A novel artificial intelligent model for predicting air overpressure using brain inspired emotional neural network

Temeng, Victor Amoako; Ziggah, Yao Yevenyo

doi:10.1016/j.ijmst.2020.05.020

Cited by 49 publications

(15 citation statements)

References 34 publications

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“…One of the main reasons for the slow convergence and poor accuracy of the artificial bee colony algorithm is that only one dimension is updated at a time when the food source is updated, which seriously affects the efficiency of the search [ 16 ]. To overcome this problem, this section proposes a new update strategy that incorporates a multidimensional update strategy and a high-quality bee guidance strategy.…”

Section: Analysis Of Swarm Algorithm Combined With Neural Network Algorithm In English Course Recommendation Techniquementioning

confidence: 99%

[Retracted] Application of the Deep Learning Algorithm and Similarity Calculation Model in Optimization of Personalized Online Teaching System of English Course

Fang

2021

Computational Intelligence and Neuroscience

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This study provides an in-depth study and analysis of English course recommendation techniques through a combination of bee colony algorithm and neural network algorithm. In this study, the acquired text is trained with a document vector by a deep learning model and combined with a collaborative filtering method to recommend suitable courses for users. Based on the analysis of the current research status and development of the technology related to course resource recommendation, the deep learning technology is applied to the course resource recommendation based on the current problems of sparse data and low accuracy of the course recommendation. For the problem that the importance of learning resources to users changes with time, this study proposes to fuse the time information into the neural collaborative filtering algorithm through the clustering classification algorithm and proposes a deep learning-based course resource recommendation algorithm to better recommend the course that users want to learn at this stage promptly. Secondly, the course cosine similarity calculation model is improved for the course recommendation algorithm. Considering the impact of the number of times users rate courses and the time interval between users rating different courses on the course similarity calculation, the contribution of active users to the cosine similarity is reduced and a time decay penalty is given to users rating courses at different periods. By improving the hybrid recommendation algorithm and similarity calculation model, the error value, recall, and accuracy of course recommendation results outperform other algorithmic models. The requirements analysis identifies the personalized online teaching system with rural primary and secondary school students as the main service target and then designs the overall architecture and functional modules of the recommendation system and the database table structure to implement the user registration, login, and personal center functional modules, course publishing, popular recommendation, personalized recommendation, Q&A, and rating functional modules.

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Section: Analysis Of Swarm Algorithm Combined With Neural Network Algorithm In English Course Recommendation Techniquementioning

confidence: 99%

[Retracted] Application of the Deep Learning Algorithm and Similarity Calculation Model in Optimization of Personalized Online Teaching System of English Course

Fang

2021

Computational Intelligence and Neuroscience

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“…Management risk factors are the management conditions that cause or increase the risk of accidents or enlarge the degree of loss and are the direct or indirect causes of accidents and accident risk losses. Management conditions are first reflected in the quality of the manager [17]. e manager is responsible for the planning, organization, and supervision of the swimming pool operation and management activities to ensure the normal operation of the swimming pool, create a good safe environment, and better serve the swimming consumers.…”

Section: Analysis Of Intelligent Management Model Of Swimming Place W...mentioning

confidence: 99%

Nonlinear Random Matrix-Based Intelligent Management Model for Swimming Place Waters

Yuan

Han

2022

Mathematical Problems in Engineering

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In this paper, a nonlinear random matrix approach is used to analyze the management of swimming place waters, and in this way, an intelligent management model is designed and applied to the actual swimming place waters management. Firstly, some basic small deviation results of the random matrix are presented. Then, several types of small deviation inequalities are obtained for the maximum eigenvalues of independent stochastic semi-positive definite (PSD) matrices. These small deviation inequalities are independent of the matrix dimension, and the results apply to high-dimensional and even infinite-dimensional matrices. For the inverse eigenvalue problem of higher-order doubly random matrices, the conclusion that two symmetric doubly random matrices are combined into a higher-order symmetric doubly random matrix is proved. In other words, the method of constructing new symmetric random matrices using smaller matrices with known spectra is applied to the inverse eigenvalue problem of higher-order symmetric random matrices, and sufficient conditions for the existence of the solution of the inverse eigenvalue problem of higher-order random matrices are derived, avoiding the discussion of the parity of the number of eigenvalues. This paper focuses on realistic management, to explore the standardization of safety management of swimming venues, and is based on modern management theory, risk management theory and methods, using research integration method, fieldwork method, questionnaire survey method, expert interview method, and other research methods, detailing the methods and conclusions of risk identification in safety management of swimming venues, and further exploring the safety management standards of swimming venues on this basis. And, combined with risk management theory, depending on the probability of occurrence and the degree of harm of risk, four basic disposal methods such as risk avoidance, risk control, risk transfer, and risk self-retaining are proposed.

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“…SVRs have been compared to Response Surface Methodologies for predictive applications (Mia and Dhar, 2019 ). Similarly, these models have been used for prediction purposes (Temeng et al, 2020 ), but lack implementations on systems lacking features within data points.…”

Section: Introductionmentioning

confidence: 99%

Integration of machine learning with complex industrial mining systems for reduced energy consumption

Harmse

Laar

Pelser

et al. 2022

Front. Artif. Intell.

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The deep-level mining industry is experiencing narrowing profit margins due to increasing operating costs and decreasing production. The industry is known for its lack of dynamic control across complex integrated systems running deep underground, making IoT technologies difficult to implement. An important integrated system in a typical underground mine is the refrigeration-ventilation system. In practice, the two systems are still controlled independently, often due to a lack of continuous measurements. However, their integrated effects ultimately affect energy usage and production. This study develops and compares various machine learning prediction techniques to predict the integrated behavior of a key component operating on the boundary of the refrigeration-ventilation system, while also addressing the lack of continuous measurements. The component lacks sensors and the developed industrial machine learning models negate the effect thereof using integrated control. The predictive models are compared based on accuracy, prediction time, as well as the amount of data required to obtain the required level of accuracy. The “Support Vector Machines” method achieved the lowest average error (1.97%), but the “Artificial Neural Network” method is more robust (with a maximum percentage error of 12.90%). A potential energy saving of 215 kW or 2.9% of the ventilation and refrigeration system, equivalent to R1.33-million per annum ($82 9001) is achievable using the “Support Vector Machines” method.

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A novel artificial intelligent model for predicting air overpressure using brain inspired emotional neural network

Cited by 49 publications

References 34 publications

[Retracted] Application of the Deep Learning Algorithm and Similarity Calculation Model in Optimization of Personalized Online Teaching System of English Course

[Retracted] Application of the Deep Learning Algorithm and Similarity Calculation Model in Optimization of Personalized Online Teaching System of English Course

Nonlinear Random Matrix-Based Intelligent Management Model for Swimming Place Waters

Integration of machine learning with complex industrial mining systems for reduced energy consumption

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