3.327 hasil untuk machine learning
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An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine
…in credit card transactions using an optimized light gradient boosting machine (OLightGBM). In the proposed approach, a Bayesian-based hyperparameter optimization algorithm is intelligently integrated to tune the parameters of a light gradient boosting machine (LightGBM). To demonstrate the…
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Using digital technology to promote higher education learning: The importance of different learning activities and their relations to learning outcomes
Digital technologies can have positive effects on student learning in higher education. Based on the ICAP framework, they should be particularly effective when teachers use them to encourage student engagement in constructive and interactive as opposed to passive and active learning activities…
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Contrastive Representation Learning: A Framework and Review
Contrastive Learning has recently received interest due to its success in self-supervised representation learning in the computer vision domain. However, the origins of Contrastive Learning date as far back as the 1990s and its development has spanned across many fields and domains including Metric…
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Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis
This paper reviews the literature on integrating AI in e-learning, from the viewpoint of cognitive neuropsychology, for Personalized Learning (PL) and Adaptive Assessment (AA). This review follows the PRISMA systematic review methodology and synthesizes the results of 85 studies that were selected…
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Neural heterogeneity promotes robust learning
…and found that heterogeneity substantially improved task performance. Learning with heterogeneity was more stable and robust, particularly for tasks with a rich temporal structure. In addition, the distribution of neuronal parameters in the trained networks is similar to those observed…
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Multimodal Learning With Transformers: A Survey
Transformer is a promising neural network learner, and has achieved great success in various machine learning tasks. Thanks to the recent prevalence of multimodal applications and Big Data, Transformer-based multimodal learning has become a hot topic in AI research. This paper presents a…
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A survey on active learning and human-in-the-loop deep learning for medical image analysis
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Detection of coronavirus Disease (COVID-19) based on Deep Features and Support Vector Machine
…spread can be taken. In this paper, the deep feature plus support vector machine (SVM) based methodology is suggested for detection of coronavirus infected patient using X-ray images. For classification, SVM is used instead of deep learning based classifier, as the later one need a large dataset…
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Fast-Convergent Federated Learning
Federated learning has emerged recently as a promising solution for distributing machine learning tasks through modern networks of mobile devices. Recent studies have obtained lower bounds on the expected decrease in model loss that is achieved through each round of federated learning. However…
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Meta-Learning in Neural Networks: A Survey
The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent years. Contrary to conventional approaches to AI where tasks are solved from scratch using a fixed learning algorithm, meta-learning aims to improve the learning algorithm itself, given the experience…
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Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better
Deep learning has revolutionized the fields of computer vision, natural language understanding, speech recognition, information retrieval, and more. However, with the progressive improvements in deep learning models, their number of parameters, latency, and resources required to train, among…
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Deep learning-based landslide susceptibility mapping
…comprehensively by comparing it to several state-of-the-art benchmark machine learning techniques including the support vector machine (SVM), logistic regression (LR), Gaussian naïve Bayes (GNB), multilayer perceptron (MLP), Bernoulli Naïve Bayes (BNB) and decision tree (DT) classifiers. The…
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A comprehensive survey on support vector machine classification: Applications, challenges and trends
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Ensemble Learning for Disease Prediction: A Review
Machine learning models are used to create and enhance various disease prediction frameworks. Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions than a single classifier. Although numerous studies have…
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Classifying Crop Leaf Diseases using Different Deep Learning Models with Transfer Learning
Within the scope of the research, we put forward a technique of exactly confirming the distinctiveness of agricultural leaf pathologies with the assist of deep mastering algorithms and switch getting to know generation. We have pre-skilled models like VGG19, MobileNet, InceptionV3, EfficientNetB0…
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Forest fire and smoke detection using deep learning-based learning without forgetting
…there is no sufficient dataset available. To address this issue, transfer learning is exercised on pre-trained models. However, the models may lose their classification abilities on the original datasets when transfer learning is applied. To solve this problem, we use learning without forgetting…
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TabNet: Attentive Interpretable Tabular Learning
We propose a novel high-performance and interpretable canonical deep tabular data learning architecture, TabNet. TabNet uses sequential attention to choose which features to reason from at each decision step, enabling interpretability and more efficient learning as the learning capacity is used for…
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A comparison review of transfer learning and self-supervised learning: Definitions, applications, advantages and limitations
Deep learning has emerged as a powerful tool in various domains, revolutionising machine learning research. However, one persistent challenge is the scarcity of labelled training data, which hampers the performance and generalisation of deep learning models. To address this limitation, researchers…
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Communication-efficient federated learning
Federated learning (FL) enables edge devices, such as Internet of Things devices (e.g., sensors), servers, and institutions (e.g., hospitals), to collaboratively train a machine learning (ML) model without sharing their private data. FL requires devices to exchange their ML parameters iteratively…
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Brain Tumor Analysis Using Deep Learning and VGG-16 Ensembling Learning Approaches
A brain tumor is a distorted tissue wherein cells replicate rapidly and indefinitely, with no control over tumor growth. Deep learning has been argued to have the potential to overcome the challenges associated with detecting and intervening in brain tumors. It is well established that the…
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