This issue focuses on new research work in the area of Machine Learning. Machine Learning was motivating to learn because we could see how all the math, we had studies at university is applied in real life, and it's not only interesting, but also very useful.
Also, just the thought that given the data we can extract something useful from it is already very motivating. For example, if you measure, we weight every day, then, when we accumulate enough data, we can extract some helpful stuff about it that overwise we won't be able to learn. Another motivation could be money. Data science is quite a hot topic nowadays and data scientists are paid quite well - companies have tons and tons of data and they need people who know how to mine something useful from this data. And there are more and more "data driven" companies that need people who can mine insight from the raw floods of information.
From the above perspective, papers submitted in this area should have a strong application perspective rather than purely theoretical goal.
Specific subfields covered by this section include AI algorithms and methods, deep learning, natural language processing, big data text analytics, information extraction, Data Mining, Classification, Clustering, Association rules, Text Mining, Opinion Mining and so on.
Aims and Scope:
- Machine Learning
- Data Mining
- Text mining
- Opinion Mining
- Sentiment Analysis
- Text Classification