Multiclass SVM algorithms for wind speed prediction

Wani, M. Arif; Bhat, Heena Farooq

doi:10.1109/icrera.2017.8191231

Cited by 8 publications

(2 citation statements)

References 32 publications

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“…Classical statistical methods have a clear physical meaning and are easily explained. Academic researchers have developed a variety of such methods, ranging from linear regression [9] and support vector machine [10], [11] to the Autoregressive Integrated Moving Average (ARIMA) model [12]- [14]. Williams conducted a representative study on applying the ARIMA model, which considered the effect of seasons on traffic forecasts.…”

Section: Related Workmentioning

confidence: 99%

Short-Term Traffic Speed Forecasting Using a Deep Learning Method Based on Multitemporal Traffic Flow Volume

et al. 2022

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Accurate traffic speed forecasting not only can help traffic management departments make better judgments and improve the efficacy of road monitoring but also can help drivers plan their driving routes and arrive safely and smoothly at their destination. This paper focuses on the lack of traffic speed data and proposes a method for traffic speed forecasting based on the multitemporal traffic flow volume of the previous and later moment states. First, according to traffic flow volume data, the different traffic patterns of previous and later moment states were extracted. Second, the performance of five forecasting models, namely, long short-term memory (LSTM), backpropagation (BP), classification and regression trees, k-nearest neighbor, and support vector regression, were compared. Finally, the model with the best prediction results was used to conduct sensitivity analysis experiments for different traffic patterns. Through a real-data case study, we found that the LSTM model has the highest prediction accuracy compared to other models in both time and space. This traffic pattern "previous = 3 and later = 3" can forecast traffic speed more accurately, and its forecasting ability is robust across a range of scenarios.

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Section: Related Workmentioning

confidence: 99%

Short-Term Traffic Speed Forecasting Using a Deep Learning Method Based on Multitemporal Traffic Flow Volume

et al. 2022

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“…The SVM is a supervised learning algorithm used to categorize reserved blueprint of data [7−9]. SVM has been applied to various other domains which incorporate wind speed prediction [10], fingerprint recognition 319 [11], face recognition [12], global solar radiation forecasting [13] and also evaluates various information retrieval algorithms with the use of linear algebra [14]. The functional proteins included in organisms at the upper level are not adjacent.…”

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confidence: 99%

PSSM amino-acid composition based rules for gene identification

Bhat¹,

Wani²

2018

IJATEE

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With the advancement of genome sequence data, a large number of gene predicting programs have come into existence. Big data associated with the problem of identification of genes can be projected into subspaces or clusters so that the given problem can be divided into sub-problems. Each sub-problem can then be optimized with an appropriate model. One of the approaches of dividing a given problem into subproblems involves projecting the big data to various sub-space grids [1−4], where each sub-space grid represents a sub-problem. Each sub-problem can then be represented independently with various models which can be combined by using a rule based system [5].The gene identification from large genome sequence is found to be one of the significant issues to solve in the field of bioinformatics. There is an essential requirement of developing gene finding methods and their corresponding functions. One of the foremost problems in the process of gene forecasting is to discover the protein coding genes in genomic DNA sequence.

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