Harnessing the power of Machine Learning for Automating the Repetitive Tasks
DOI:
https://doi.org/10.26438/ijcse/v6si3.108112Keywords:
Smart work, Machine Learning, Automating, Smart assistantsAbstract
Why to do hard work? When smart work pays off! There are about 7.6 billion people in the world who do many tasks every day, in which most of the tasks are repetitive. Repetitive tasks can be assisted and done by employing machine learning. Data is generated from these repetitive tasks, and this voluminous data is managed by Big Data Analytics and it is analyzed by Machine Learning and provides smart solutions. First of all Machine Learning creates a study pattern based on our daily routines and this data will be at a level of complexity that human minds will fail to comprehend. Machine Learning will make it possible for automated system to outthink the human brain by integrating broad information sets and finding correlations. A large number of repetitive tasks that involve manual labor can be automated through Machine Learning. Advances in Machine learning signify a future when devices run on self-learning algorithms and operate independently. They may deduce their own conclusions within certain parameters and develop a context based behavior to interact with human more directly than before. This could mean automating tasks of professionals like doctors (analyzing reports), advocates (for analyzing vast number of judgments and concluding outcomes), etc., even for routine jobs Machine Learning could uncover new potentials and enable human to make the best of their talents. In this article we would focus on how to minimize the time and energy spent on the repetitive and tedious tasks by assigning them to smart assistants using Machine Learning.
References
ABDULSALAM YASSINE, SHAILENDRA SINGH and ATIF ALAMRI, “Mining Human Activity Patterns From Smart Home Big Data for Health Care Applications”
Taking the Human Out of the Loop: A Review of Bayesian Optimization The paper introduces the reader to Bayesian optimization, highlighting its methodical aspects and showcasing its applications. By Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, and Nando de Freitas
Decentralizing Privacy: Using Block chain to Protect Personal Data Guy Zyskind MIT Media Lab Cambridge, Massachusetts Email: guyz@mit.edu Oz Nathan Tel-Aviv University Tel-Aviv, Israel Email: oznathan@gmail.com Alex ’Sandy’ Pentland MIT Media Lab Cambridge, Massachusetts Email: pentland@mit.edu
Analytics vidya ( website)
Available: https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/
Digital doughnut (website ) Available: https://www.digitaldoughnut.com/articles/2017/june/machine-learning-accelerates-transformation
Matlab&Simulink (website)
Available:
https://www.mathworks.com/discovery/machine-learning.html
kdnuggets (website)
Available:
https://www.kdnuggets.com/2016/08/10-algorithms-machine-learning-engineers.html
dezyre (website)
Available:
https://www.dezyre.com/article/top-10-machine-learning-algorithms/202
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
