scholarly journals Data Mining and Analytics in the Process Industry: The Role of Machine Learning

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 20590-20616 ◽  
Author(s):  
Zhiqiang Ge ◽  
Zhihuan Song ◽  
Steven X. Ding ◽  
Biao Huang
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Process Industry

Current Trends

2019 ◽  
pp. 181-198
Author(s):  
Jayanthi Jagannathan ◽  
Anitha Elavarasi S.
Keyword(s):  
Machine Learning ◽  
Manufacturing Industry ◽  
Predictive Analytics ◽  
Process Industry ◽  
Healthcare Industry ◽  
Current Trends ◽  
Smart Campus ◽  
Industry Automation ◽  
The Internet Of Things

This chapter addresses the key role of machine learning and artificial intelligence for various applications of the internet of things. The following are the most significant applications of IoT: (1) manufacturing industry: automation of industries is on the rise; there is an urge for analyzing the energy in the process industry; (2) anomaly detection: to detect the existing fault and abnormality in functioning by using ML algorithms thereby avoiding the adverse effect during its operation; (3) smart campus: in-order to efficiently handle the energy in buildings, smart campus systems are developed; (4) improving product decisions: with the help of the predictive analytics system products are designed and developed based on the user's requirements and usability; (5) healthcare industry: IoT with machine learning provides numerous ways for the betterment of the human wellbeing. In this chapter, the most predominant approaches to machine learning that can be useful in the IoT applications to achieve a significant set of outcomes will be discussed.

scholarly journals Intrusion Detection in Cyber Security: Role of Machine Learning and Data Mining in Cyber Security

2020 ◽  
Vol 5 (3) ◽  
pp. 72-81 ◽  
Author(s):  
Gillala Rekha ◽  
Shaveta Malik ◽  
Amit Kumar Tyagi ◽  
Meghna Manoj Nair
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Intrusion Detection ◽  
Cyber Security

scholarly journals Role of Machine Learning and Data Mining in Internet Security: Standing State with Future Directions

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Bilal Ahmad ◽  
Wang Jian ◽  
Zain Anwar Ali
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Information Technology ◽  
Network Security ◽  
Intrusion Detection ◽  
Internet Security ◽  
Network Activity ◽  
Safety Measures ◽  
Future Directions

As time progresses with vast development of information technology, a large number of industries are more dependent on network connections for sensitive business trading and security matters. Communications and networks are highly vulnerable to threats because of increase in hacking. Personnel, governments, and armed classified networks are more exposed to difficulties, so the need of the hour is to install safety measures for network to prevent illegal modification, damage, or leakage of serious information. Intrusion detection, an important entity towards network security, has the ability to observe network activity as well as detect intrusions/attacks. This study highlights the developing research about the application of machine learning and data mining in Internet security. We provide background, enthusiasm, discussion of challenges, and recommendations for the application of ML/DM in the field of intrusion detection.

scholarly journals The Role of Machine Learning and Data Mining Techniques in Predicting Students’ Academic Performance

2021 ◽  
Vol 10 (8) ◽  
pp. 198-207
Author(s):  
Dr. Aliyu Y. Rufai ◽  
Dr.Hassan U. Suru ◽  
James Afrifa
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Academic Performance ◽  
Educational Data Mining ◽  
Large Data ◽  
Complex Problem ◽  
Data Set ◽  
Early Action ◽  
Methods And Techniques

The advancement in Information Technology makes it easier and cheaper to collect large amounts of data, but if this data is not further analyzed, it remains only huge amounts of data. These large amounts of data set have motivated research and development in various fields to extract meaningful information with a view of analyzing it to solve complex problem. With new methods and techniques, data can be analyze and be of great advantage. Data mining and machine learning are two computing disciplines that enable analysis of large data sets using different techniques. This paper gave an overview of several applications using these disciplines in education, with focus on student’s academic performance prediction. Early prediction of students’ performance is useful in taking early action of improving learning outcome. The perfect methods for this are machine learning and data mining. This paper also discusses special use of data mining in education, called educational data mining. Educational Data Mining (EDM) uses different methods and techniques from machine learning, statistics, data mining and data analysis, to analyze data collected during teaching and learning. The goal of this paper is to introduce the role of machine learning and data mining in predicting student’s academic performance and to present its applications and benefits

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

Sign in / Sign up

Export Citation Format

Share Document