New Hybrid Statistical Method and Machine Learning for PM10 Prediction

2019 ◽  
pp. 142-155
Author(s):  
Suhartono ◽  
Hendri Prabowo ◽  
Dedy Dwi Prastyo ◽  
Muhammad Hisyam Lee
Keyword(s):  
Machine Learning ◽  
Statistical Method

scholarly journals Statistics and Machine Learning Experiments in English and Romanian Poetry

Sci ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 92
Author(s):  
Ovidiu Calin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Statistical Method ◽  
Information Entropy ◽  
English Language ◽  
Quantitative Approach ◽  
Statistical Measures ◽  
Informational Energy ◽  
N Gram ◽  
Average Information

This paper presents a quantitative approach to poetry, based on the use of several statistical measures (entropy, informational energy, N-gram, etc.) applied to a few characteristic English writings. We found that English language changes its entropy as time passes, and that entropy depends on the language used and on the author. In order to compare two similar texts, we were able to introduce a statistical method to asses the information entropy between two texts. We also introduced a method of computing the average information conveyed by a group of letters about the next letter in the text. We found a formula for computing the Shannon language entropy and we introduced the concept of N-gram informational energy of a poetry. We also constructed a neural network, which is able to generate Byron-type poetry and to analyze the information proximity to the genuine Byron poetry.

Forecasting the Number of Customers Visiting Restaurants Using Machine Learning and Statistical Method

2021 ◽  
pp. 189-197
Author(s):  
Takashi Tanizaki ◽  
Shunsuke Kozuma ◽  
Takeshi Shimmura
Keyword(s):  
Machine Learning ◽  
Statistical Method ◽  
Number Of Customers

scholarly journals Statistics and Machine Learning Experiments in English and Romanian Poetry

Sci ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 78
Author(s):  
Ovidiu Calin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Statistical Method ◽  
Information Entropy ◽  
English Language ◽  
Quantitative Approach ◽  
Statistical Measures ◽  
Informational Energy ◽  
N Gram ◽  
Average Information

This paper presents a quantitative approach to poetry, based on the use of several statistical measures (entropy, informational energy, N-gram, etc.) applied to a few characteristic English writings. We found that English language changes its entropy as time passes, and that entropy depends on the language used and on the author. In order to compare two similar texts, we were able to introduce a statistical method to asses the information entropy between two texts. We also introduced a method of computing the average information conveyed by a group of letters about the next letter in the text. We found a formula for computing the Shannon language entropy and we introduced the concept of N-gram informational energy of a poetry. We also constructed a neural network, which is able to generate Byron-type poetry and to analyze the information proximity to the genuine Byron poetry.

Design of Noncoherent Communications: From Statistical Method to Machine Learning

2020 ◽  
Vol 27 (1) ◽  
pp. 76-83
Author(s):  
Jianhao Huang ◽  
Muhang Lan ◽  
Han Zhang ◽  
Chuan Huang ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Statistical Method ◽  
Noncoherent Communications

scholarly journals Solar power generation forecasting using ensemble approach based on deep learning and statistical methods

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mariam AlKandari ◽  
Imtiaz Ahmad
Keyword(s):  
Machine Learning ◽  
Power Generation ◽  
Statistical Method ◽  
Hybrid Model ◽  
Solar Power ◽  
Solar Power Generation ◽  
Machine Learning Methods ◽  
Linear Approach ◽  
Energy Plants ◽  
Renewable Energy Plants

Solar power forecasting will have a significant impact on the future of large-scale renewable energy plants. Predicting photovoltaic power generation depends heavily on climate conditions, which fluctuate over time. In this research, we propose a hybrid model that combines machine-learning methods with Theta statistical method for more accurate prediction of future solar power generation from renewable energy plants. The machine learning models include long short-term memory (LSTM), gate recurrent unit (GRU), AutoEncoder LSTM (Auto-LSTM) and a newly proposed Auto-GRU. To enhance the accuracy of the proposed Machine learning and Statistical Hybrid Model (MLSHM), we employ two diversity techniques, i.e. structural diversity and data diversity. To combine the prediction of the ensemble members in the proposed MLSHM, we exploit four combining methods: simple averaging approach, weighted averaging using linear approach and using non-linear approach, and combination through variance using inverse approach. The proposed MLSHM scheme was validated on two real-time series datasets, that sre Shagaya in Kuwait and Cocoa in the USA. The experiments show that the proposed MLSHM, using all the combination methods, achieved higher accuracy compared to the prediction of the traditional individual models. Results demonstrate that a hybrid model combining machine-learning methods with statistical method outperformed a hybrid model that only combines machine-learning models without statistical method.

scholarly journals Statistics and Machine Learning Experiments in Poetry

Sci ◽  
2020 ◽  
Vol 2 (3) ◽  
pp. 48
Author(s):  
Ovidiu Calin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Statistical Method ◽  
Information Entropy ◽  
English Language ◽  
Statistical Measures ◽  
Entropy Information ◽  
N Gram ◽  
Information Energy ◽  
Average Information

This paper presents a quantitative approach to poetry, based on the use of several statistical measures (entropy, information energy, N-gram, etc.) applied to a few characteristic English writings. We found that English language changes its entropy as time passes, and that entropy depends on the language used and on the author. In order to compare two similar texts, we were able to introduce a statistical method to asses the information entropy between two texts. We also introduced a method of computing the average information conveyed by a group of letters about the next letter in the text. We found a formula for computing the Shannon language entropy and we introduced the concept of N-gram informational energy of a poetry. We also constructed a neural network, which is able to generate Byron-type poetry and to analyze the information proximity to the genuine Byron poetry.

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