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Federated mutual learning: a collaborative machine learning method for heterogeneous data, models, and objectives
Federated learning (FL) is a novel technique in deep learning that enables clients to collaboratively train a shared model while retaining their...
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Machine learning and deep learning algorithms in detecting COVID-19 utilizing medical images: a comprehensive review
The public’s health is seriously at risk from the coronavirus pandemic. Millions of people have already died as a result of this devastating illness,...
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Exploring the effects of personalized recommendations on student’s motivation and learning achievement in gamified mobile learning framework
In this research, a GAmified Mobile Leaning Framework (GAMOLEAF) developed as a new intelligent application designed for mobile devices to ensure...
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Kronecker-factored Approximate Curvature with adaptive learning rate for optimizing model-agnostic meta-learning
Model-agnostic meta-learning (MAML) highlights the ability to quickly adapt to new tasks with only a small amount of labeled training data among many...
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Enhancing e-learning effectiveness: analyzing extrinsic and intrinsic factors influencing students’ use, learning, and performance in higher education
As a result of the pandemic, but also of the rapid advancement of technology in general, e-learning has emerged as a popular method of education,...
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Modeling undergraduate students’ learning dynamics between self-regulated learning patterns and community of inquiry
In online STEM courses, self-regulated learning (SRL) serves a critical role in academic success because students are required to monitor and...
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Seeing is Learning in High Dimensions: The Synergy Between Dimensionality Reduction and Machine Learning
High-dimensional data are a key study object for both machine learning (ML) and information visualization. On the visualization side, dimensionality...
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The comparison of two incidental learning scenarios on a digital learning platform from the cognitive load perspective
Incidental learning is a type of informal learning occurring consciously with unintentional acts. Within the scope of this study, informal learning...
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Classical learning or deep learning: a study on food photo aesthetic assessment
Food photo aesthetic assessment has gained increasing attention in both commercial activity and social life. However, there has been little research...
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Exploring students’ self-directed learning strategies and satisfaction in online learning
The past ten years have witnessed a tremendous increase in the number of online courses, and the COVID-19 pandemic suddenly accelerated online...
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What rationale would work? Unfolding the role of learners’ attitudes and motivation in predicting learning engagement and perceived learning outcomes in MOOCs
The aim of this study is to gain insight into the interplay between attitudes, motivation, learning engagement, and perceived learning outcomes in...
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Learning more discriminative local descriptors with parameter-free weighted attention for few-shot learning
Few-shot learning for image classification comes up as a hot topic in computer vision, which aims at fast learning from a limited number of labeled...
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Strengthening KMS Security with Advanced Cryptography, Machine Learning, Deep Learning, and IoT Technologies
This paper presents an innovative approach to strengthening Key Management Systems (KMS) against the escalating landscape of cyber threats by...
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DisRot: boosting the generalization capability of few-shot learning via knowledge distillation and self-supervised learning
Few-shot learning (FSL) aims to adapt quickly to new categories with limited samples. Despite significant progress in utilizing meta-learning for...
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A Transfer Learning-Based CNN Deep Learning Model for Unfavorable Driving State Recognition
The detection of unfavorable driving states (UDS) of drivers based on electroencephalogram (EEG) measures has received continuous attention from...
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Simple knowledge graph completion model based on PU learning and prompt learning
Knowledge graphs (KGs) are important resources for many artificial intelligence tasks but usually suffer from incompleteness, which has prompted...
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Assessment of learning parameters for students' adaptability in online education using machine learning and explainable AI
Technology Enabled Learning (TEL) has a major impact on the learning adaptability of the learners. During the COVID-19 pandemic, there has been a...
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Collaborative learning of supervision and correlation for generalized zero-shot extreme multi-label learning
Generalized zero-shot extreme multi-label learning (GZXML) aims to predict relevant labels for unknown instances from a set of seen and unseen labels...
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Ensemble learning based-features extraction for brain mr images classification with machine learning classifiers
In general, different neuroimaging methods including Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography...
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Transfer learning-based quantized deep learning models for nail melanoma classification
Skin cancer, particularly melanoma, has remained a severe issue for many years due to its increasing incidences. The rising mortality rate associated...
