Applications of Deep Learning to MRI Images: A Survey by Jin Liu, Yi Pan, Min Li, Ziyue Chen, Lu Tang, Chengqian Lu, Jianxin Wang

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An Event Causality Identification Framework Using Ensemble Learning by Xiaoyang Wang, Wenjie Luo, Xiudan Yang

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PERCEPTION OF ONLINE VERSUS PHYSICAL LEARNING AMONGUNIVERSITY STUDENTS by Ramna Shafique, Muhammad Abdullah, Tooba Nasir, Sundas Salim, Sabrina Jam, Bina Fawad

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Social context affects sequence modification learning in birdsong by Lioba Fortkord, Lena Veit

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User Model for Virtual Learning Based on Adaptive Gamification by Intan Yusrina Zairon, Tengku Siti Meriam Tengku Wook, Syahanim Mohd Salleh, Hadi Affendy Dahlan

Subjects: “…Learning style…”
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Learning with semantic ambiguity for unbiased scene graph generation by Shanjin Zhong, Yang Cao, Qiaosen Chen, Jie Gong

“…In response to these challenges, we propose a model-agnostic method named Mixup and Balanced Relation Learning (MBRL). This method assigns soft labels to samples exhibiting semantic ambiguities and optimizes model training by adjusting the loss weights for fine-grained and low-frequency relation samples. …”
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A novel machine learning approach for portfolio optimization by Saman Haratizadeh, Fatemeh Rezaee

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Environmental Responsibility, Active Social Learning, and Political Action by David Hayes

“…In relation to this movement and its potentiality, there will be a particular focus on what is known as active social learning and the ways in which community views can be formalised in order to facilitate a dialogical relationship with representative structures. …”
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Travel route recommendation with a trajectory learning model by Xiangping Wu, Zheng Zhang, Wangjun Wan

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Deep Learning-Based Damage Assessment in Cherry Leaves by Burakhan Cubukcu, Hazel BOZCU

Subjects: “…transfer learning…”
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A convergent Deep Learning algorithm for approximation of polynomials by Després, Bruno

“…We propose a novel algorithm, where the functional equation is solved with a converging iterative algorithm which can be realized as a Machine Learning training method iteratively with respect to the number of layers. …”
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Predicting Spread Probability of Learning-Effect Computer Virus by Wei-Chang Yeh, Edward Lin, Chia-Ling Huang

“…The proposed BAT utilizes the probability derived from PageRank from the scale-free network together with the consideration of state vectors with both the temporal and learning effects. The performance of the proposed algorithm was verified via numerous experiments.…”
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Virtual Learning Environments from localization point of view by Valentina Dagienė, Tatjana Jevsikova

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A Simple Mathematical Model of Cyclic Circadian Learning by J. Šimon, M. Bulko

“…The usability of this model is limited to scenarios where logical bonds (connections to prior learning) are not created and mnemonic devices cannot be utilized during the learning process.…”
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Learning-Based Dark and Blurred Underwater Image Restoration by Yifeng Xu, Huigang Wang, Garth Douglas Cooper, Shaowei Rong, Weitao Sun

“…Due to the high-quality training data, the proposed restoration algorithm based on deep learning achieves inspiring results for underwater images taken in a low-light environment. …”
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Machine learning in medicine: what clinicians should know by Jordan Zheng Ting Sim, Qi Wei Fong, Weimin Huang, Cher Heng Tan

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Recent Machine Learning Progress in Image Analysis and Understanding by Shengping Zhang, Huiyu Zhou, Lei Zhang

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One-Shot Segmentation of Battery Cells with Gradual Learning by Johann Christopher Engster, Nele Blum, Laura Hellwege, Thorsten M. Buzug, Maik Stille

“…One aspect is the visualization of inner structures, which can be achieved by computed tomography (CT) as a non-destructive testing (NDT) method. Deep-learning tools can learn and perform different image processing tasks in the obtained volume quickly. …”
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CONCERING THE INTERRELATION OF E-LEARNING CONCEPTS IN HIGHER EDUCATION by B. E. Starichenko, I. N. Semenova, A. V. Slepukhin

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Multitask Learning with Local Attention for Tibetan Speech Recognition by Hui Wang, Fei Gao, Yue Zhao, Li Yang, Jianjian Yue, Huilin Ma

“…Inspired by the attention mechanism, we introduce the local attention to automatically tune the weights of feature frames in a window and pay different attention on context information for multitask learning. The experimental results show that our method improves the accuracies of speech recognition for all Tibetan dialects in three-task learning, compared with the baseline model. …”
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