Improving Causality Induction with Category Learning by Yi Guo, Zhihong Wang, Zhiqing Shao

“…This paper confirms the expectation that the precision and coverage of causality induction can be remarkably improved by means of causal and category learning.…”
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Identifying Transfer Learning in the Reshaping of Inductive Biases by Anna Székely, Balázs Török, Mariann Kiss, Karolina Janacsek, Dezső Németh, Gergő Orbán

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Prediction of induction motor faults using machine learning by Ademola Abdulkareem, Tochukwu Anyim, Olawale Popoola, John Abubakar, Agbetuyi Ayoade

“…This research study centers on the development of a versatile machine-learning model for predicting faults in induction motors within industrial environments. …”
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ILA4: Overcoming missing values in machine learning datasets – An inductive learning approach by Ammar Elhassan, Saleh M. Abu-Soud, Firas Alghanim, Walid Salameh

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OntoLearn Reloaded: A Graph-Based Algorithm for Taxonomy Induction by Paola Velardi, Stefano Faralli, Roberto Navigli

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Analysis of multiple faults in induction motor using machine learning techniques by Puja Pohakar, Ravi Gandhi, Surender Hans, Gulshan Sharma, Pitshou N. Bokoro

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Transfer Learning for Induction Motor Health Monitoring: A Brief Review by Prashant Kumar

“…These sophisticated deep learning algorithms have been widely used for induction motor health monitoring due to their autonomous feature extraction abilities and end-to-end learning capabilities. …”
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Model-Based Graph Reinforcement Learning for Inductive Traffic Signal Control by Francois-Xavier Devailly, Denis Larocque, Laurent Charlin

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Explainability in RIONA Algorithm Combining Rule Induction and Instance-Based Learning by Grzegorz Góra, Andrzej Skowron, Arkadiusz Wojna

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Privacy-Preserving Rule Induction Using CKKS by Jihyeon Choi, Jina Choi, Younho Lee, Jung-Sik Hong

Subjects: “…Rule induction…”
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Reliability-enhanced data cleaning in biomedical machine learning using inductive conformal prediction. by Xianghao Zhan, Qinmei Xu, Yuanning Zheng, Guangming Lu, Olivier Gevaert

“…Accurately labeling large datasets is important for biomedical machine learning yet challenging while modern data augmentation methods may generate noise in the training data, which may deteriorate machine learning model performance. …”
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Induction of gender-like linguistic categories using noun-marking and blocking of learning trials by Roman Taraban

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Recognition of MI-EEG signals using extended-LSR-based inductive transfer learning by Zhibin Jiang, Zhibin Jiang, Keli Hu, Keli Hu, Jia Qu, Zekang Bian, Zekang Bian, Donghua Yu, Donghua Yu, Jie Zhou, Jie Zhou

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Unsupervised Declarative Knowledge Induction for Constraint-Based Learning of Information Structure in Scientific Documents by Yufan Guo, Roi Reichart, Anna Korhonen

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Physics Learning Design of Faraday's Induction Law Material Using PhET Simulation by S. T. M. Famani, M. R. S. S. N. Ayub, D N Sudjito

“…The purpose of this research is to make a learning design on Faraday’s Induction Law using a PhET Faraday’s Electromagnetic Lab simulation and to investigate the effectiveness of the design on students' understanding. …”
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Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis by Kevin Barrera-Llanga, Jordi Burriel-Valencia, Angel Sapena-Bano, Javier Martinez-Roman

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Enhancing Problem-Solving Skills and Motivation through Cooperative Learning by Ahlam Debache

“… In the present paper, the goal set to assist learners in the process of strategically seeking as well as applying information is given a major consideration in the realm of education. the advantage of raising awareness of problem solving skills and encouraging learners to apply them are two main requirements which go hand in hand to increase performance throughout learning, for we noticed that being informed about some pieces of information does not inevitably necessitate its application but an active involvement into cooperative problem-solving activities fosters higher-order thinking skills namely deductive and inductive types of thinking within cooperative learning environment. …”
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Inductive and Transfer Learning‐Based Hybrid Model Techniques for Accurate and Automated Diagnosis of Neurological Diseases by Saroj Kumar Pandey, Yogesh Kumar Rathore, Sunakshi Mehra, Anurag Sinha, Tarun Raj Kumar, Ankit Kumar, Rekh Ram Janghel, Ayodele Lasisi, Quadri Noorulhasan Naveed, Md. Sazid Reza

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An Improved Fault Diagnosis Strategy for Induction Motors Using Weighted Probability Ensemble Deep Learning by Usman Ali, Umer Ramzan, Waqas Ali, Khaled Ali Al-Jaafari

Subjects: “…Induction motor…”
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Data science and automation in the process of theorizing: Machine learning's power of induction in the co-duction cycle. by Daan Kolkman, Gwendolyn K Lee, Arjen van Witteloostuijn

“…When used as a part of a full research process, including inductive, deductive and abductive steps, machine learning can offer explanatory insights and provide a solid basis for theorization. …”
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