Modeling of carbon dioxide fixation by microalgae using hybrid artificial intelligence (AI) and fuzzy logic (FL) methods and optimization by genetic algorithm (GA)

Kushwaha, Omkar S.; Uthayakumar, Haripriyan; Kumaresan, K.

doi:10.1007/s11356-022-19683-0

Cited by 25 publications

(8 citation statements)

References 104 publications

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“…To sum up, the postproduction parameter C sum in film and television production theory is the key to choosing innovative strategy of the artificial intelligence algorithm, and it is also the main postproduction parameter optimized in this paper as stated by Kushwaha et al [14].…”

Section: Film and Television Production Eorymentioning

confidence: 97%

Study Film and Television Postproduction and Innovation Strategy Based on an Artificial Intelligence Algorithm

Han

Shen

2022

Mobile Information Systems

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In the process of choosing the best scheme in the artificial intelligence algorithm, it is impossible to accurately judge the nonlinear relationship between the innovation strategy and the film and television postproduction scheme. An improved artificial intelligence algorithm based on the integration of dynamic factors and the artificial intelligence algorithm is proposed to reduce the disturbance ability of the artificial intelligence algorithm and improve the analysis level of film and television postproduction and innovation strategy. Firstly, the initial innovation strategy set of the production set is established by using dynamic factors, which makes it discrete and reduces the influence of the scheme selection error on the results. Then, the production set is divided into dynamic subproduction sets by using the film and television production theory, and each subproduction set seeks its own parallel innovation strategy. Finally, under the guidance of film and television production theory, each subproduction set shares the matching of optimal solutions. Through MATLAB simulation analysis and verification, the improved dynamic artificial intelligence algorithm can improve the accuracy of judging the innovation strategy of film and television works in an uncertain environment and shorten the convergence time of global feature solution and is superior to the original selection method of film and television production strategy. In addition, under that condition the initial weight scheme and the threshold scheme are set. The artificial intelligence algorithm is used to analyze the innovation strategy selection of youth idol works. The results show that under different film and television production requirements, the innovation strategy selection judgment of the artificial intelligence algorithm is accurate and superior to the original film and television production strategy selection method, which further verifies the effectiveness of the artificial intelligence algorithm proposed in this paper.

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Section: Film and Television Production Eorymentioning

confidence: 97%

Study Film and Television Postproduction and Innovation Strategy Based on an Artificial Intelligence Algorithm

Han

Shen

2022

Mobile Information Systems

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“…AI optimizes energy consumption within CDR facilities by analyzing energy usage patterns and considering factors like costs and availability. This includes strategies such as load balancing, scheduling energy-intensive operations during low-demand periods, and integrating renewable energy sources [76]. AI also optimizes resource allocation and utilization within CDR facilities by analyzing data on availability, costs, and carbon removal targets.…”

Section: Efficiency Improvementsmentioning

confidence: 99%

Integration of Carbon Dioxide Removal (CDR) Technology and Artificial Intelligence (AI) in Energy System Optimization

Li,

Luo,

Liu

et al. 2024

Processes

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In response to the urgent need to address climate change and reduce carbon emissions, there has been a growing interest in innovative approaches that integrate AI and CDR technology. This article provides a comprehensive review of the current state of research in this field and aims to highlight its potential implications with a clear focus on the integration of AI and CDR. Specifically, this paper outlines four main approaches for integrating AI and CDR: accurate carbon emissions assessment, optimized energy system configuration, real-time monitoring and scheduling of CDR facilities, and mutual benefits with mechanisms. By leveraging AI, researchers can demonstrate the positive impact of AI and CDR integration on the environment, economy, and energy efficiency. This paper also offers insights into future research directions and areas of focus to improve efficiency, reduce environmental impact, and enhance economic viability in the integration of AI and CDR technology. It suggests improving modeling and optimization techniques, enhancing data collection and integration capabilities, enabling robust decision-making and risk assessment, fostering interdisciplinary collaboration for appropriate policy and governance frameworks, and identifying promising opportunities for energy system optimization. Additionally, this paper explores further advancements in this field and discusses how they can pave the way for practical applications of AI and CDR technology in real-world scenarios.

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“…Generally speaking, the following strategy can be used to determine the initial population: (1) based on the internal knowledge of the problem, the optimal solution is attempted to understand the spatial distribution area in the entire problem space, and then within this distribution area, the initial population is determined. (2) Several individuals are randomly generated and the best individual is selected to join the initial population [12]. As shown in Figure 2, this process is repeated until the number of individuals in the initial population reaches the predetermined size.…”

Section: Ga Algorithmmentioning

confidence: 99%

Evaluation of Multi-level Employment Psychological Pressure among College Students Based on Dynamic Optimization Models

2024

appep

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In universities, the primary task of vocational education is to cultivate students' ability to adapt to social work, so employment is very important for college students. They need to think about how to find a job and how to apply what they have learned to their work. Employment is a huge psychological burden for college students, especially in the current labor market where many people are unable to find their ideal jobs, which has a certain impact on their psychology, such as conflicts, anxiety, depression, etc. Psychological problems have a significant impact on their employment and even mental health. Therefore, establishing a correct career choice perspective, cultivating various psychological qualities, and maintaining a good psychological state for college students in their career, as well as making effective adjustments to themselves, plays a very important role in their future. Therefore, this article attempted to use the GA-BP (genetic algorithm-back propagation) algorithm to construct a dynamic optimization model for evaluating the multi-level employment psychological pressure of college students. Firstly, the influencing factors of employment psychological pressure of college students were obtained from a large number of literature, and then the various influencing factors of students were investigated and collected as sample data for the GA-BP algorithm. The experiment showed that the combination of GA algorithm and BP algorithm could achieve the highest accuracy in predicting the employment psychological pressure of college students. The average accuracy of the three training results was 0.9, which was higher than the optimized accuracy of momentum algorithm, LM algorithm, adaptive algorithm, and CG algorithm.

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Modeling of carbon dioxide fixation by microalgae using hybrid artificial intelligence (AI) and fuzzy logic (FL) methods and optimization by genetic algorithm (GA)

Cited by 25 publications

References 104 publications

Study Film and Television Postproduction and Innovation Strategy Based on an Artificial Intelligence Algorithm

Study Film and Television Postproduction and Innovation Strategy Based on an Artificial Intelligence Algorithm

Integration of Carbon Dioxide Removal (CDR) Technology and Artificial Intelligence (AI) in Energy System Optimization

Evaluation of Multi-level Employment Psychological Pressure among College Students Based on Dynamic Optimization Models

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