scholarly journals Preface to the special issue on analysis in machine learning and data science

2020 ◽  
Vol 19 (8) ◽  
pp. i-iii
Author(s):  
Andreas Chirstmann ◽  
◽  
Qiang Wu ◽  
Ding-Xuan Zhou ◽  
◽  
...  
Information ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 171
Author(s):  
Alexandru Telea ◽  
Andreas Kerren

Recent developments at the crossroads of data science, datamining,machine learning, and graphics and imaging sciences have further established information visualization and visual analytics as central disciplines that deliver methods, techniques, and tools for making sense of and extracting actionable insights and results fromlarge amounts of complex,multidimensional, hybrid, and time-dependent data.[...]


2020 ◽  
Vol 20 (S11) ◽  
Author(s):  
Li Shen ◽  
Xinghua Shi ◽  
Zhongming Zhao ◽  
Kai Wang

AbstractThe 2020 International Conference on Intelligent Biology and Medicine (ICIBM 2020) provided a multidisciplinary forum for computational scientists and experimental biologists to share recent advances on all aspects of intelligent computing, informatics and data science in biology and medicine. ICIBM 2020 was held as a virtual conference on August 9–10, 2020, including four live sessions with forty-one oral presentations over video conferencing. In this special issue, ten high-quality manuscripts were selected after peer-review from seventy-five submissions to represent the medical informatics and decision making aspect of the conference. In this editorial, we briefly summarize these ten selected manuscripts.


2018 ◽  
Vol 48 (5) ◽  
pp. 673-684 ◽  
Author(s):  
Matthew L. Jones

In the last two decades, a highly instrumentalist form of statistical and machine learning has achieved an extraordinary success as the computational heart of the phenomenon glossed as “predictive analytics,” “data mining,” or “data science.” This instrumentalist culture of prediction emerged from subfields within applied statistics, artificial intelligence, and database management. This essay looks at representative developments within computational statistics and pattern recognition from the 1950s onward, in the United States and beyond, central to the explosion of algorithms, techniques, and epistemic values that ultimately came together in the data sciences of today. This essay is part of a special issue entitled Histories of Data and the Database edited by Soraya de Chadarevian and Theodore M. Porter.


2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.


2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
...  

Author(s):  
Ritu Khandelwal ◽  
Hemlata Goyal ◽  
Rajveer Singh Shekhawat

Introduction: Machine learning is an intelligent technology that works as a bridge between businesses and data science. With the involvement of data science, the business goal focuses on findings to get valuable insights on available data. The large part of Indian Cinema is Bollywood which is a multi-million dollar industry. This paper attempts to predict whether the upcoming Bollywood Movie would be Blockbuster, Superhit, Hit, Average or Flop. For this Machine Learning techniques (classification and prediction) will be applied. To make classifier or prediction model first step is the learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations. Methods: All the techniques related to classification and Prediction such as Support Vector Machine(SVM), Random Forest, Decision Tree, Naïve Bayes, Logistic Regression, Adaboost, and KNN will be applied and try to find out efficient and effective results. All these functionalities can be applied with GUI Based workflows available with various categories such as data, Visualize, Model, and Evaluate. Result: To make classifier or prediction model first step is learning stage in which we need to give the training data set to train the model by applying some technique or algorithm and after that different rules are generated which helps to make a model and predict future trends in different types of organizations Conclusion: This paper focuses on Comparative Analysis that would be performed based on different parameters such as Accuracy, Confusion Matrix to identify the best possible model for predicting the movie Success. By using Advertisement Propaganda, they can plan for the best time to release the movie according to the predicted success rate to gain higher benefits. Discussion: Data Mining is the process of discovering different patterns from large data sets and from that various relationships are also discovered to solve various problems that come in business and helps to predict the forthcoming trends. This Prediction can help Production Houses for Advertisement Propaganda and also they can plan their costs and by assuring these factors they can make the movie more profitable.


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