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Fostering success in online English education: Exploring the effects of ICT literacy, online learning self-efficacy, and motivation on deep learning
This research explores how motivation and online learning self-efficacy (OLSE) act as mediators in the association between information and...
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Forecasting VIX using Bayesian deep learning
Recently, deep learning techniques are gradually replacing traditional statistical and machine learning models as the first choice for price...
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Deep ensemble transfer learning framework for COVID-19 Arabic text identification via deep active learning and text data augmentation
Since the declaration of COVID-19 as an epidemic by the World Health Organization in September 2019, the task of monitoring and managing the spread...
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Enhancing Cardiovascular Health Monitoring Through IoT and Deep Learning Technologies
Monitoring cardiovascular conditions is crucial in healthcare due to their significant impact on overall wellness and their role in mitigating...
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Control learning rate for autism facial detection via deep transfer learning
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects social interaction and communication. Early detection of ASD can...
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Deep reinforcement learning in mobile robotics – a concise review
Mobile robotics is one of the emerging research area in the robotics. The recently evolving techniques, artificial intelligence and precise hardware...
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Image steganalysis using deep learning models
In the domain of digital steganography, the problem of efficient and accurate steganalysis is of utmost importance. Steganalysis seeks to detect the...
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Deep Machine Learning in Optimization of Scientific Research Activities
Abstract—This article provides a general overview of machine learning, a subdomain of artificial intelligence. The substance of the deep learning...
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Machine Learning and Deep Learning Algorithms
This chapter provides a brief overview of how machine learning and deep learning algorithms are trained for biomedical natural language processing...
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Deep video representation learning: a survey
This paper provides a review on representation learning for videos . We classify recent spatio-temporal feature learning methods for sequential visual...
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Attractor Inspired Deep Learning for Modelling Chaotic Systems
Predicting and understanding the behavior of dynamic systems have driven advancements in various approaches, including physics-based models and...
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Image classification of intracranial tumor using deep residual learning technique
Classifying brain tumours is essential for diagnosing tumour progression and planning effective treatments. Different imaging modalities are used to...
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Skin cancer detection using ensemble of machine learning and deep learning techniques
Skin cancer is one of the most common forms of cancer, which makes it pertinent to be able to diagnose it accurately. In particular, melanoma is a...
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LSNet: a deep learning based method for skin lesion classification using limited samples and transfer learning
When analyzing skin lesion image data using deep learning, the lack of a sufficient amount of effective training data poses a challenge. Although...
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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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A Survey on ensemble learning under the era of deep learning
Due to the dominant position of deep learning (mostly deep neural networks) in various artificial intelligence applications, recently, ensemble...
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Development and trending of deep learning methods for wind power predictions
With the increasing data availability in wind power production processes due to advanced sensing technologies, data-driven models have become...
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Abstraction, mimesis and the evolution of deep learning
Deep learning developers typically rely on deep learning software frameworks (DLSFs)—simply described as pre-packaged libraries of programming...
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MobilityDL: a review of deep learning from trajectory data
Trajectory data combines the complexities of time series, spatial data, and (sometimes irrational) movement behavior. As data availability and...
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Relationship constraint deep metric learning
AbstractDeep metric learning (DML) models aim to learn semantically meaningful representations in which similar samples are pulled together and...
