Wet Laboratory Versus Computer Simulation for Learning Endobronchial Ultrasound: A Randomized Trial by David R Stather, Paul MacEachern, Alex Chee, Elaine Dumoulin, Christopher A Hergott, Alain Tremblay

“…BACKGROUND: Linear endobronchial ultrasound with transbronchial needle aspiration (EBUS-TBNA) is a revolutionary bronchoscopic procedure that is challenging to learn.…”
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Sign Prediction on Unlabeled Social Networks Using Branch and Bound Optimized Transfer Learning by Weiwei Yuan, Jiali Pang, Donghai Guan, Yuan Tian, Abdullah Al-Dhelaan, Mohammed Al-Dhelaan

“…Due to the insufficiency of labeled data, transfer learning has been adopted to leverage the auxiliary data to improve the prediction of signs in target domain. …”
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Learning Coupled Meteorological Characteristics Aids Short-Term Photovoltaic Interval Prediction Methods by Yue Guo, Yu Song, Zilong Lai, Xuyang Wang, Licheng Wang, Hui Qin

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Improved BCI calibration in multimodal emotion recognition using heterogeneous adversarial transfer learning by Mehmet Ali Sarikaya, Gökhan Ince

“…This study presents a novel approach that utilizes heterogeneous adversarial transfer learning (HATL) to synthesize electroencephalography (EEG) data from various other signal modalities, reducing the need for lengthy calibration phases. …”
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Machine Learning and Artificial Intelligence Systems Based on the Optical Spectral Analysis in Neuro-Oncology by Tatiana Savelieva, Igor Romanishkin, Anuar Ospanov, Sergey Goryaynov, Galina Pavlova, Igor Pronin, Victor Loschenov

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Research on CSI feedback of RIS-assisted massive MIMO system based on manifold learning by QIAN Mujun, YU Shunchi, LIU Chen, SONG Yunchao, LU Feng

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Resonant Frequency Modeling of Microwave Antennas Using Gaussian Process Based on Semisupervised Learning by Jing Gao, Yubo Tian, Xie Zheng, Xuezhi Chen

“…This paper proposes a semisupervised learning Gaussian Process (GP), which combines unlabeled samples to improve the accuracy of the GP model and reduce the number of labeled training samples required. …”
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Machine Learning-Based Offloading Strategy for Lightweight User Mobile Edge Computing Tasks by Shuchen Zhou, Waqas Jadoon, Junaid Shuja

“…This paper presents an in-depth study and analysis of offloading strategies for lightweight user mobile edge computing tasks using a machine learning approach. Firstly, a scheme for multiuser frequency division multiplexing approach in mobile edge computing offloading is proposed, and a mixed-integer nonlinear optimization model for energy consumption minimization is developed. …”
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Application of machine learning in diabetes prediction based on electronic health record data analysis by Yang Zihan

“…With the application of electronic health records (EHRs) in the medical field, the use of machine learning to predict disease has become one of the important research hotspots in the healthcare industry. …”
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Building occupancy type classification and uncertainty estimation using machine learning and open data by Tom Narock, J. Michael Johnson, Justin Singh-Mohudpur, Arash Modaresi Rad

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Learning team-based quality improvement in a virtual setting: a qualitative study by Einar Hovlid, Gunnar Husabø, Eivind Alexander Valestrand, Miriam Hartveit

“…Objective The objective was to explore students’ perceptions of learning quality improvement (QI) in a virtual setting and identify factors that promote or inhibit virtual learning.Design We used an exploratory case study design with focus group interviews. …”
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Physics-informed deep learning quantifies propagated uncertainty in seismic structure and hypocenter determination by Ryoichiro Agata, Kazuya Shiraishi, Gou Fujie

“…Here, we address this issue by employing a physics-informed deep learning (PIDL) approach that quantifies uncertainty in two-dimensional seismic velocity structure modeling and its propagation to hypocenter determination by introducing neural network ensembles trained on active seismic survey data, earthquake observation data, and the physical equation of wavefront movement. …”
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Retracted: A Novel Deep Learning Model for Analyzing Psychological Stress in College Students by Journal of Electrical and Computer Engineering

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LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio Tagging by Charilaos Papaioannou, Emmanouil Benetos, Alexandros Potamianos

Subjects: “…Few-shot learning…”
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A Novel Semi-Supervised Learning Method Based on Fast Search and Density Peaks by Fei Gao, Teng Huang, Jinping Sun, Amir Hussain, Erfu Yang, Huiyu Zhou

“…In this paper, two new semi-supervised learning methods are proposed: a semi-supervised learning method based on fast search and density peaks (S2DP) and an iterative S2DP method (IS2DP). …”
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Deep-Learning Based Depth-Tracking of Stacking-Faults in Epitaxially Grown Silicon Wafers by Theresa Trötschler, Saed Al-Hajjawi, Siddharth Raghavendran, Jonas Haunschild, Matthias Demant, Stefan Rein

“…This work presents a non-destructive and automated characterization method on full-size wafers to locate stacking faults and determine their layer of origin to identify process-based root causes. A deep learning model and a quantification via geometric defect properties is realized on dark field microscope images, with the potential to be transferred to inline images measured in dark field mode with high-resolution cameras. …”
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LEARNING CYCLE MANAGEMENT BASED ON LOCAL WISDOM IN INSTILLING THE CHARACTER OF RELIGIOUS STUDENTS IN MADRASA by Siti Masruroh, Zakiyah Bz

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Machine Learning Does Not Improve Humeral Torsion Prediction Compared to Regression in Baseball Pitchers by Garrett S Bullock, Charles A Thigpen, Gary S Collins, Nigel K Arden, Thomas K Noonan, Michael J Kissenberth, Ellen Shanley

“…While machine learning models demonstrated improved RMSE compared to the regression, the machine learning models displayed poorer calibration compared to regression. …”
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Gene Selection Based Cancer Classification With Adaptive Optimization Using Deep Learning Architecture by Anju Das, N. Neelima, K. Deepa, Tolga Ozer

“…To address these challenges and enhance classification outcomes, this research employs an efficient framework based on a deep learning (DL) approach. Initially, the data is collected from five gene cancer datasets, which are then augmented to maximize the data size. …”
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Serial visual reversal learning in captive black-handed spider monkeys, Ateles geoffroyi by Jules Dorschner, Laura Teresa Hernandez Salazar, Matthias Laska

Subjects: “…Serial visual reversal learning…”
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