A novel weather prediction model using a hybrid mechanism based on MLP and VAE with fire-fly optimization algorithm

Vuyyuru, Veera Ankalu.; Rao, G. Appa; Murthy, Y. V. Srinivasa

doi:10.1007/s12065-021-00589-8

Cited by 21 publications

(9 citation statements)

References 38 publications

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“…Research institutions or companies are faced with problems such as lack of prediction accuracy of recommendation algorithms, the cold start of recommendation systems, and how to achieve maximum structural engineering of recommendation systems. Solving these problems requires continued research and practice by a wide range of scholars [ 4 ]. Initializing the weight with the smallest possible value will easily lead to too long network training time, too many iterations, and easy to fall into a local optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization

Sun

2022

Computational Intelligence and Neuroscience

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In this paper, we use a variational fuzzy neural network algorithm to conduct an in-depth analysis and research on the optimization of music intelligent marketing strategy. The music recommendation system proposed in this paper includes a user modelling module, audio feature extraction module, and recommendation algorithm module. The basic idea of the recommendation algorithm is as follows: firstly, the historical behavioural information of music users is collected, and the user preference model is constructed by using the method of matrix decomposition of the hidden semantic model; then, the audio resources in the system are preprocessed and the spectrum map that can represent the music features is extracted; the similarity between the user’s preferred features and the music potential features are calculated to generate recommendations for the target user. The user-music dataset for model training and testing is constructed in-house, and the network model structure used for system experiments is designed based on a typical convolutional neural network model, while the model training tuning parameters are compared and selected. Finally, the model is trained and tested in this paper, and the system is evaluated in terms of both prediction rating accuracy and recommendation list generation accuracy using root mean square error, accuracy, recall, and F1 value as recommendation quality evaluation metrics. The experimental results show that the recommendation algorithm in this paper has certain feasibility and effectiveness. Compared with other traditional music recommendation algorithms, this paper makes full use of the powerful advantage of deep neural networks to automatically extract features and obtain higher-level music feature representations from the audio content, while incorporating the historical behavioural information of user interactions with music, which can effectively alleviate the problems such as cold start in recommendation systems.

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Section: Introductionmentioning

confidence: 99%

Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization

Sun

2022

Computational Intelligence and Neuroscience

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“…AI models commonly used for drought forecasting or similar applications with the potential to be used for drought prediction include fuzzy logic based models ( [126][127][128][129]), genetic algorithm ( [130,131]), genetic programming ( [132]), clustering methods such as K-means and nearest neighbour [133][134][135], as well as a variety of ML models such as artificial neural networks (ANNs; [26,118,136,142-]), support vector regression ( [72]), support vector machine (SVM; [143,144]), decision tree ( [145,146]) and random forest ( [147][148][149][150][151]). More recently, boosting algorithms, such as XGBoost [152] and deep generative models [153][154][155] including variational autoencoders (VAEs; [156]), and generative adversarial networks (GANs; [157]) have shown great promise for drought forecasting performance improvement. Further, models that focus on predicting a distribution rather than a value, e.g.…”

Section: Artificial Intelligence and Machine Learning Modelsmentioning

confidence: 99%

“…A more attractive and powerful category of AI models, with significant potential applications to drought prediction, is the so-called deep generative models. Broadly, generative models combine the benefits of neural networks and probabilistic methods for a wide range of applications [153]. Two key architectures in this class include the variational autoencoders (VAEs; [156]) and GANs ( [157]).…”

Section: Artificial Intelligence and Machine Learning Modelsmentioning

confidence: 99%

Status and prospects for drought forecasting: opportunities in artificial intelligence and hybrid physical–statistical forecasting

AghaKouchak

Pan

Mazdiyasni

et al. 2022

Phil. Trans. R. Soc. A.

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Despite major improvements in weather and climate modelling and substantial increases in remotely sensed observations, drought prediction remains a major challenge. After a review of the existing methods, we discuss major research gaps and opportunities to improve drought prediction. We argue that current approaches are top-down, assuming that the process(es) and/or driver(s) are known—i.e. starting with a model and then imposing it on the observed events (reality). With the help of an experiment, we show that there are opportunities to develop bottom-up drought prediction models—i.e. starting from the reality (here, observed events) and searching for model(s) and driver(s) that work. Recent advances in artificial intelligence and machine learning provide significant opportunities for developing bottom-up drought forecasting models. Regardless of the type of drought forecasting model (e.g. machine learning, dynamical simulations, analogue based), we need to shift our attention to robustness of theories and outputs rather than event-based verification. A shift in our focus towards quantifying the stability of uncertainty in drought prediction models, rather than the goodness of fit or reproducing the past, could be the first step towards this goal. Finally, we highlight the advantages of hybrid dynamical and statistical models for improving current drought prediction models. This article is part of the Royal Society Science+ meeting issue ‘Drought risk in the Anthropocene’.

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“…Various studies have proved that the VAE network with the MLP structure is effective. 33 However, for the spectra data that focus more on determining the absolute peak difference, the MLP network is unstable and producing a significant single value is easy, which is prone to gradient explosion during ELBO calculations. Therefore, the number of the FC layers needs to be reduced.…”

Section: Convolutional Variational Autoencoder Deep Embedding Clusteringmentioning

confidence: 99%

Unsupervised convolutional variational autoencoder deep embedding clustering for Raman spectra

et al. 2022

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A novel weather prediction model using a hybrid mechanism based on MLP and VAE with fire-fly optimization algorithm

Cited by 21 publications

References 38 publications

Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization

Variational Fuzzy Neural Network Algorithm for Music Intelligence Marketing Strategy Optimization

Status and prospects for drought forecasting: opportunities in artificial intelligence and hybrid physical–statistical forecasting

Unsupervised convolutional variational autoencoder deep embedding clustering for Raman spectra

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