Cognitive Automation Research Articles

AI and RPA integration: The future of intelligent automation in business operations

Artificial intelligence and Robotic process automation, commonly known as Cognitive automation, are now revolutionizing business processes and delivering Intelligent automation solutions. The objective of this paper is to explain how AI is connected to RPA and how these two technologies improve proper business functioning. Since organizations are trying to make their processes more efficient, the demand for such automation tools that execute simple tasks and learn from data is greatest. To that end, the present study adopted a mixed-methods approach based on quantitative and qualitative research procedures. The primary data were obtained through the questionnaires of those organizations that have already implemented AI and RPA and the four interviews of the industry specialists. Furthermore, primary data supplemented by second data sourced from peer-reviewed journals and white papers where relevant were considered in substantiation of the research findings. The paper outlines the advantages of IA for organizations, including increased operation efficiency, better customer experience, and higher employee morale. Key insights include confirming that organizations with AI and RPA achieve much lower operating expenses and the duration required to accomplish repetitive activities. However, the study also reveals issues using these technologies, such as data security and retraining costs. In general, this study adds to the knowledge of effectively managing the strategic directions in BPO using AI and RPA integration while providing insights to companies on the approaches towards realizing this type of transformational change. In other words, there has been a gradual accumulation in the evolution of business automation that has characterized the industrial and economic revolution over the centuries.

Correlation Of Functional Disorders In Cerebral Blood Flow With Cognitive Style Of Freshmen Students

In 97 healthy male freshmen students, we studied the parameters of arterial blood flow intensity (index of vascular resistance RI, amplitude-frequency index AFI), venous outflow (venous outflow index VOI), and tone and elasticity of cerebral vessels (diastolic index DIA, dicrotic index DIC, maximum blood velocity of fast filling in the artery Vmax, average blood velocity of slow filling in the artery Vav). Separately, we assessed the effectiveness of such mental operations as classification and analysis and studied the cognitive styles. In 35.9% of male freshmen, we revealed the functional dystonia of cerebral vessels with a pronounced inter-lacunar redistribution of blood flow to the region of internal carotid arteries. Their cognitive style had low rigidity, flexible control, strong cognitive automation, and low information processing speed. The cognitive style in freshmen without dystonic disorders was characterized by a balance of rigidity and flexibility of thinking, with a medium information processing speed.

Towards Semantic Integration of Machine Vision Systems to Aid Manufacturing Event Understanding

A manufacturing paradigm shift from conventional control pyramids to decentralized, service-oriented, and cyber-physical systems (CPSs) is taking place in today’s 4th industrial revolution. Generally accepted roles and implementation recipes of cyber systems are expected to be standardized in the future of manufacturing industry. The authors intend to develop a novel CPS-enabled control architecture that accommodates: (1) intelligent information systems involving domain knowledge, empirical model, and simulation; (2) fast and secured industrial communication networks; (3) cognitive automation by rapid signal analytics and machine learning (ML) based feature extraction; (4) interoperability between machine and human. Semantic integration of process indicators is fundamental to the success of such implementation. This work proposes an automated semantic integration of data-intensive process signals that is deployable to industrial signal-based control loops. The proposed system rapidly infers manufacturing events from image-based data feeds, and hence triggers process control signals. Two image inference approaches are implemented: cloud-based ML model query and edge-end object shape detection. Depending on use cases and task requirements, these two approaches can be designated with different event detection tasks to provide a comprehensive system self-awareness. Coupled with conventional industrial sensor signals, machine vision system can rapidly understand manufacturing scenes, and feed extracted semantic information to a manufacturing ontology developed by either expert or ML-enabled cyber systems. Moreover, extracted signals are interpreted by Programmable Logical Controllers (PLCs) and field devices for cognitive automation towards fully autonomous industrial systems.

Neural-Architecture-Search-Based Multiobjective Cognitive Automation System

Currently, deep-learning-based cognitive automation for decision-making in industrial informatics is a new hot topic in the field of cognitive computing, among which multiobjective architecture optimization is of great difficulty in the research area. When the existing algorithms face multiobjective cognitive model problems, it often takes a lot of time to continuously set different search preference parameters to generate a new search process. This article mainly aims to solve the problem in a multiobjective neural architecture search process, and the key issue is how to adapt user preferences during architectural search. We propose a new algorithm: linear-prefer coevolutionary algorithm. Compared to the original user-constrained method and the Pareto-dominant NSGA-II algorithm, we have faster adaptation time and better quality of adaptation. At the same time, it can respond to user's needs at a relatively faster pace during the reasoning phase. Based on a large number of comparative test results, our algorithm is superior to the traditional cognitive automation algorithms for the multiobjective problem in search quality.

Influencing information systems practice: The action principles approach applied to robotic process and cognitive automation

The article formalizes an action principles approach for investigating and influencing the adoption of emerging information systems phenomena, particularly for new technologies. It draws upon recent research into robotic process and cognitive automation to demonstrate the concepts and methodology for a further mode of research into practice that is distinguishable from action research and design science. The authors present definitions, research assumptions, and evaluation criteria and provide a six-step process for generating a set of action principles, updatable by new empirical evidence. The process is illustrated by research into 22 automation cases that eventually arrived at 39 action principles for effective deployment of the automation technologies under review. The major objective is to provide guidelines to prospective researchers. A secondary objective is to provide major insights into the management of robotic process and cognitive automation. This provides opportunities for further theorization and research by academics, and more considered action by practitioners. The authors also discuss the value and limitations of the action principles approach, and how the knowledge generated can be disseminated. The article offers a way of doing research on the applied side of information systems that is timely, does justice to the phenomena under investigation, and provides insights for multiple parties.

The Future of Work in a Jobless Society: Globalization, Smart Digitalization, and Cognitive Automation

Research background: The future of work is undoubtedly one of the toughest challenges faced by many researchers and managers all over the word. The new era in digital globalization and smart digitalization, the trends in robotization and artificial intelligence have changed the labour market. Due to accelerated technology, many companies are ready to adopt digital solutions, stationary robots and drones with significant consequences over the declining jobs. The new human-machine frontier will determine a different outlook work in a jobless society, where many roles become automated, while human’s role in these processes is minimized. Purpose of the article: In our opinion, globalization and impact of artificial intelligence on the future of work will be significant. In this paper we try to analyse and clarify the issues in question in terms of smart digitalization, cognitive automation, human-machine frontier and changing employment types. The data used for this research was obtained from previous study conducted by World Bank and OECD. Methods: In order to fulfil our goal, we apply the methods of comparison, analysis, deduction and our estimates for identifying the trends that are shaping the future of jobs and the evolution of jobs caused by technological change. Findings & Value added: In the near future, innovation will continue to accelerate and many artisan jobs are being lost to computerization and office automation. Finally, we formulate our own conclusion and view about digitalization and opportunities to create new jobs, increase productivity, and cost reduction, through innovation and accelerating change.

Smart Factory Performance, Cognitive Automation, and Industrial Big Data Analytics in Sustainable Manufacturing Internet of Things

Smart Factory Performance, Cognitive Automation, and Industrial Big Data Analytics in Sustainable Manufacturing Internet of Things

Real-Time Process Monitoring, Industry 4.0 Wireless Networks, and Cognitive Automation in Cyber-Physical System-based Manufacturing

Real-Time Process Monitoring, Industry 4.0 Wireless Networks, and Cognitive Automation in Cyber-Physical System-based Manufacturing

Sustainable Industry 4.0 Wireless Networks, Smart Factory Performance, and Cognitive Automation in Cyber-Physical System-based Manufacturing

Sustainable Industry 4.0 Wireless Networks, Smart Factory Performance, and Cognitive Automation in Cyber-Physical System-based Manufacturing

Big Data-driven Algorithmic Governance in Sustainable Smart Manufacturing: Robotic Process and Cognitive Automation Technologies

Big Data-driven Algorithmic Governance in Sustainable Smart Manufacturing: Robotic Process and Cognitive Automation Technologies

Cognitive automation: A new era of knowledge work?

There have been a number of breakthroughs in artificial intelligence since the beginning of the 21st century with machines now outperforming humans in cognitive tasks such as object detection, face recognition, language translation, and complex decision strategies. Computational advances enable machines to process and analyse information at a scale far beyond human capabilities and has led to a rise in demand for intelligent process automation (IPA) services. This article considers the potential for cognitive algorithms to disrupt knowledge work in the modern workplace. Benefits include augmenting and accelerating the intelligence-decision-action cycle that is central to knowledge work. However, there are also risks from becoming over-reliant on algorithms in ambiguous and uncertain real-world situations. The value from next-generation knowledge systems will come from bridging human and artificial intelligence for insights and innovation.

Cognitive Automation and its Impact on Additive Manufacturing

Cognitive Automation and its Impact on Additive Manufacturing

Cognitive Automation for Smart Decision-Making in Industrial Internet of Things

Classical automated schemes in the industrial Internet of Things (IIoT) are challenged by the problems related to huge record storage and the way they respond. To properly manage the manufacturing settings, cognitive systems aim to find a way to efficiently adapt their actions based on uncertainty management and sensory data. However, due to the lack of existing IT integration, cognitive systems are not fully exploited by organizations. In this article, we provide a novel decision-making process in industrial informatics during information transmission, manufacturing, and storing records through the simple additive weighting and analytic hierarchy process. The proposed mechanism is analyzed and validated rigorously using various sensing and decision-making parameters against a baseline solution in industrial parameter settings. The simulation results suggest that the proposed mechanism leads to 87% efficiency in terms of better detection of the sensor node, decision-making, and alteration of transmitted data during analyses of product manufacturing in the IIoT.

Implementing cognitive automation for employee management in Ganitec University of Science and Technology

In May 2016, it was more than 4 months, since the Ganitec University of Science and Technology went through an exercise for implementing an information system to manage the attendance, leave particulars and payroll of their employee. The management decided to replace their existing manual system with a new information system which was labeled “Cognitive Employee Management System.” In May 2016, spearheaded by a couple of faculty members, the Cognitive Employee Management System was finally implemented by a team of three students headed by the coordinator of the Information Systems Department of the Ganitec University of Science and Technology. Although the director envisaged lots of benefits, the changes were not really working out as he had foreseen. Why were people apprehensive about using the Cognitive Employee Management System when the benefits were apparent? How should the project rollout be planned in the Ganitec University of Science and Technology?

Lifespan change in grammaticalisation as frequency-sensitive automation: William Faulkner and thelet aloneconstruction

AbstractThis paper explores the added value of studying intra- and inter-speaker variation in grammaticalisation based on idiolect corpora. It analyses the usage patterns of the Englishlet aloneconstruction in a self-compiled William Faulkner corpus against the backdrop of aggregated community data. Vast individual differences (early Faulkner vs. late Faulkner vs. peers) in frequencies of use are observed, and these frequency differences correlate with different degrees of grammaticalisation as measured in terms of host-class and syntactic context expansion. The corpus findings inform general issues in current cognitive-functional research, such as the from-corpus-to-cognition issue and the cause/consequence issue of frequency. They lend support to the usage-based view of grammaticalisation as a lifelong, frequency-sensitive process of cognitive automation. To substantiate this view, this paper proposes a self-feeding cycle of constructional generalisation that is driven by the interplay of frequency, entrenchment, partial sanction and habituation.

Off-line programming of a flexible and adaptive production line for composite-metal multi-material manufacturing based on OPC-UA communication

Transport sector is working on the development of innovative lightweight structural solutions in order to improve fuel consumption replacing single-material components by layered composite-metal multi-material, which are manufactured predominantly in manual or only partly automated systems. In order to overcome these issues, it was developed an end-to-end engineering approach supporting a Hybrid Digital-Twin, covering all the manufacturing chain, making use of digital technologies to enable data flow from design, simulation, CAD/CAM, process monitoring and control to quality assurance, enabling a real-time adaptive control of the manufacturing systems involved, aimed at ‘Cognitive Automation’. Based on the inputs from the Self-Adaptive System (SAS), the CAD/CAM system, embedded in FreeCAD software, will update the high-level multi-stage operation policy to correct the existing deviations and will extract robot planning of each manufacturing stage. This global operation policy will modify the parameters of a real machine integrating an OPC-UA interoperability across devices and processes.

Cognitive Automation, Big Data-driven Manufacturing, and Sustainable Industrial Value Creation in Internet of Things-based Real-Time Production Logistics

Cognitive Automation, Big Data-driven Manufacturing, and Sustainable Industrial Value Creation in Internet of Things-based Real-Time Production Logistics

Cognitive Automation, Business Process Optimization, and Sustainable Industrial Value Creation in Artificial Intelligence Data-driven Internet of Things Systems

Cognitive Automation, Business Process Optimization, and Sustainable Industrial Value Creation in Artificial Intelligence Data-driven Internet of Things Systems

Improving devices communication in Industry 4.0 wireless networks

Internet of Things (IoT) and cyberphysical system (CPS) technologies play huge roles in the context of Industry 4.0. These technologies introduce cognitive automation to implement the concept of intelligent production, leading to smart products and services. One of the technological challenges related to Industry 4.0 is to provide support to big data cloud based applications which demand QoS-enabled Internet connectivity for information gathering, exchange, and processing. In order to deal with this challenge, in this article, a QoS-aware cloud based solution is proposed by adapting a recently proposed seamless resources sharing architecture to the IoT scenario. The resulting solution aims at improving device to cloud communications considering the coexistence of different wireless networks technologies, particularly in the domain of Industry 4.0. Results are obtained via simulations of three QoS demanding industrial applications. The outcomes of the simulations show that both delay and jitter QoS metrics are kept below their specific thresholds in the context of VoIP applications used for distributed manipulators fine tuning control. In the case of video-based production control, the jitter was controlled to meet the application demands, and even the throughput for best-effort supervisory systems HTTP access is guaranteed.

Real-World Implementation of Cyber-Physical Production Systems in Smart Manufacturing: Cognitive Automation, Industrial Processes Assisted by Data Analytics, and Sustainable Value Creation Networks

Real-World Implementation of Cyber-Physical Production Systems in Smart Manufacturing: Cognitive Automation, Industrial Processes Assisted by Data Analytics, and Sustainable Value Creation Networks