Application of cascade feed forward neural network to predict coagulant dose

Wadkar, Dnyaneshwar Vasant; Karale, Rahul; Wagh, Manoj Pandurang

doi:10.1080/23249676.2021.1927210

Cited by 14 publications

(5 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[21][22][23][24][25] Heddam 26 and Hong 27 adopted an adaptive neuro-fuzzy inference system model with pH, TUR, dissolved oxygen (DO), CON, and temperature as model input parameters for coagulant dosage in DWPT. Wadkar 28 applied a cascade feeds forward neural network to predict coagulant dose. Haghiri 29 used PH, temperature, alkalinity, and TUR as training parameters for the multi-layer perceptron model to determine the coagulation dosage in WWTP.…”

Section: Introductionmentioning

confidence: 99%

Exploring potential dual-stage attention based recurrent neural network machine learning application for dosage prediction in intelligent municipal management

Fang

Zang

Zhai

et al. 2023

Environ. Sci.: Water Res. Technol.

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Exploring potential dual-stage attention based recurrent neural network machine learning application for dosage prediction in intelligent municipal management

Fang

Zang

Zhai

et al. 2023

Environ. Sci.: Water Res. Technol.

View full text Add to dashboard Cite

show abstract

“…To finish, it attains the minimal of this quadratic. This function dynamically [28]modified by updating to the parameter between Gauss-Newton update and Gradient-Descent update.…”

Section: Levenberg Marquardt Optimization (Lm)mentioning

confidence: 99%

Application of Neural Network Algorithms to Implement Drug Abuse Research on Identifying Cannabis Use Disorder through Analysing Personality Traits

D. Kumaresan, Aranga. Arivarasan

2023

tjjpt

View full text Add to dashboard Cite

Frequent cannabis use will certainly lead to a reasonable mental complication. Daily and long-time period cannabis use is associated with more intellectual loss. This is identified as cannabis use disorder (CUD). The CUD starting at the teen age is the strongest predictors of cognitive impairment. However, it's miles uncertain which comes first. Whether CUD results in early onset cannabis use or cannabisearly use in lives reasons the CUD. The aim of this paper is to investigate the most influential features origins the CUD among the cannabis user’s personality trails. Depending on the self-extracted data collected from 1885 individuals three of the NN models were built. These three NN models apply the 10-Fold cross validation and 30-70 Hold-Out validation methods to provide the reliability of the prediction results. Among the three methods the RNN Hold-Out method achieves 99.19% as best accuracy result.

show abstract

“…One area that remains largely unexplored in these studies is the integration of autonomous learning principles into English language teaching methodologies [4]. While the focus is primarily on computational and statistical techniques such as neural networks, Bayesian networks, and machine learning algorithms, there is a notable absence of discussion surrounding the application of autonomous learning in the context of English language education [5]. Autonomous learning entails empowering students to take control of their learning journey, fostering selfdirectedness, and enabling them to set goals, monitor progress, and make decisions about their learning path [6].…”

Section: Introductionmentioning

confidence: 99%

Application of Frbf Network Autonomous Learning Algorithm Based on Intrinsic Motivation in English Network Teaching

Chen

2024

jes

View full text Add to dashboard Cite

An autonomous learning algorithm based on intrinsic motivation represents a cutting-edge approach to network teaching in English education. By harnessing intrinsic motivation, this algorithm empowers learners to autonomously engage with English language materials and activities, driven by their innate curiosity and desire for mastery. Through a combination of personalized learning pathways, adaptive feedback mechanisms, and gamified elements, the algorithm fosters a sense of ownership and agency in learners, leading to more effective and enjoyable learning experiences. Learners are encouraged to explore, experiment, and persist in their learning journey, guided by their intrinsic drive to understand and communicate in English. This paper introduces an innovative application of the FRBF network autonomous learning algorithm, grounded in intrinsic motivation principles, in the domain of English network teaching, further enhanced by the LogRegression Federated Radial Basic Function (LogR-FRBF). This approach leverages intrinsic motivation to foster autonomous learning behaviors among students, encouraging active engagement and exploration in English language acquisition. The LogR-FRBF model facilitates personalized learning pathways by integrating federated learning techniques with radial basic functions, enabling adaptive and context-aware recommendations tailored to individual learner needs and preferences. Through simulated experiments and empirical validations, the efficacy of the proposed framework is assessed, with promising results. The LogR-FRBF autonomous learning algorithm demonstrates superior performance in English network teaching, achieving higher levels of learner engagement, proficiency, and satisfaction compared to traditional methods. The LogR-FRBF autonomous learning algorithm demonstrated remarkable performance, achieving an average proficiency improvement of 25% among students compared to conventional teaching methods. Moreover, learner engagement levels increased by 30%, indicating heightened interest and participation in English language learning activities. Additionally, satisfaction surveys revealed a significant positive impact, with 90% of students expressing higher levels of satisfaction with the LogR-FRBF-enhanced autonomous learning experience. The LogR-FRBF autonomous learning algorithm demonstrated remarkable performance, achieving an average proficiency improvement of 25% among students compared to conventional teaching methods. Moreover, learner engagement levels increased by 30%, indicating heightened interest and participation in English language learning activities. Additionally, satisfaction surveys revealed a significant positive impact, with 90% of students expressing higher levels of satisfaction with the LogR-FRBF-enhanced autonomous learning experience.

show abstract

Application of cascade feed forward neural network to predict coagulant dose

Cited by 14 publications

References 38 publications

Exploring potential dual-stage attention based recurrent neural network machine learning application for dosage prediction in intelligent municipal management

Exploring potential dual-stage attention based recurrent neural network machine learning application for dosage prediction in intelligent municipal management

Application of Neural Network Algorithms to Implement Drug Abuse Research on Identifying Cannabis Use Disorder through Analysing Personality Traits

Application of Frbf Network Autonomous Learning Algorithm Based on Intrinsic Motivation in English Network Teaching

Contact Info

Product

Resources

About