Data Mining Based Artificial Intelligence
Dr. Sathishkumar V E.
Dr. K Venkatachalam
Special Issue Information
Dear Colleagues:
Big data analytics using machine learning is a rapidly growing field with numerous applications across various industries. In recent years, the amount of data being generated has increased exponentially, leading to a need for effective and efficient methods for analysing and extracting insights from this data. Machine learning algorithms are well-suited for this task, as they are able to automatically learn patterns and relationships in data, without the need for explicit programming.
As such, we propose a special issue on the topic of big data analytics using machine learning for our journal. This special issue will focus on the latest research and developments in this field, including the use of machine learning algorithms for big data analytics in various industries, such as healthcare, finance, and retail. It will also cover the challenges and opportunities associated with big data analytics using machine learning, such as data privacy and security, and the need for robust and scalable algorithms.
We envision this special issue to be a comprehensive resource for researchers, practitioners, and policymakers interested in big data analytics using machine learning. It will provide a platform for the dissemination of innovative and high-quality research, as well as facilitate the exchange of ideas and best practices among experts in the field.
We believe that this special issue will make a significant contribution to the advancement of the field of big data analytics using machine learning, and we look forward to receiving and reviewing submissions from researchers around the world.
This special issue invites original research articles, case studies, and review articles that focus on the following (but not limited to) topics related to data mining-based AI:
• Machine learning algorithms for data mining
• Deep learning for data mining
• Natural language processing for data mining
• Data mining for predictive analytics
• Data mining for image and video analysis
• Data mining for network analysis
• Data mining for anomaly detection
• Data mining for recommendation systems
• Data mining for customer segmentation
• Data mining for fraud detection
• Data mining for healthcare data analysis
• Data mining for finance data analysis
• Data mining for social media data analysis
• Data mining for agricultural data analysis
• Data mining for environmental data analysis
Dr. Sathishkumar V E
Dr. Venkatachalam K
Guest Editors
