Reduction of monoclonal antibody viscosity using interpretable machine learning by Emily K. Makowski, Hsin-Ting Chen, Tiexin Wang, Lina Wu, Jie Huang, Marissa Mock, Patrick Underhill, Emma Pelegri-O’Day, Erick Maglalang, Dwight Winters, Peter M. Tessier

“…Here, we report an interpretable machine learning model for predicting antibody (IgG1) variants with low viscosity using only the sequences of their variable (Fv) regions. …”
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Ethnomathematical exploration of the Remo dance as a source for mathematics learning by Eka Sulistyawati, Brenda Indah Nirmala, Dewi Hamidah

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Evaluating the method reproducibility of deep learning models in biodiversity research by Waqas Ahmed, Vamsi Krishna Kommineni, Birgitta König-Ries, Jitendra Gaikwad, Luiz Gadelha, Sheeba Samuel

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Application of Additive Manufacturing and Deep Learning in Exercise State Discrimination by Zhilong Zhao, Jiaxi Yang, Jiahao Liu, Shijie Soong, Yiming Wang, Juan Zhang

“…To address these challenges, this paper introduces 3D printing and deep learning to design a smart wearable sensing device to detect different states of sports fatigue. …”
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Beyond the Screens: Reflections of Remote Learning on Adolescents’ Mental Health by Nayara Gonçalves Barbosa, Thaianne Cristine Gadagnoto, Lise Maria Carvalho Mendes, Giovanna Cristina Machado-Kayzuka, Flávia Azevedo Gomes-Sponholz, Lucila Castanheira Nascimento

“…Abstract: Remote learning during the COVID-19 pandemic demanded adaptations from adolescents who showed a noticeable dependency on screens. …”
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In Search of Chaos and Complexity of a Cognitive Language-Learning System by Gaofei Luo, Sayan Mukherjee

“…In this article, we investigate the long-term dynamics of a known cognitive-based language-learning system under the variation of a system parameter. …”
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Prediction of Defective Software Modules Using Class Imbalance Learning by Divya Tomar, Sonali Agarwal

“…The objective of software defect prediction system is to find as many defective software modules as possible without affecting the overall performance. The learning process of a software defect predictor is difficult due to the imbalanced distribution of software modules between defective and nondefective classes. …”
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Optimizing Learning and Cognitive Activities of University Students in the Internet Environment by L. K. Raitskaya

Subjects: “…self-reliant learning and cognitive activities…”
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Fault Diagnosis of Batch Reactor Using Machine Learning Methods by Sujatha Subramanian, Fathima Ghouse, Pappa Natarajan

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OPTIMIZING STUDENT LEARNING QUALITY THROUGH DIFFERENTIATED INSTRUCTION APPROACHES by Fathi Hidayah, Eny Susiani, Ana Aniati, Bannaga Taha Hussen

Subjects: “…quality of learning…”
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SURVEY OF MACHINE LEARNING APPLICATIONS IN MEDICAL IMAGING. ALGORITHMS AND TECHNOLOGIES by Marius Eremia Vlaicu POPA, Mihai CRUCERU, Bogdan DIACONU

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Automatically generated language learning exercises for Finno-Ugric languages by Zsanett Ferenczi

Subjects: “…computer-assisted language learning…”
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Machine Learning Applications to Dust Storms: A Meta-Analysis by Reem K. Alshammari, Omer Alrwais, Mehmet Sabih Aksoy

Subjects: “…Machine learning…”
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Hybrid neural networks for continual learning inspired by corticohippocampal circuits by Qianqian Shi, Faqiang Liu, Hongyi Li, Guangyu Li, Luping Shi, Rong Zhao

“…Abstract Current artificial systems suffer from catastrophic forgetting during continual learning, a limitation absent in biological systems. …”
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Revitalizing Physiology Education: Integrating Notability and Video-Based Learning by James Davis

“…Journal of Teaching and Learning with Technology…”
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Living, Caring, Learning - Building family relationships in haemophilia care by Shoemark Robyn

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Lighting Spectrum Optimization With Deep Learning for Moss Species Classification by Kenichi Ito, Pauli Falt, Markku Hauta-Kasari, Shigeki Nakauchi

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Designing a Collaborative Learning Platform Based on Learnersourcing and Gamification by Alexandru Smarandache, Elvira Popescu

“… In traditional, old-fashioned educational settings, students are passive consumers of learning content and do not actively contribute to the overall improvement of the learning process. …”
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iMESc – an interactive machine learning app for environmental sciences by Danilo Cândido Vieira, Danilo Cândido Vieira, Fabiana S. Paula, Luciana Erika Yaginuma, Gustavo Fonseca

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Modified Hybrid Discriminant Analysis Methods and Their Applications in Machine Learning by Liwen Huang

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