Students’ Perspectives on Learning Practical Nursing Skills: A Focus Group Study in Norway by A. G. Gregersen, M. T. Hansen, S. E. A. Brynhildsen, V. A. Grøndahl, A. C. Leonardsen

“…Studies emphasize a need to uncover whether the technical aspect of nursing skills, in general, is challenging in students’ learning. The aim of this study was to explore students’ perspectives on practical nursing skills and how they can best learn these. …”
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Faster implicit motor sequence learning of new sequences compatible in terms of movement transitions by Susanne Dyck, Christian Klaes

“…npj Science of Learning…”
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The Application of Differing Machine Learning Algorithms and Their Related Performance in Detecting Skin Cancers and Melanomas by Suboh Alkhushayni, Du’a Al-zaleq, Luwis Andradi, Patrick Flynn

“…When comparing different existing implementations of machine learning against public datasets and several we seek to create, we attempted to create a more accurate model that can be readily adapted to use in clinical settings. …”
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Teacher’s strategies for stimulating EFL students’ active learning in an Indonesian senior high school by Nikmatus Solikhah, Muhammad Jauhari Sofi

Subjects: “…active learning…”
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Overcoming or overstepping? Boundary infrastructure for learning in the context of continuous improvement research-practice partnership by Angel X. Bohannon, Caitlin C. Farrell, Spencer Cook

“…Then we describe how the county office of education created important opportunities for learning through a boundary infrastructure: the network of people, practices, and objects that supported the movement of ideas between research, practice, and CI communities.DiscussionWe highlight the possibilities of county offices as critical actors in CI efforts. …”
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MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning by Guangyuan Xu, Ruifeng Guo, Zhenyu Yin, Feiqing Zhang

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A Topological Approach to Enhancing Consistency in Machine Learning via Recurrent Neural Networks by Muhammed Adil Yatkin, Mihkel Kõrgesaar, Ümit Işlak

Subjects: “…sequence to sequence learning…”
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AI-assisted grammar learning: Improving present perfect tense proficiency in EFL students by Barkın Baran Yener, Hasan Selçuk

Subjects: “…ai-assisted language learning…”
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Response to Novelty Correlates with Learning Rate in a Go/No-Go Task in Göttingen Minipigs by Nanna Marie Lind, Anette Moustgaard

“…Novelty-seeking and harm-avoidance personality traits influence Go/No-go (GNG) learning in humans. Animal studies have also indicated a link between response to novelty and spatial discrimination learning. …”
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IMPLEMENTATION OF LEARNING MANAGEMENT SYSTEMS WITH GENERATIVE ARTIFICIAL INTELLIGENCE FUNCTIONS IN THE POST-PANDEMIC ENVIRONMENT by Denis-Cătălin Arghir

Subjects: “…online learning…”
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Towards accurate anomaly detection for cloud system via graph-enhanced contrastive learning by Zhen Zhang, Zhe Zhu, Chen Xu, Jinyu Zhang, Shaohua Xu

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Developing Train Station Parking Algorithms: New Frameworks Based on Fuzzy Reinforcement Learning by Wei Li, Kai Xian, Jiateng Yin, Dewang Chen

“…To increase the parking accuracy, robustness, and self-learning ability, we propose new train station parking frameworks by using the reinforcement learning (RL) theory combined with the information of balises. …”
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Deep Learning for an Innovative Photo Energy Model to Estimate the Energy Distribution in Smart Apartments by Komala C R, S. Vimal, G. Ravindra, P. Hariramakrishnan, Shaik Razia, S. Geerthik, K. Raja, V. Mohanavel, Nedumaran Arappali

“…In this paper, an energy distribution model was proposed to estimate the photo energy with the help of deep learning model. A small apartment not only uses less energy but also requires lower construction costs. …”
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Advancements in Frank’s sign Identification using deep learning on 3D brain MRI by Sungman Jo, Jun Sung Kim, Min Jeong Kwon, Jieun Park, Jeong Lan Kim, Jin Hyeong Jhoo, Eosu Kim, Leonard Sunwoo, Jae Hyoung Kim, Ji Won Han, Ki Woong Kim

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A deep learning based detection algorithm for anomalous behavior and anomalous item on buses by Shida Liu, Yu Bi, Qingyi Li, Ye Ren, Honghai Ji, Li Wang

“…The algorithm is based on the deep learning YOLOv5 (You Only Look Once) algorithm with improvements. …”
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Identification and validation of key autophagy-related genes in lupus nephritis by bioinformatics and machine learning. by Su Zhang, Weitao Hu, Yelin Tang, Xiaoqing Chen

“…Differentially expressed autophagy-related genes (DE-ARGs) among DEGs, key module genes and autophagy-related genes (ARGs) were obtained by venn plot, and subjected to protein-protein interaction network construction. Two machine learning methods were applied to identify signature genes. …”
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Diagnosis of COVID-19 Using a Deep Learning Model in Various Radiology Domains by Yousef Alhwaiti, Muhammad Hameed Siddiqi, Madallah Alruwaili, Ibrahim Alrashdi, Saad Alanazi, Muhammad Hasan Jamal

“…This study proposes a generalized adaptive deep learning model that helps the front-line paramedical employees to easily detect COVID-19 in different radiology domains. …”
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Evaluating Explainable AI Methods in Deep Learning Models for Early Detection of Cerebral Palsy by Kimji N. Pellano, Inga Strumke, Daniel Groos, Lars Adde, Espen F. Alexander Ihlen

“…This paper tests the reliability and applicability of Explainable AI (XAI) methods using a deep learning method that predicts CP by analyzing skeletal data extracted from video recordings of infant movements. …”
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Tunnel squeezing prediction based on partially missing dataset and optimized machine learning models by Peng Guan, Guangzhao Ou, Feng Liang, Weibang Luo, Qingyong Wang, Chengyuan Pei, Xuan Che

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Rapid Detection of Hybrid Maize Parental Lines Using Stacking Ensemble Machine Learning by M. Aqil, M. Azrai, M. J. Mejaya, N. A. Subekti, F. Tabri, N. N. Andayani, Rahma Wati, S. Panikkai, S. Suwardi, Z. Bunyamin, E. Roy, M. Muslimin, M. Yasin, E. Prakasa

“…The research’s primary contribution is the integration of two widely used transfer learning architectures, Inception V3 and SqueezeNet, with stacking ensemble machine learning using four algorithms (logistic regression, support vector machine, random forest, and k-nearest neighbors) for rapid classification of tassel images. …”
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