Controlling quadrupedal robots presents significant challenges due to the complexity of their locomotion. Traditional robot control methods often struggle to control the various movements of these robots. In this stud...
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The recognition of human activity is significant in such domains as smart environments, security, and health. A novel approach to IoT-based women's safety monitoring at educational campuses using a hybrid deep lea...
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The financial market is a dynamic environment, which complicates accurate forecasting. Forecasting financial markets is essential for informed investment decisions and effective risk management. The proposed work seek...
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This research makes use of deep learning for creating an automatic traffic surveillance model to detect the various vehicles under extreme climatic weather conditions. The model proposed is an extension of the standar...
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The exponential growth of mobile messaging platforms has led to a volume of suspicious and malicious messages that pose significant risks to user privacy, financial security, and information integrity. This paper pres...
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This study compares different scheduling algorithms used in edge computing for real-time video processing. Instead of sending video data to cloud servers, the video is processed directly at the edge, reducing delays a...
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This research focuses on the development and analysis of a secure data storage platform employing state-of-the-art cybersecurity techniques. With cyber threats growing in complexity, ensuring data integrity, confident...
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Hyperparameter Optimization (HPO) plays a significant role in enhancing the performance of machine learning models. However, as the size and complexity of (deep) neural architectures continue to increase, conducting H...
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ISBN:
(纸本)9783031777301;9783031777318
Hyperparameter Optimization (HPO) plays a significant role in enhancing the performance of machine learning models. However, as the size and complexity of (deep) neural architectures continue to increase, conducting HPO has become very expensive in terms of time and computational resources. Existing methods that automate this process still demand numerous evaluations to find the optimal hyperparameter configurations. In this paper, we present a novel approach based on model-based reinforcement learning to effectively improve sample efficiency while minimizing resource consumption. We formulate the HPO task as a Markov decision process and develop a predictive dynamics model for efficient policy optimization. Additionally, we employ the Deep Sets framework to encode the state space, which is then leveraged in meta-learning for transfer of knowledge across multiple datasets, enabling the model to quickly adapt to new datasets. Empirical studies demonstrate that our approach outperforms alternative techniques on publicly available datasets in terms of sample efficiency and accuracy.
Skin cancer is one of the most prevalent and life-threatening diseases worldwide, requiring early detection for effective treatment. This study investigates a deep learning-based approach for classifying skin cancer i...
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The increasing adoption of EVs requires efficient battery management systems for optimal performance, safety, and increased battery life. An investigation of an IoT-based intelligent smart battery management system fo...
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