a b " 'This place used to be green': the brutal impact of oil in the Niger Delta". the Guardian. 2019-12-06 . Retrieved 2022-03-25. This is one of the major environmental issues in Bayelsa State due to the activities of major oil companies. [51] Oil spillage has affected farmlands, aquatic life and the health of the people. [52] Almost every day, Udengs Eradiri is informed of another oil spill in Bayelsa state, in the Niger Delta.

FUOTUOKE Resumption Date for Academic Activities 2020/2021". InfoGuideNigeria.com. 2022-10-20 . Retrieved 2023-01-06. Timipre Sylva, former governor of Bayelsa state, current Nigeria Minister of State for Petroleum Resources. [78]Li G, Shi J, Zhou J. Bayesian adaptive combination of short-term wind speed forecasts from neural network models. Renewable Energy. 2011;36(1):352–359. Governor Diri, Odi School alumni raise funds for science laboratory project". Tribune Online. 2022-03-16 . Retrieved 2022-03-23. Ishiguro, M. and H. Akaike, (1980) A Bayesian Approach to the Trading-day Adjustment of Monthly Data in Time Series Analysis: Proceedings of the International Conference held at Houston, August 1980, O. D. Anderson and M. R. Perryman eds., Amsterdam: North-Holland (to appear).

In response to environmental degradation in the state caused by the oil industry, movements such as the "Rise for Bayelsa [32]" campaign have emerged to push for protecting the local water supply. [33] In 2019, the Bayelsa State government launched the first formal inquiry into the crisis of oil pollution in the state. [34] [35] Economy [ edit ] Référence rapide des codes de la CITE-P et de la CITE-A dans la CITE 2011", Guide opérationnel CITE 2011, OECD, pp.117–118, 2016-01-25, doi: 10.1787/9789264248823-16-fr, ISBN 9789264248830 , retrieved 2021-09-10 Amarasinghe K, Marino DL, Manic M. Deep neural networks for energy load forecasting. In: 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE); 2017. p. 1483–1488.

Deep learning refers to a special type neural networks which consists of multiple processing layers and enables high-level abstraction to model data [ 55]. In the literature, deep learning commonly refers to the outstanding models such as recurrent networks (RNNs), convolutional neural networks (CNNs), deep belief networks, and long short-term memory networks (LSTMs) [ 56, 57]. In recent years, with more computing power and massive datasets, deep learning models have demonstrated excellent performance in different fields, such as sentiment analysis [ 58], image analysis [ 59] and natural language processing [ 60].

Mirikitani DT, Nikolaev N. Recursive Bayesian Recurrent Neural Networks for Time-Series Modeling. Neural Networks, IEEE Transactions on. 2010;21(2):262–274. pmid:20040415 Koprinska I, Wu D, Wang Z. Convolutional neural networks for energy time series forecasting. In: 2018 International Joint Conference on Neural Networks (IJCNN). IEEE; 2018. p. 1–8.

4 Experiments and results

You can ask a GP to refer you to a physiotherapist, or it may be possible to refer yourself directly. Learn About Bayelsa State, Nigeria | People, Local Government and Business Opportunities in Bayelsa". Overview of Nigeria |NgEX . Retrieved 2018-07-29. Shiskin, J., A. H. Young, and J. C. Musgrave, (1976) The X-11 Variant of the Census Method II Seasonal Adjustment Program Technical Paper No. 15 Bureau of the Census, U.S. Department of Commerce. Citation Needed", Retcon Game, University Press of Mississippi, 2017-04-03, doi: 10.14325/mississippi/9781496811325.003.0047, ISBN 9781496811325 , retrieved 2022-09-01

The languages of the Ijaw [14] are widely spoken in Bayelsa State, along with Isoko and Urhobo. The state is the primary home for the Ijaws, their ancestral home. The state is also the ancestral home of the Urhobo people in the Sagbama local government area. [15] The Bayelsa region experiences tropical monsoon weather. All year long, there are high temperatures and a lot of rain. In Bayelsa, the average annual temperature is 56 degrees, and there are roughly 675 inches of rain each year. With an average humidity of 82% and a UV-index of 6, it is dry for 47 days out of the year. [45] Environmental issues [ edit ] Soot pollution [ edit ]Neal RM. Bayesian Learning via Stochastic Dynamics. In: Advances in Neural Information Processing Systems 5. Morgan-Kaufmann; 1993. p. 475–482. The State is also well endowed with enormous deposits of hydrocarbons which have remained the mainstay of the nation and State’s economy for over five decades. It is made up of 8 Local Government Areas with Ekeremor, Kaiama, Nembe, Ogbia Town, Oporoma, Sagbama, Twon-Brass and Yenagoa, which is also the capital of the State.