Machine learning technique-based data-driven model of exploring effects of electrolyte additives on LiNi0.6Mn0.2Co0.2O2/graphite cell

2021 ◽  
Vol 42 ◽  
pp. 103012
Author(s):  
Van Minh Duong ◽  
Thanh Nhan Tran ◽  
Akhil Garg ◽  
Thinh Gia Phung ◽  
Van Man Tran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Driven ◽  
Machine Learning Technique ◽  
Electrolyte Additives ◽  
Graphite Cell ◽  
Learning Technique

scholarly journals Evaluation of the COVID-19 Era by Using Machine Learning and Interpretation of Confidential Dataset

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2910
Author(s):  
Andreas Andreou ◽  
Constandinos X. Mavromoustakis ◽  
George Mastorakis ◽  
Jordi Mongay Batalla ◽  
Evangelos Pallis
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Research Study ◽  
Nonlinear Least Squares ◽  
Data Driven ◽  
Machine Learning Technique ◽  
Marquardt Algorithm ◽  
Learning Technique ◽  
Iot Devices ◽  
Algorithmic Techniques

Various research approaches to COVID-19 are currently being developed by machine learning (ML) techniques and edge computing, either in the sense of identifying virus molecules or in anticipating the risk analysis of the spread of COVID-19. Consequently, these orientations are elaborating datasets that derive either from WHO, through the respective website and research portals, or from data generated in real-time from the healthcare system. The implementation of data analysis, modelling and prediction processing is performed through multiple algorithmic techniques. The lack of these techniques to generate predictions with accuracy motivates us to proceed with this research study, which elaborates an existing machine learning technique and achieves valuable forecasts by modification. More specifically, this study modifies the Levenberg–Marquardt algorithm, which is commonly beneficial for approaching solutions to nonlinear least squares problems, endorses the acquisition of data driven from IoT devices and analyses these data via cloud computing to generate foresight about the progress of the outbreak in real-time environments. Hence, we enhance the optimization of the trend line that interprets these data. Therefore, we introduce this framework in conjunction with a novel encryption process that we are proposing for the datasets and the implementation of mortality predictions.

Machine learning technique for data-driven fault detection of nonlinear processes

2019 ◽  
Vol 31 (4) ◽  
pp. 865-884 ◽  
Author(s):  
Maroua Said ◽  
Khaoula ben Abdellafou ◽  
Okba Taouali
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Data Driven ◽  
Nonlinear Processes ◽  
Machine Learning Technique ◽  
Learning Technique

Property-Oriented Material Design Based on a Data-Driven Machine Learning Technique

2020 ◽  
Vol 11 (10) ◽  
pp. 3920-3927 ◽  
Author(s):  
Qionghua Zhou ◽  
Shuaihua Lu ◽  
Yilei Wu ◽  
Jinlan Wang
Keyword(s):  
Machine Learning ◽  
Material Design ◽  
Data Driven ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Oriented Material

What Should Investors Care About? Mutual Fund Ratings by Analysts vs. Machine Learning Technique

2020 ◽  
Author(s):  
Si Cheng ◽  
Ruichang Lu ◽  
Xiaojun Zhang
Keyword(s):  
Machine Learning ◽  
Mutual Fund ◽  
Machine Learning Technique ◽  
Learning Technique

scholarly journals The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

Voice Pathology Detection Using Machine Learning Technique

2020 ◽  
Author(s):  
Fahad Taha AL-Dhief ◽  
Nurul Mu'azzah Abdul Latiff ◽  
Nik Noordini Nik Abd. Malik ◽  
Naseer Sabri ◽  
Marina Mat Baki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Voice Pathology Detection

Realization of a temperature sensor using both two- and three-dimensional photonic structures through a machine learning technique

2021 ◽  
Author(s):  
Kaliprasanna Swain ◽  
Subhra Rani Mondal ◽  
Mihir Narayan Mohanty ◽  
Susanta Kumar Tripathy ◽  
Gopinath Palai
Keyword(s):  
Machine Learning ◽  
Temperature Sensor ◽  
Three Dimensional ◽  
Machine Learning Technique ◽  
Photonic Structures ◽  
Learning Technique

Evaluation of caridean ecological distribution in the Ubatuba region, southeastern Brazilian coast using unsupervised machine learning technique

2021 ◽  
Author(s):  
Alexandre Oliveira Marques ◽  
Aline Nonato Sousa ◽  
Veronica Pereira Bernardes ◽  
Camila Hipolito Bernardo ◽  
Danielle Monique Reis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brazilian Coast ◽  
Ecological Distribution ◽  
Machine Learning Technique ◽  
Unsupervised Machine Learning ◽  
Learning Technique
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