic modeling to identify and compare latent topics of conversation in recruitment calls by Morehouse School of Medicine RAs between February 2021 and April 2022 by estimating expected topic proportions in the corpus as a function of enrollment and participation in AoURP. …”
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Tbx1 haploinsufficiency leads to local skull deformity, paraflocculus and flocculus dysplasia, and motor-learning deficit in 22q11.2 deletion syndrome by Tae-Yeon Eom, J. Eric Schmitt, Yiran Li, Christopher M. Davenport, Jeffrey Steinberg, Audrey Bonnan, Shahinur Alam, Young Sang Ryu, Leena Paul, Baranda S. Hansen, Khaled Khairy, Stephane Pelletier, Shondra M. Pruett-Miller, David R. Roalf, Raquel E. Gur, Beverly S. Emanuel, Donna M. McDonald-McGinn, Jesse N. Smith, Cai Li, Jason M. Christie, Paul A. Northcott, Stanislav S. Zakharenko

“…These findings suggest a previously unrecognized pathogenic structure/function relation in 22q11DS in which local skeletal deformity and cerebellar dysplasia result in behavioral deficiencies.…”
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Structure- and machine learning-guided engineering demonstrate that a non-canonical disulfide in an anti-PD-1 rabbit antibody does not impede antibody developability by Wei-Ching Liang, Hongkang Xi, Dawei Sun, Luigi D’Ascenzo, Jonathan Zarzar, Nicole Stephens, Ryan Cook, Yinyin Li, Zhengmao Ye, Marissa Matsumoto, Jian Payandeh, Matthieu Masureel, Yan Wu

“…Next, and prompted by recent developments in machine learning (ML)-guided protein engineering, we used an unbiased ML- and structure-guided approach to rapidly and efficiently generate a different variant with recovered affinity. …”
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Évaluer les savoirs de l’expérience by Isabelle Cherqui-Houot

“…After an exploration of the procedures for accreditation of prior experiential learning in five universities, this contribution will analyse the practices of coaching and the functioning of juries in these universities. …”
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How Gestures Support Children’s Linguistic–Communicative and Cognitive Development by Gökçen Avcılar, Bilge Bilir, Şevval Cihankaya, Zeynep Çoşkun, İlayda Piri, Ercenur Ünal

“…This article focuses on two functions of gestures: (1) how gestures produced with speech facilitate communication, (2) how producing gestures or observing others’ gestures facilitates learning and cognitive development. …”
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Implementation of the Moodle System Into EFL Classes by Nuket Gunduz, Deniz Ozcan

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Enhancing the educational interaction in family medicine registrar training in the clinical context by Bob Mash, Jo Goedhuys, Franky D'Argent

“…The relationship between registrar and trainer functions best when the trainer consciously facilitates the registrars learning and considers all their interactions as educational opportunities. …”
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MELGYM: A dynamic control interface for MELCOR simulations by Antonio Manjavacas, Juan Gómez-Romero, Damien Ernst, Manuel A. Vázquez-Barroso, Francisco Martín-Fuertes

“…MELGYM enables the training of reinforcement learning agents on MELCOR simulations, introducing real-time interaction and enabling the definition of reward functions based on simulation outcomes.…”
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Phenoflow-ML: A next-generation phenotyping framework to capture ML-based phenotypes by Antonio Lopez-Martinez-Carrasco

“…This infrastructure is open-access and extensible, and its development was motivated by an important limitation of the state-of-the-art phenotyping tools: the lack of support for phenotypes based on machine learning. Some advantages of Phenoflow-ML are (a) its modular design, which improves code reuse and implementing new functionality, (b) the use of CWL, permitting the generation of portable and standard ML-based phenotype definitions for easy sharing between practitioners and institutions and enhancing research reproducibility, (c) the extensive documentation provided, and (d) the solid base of users and continuous growth since the initial versions. …”
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Pemanfaatan Laboratorium Konseling Sebagai Sumber Belajar Bimbingan Konseling by Purbatua Manurung

“…Data analysis uses content analysis techniques where each source or literature used is adjusted and the context is seen for its suitability with other literature from books, journals or other research. In relation to learning resources, the counseling laboratory functions as (1) to provide completeness for the theoretical lessons that have been received so that theory and practice are not two separate things. …”
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Deep Learning–Based Chronic Obstructive Pulmonary Disease Exacerbation Prediction Using Flow-Volume and Volume-Time Curve Imaging: Retrospective Cohort Study by Eun-Tae Jeon, Heemoon Park, Jung-Kyu Lee, Eun Young Heo, Chang Hoon Lee, Deog Kyeom Kim, Dong Hyun Kim, Hyun Woo Lee

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An empirical study of the naïve REINFORCE algorithm for predictive maintenance by Rajesh Siraskar, Satish Kumar, Shruti Patil, Arunkumar Bongale, Ketan Kotecha, Ambarish Kulkarni

“…For the REINFORCE, a basic hyperparameter sensitivity and interaction analysis is conducted to better understand the dynamics and present results for the hyperparameters learning rate, discount factor $$\gamma$$ γ and the network activation functions (ReLU and Tanh). …”
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