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Home / Journals / American Journal of Data Mining and Knowledge Discovery / Data Mining and Machine Learning Applications
Data Mining and Machine Learning Applications

Special Issue Flyer (PDF)

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Lead Guest Editor:
Issa Atoum
Department of Software Engineering, The World Islamic Sciences and Education, Amman, Jordan
Guest Editors
Indranath Chatterjee
Assistant Professor, JK Lakshmipa University
Jaipur, Rajasthan, India
Ahmed Otoom
Social Security Funds
Amman, Jordan
Alumnus with Department of Electrical Engineering, University of Bridgeport
Bridgeport, Connecticut, USA
Maruthi Rohit Ayyagari
University of Dallas
Irving, Texas, USA
Abdallah Gharib
University of Malaysia
Sarawak, Malaysia
Raed Alazaidah
School of Computing, Northern University of Malaysia
Sintok, Kedah, Malaysia
Mohd Saifuzzaman
Daffodil International University
Dhaka, Bangladesh
With the existence of gigantic data daily, the business strives to extract interesting patterns and take proactive decisions. The application of data mining and machine learning can gain success in the field of business as well as in the technical fields. In the last years, the applications over customers and their behaviors have increased dramatically as a way of driving forces to enhance business process and marketing strategies. Therefore, such applications provide business with intelligent capabilities for interpretation and decision-making processes for gaining profit of the business organization. Moreover, data mining and machine learning community have been applied in technical fields. It has been applied in software engineering in requirements engineering, program comprehension, maintenance, and software components analysis. Data mining and machine learning applications have also proven a useful tool in cybersecurity solutions for detecting vulnerabilities and proactively preventing attacks.
The goal of this special issue is to cover a variety of topics and issues related to data mining applications. The special issue also welcomes manuscripts in theories and algorithms of data mining. Each manuscript will be reviewed by academics and practitioners who have worked in similar problem areas. The targeted readerships are technical and academic, as well as professionals in the domain.

Aims and Scope:

  1. Topics include but are not limited to:
  2. Applications of Data mining and Machine Learning
  3. Data Mining Process
  4. Data Transformations
  5. Data Analysis and Interpretations
  6. Pattern Recognition and Association Analysis
  7. Data Warehousing
  8. Image Processing
  9. Information Retrieval
  10. Risk Management Applications
  11. Artificial Intelligence Applications
  12. Software Engineering Applications
  13. Cybersecurity Applications
  14. Deep Learning
  15. Empirical Case studies
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