JOVIAL J73 Automated Verification System: Implementation Phase.

The modern tools of today's concurrent engineering (CE) based management are a wide array of instruments, plans, software, technical skills, human capabilities and resources. This paper aims to emphasize the benefits of such a system implementation in a business environment based on case studies retrieved from scientific literature and author's personal experience in a private working environment. Constant team sessions, briefs, workshops and brainstorming sessions proved to be very powerful tools for the management teams subjected to these studies in order for them to develop fully stable concurrent teams. The paper also highlights the fact that even from the implementation phase the workflow within the CE offers excellent opportunities for continuous improvements of the system and fine tunings of tools that are correspondent to it. As result, CE proves to be one of the best systems around in terms of efficiency, scalability and of course, results.

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Automated Verification System for Functional Coverage Extraction

2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA) ◽

10.1109/iceca.2018.8474836 ◽

2018 ◽

Author(s):

Palak Jain ◽

Mihir V. Shah ◽

Bhavin Patel

Keyword(s):

Automated Verification ◽

Verification System ◽

Functional Coverage

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Risk Management and Business Processes Reengineering, Success Drivers for ERP Projects

Information Systems Reengineering for Modern Business Systems ◽

10.4018/978-1-4666-0155-0.ch008 ◽

2012 ◽

pp. 146-184 ◽

Cited By ~ 1

Author(s):

Hussam Eldin I. Agha

Keyword(s):

Risk Management ◽

Best Practices ◽

Business Processes ◽

Organization Culture ◽

Management Model ◽

System Implementation ◽

Implementation Phase ◽

Erp System ◽

Is Projects

By using a case study example and focusing on the implementation phase, this chapter presents and emphasises the validity of specific risk management model. Briefly, an overview of another two ERP projects risk management’s models will be provided; the first model deals with ERP as technology where risks are managed in accordance to Information Technology and Information System (IT & IS) projects governance. The second model considers the integrated and interdisciplinary nature of ERP systems and introduces the term “ERP Governance” as a model that demands balance between IT and business governance. The third model, while similar to the second, relies heavily on best-practises and assessment frameworks from the industry. Throughout this chapter, the author explores this model, its importance, and application in contexts similar in nature to those found in the hierarchically structured intergovernmental organizations. Finally, the author concludes that embedding risk management best practices into the organization culture and business processes can positively influence the success of ERP system implementation. In other words, earlier adoption of risk management best practices for ERP system implementation will help both business & projects to meet their intended objectives and thus positively influence the success of ERP projects by ensuring that prescribed goals are met.

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FORTRAN Automated Verification System (FAVS). Volume II. User's Manual.

10.21236/ada065447 ◽

1979 ◽

Author(s):

Dorothy M. Andrews ◽

Rich A. Melton

Keyword(s):

Automated Verification ◽

Verification System

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An integrated approach on verification of signatures using multiple classifiers (SVM and Decision Tree): A multi-classification approach

International Journal of ADVANCED AND APPLIED SCIENCES ◽

10.21833/ijaas.2022.01.012 ◽

2022 ◽

Vol 9 (1) ◽

pp. 99-109

Author(s):

Jindal et al. ◽

Keyword(s):

Decision Tree ◽

Integrated Approach ◽

Signature Verification ◽

Automated Verification ◽

Support Vector ◽

Learning Support ◽

Static Characteristics ◽

Multiple Classifiers ◽

Verification System ◽

Multi Classification

A signature is a handwritten representation that is commonly used to validate and recognize the writer individually. An automated verification system is mandatory to verify the identity. The signature essentially displays a variety of dynamics and the static characteristics differ with time and place. Many scientists have already found different algorithms to boost the signature verification system function extraction point. The paper is aimed at multiplying two different ways to solve the problem in digital, manual, or some other means of verifying signatures. The various characteristics of the signature were found through the most adequately implemented methods of machine learning (support vector and decision tree). In addition, the characteristics were listed after measuring the effects. An experiment was performed in various language databases. More precision was obtained from the feature.

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