Smart autonomous robotics must be able to navigate in complicated surroundings. Developing complex navigational platforms to transport autonomous robotics from one location to another has taken years of engineering an...
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Sleep Apnoea is a sleep disorder where inhaling and exhaling repeatedly pauses and continues;it can be termed breathing difficulties, where the throat muscles irregularly relax and block the airway during sleep. Other...
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Quantitative algorithmic trading and machinelearning have transformed financial markets by introducing advanced computational techniques for market analysis, decision-making, risk management, and order execution. Thi...
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ISBN:
(纸本)9798350354843;9798350354836
Quantitative algorithmic trading and machinelearning have transformed financial markets by introducing advanced computational techniques for market analysis, decision-making, risk management, and order execution. This paper explores Deep Reinforcement learning (DRL) for developing day trading strategies within the Ibovespa futures market, the world's most traded stock index futures contract. We chose the Ibovespa market for its high liquidity and volatility, which present unique challenges and opportunities for trading. We propose a novel methodology for the development, evaluation, and deployment of DRL-powered strategies. This methodology involves model generation, approval, selection, and execution, supported by comprehensive metrics for assessing returns, risk, and stability. We introduce the FUT-DRL trading strategy, a new approach that models the trading problem as a Partially Observable Markov Decision Process using DRL agents and the Deep Q-Network algorithm to navigate market complexities. Our empirical study, utilizing a diverse five-year dataset under varied conditions, shows that FUT-DRL outperforms traditional strategies, such as trend-following and mean-reversion, achieving an annualized return over 100% and a Probabilistic Sharpe Ratio of 0.96. This research advances the integration of machinelearning with quantitative trading, offering a robust framework for strategy development, and highlighting DRL's effectiveness in the volatile Ibovespa market, demonstrating the potential of adaptive and intelligent systems to evolve trading strategies in rapidly changing financial markets.
In the US, ischemic heart disease (IHD) and CAD, also known as chronic heart disease (CHD), are major causes of death for people of all racial and ethnic backgrounds, regardless of gender. Cardiovascular disease claim...
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An innovative educational tool called the 'Auto-mated Question Generation and Answer Evaluation System' was created to make the assessment process more efficient. By automating the creation of questions, it co...
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Agriculture is the foundation of all the countries. Due to the decreased size of a farming parkland, it has become a most important issue in picking the maximum fitting crop based on current factors in a particular fi...
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Condition monitoring of transformers is crucial to maintain and enhance their life longevity. Many traditional approaches have been used in the past to predict the health and identify the faults occurring in transform...
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ISBN:
(纸本)9798331516970;9798331516963
Condition monitoring of transformers is crucial to maintain and enhance their life longevity. Many traditional approaches have been used in the past to predict the health and identify the faults occurring in transformers. In this paper, some traditional and newer machinelearning algorithms are used to predict the type of fault in a transformer. Feature engineering is applied to study its effect on the accuracy of fault classification. The results prove the adaptability of the algorithms to small and medium size datasets. But it also leads to an important observation that the accuracy of these algorithms decreases with increase in size of the dataset.
Agriculture is of vital importance to human life as it is the main source of livestock production and contributes significantly to the country's employment opportunities and economy. Ensuring high standards of pro...
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Recent advancements in artificial intelligence (AI) and machinelearning (ML) have significantly enhanced various fields, including intelligent transportation systems (ITS). A notable area of progress is vehicle class...
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Recent advancements in artificial intelligence (AI) and machinelearning (ML) have significantly enhanced various fields, including intelligent transportation systems (ITS). A notable area of progress is vehicle classification, crucial for improving road safety and traffic management. Traditional vehicle classification methods often struggle with accuracy and efficiency in complex conditions. This study introduces a novel method using Neuro-Evolutionary Algorithms (NEAs) to optimize vehicle classification, combining neural networks with evolutionary computation for robust framework design and parameter optimization. NEAs adaptively refine neural network architectures, particularly enhancing their performance in diverse driving scenarios. Implemented in Google Colab, our NEA-optimized models demonstrated a remarkable classification accuracy of 98.35%, outperforming traditional and contemporary methods. This approach not only advances vehicle classification accuracy but also sets the stage for future developments in ITS, promoting safer, more efficient mobility.
As digital interactions proliferate, the imperative to fortify data privacy and security becomes paramount. This paper explores advanced techniques - encryption algorithms, biometric authentication, machinelearning f...
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