Short-term Prediction of Lightning in Southern Africa using Autoregressive Machine Learning Techniques

2021 ◽  
Author(s):  
Yaseen Essa ◽  
Hugh G.P. Hunt ◽  
Ritesh Ajoodha
Keyword(s):  
Machine Learning ◽  
Southern Africa ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Term Prediction ◽  
Learning Techniques ◽  
Short Term Prediction

scholarly journals Utility of Big Data in Predicting Short-Term Blood Glucose Levels in Type 1 Diabetes Mellitus Through Machine Learning Techniques

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4482 ◽  
Author(s):  
Ignacio Rodríguez-Rodríguez ◽  
Ioannis Chatzigiannakis ◽  
José-Víctor Rodríguez ◽  
Marianna Maranghi ◽  
Michele Gentili ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Average Error ◽  
Short Term ◽  
Glucose Levels ◽  
Learning Techniques ◽  
Short Term Prediction

Machine learning techniques combined with wearable electronics can deliver accurate short-term blood glucose level prediction models. These models can learn personalized glucose–insulin dynamics based on the sensor data collected by monitoring several aspects of the physiological condition and daily activity of an individual. Until now, the prevalent approach for developing data-driven prediction models was to collect as much data as possible to help physicians and patients optimally adjust therapy. The objective of this work was to investigate the minimum data variety, volume, and velocity required to create accurate person-centric short-term prediction models. We developed a series of these models using different machine learning time series forecasting techniques suitable for execution within a wearable processor. We conducted an extensive passive patient monitoring study in real-world conditions to build an appropriate data set. The study involved a subset of type 1 diabetic subjects wearing a flash glucose monitoring system. We comparatively and quantitatively evaluated the performance of the developed data-driven prediction models and the corresponding machine learning techniques. Our results indicate that very accurate short-term prediction can be achieved by only monitoring interstitial glucose data over a very short time period and using a low sampling frequency. The models developed can predict glucose levels within a 15-min horizon with an average error as low as 15.43 mg/dL using only 24 historic values collected within a period of sex hours, and by increasing the sampling frequency to include 72 values, the average error is reduced to 10.15 mg/dL. Our prediction models are suitable for execution within a wearable device, requiring the minimum hardware requirements while at simultaneously achieving very high prediction accuracy.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

Short-term wind speed predictions with machine learning techniques

2015 ◽  
Vol 128 (1) ◽  
pp. 57-72 ◽  
Author(s):  
M. A. Ghorbani ◽  
R. Khatibi ◽  
M. H. FazeliFard ◽  
L. Naghipour ◽  
O. Makarynskyy
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Learning Techniques

scholarly journals COMPARATIVE ANALYSIS AND EVALUATION OF THE APPLICATION OF DEEP LEARNING TECHNIQUES TO CYBERSECURITY DATASETS

10.6036/10007 ◽  
2021 ◽  
Vol 96 (5) ◽  
pp. 528-533
Author(s):  
XAVIER LARRIVA NOVO ◽  
MARIO VEGA BARBAS ◽  
VICTOR VILLAGRA ◽  
JULIO BERROCAL
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Performance ◽  
New Technologies ◽  
Short Term Memory ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Term Memory ◽  
Learning Techniques ◽  
Long Short Term Memory

Cybersecurity has stood out in recent years with the aim of protecting information systems. Different methods, techniques and tools have been used to make the most of the existing vulnerabilities in these systems. Therefore, it is essential to develop and improve new technologies, as well as intrusion detection systems that allow detecting possible threats. However, the use of these technologies requires highly qualified cybersecurity personnel to analyze the results and reduce the large number of false positives that these technologies presents in their results. Therefore, this generates the need to research and develop new high-performance cybersecurity systems that allow efficient analysis and resolution of these results. This research presents the application of machine learning techniques to classify real traffic, in order to identify possible attacks. The study has been carried out using machine learning tools applying deep learning algorithms such as multi-layer perceptron and long-short-term-memory. Additionally, this document presents a comparison between the results obtained by applying the aforementioned algorithms and algorithms that are not deep learning, such as: random forest and decision tree. Finally, the results obtained are presented, showing that the long-short-term-memory algorithm is the one that provides the best results in relation to precision and logarithmic loss.

Short-term Prediction of Localized Heavy Rain from Radar Imaging and Machine Learning

2018 ◽  
Vol 7 (2) ◽  
pp. 107-115 ◽  
Author(s):  
Swe Swe Aung ◽  
Senaha Yu ◽  
Ohsawa Shin ◽  
Nagayama Itaru ◽  
Tamaki Shiro
Keyword(s):  
Machine Learning ◽  
Heavy Rain ◽  
Radar Imaging ◽  
Short Term ◽  
Term Prediction ◽  
Short Term Prediction
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