scholarly journals From Classical Machine Learning to Deep Neural Networks: A Simplified Scientometric Review

2021 ◽  
Vol 11 (12) ◽  
pp. 5541
Author(s):  
Ravil I. Mukhamediev ◽  
Adilkhan Symagulov ◽  
Yan Kuchin ◽  
Kirill Yakunin ◽  
Marina Yelis
Keyword(s):  
Machine Learning ◽  
Publication Activity ◽  
Short Term ◽  
Scientific Publications ◽  
Average Prediction Error ◽  
Research Areas ◽  
Quantitative Estimates ◽  
Key Terms ◽  
Research Domains ◽  
Dynamic Indicators

There are promising prospects on the way to widespread use of AI, as well as problems that need to be overcome to adapt AI&ML technologies in industries. The paper systematizes the AI sections and calculates the dynamics of changes in the number of scientific articles in machine learning sections according to Google Scholar. The method of data acquisition and calculation of dynamic indicators of changes in publication activity is described: growth rate (D1) and acceleration of growth (D2) of scientific publications. Analysis of publication activity, in particular, showed a high interest in modern transformer models, the development of datasets for some industries, and a sharp increase in interest in methods of explainable machine learning. Relatively small research domains are receiving increasing attention, as evidenced by the negative correlation between the number of articles and D1 and D2 scores. The results show that, despite the limitations of the method, it is possible to (1) identify fast-growing areas of research regardless of the number of articles, and (2) predict publication activity in the short term with satisfactory accuracy for practice (the average prediction error for the year ahead is 6%, with a standard deviation of 7%). This paper presents results for more than 400 search queries related to classified research areas and the application of machine learning models to industries. The proposed method evaluates the dynamics of growth and the decline of scientific domains associated with certain key terms. It does not require access to large bibliometric archives and allows to relatively quickly obtain quantitative estimates of dynamic indicators.

scholarly journals AI for short term forecasts (nowcasting) at DWD, status and perspectives.

2021 ◽  
Author(s):  
Richard Müller
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Weather Forecast ◽  
Autonomous Driving ◽  
Short Term ◽  
Simulation And Optimization ◽  
Success Stories ◽  
Research Areas ◽  
Research Activities ◽  
German Aerospace

<p>Artificial Intelligence (AI) is nowadays a central key for the improvement of methods in many modern applications and research areas, e.g. autonomous driving, image / face recognition as well as system simulation and optimization. The AI success stories are increasing quite rapidly. However, AI is still largely underrepresented in weather services.</p><p>DWD was among the first European weather services who used modern AI methods within the scope of short term weather forecast. The respective research activities and applications are currently performed in close cooperation with the University of Saarbrücken and Mainz as well as the German Aerospace Center, whereby DWD acts mainly on implementation and steering of the research. Further, on an international level, DWD agreed recently on a closer cooperation with the new AI section of the South-Korean Meteorological Agency as well.</p><p>This presentation will provide an overview about the AI applications at DWD covering the following topics:</p><p>- The application of computer vision for the short term forecasting (nowcasting) of radiation, fronts, thunderstorms and precipitation based on satellite data.</p><p>- The development and application of machine learning for the early detection of thunderstorms as well as for the improvement of short term forecasts of mature Cbs.</p><p>- The operational usage of neuronal networks for the detection of volcanic ash (satellite data).</p><p>- AI concepts for a seamless integrated forecasting system.</p><p>However, AI, in particular machine learning, is also linked with handicaps. Thus the pros and cons of AI, will be discussed in comparison to classical methods. Within this scope, recent validation results will be considered as well.</p><p> </p>

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.

Predicting the citation and impact factor of terms for scientific publications using machine learning algorithms

2020 ◽  
Author(s):  
Aleksey Klokov ◽  
Evgenii Slobodyuk ◽  
Michael Charnine
Keyword(s):  
Machine Learning ◽  
Semantic Processing ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Scientific Publications ◽  
Text Data ◽  
Semantic Relationships ◽  
Subject Areas ◽  
The Subject ◽  
Scientific Environment

The object of the research when writing the work was the body of text data collected together with the scientific advisor and the algorithms for processing the natural language of analysis. The stream of hypotheses has been tested against computer science scientific publications through a series of simulation experiments described in this dissertation. The subject of the research is algorithms and the results of the algorithms, aimed at predicting promising topics and terms that appear in the course of time in the scientific environment. The result of this work is a set of machine learning models, with the help of which experiments were carried out to identify promising terms and semantic relationships in the text corpus. The resulting models can be used for semantic processing and analysis of other subject areas.

scholarly journals A Systematic Survey of ML Datasets for Prime CV Research Areas—Media and Metadata

Data ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 12
Author(s):  
Helder F. Castro ◽  
Jaime S. Cardoso ◽  
Maria T. Andrade
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Qualitative Analysis ◽  
Continuous Growth ◽  
Scientific Databases ◽  
Extensive Survey ◽  
Research Areas ◽  
Current State ◽  
Global View ◽  
Cooperative Production

The ever-growing capabilities of computers have enabled pursuing Computer Vision through Machine Learning (i.e., MLCV). ML tools require large amounts of information to learn from (ML datasets). These are costly to produce but have received reduced attention regarding standardization. This prevents the cooperative production and exploitation of these resources, impedes countless synergies, and hinders ML research. No global view exists of the MLCV dataset tissue. Acquiring it is fundamental to enable standardization. We provide an extensive survey of the evolution and current state of MLCV datasets (1994 to 2019) for a set of specific CV areas as well as a quantitative and qualitative analysis of the results. Data were gathered from online scientific databases (e.g., Google Scholar, CiteSeerX). We reveal the heterogeneous plethora that comprises the MLCV dataset tissue; their continuous growth in volume and complexity; the specificities of the evolution of their media and metadata components regarding a range of aspects; and that MLCV progress requires the construction of a global standardized (structuring, manipulating, and sharing) MLCV “library”. Accordingly, we formulate a novel interpretation of this dataset collective as a global tissue of synthetic cognitive visual memories and define the immediately necessary steps to advance its standardization and integration.

scholarly journals Short-Term Prediction of COVID-19 Cases Using Machine Learning Models

2021 ◽  
Vol 11 (9) ◽  
pp. 4266
Author(s):  
Md. Shahriare Satu ◽  
Koushik Chandra Howlader ◽  
Mufti Mahmud ◽  
M. Shamim Kaiser ◽  
Sheikh Mohammad Shariful Islam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Learning Models ◽  
Short Term ◽  
Infected People ◽  
Infected Case ◽  
First Case ◽  
Novel Coronavirus ◽  
Short Term Prediction ◽  
Machine Learning Models

The first case in Bangladesh of the novel coronavirus disease (COVID-19) was reported on 8 March 2020, with the number of confirmed cases rapidly rising to over 175,000 by July 2020. In the absence of effective treatment, an essential tool of health policy is the modeling and forecasting of the progress of the pandemic. We, therefore, developed a cloud-based machine learning short-term forecasting model for Bangladesh, in which several regression-based machine learning models were applied to infected case data to estimate the number of COVID-19-infected people over the following seven days. This approach can accurately forecast the number of infected cases daily by training the prior 25 days sample data recorded on our web application. The outcomes of these efforts could aid the development and assessment of prevention strategies and identify factors that most affect the spread of COVID-19 infection in Bangladesh.

Sign in / Sign up

Export Citation Format

Share Document