Building Intelligent Conveyor System using classification techniques in a logistics Industry
Poornachand P1, Vijayaramaraju Poosapati2, Abinash Tripathy3, Vedavathi Katneni4
1Poornachand P, Computer Science Department, Raghu Engineering College, Visakhapatnam, India.
2Vijayaramaraju Poosapati*, Computer Science Department, GITAM University, Visakhapatnam, India.
3Abinash Tripathy, Professor, Raghu Engineering College, Visakhapatnam, India.
4Vedavathi Katneni, Head of the Department, Computer Science Department, GITAM, Visakhapatnam.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 2116-2121 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8484088619/2019©BEIESP | DOI: 10.35940/ijeat.F8484.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Machine learning techniques plays an important role in knowledge discovery and assists humans in decision making. They help to detect patterns and predict the actions/outcome. In a complex industrial environment mode of operations of a machine depends on various internal and external parameters which are often done using expert judgement method which is not accurate and results in machine breakdown thereby resulting in unplanned outage. In this paper, we discussed and demonstrated how machine learning algorithms can help to handle conveyor systems autonomously in an optimum way without any human intervention. A conveyor belt system operational data is used to select the appropriate classification technique for the selected dataset. The details of the dataset collected, algorithms used and the test results are discussed in this paper.
Keywords: Classification, Machine Learning Algorithms, Industrial automation, Autonomous conveyor systems.