Application of a New Hybrid Machine Learning (Fuzzy-PSO) for Detection of Breast’s Tumor

Published: Jan. 19, 2023

Breast cancer is the second leading cause of death after lung cancer. The only possible way to save patients' lives early diagnosis disease; Because if this disease diagnosed in stages and with a high level accuracy, chance survival increases. Different fuzzy-based soft computing techniques have been proposed. In research, proposed fuzzy hybrid algorithm - particle swarm has used detect type breast tumors based on analysis features mammography images. method study, algorithm, remarkable performance 94.58% diagnosis. results obtained from study can be for timely providing effective treatments

Language: Английский

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A reinforcement learning-based online learning strategy for real-time short-term load forecasting

Energy, Journal Year: 2024, Volume and Issue: 305, P. 132344 - 132344

Published: July 9, 2024

Real-time Short-Term Load Forecasting (STLF) is crucial for energy management and power system operations. Conventional Machine Learning (ML) methodologies STLF are often challenged by the inherent variability in demand. To tackle challenge associated with variability, this paper presents a novel Reinforcement (RL)-enhanced method. Different from conventional methods, our method dynamically improves model selecting optimal training data to capture recent usage trends possible variations demand patterns. By doing so, can significantly reduce impact of unforeseen fluctuations real-time forecasting. In addition RL-enhanced method, we propose comprehensive evaluation framework, encompassing three key dimensions: accuracy, runtime efficiency, robustness. Tested on distinct real-world datasets, demonstrates superior forecasting performance across metrics achieving accurate robust predictions under varying scenarios. Furthermore, approach provides uncertainty bounds practical applications. These results underscore significant advancements made RL-based precision, We have algorithm openly accessible online promote continued development advancement methods.

Language: Английский

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A Review of Deep Reinforcement Learning Methods and Military Application Research

Mathematical Problems in Engineering, Journal Year: 2023, Volume and Issue: 2023(1)

Published: Jan. 1, 2023

In the area of artificial intelligence, deep reinforcement learning has grown in significance. It accomplished extraordinary feats and offers a fresh approach to previously challenging challenges, such as controlling robotic arm discovering game strategies. The two primary categories methods—deep based on value function policy gradient—are initially explained this study. limitations current approaches difficulties faced by methods related domains are further sorted out, then future application directions military sphere examined. Finally, growing trend for techniques is anticipated applications.

Language: Английский

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Enhanced short-term flow prediction in power dispatching network using a transfer learning approach with GRU-XGBoost module ding

Frontiers in Energy Research, Journal Year: 2024, Volume and Issue: 12

Published: Aug. 1, 2024

The power dispatching network forms the backbone of efforts to automate and modernize grid dispatching, rendering it an indispensable infrastructure element within system. However, accurately forecasting future flows remains a formidable challenge due network’s intricate nature, variability, extended periods missing data resulting from equipment maintenance anomalies. Vital enhancing prediction precision is interpolation values aligned with distribution across other time points, facilitating effective capture nonlinear patterns historical flow sequences. To address this, we propose transfer learning approach leveraging gated recurrent unit (GRU) for interpolating sequence. Subsequently, decompose generation predictions into two stages: first, extracting features using GRU, then generating robust via eXtreme Gradient Boosting (XGBoost). This integrated process termed GRU-XGBoost module, applied in experiments on four sequences obtained company southern China. Our experimental findings illustrate that proposed model outperforms both machine neural models, underscoring its superiority short-term power-dispatching networks.

Language: Английский

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Artificial intelligence and machine learning in future energy systems (state-of-the-art, future development)

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 3 - 30

Published: Jan. 1, 2024

Language: Английский

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