Prediction of fish mortality based on a probabilistic anomaly detection approach for recirculating aquaculture system facilities

Lopes, Bruna Daniela Mendes; Silva, Luís C.B.; Blanquet, Isidro; Georgieva, Pétia; Marques, Carlos

doi:10.1063/5.0045047

Cited by 12 publications

(3 citation statements)

References 62 publications

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“…Both exceeded 0.85, indicating good reliability and that the questionnaire met the survey requirements. e calibration process for the split-half reliability test used the Spearman-Brown formula [25][26][27]:…”

Section: Methodsmentioning

confidence: 99%

Machine Learning-Based Classification and Evaluation of Regional Ethnic Traditional Sports Tourism Resources

Lyu

2022

Mobile Information Systems

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Traditional sports in ethnic minority regions are a valuable cultural heritage. Regional ethnic traditional sports are not only a sports business but also a tourism resource. The construction of a reasonable regional sports tourism resource classification model is fundamental to the development of sports tourism resources. However, the existing sports tourism resources classification is mostly constructed manually based on the national standard tourism resources classification system. The efficiency and accuracy of the traditional manual classification are poor and cannot reflect the characteristics of regional ethnic traditional sports tourism. In order to solve the above problems, a machine learning-based classification method for regional ethnic traditional sports tourism resources is proposed. Firstly, the relevant concepts and characteristics of traditional sports tourism resources are introduced. Then, taking the development of traditional sports of ethnic minorities in Yunnan Province as the research object, SWOT analysis, literature, interview, questionnaire, and mathematical statistics are used to investigate and analyse the overall status of the development of regional ethnic traditional sports. Secondly, a classification evaluation method based on an optimised back-propagation (BP) neural network is proposed. Finally, the optimised BP neural network model is applied to the classification of traditional sports tourism resources. The experimental results show that the optimised BP model performs well in the classification of traditional sports tourism resources, verifying its effectiveness.

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Section: Methodsmentioning

confidence: 99%

Machine Learning-Based Classification and Evaluation of Regional Ethnic Traditional Sports Tourism Resources

Lyu

2022

Mobile Information Systems

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“…Such categorization is determined by the central characteristics of these problems, which are concerned with the prediction and optimization of production values using historical data records (structured data) [ 104 , 105 ]. Besides, production forecasting contains uncertainties regarding external factors of the production systems, which encourage the use of probabilistic methods, such as the studies carried out by [ 106 , 107 ].…”

Section: A Taxonomy Of Ci-based Problems In the Food Supply Chainmentioning

confidence: 99%

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

Angarita-Zapata

Alonso-Vicario

Masegosa

et al. 2021

Sensors

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In the last few years, the Internet of Things, and other enabling technologies, have been progressively used for digitizing Food Supply Chains (FSC). These and other digitalization-enabling technologies are generating a massive amount of data with enormous potential to manage supply chains more efficiently and sustainably. Nevertheless, the intricate patterns and complexity embedded in large volumes of data present a challenge for systematic human expert analysis. In such a data-driven context, Computational Intelligence (CI) has achieved significant momentum to analyze, mine, and extract the underlying data information, or solve complex optimization problems, striking a balance between productive efficiency and sustainability of food supply systems. Although some recent studies have sorted the CI literature in this field, they are mainly oriented towards a single family of CI methods (a group of methods that share common characteristics) and review their application in specific FSC stages. As such, there is a gap in identifying and classifying FSC problems from a broader perspective, encompassing the various families of CI methods that can be applied in different stages (from production to retailing) and identifying the problems that arise in these stages from a CI perspective. This paper presents a new and comprehensive taxonomy of FSC problems (associated with agriculture, fish farming, and livestock) from a CI approach; that is, it defines FSC problems (from production to retail) and categorizes them based on how they can be modeled from a CI point of view. Furthermore, we review the CI approaches that are more commonly used in each stage of the FSC and in their corresponding categories of problems. We also introduce a set of guidelines to help FSC researchers and practitioners to decide on suitable families of methods when addressing any particular problems they might encounter. Finally, based on the proposed taxonomy, we identify and discuss challenges and research opportunities that the community should explore to enhance the contributions that CI can bring to the digitization of the FSC.

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“…Anomaly detection algorithms are widely relevant and applicable to various fields, such as cyber-attack detection, credit card fraud detection, financial loan approval, medical drug research, etc. [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Research on Security Anomaly Detection for Big Data Platforms Based on Quantum Optimization Clustering

Deng¹,

Wan²,

Guo³

2022

Mathematical Problems in Engineering

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Due to the explosive growth of data in the Internet, more and more applications are being deployed on Big Data platforms. However, as the scale of data continues to increase, the probability of anomalies in the platform is also increasing. However, traditional anomaly detection techniques cannot effectively handle the massive amount of historical data and can hardly meet the security requirements of big data platforms. In order to solve the above problems, this paper proposes a security anomaly detection method for big data platforms based on quantum optimization clustering. Firstly, a framework of big data platform anomaly detection system is designed based on distributed software architecture through Hadoop and Spark big data open source technology. The system achieves effective detection of network anomalies by collecting and analyzing big data platform server log data. Secondly, an offline anomaly detection algorithm based on quantum ant colony optimized affinity propagation clustering is designed for various anomalies mined from historical data. The bias parameters of the affinity propagation clustering are treated as individual ants to construct an ant colony, and the clustering accuracy is set as fitness. Finally, in order to improve the accuracy of the optimal path search of the ant colony, quantum bit encoding is applied to the ant colony position to refine the granularity of the individual ant colony position update. The experimental results show that the proposed method can effectively complete the anomaly clustering detection of massive data. With a reasonable threshold, the quantum ant colony–based affinity propagation clustering has high detection accuracy.

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Prediction of fish mortality based on a probabilistic anomaly detection approach for recirculating aquaculture system facilities

Cited by 12 publications

References 62 publications

Machine Learning-Based Classification and Evaluation of Regional Ethnic Traditional Sports Tourism Resources

Machine Learning-Based Classification and Evaluation of Regional Ethnic Traditional Sports Tourism Resources

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective

Research on Security Anomaly Detection for Big Data Platforms Based on Quantum Optimization Clustering

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