An intelligent aerator algorithm inspired-by deep learning

Deng, Hongjie; Peng, Lingxi; Jia-jing, Zhang; Tang, Chunming; Fang, Haoliang; Liu, Haohuai

doi:10.3934/mbe.2019148

Cited by 14 publications

(6 citation statements)

References 19 publications

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“…For example, an attention-based RNN model can achieve a clear and effective representation of time-space relationships and its learning ability is superior to that of other methods for both short-and long-term predictions of dissolved oxygen (Liu et al 2019). These models can be continuously optimized during the prediction process to improve their prediction accuracies (Deng et al 2019).…”

Section: Water Quality Predictionmentioning

confidence: 99%

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Deep learning for smart fish farming: applications, opportunities and challenges

Yang

Zhang

Liu

et al. 2020

Reviews in Aquaculture

192

116

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The rapid emergence of deep learning (DL) technology has resulted in its successful use in various fields, including aquaculture. DL creates both new opportunities and a series of challenges for information and data processing in smart fish farming. This paper focuses on applications of DL in aquaculture, including live fish identification, species classification, behavioural analysis, feeding decisions, size or biomass estimation, and water quality prediction. The technical details of DL methods applied to smart fish farming are also analysed, including data, algorithms and performance. The review results show that the most significant contribution of DL is its ability to automatically extract features. However, challenges still exist; DL is still in a weak artificial intelligence stage and requires large amounts of labelled data for training, which has become a bottleneck that restricts further DL applications in aquaculture. Nevertheless, DL still offers breakthroughs for addressing complex data in aquaculture. In brief, our purpose is to provide researchers and practitioners with a better understanding of the current state of the art of DL in aquaculture, which can provide strong support for implementing smart fish farming applications.

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Section: Water Quality Predictionmentioning

confidence: 99%

“…These models can be continuously optimized during the prediction process to improve their prediction accuracies (Deng et al . 2019).…”

Section: Applications Of Deep Learning In Smart Fish Farmingmentioning

confidence: 99%

Deep learning for smart fish farming: applications, opportunities and challenges

Yang

Zhang

Liu

et al. 2020

Reviews in Aquaculture

192

116

View full text Add to dashboard Cite

show abstract

“…It has a rapid onset and requires surgical treatment in severe cases. The typical symptom is fever [ 1 ], and patients usually suffer from intense pain in the upper right abdomen [ 2 ]. It is generally treated by the surgery.…”

Section: Introductionmentioning

confidence: 99%

Influence of Percutaneous Drainage Surgery and the Interval to Perform Laparoscopic Cholecystectomy on Acute Cholecystitis through Genetic Algorithm-Based Contrast-Enhanced Ultrasound Imaging

Cheng

Gao

et al. 2022

Computational Intelligence and Neuroscience

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To discuss the optimal interval time between genetic algorithm-based ultrasound imaging-guided percutaneous drainage surgery (PTGD) and laparoscopic cholecystectomy (LC), 64 cholecystitis patients were selected as the research objects and evenly divided into experimental group (intelligent algorithm was adopted to recognize patients’ ultrasonic images) and control group (professional doctors carried out diagnosis). 92 acute cholecystitis patients undergoing PTGD were divided into three groups. 30 out of the 92 patients received LC within 2 months and were defined as the early group. 32 were performed with LC within 2 to 4 months and were defined as the metaphase group. 28 underwent LC over 4 months and were defined as the late-stage group. The average operation time, the transition from LC to laparotomy, the average postoperative hospital stay, and the incidence of complications of the three groups were compared. The results revealed that the comparison of the diagnostic accuracy and comprehensive effectiveness between experimental group and control group demonstrated that the differences were statistically significant ( P < 0.05 ). When the optimal interval of implementing LC after PTGD was realized, the corresponding values of the early group were 88.5 minutes, 16.67%, 8.13 days, and 13.75%. Those of the metaphase group were 49.91 minutes, 3.13%, 4.97 days, and 9.52%. Those of the late stage group were 68.78 minutes, 10.71%, 7.09 days, and 11.96%. To sum up, the diagnostic accuracy and comprehensive effectiveness of intelligent algorithm were higher than those of conventional ultrasound, and the optimal interval time of implementing LC after PTGD was 2 to 4 months.

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“…Based on the above problems, this study was expected to determine the relationship between the pandemic trends and the social-economic development. A smart pandemic forecasting model named LDDPG was constructed, based on theses data using the LSTM ( L ong Short-Term Memory) ( Peng et al., 2021 , Peng et al., 2016 , Deng et al., 2019 ) and the DDPG ( D eep D eterministic P olicy G radient) algorithms. The model combined the pandemic and economic data over a time to adjust the prediction parameters dynamically and make reasonable predictions of the social-economic trends, based on daily updated pandemic statistics as the main reference indicators, to help the decision-makers assess the benefits of the current decisions and reference for next decisions.…”

Section: Introductionmentioning

confidence: 99%

Self-correcting error-based prediction model for the COVID-19 pandemic and analysis of economic impacts

Tang

et al. 2021

Sustainable Cities and Society

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An intelligent aerator algorithm inspired-by deep learning

Cited by 14 publications

References 19 publications

Deep learning for smart fish farming: applications, opportunities and challenges

Deep learning for smart fish farming: applications, opportunities and challenges

Influence of Percutaneous Drainage Surgery and the Interval to Perform Laparoscopic Cholecystectomy on Acute Cholecystitis through Genetic Algorithm-Based Contrast-Enhanced Ultrasound Imaging

Self-correcting error-based prediction model for the COVID-19 pandemic and analysis of economic impacts

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