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A Modified Stochastic Gradient Descent Optimization Algorithm With Random Learning Rate for Machine Learning and Deep Learning
An optimization algorithm is essential for minimizing loss (or objective) functions in machine learning and deep learning. Optimization algorithms...
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Role of information technology in blended learning, flipped learning and e-learning
Every aspect of our everyday lives is being revolutionized by the advent and development of information technology, which has resulted in increased...
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Emerging trends in federated learning: from model fusion to federated X learning
Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. As...
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Target adaptive extreme learning machine for transfer learning
Extreme learning machines (ELM) have been applied in several fields due to their simplicity and computational efficiency. However, ELM hurts the...
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Integrating curriculum learning with meta-learning for general rhetoric identification
Rhetoric is abundant and universal across different human languages. In this paper, we propose a novel curriculum learning integrated with...
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Leveraging ensemble learning for stealth assessment model with game-based learning environment
A distinguishing feature of intelligent game-based learning environment is its capacity for assisting stealth assessment. Stealth assessment gathers...
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A Procedural Constructive Learning Mechanism with Deep Reinforcement Learning for Cognitive Agents
Recent advancements in AI and deep learning have created a growing demand for artificial agents capable of performing tasks within increasingly...
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Learning domain-heterogeneous speaker recognition systems with personalized continual federated learning
Speaker recognition, the process of automatically identifying a speaker based on individual characteristics in speech signals, presents significant...
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Robot autonomous gras** and assembly skill learning based on deep reinforcement learning
This paper proposes a deep reinforcement learning-based framework for robot autonomous gras** and assembly skill learning. Meanwhile, a deep...
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An Enhanced Ensemble Learning Method for Sentiment Analysis based on Q-learning
Ensemble learning is a powerful technique for combining multiple classifiers to achieve improved performance. However, the challenge of applying...
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Better value estimation in Q-learning-based multi-agent reinforcement learning
In many real-life scenarios, multiple agents necessitate cooperation to accomplish tasks. Benefiting from the significant success of deep learning,...
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UAV Head-On Situation Maneuver Generation Using Transfer-Learning-Based Deep Reinforcement Learning
Recently, the demand for unmanned aerial vehicle technology has increased. In particular, AI pilots through reinforcement learning (RL) are more...
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Classification of Different Plant Species Using Deep Learning and Machine Learning Algorithms
In the present situation, a lot of research has been directed towards the potency of plants. These natural resources contain characteristics valuable...
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ETQ-learning: an improved Q-learning algorithm for path planning
Path planning algorithm has always been the core of intelligent robot research; a good path planning algorithm can significantly enhance the...
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Detecting Suicidality in Arabic Tweets Using Machine Learning and Deep Learning Techniques
Social media platforms have revolutionized traditional communication techniques by allowing people to connect instantaneously, openly, and...
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Debiased graph contrastive learning based on positive and unlabeled learning
Graph contrastive learning (GCL) is one of the mainstream techniques for unsupervised graph representation learning, which reduces the distance...
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State-Dependent Maximum Entropy Reinforcement Learning for Robot Long-Horizon Task Learning
Task-oriented robot learning has shown significant potential with the development of Reinforcement Learning (RL) algorithms. However, the learning of...
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Model-based deep reinforcement learning for accelerated learning from flow simulations
In recent years, deep reinforcement learning has emerged as a technique to solve closed-loop flow control problems. Employing simulation-based...
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Enhancing Learning Efficiency in FACL: A Novel Fuzzy Rule Transfer Method for Transfer Learning
The concept of leveraging knowledge from previous experience to accelerate learning forms the crux of transfer learning. Within the realm of...
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Hermite broad-learning recurrent neural control with adaptive learning rate for nonlinear systems
Although conventional control systems are simple and widely used, they may not be effective for complex and uncertain systems. This study proposes a...
