Applications of Artificial Intelligence in Machine Learning: Review and Prospect
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TLDR
A brief review and future prospect of the vast applications of machine learning has been made.Abstract:
Machine learning is one of the most exciting recent technologies in Artificial Intelligence. Learning algorithms in many applications that’s we make use of daily. Every time a web search engine like Google or Bing is used to search the internet, one of the reasons that works so well is because a learning algorithm, one implemented by Google or Microsoft, has learned how to rank web pages. Every time Facebook is used and it recognizes friends' photos, that's also machine learning. Spam filters in email saves the user from having to wade through tons of spam email, that's also a learning algorithm. In this paper, a brief review and future prospect of the vast applications of machine learning has been made.read more
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References
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TL;DR: The Semantic Web approach is outlined and conclusions drawn as to the suitability of different annotation methods and their combination with ubiquitous computing techniques to provide novel mechanisms for both student feedback and increased understanding of the learning environment.
Book ChapterDOI
A Machine Learning Based Framework for Adaptive Mobile Learning
Ahmed Al-Hmouz,Jun Shen,Jun Yan +2 more
TL;DR: A framework is presented that depicts the process of adapting learning content to satisfy individual learner characteristics by taking into consideration his/her learning style and uses a machine learning based algorithm for acquiring, representing, storing, reasoning and updating each learners acquired profile.
Machine Learning, Reasoning, and Intelligence in Daily Life: Directions and Challenges
TL;DR: I will review several illustrative research efforts on the team, and focus on challenges, opportunities, and directions with the streaming of machine intelligence into daily life.
Bagging Support Vector Machines for Leukemia Classification
TL;DR: Experimental results revealed that bSVM showed the best performance and can be used as a biomarker for the diagnose of leukemia disease and outperformed single SVM and other classification methods.
Machine Learning in Computational Biology.
Cornelia Caragea,Vasant Honavar +1 more
TL;DR: This work states that machine learning currently offers some of the most cost-effective tools for building predictive models from biological data, e.g., for annotating new genomic sequences, for predicting macromolecular function, for identifying functionally important sites in proteins, and for discovering the networks of genetic interactions that orchestrate important biological processes.