scholarly journals Robotic Process Automation of Unstructured Data with Machine Learning

2018 ◽  
Author(s):  
Anna Wróblewska ◽  
Tomasz Stanisławek ◽  
Bartłomiej Prus-Zajączkowski ◽  
Łukasz Garncarek
Keyword(s):  
Machine Learning ◽  
Unstructured Data ◽  
Process Automation ◽  
Robotic Process Automation

Robot Process Automation (RPA) and Its Future

2022 ◽  
pp. 35-58
Author(s):  
Ozge Doguc
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Customer Satisfaction ◽  
Human Behavior ◽  
The Other ◽  
Human Intervention ◽  
Process Automation ◽  
Robotic Process Automation ◽  
Human Agent

Many software automation techniques have been developed in the last decade to cut down cost, improve customer satisfaction, and reduce errors. Robotic process automation (RPA) has become increasingly popular recently. RPA offers software robots (bots) that can mimic human behavior. Attended robots work in tandem with humans and can operate while the human agent is active on the computer. On the other hand, unattended robots operate behind locked screens and are designed to execute automations that don't require any human intervention. RPA robots are equipped with artificial intelligence engines such as computer vision and machine learning, and both robot types can learn automations by recording human actions.

Robot Process Automation (RPA) and Its Future

2020 ◽  
pp. 469-492
Author(s):  
Ozge Doguc
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Customer Satisfaction ◽  
Human Behavior ◽  
The Other ◽  
Human Intervention ◽  
Process Automation ◽  
Robotic Process Automation ◽  
Human Agent

Many software automation techniques have been developed in the last decade to cut down cost, improve customer satisfaction, and reduce errors. Robotic process automation (RPA) has become increasingly popular recently. RPA offers software robots (bots) that can mimic human behavior. Attended robots work in tandem with humans and can operate while the human agent is active on the computer. On the other hand, unattended robots operate behind locked screens and are designed to execute automations that don't require any human intervention. RPA robots are equipped with artificial intelligence engines such as computer vision and machine learning, and both robot types can learn automations by recording human actions.

scholarly journals Novel Approach for Robotic Process Automation with Increasing Productivity and Improving Product Quality using Machine Learning

2021 ◽  
Vol 10 (3) ◽  
pp. 103-109
Author(s):  
Rashmi Jha ◽  
Govind Murari Upadhyay
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Supply Chains ◽  
Product Quality ◽  
Historical Analysis ◽  
Life Cycles ◽  
Process Automation ◽  
Robotic Process Automation ◽  
Novel Approach ◽  
Positive Method

Robotic Process Automation (RPA) is one of the smartest technology evolutions in recent years. It is, a software installed on a system. RPA can be implemented in a well-defined environment with defined procedures and clarity with reference to decision making. RPA’s limitation is that it cannot be automated if it involves decision making supported by knowledgebased application. Highly invasive and intertwined supply chains are now confronted by producers, which reduce manufacturing life cycles and raise product sophistication. You therefore sense the need, at all stages of value formation, to change and adjust more rapidly. The theory of self-optimization is a positive method to coping with uncertainty and unexpected delays within supply chains, devices and processes. It would also boost manufacturing industries' stability and productivity. This paper explores the idea of development processes that are self-optimized. Following a quick historical analysis and understanding the particular needs, specifications and self-optimizing criteria of the various stages of value generation from supply chain planning and management to manufacture and assembly. Examples at both stages are used to demonstrate the self-optimization principle and to explain its simplicity and efficiency ability.. We proposed Novel approach for Robotic Process Automation with increasing productivity and improving product quality using machine learning

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