Optimization of biomass production from sugar bagasse in anaerobic digestion using genetic algorithm

Saeid, Parvane; Pazoki, Maryam; Zeinolabedini, Mohammad

doi:10.1007/s40808-022-01598-x

Cited by 15 publications

(2 citation statements)

References 22 publications

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“…Concurrently, employing the ABC algorithm during the model's pre-training phase establishes a strong foundation for learning (Saeid et al 2023). This algorithm excels at navigating the extensive search space for potential solutions, pinpointing the most favorable initial weight configurations for the neural network.…”

Section: Discussionmentioning

confidence: 99%

ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration

Mirzaee Moghaddam Kasmaee,

Ataei,

Moravvej

et al. 2024

Physiol. Meas.

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Objective. Myocarditis poses a significant health risk, often precipitated by viral infections like Coronavirus disease (COVID-19), and can lead to fatal cardiac complications. As a less invasive alternative to the standard diagnostic practice of endomyocardial biopsy, which is highly invasive and thus limited to severe cases, Cardiac Magnetic Resonance (CMR) imaging offers a promising solution for detecting myocardial abnormalities. Approach. This study introduces a deep model called ELRL-MD that combines ensemble learning and Reinforcement Learning (RL) for effective myocarditis diagnosis from CMR images. The model begins with pre-training via the Artificial Bee Colony (ABC) algorithm to enhance the starting point for learning. An array of Convolutional Neural Networks (CNNs) then works in concert to extract and integrate features from CMR images for accurate diagnosis. Leveraging the Z-Alizadeh Sani myocarditis CMR dataset, the model employs RL to navigate the dataset's imbalance by conceptualizing diagnosis as a decision-making process. Main Results. ELRL-DM demonstrates remarkable efficacy, surpassing other deep learning, conventional machine learning, and transfer learning models, achieving an F-measure of 88.2\% and a geometric mean of 90.6\%. Extensive experimentation helped pinpoint the optimal reward function settings and the perfect count of CNNs. Significance. The study addresses the primary technical challenge of inherent data imbalance in CMR imaging datasets and the risk of models converging on local optima due to suboptimal initial weight settings. Further analysis, leaving out ABC and RL components, confirmed their contributions to the model's overall performance, underscoring the effectiveness of addressing these critical technical challenges.

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Section: Discussionmentioning

confidence: 99%

ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration

Mirzaee Moghaddam Kasmaee,

Ataei,

Moravvej

et al. 2024

Physiol. Meas.

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“…A new approach, ML-ABC, has been developed to enhance the standard ABC [22]. This method promotes mutual learning between different elements of the algorithm, improving adaptability and possibly resolving the weight initialization issues that affect gradientbased methods [23]. By fostering information exchange within the optimization process, ML-ABC can avoid local minima and find better solutions [24].…”

Section: Introductionmentioning

confidence: 99%

Advanced Seismic Magnitude Classification Through Convolutional and Reinforcement Learning Techniques

Lin,

2023

IJACSA

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Earthquake Early Warning (EEW) systems are crucial in reducing the dangers associated with earthquakes. This paper delves into the realm of EEWs, focusing on rapidly determining earthquake magnitudes (EMs). Traditional methods for swift magnitude categorization often grapple with challenges such as data disparity and cumbersome processes. Our research introduces an innovative EEW model, employing a 7-second seismic waveform record from three different components provided by the China Earthquake Network Center (CENC). This empirical, quantitative study pioneers a method combining dilated convolutional techniques with a novel mutual learningbased artificial bee colony (ML-ABC) algorithm and reinforcement learning (RL) for EM classification. The proposed model utilizes an ensemble of convolutional neural networks (CNNs) to simultaneously extract feature vectors from input images, which are then amalgamated for classification. To address the imbalances in the dataset, we implement an RLbased algorithm, conceptualizing the training process as a series of decisions with individual samples representing distinct states. Within this framework, the network operates as an agent, receiving rewards or penalties based on its precision in distinguishing between the minority and majority classes.A key innovation in our approach is the initial weight pre-training using the ML-ABC method. This technique dynamically optimizes the "food source" for candidates, integrating mutual learning elements related to the initial weights. Extensive experiments were carried out on the selected dataset to ascertain the most effective parameter values, including the reward function. The findings demonstrate the superiority of our proposed model over other evaluated methods, highlighting its potential as a robust tool for EM classification in seismology. This research provides valuable insights for both seismologists and developers of EEW systems, offering a novel, efficient approach to earthquake magnitude determination.

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Smartphone detector examination for transportation mode identification utilizing imbalanced maximizing-area under the curve proximal support vector machine

Dai,

Huang

2024

SIViP

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Optimization of biomass production from sugar bagasse in anaerobic digestion using genetic algorithm

Cited by 15 publications

References 22 publications

ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration

ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration

Advanced Seismic Magnitude Classification Through Convolutional and Reinforcement Learning Techniques

Smartphone detector examination for transportation mode identification utilizing imbalanced maximizing-area under the curve proximal support vector machine

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