Investigation of Forecasting Methods of the State of Complex IT-Projects with the Use of Deep Learning Neural Networks

Morozov, Viktor; Kalnichenko, Olena; Proskurin, Maksym; Mezentseva, Olga

doi:10.1007/978-3-030-26474-1_19

Cited by 15 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Under the umbrella of the complexity of implementing and managing distributed information systems projects, Morozov et al [78] proposed DL NNs for forecasting the state of the project when impacted by the changes caused by the environment. In this way, the developed AI model will help the effective proactive management of such complex IT projects, better ensuring their satisfactory performance.…”

Section: Project Work Pdmentioning

confidence: 99%

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

et al. 2023

View full text Add to dashboard Cite

In the Industry 5.0 era, companies are leveraging the potential of cutting-edge technologies such as artificial intelligence for more efficient and green human-centric production. In a similar approach, project management would benefit from artificial intelligence in order to achieve project goals by improving project performance, and consequently, reaching higher sustainable success. In this context, this paper examines the role of artificial intelligence in emerging project management through a systematic literature review; the applications of AI techniques in the project management performance domains are presented. The results show that the number of influential publications on artificial intelligence-enabled project management has increased significantly over the last decade. The findings indicate that artificial intelligence, predominantly machine learning, can be considerably useful in the management of construction and IT projects; it is notably encouraging for enhancing the planning, measurement, and uncertainty performance domains by providing promising forecasting and decision-making capabilities.

show abstract

Section: Project Work Pdmentioning

confidence: 99%

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Such a transformation can be achieved by assisting project managers through AI-based automation of repetitive and highvolume tasks, improving project analytics for estimation and risk prediction, and enabling AI-supported actionable decision-making. The most recent systematic literature review by Taboada et al [18] concluded that AI and machine learning (ML) could be very useful in the management of IT and construction projects by enabling significant improvements in project planning [19], scheduling [20], cost and quality [21], forecasting [22], risk management [23], and decision-making competences [24][25][26].…”

Section: Ai-supported Agile Project Managementmentioning

confidence: 99%

Get Real Get Better: A Framework for Developing Agile Program Management in the U.S. Navy Supported by the Application of Advanced Data Analytics and AI

Haase,

Walker,

Berardi

et al. 2023

Technologies

View full text Add to dashboard Cite

This paper discusses the “Get Real Get Better” (GRGB) approach to implementing agile program management in the U.S. Navy, supported by advanced data analytics and artificial intelligence (AI). GRGB was designed as a set of foundational principles to advance Navy culture and support its core values. This article identifies a need for a more informed and efficient approach to program management by highlighting the benefits of implementing comprehensive data analytics that leverage recent advances in cloud computing and machine learning. The Jupiter enclave within Advana implemented by the U.S. Navy, is also discussed. The presented approach represents a practical framework that cultivates a “Get Real Get Better” mindset for implementing agile program management in the U.S. Navy.

show abstract

“…Machine learning was first named "an area of learning that enables computers to learn without explicit programming" by Arthur Samuel in 1959. A more formal definition is given by T. Mitchell: "A computer program is said to be learning from the experience of E with respect to some class of problems T and the productivity index P, if its performance at tasks in T as measured by P improves with the experience of E" [9].…”

mentioning

confidence: 99%

Optimization of Analysis and Minimization of Informationlosses in Text Mining

Mezentseva,

Kolomiiets

2020

HAIT

View full text Add to dashboard Cite

Information is one of the most important resources of today's business environment. It is difficult for any company to succeed without having sufficient information about its customers, employees and other key stakeholders. Every day, companies receive unstructured and structured text from a variety of sources, such as survey results, tweets, call center notes, phone emails, online customer reviews, recorded interactions, emails and other documents. These sources provide raw text that is difficult to understand without using the right text analysis tool. You can do text analytics manually, but the manual process is inefficient. Traditional systems use keywords and cannot read and understand language in emails, tweets, web pages, and text documents. For this reason, companies use text analysis software to analyze large amounts of text data. The software helps users retrieve textual information to act accordingly The most common manual annotation is currently the most common, which can be attributed to the high quality of annotation and its “meaningfulness”. Typical disadvantages of manual annotation systems, textual information analysis systems are the high material costs and the inherent low speed of work. Therefore, the topic of this article is to explore the methods by which you can effectively annotate reviews of various products from the largest marketplace in Ukraine. The following tasks should be solved: to analyze modern approaches to data analysis and processing; to study basic algorithms for data analysis and processing; build a program that will collect data, design the program architecture for more efficient use, based on the use of the latest technologies; clear data using minimize information loss techniques; analyze the data collected, using data analysis and processing approaches; to draw conclusions from the results of all the above works. There are quite a number of varieties of the listed tasks, as well as methods of solving them. This again confirms the importance and relevance of the topic we choose. The purpose of the study is the methods and means by which information losses can be minimized when analyzing and processing textual data. The object of the study is the process of minimizing information losses in the analysis and processing of textual data. In the course of the study, recent research on the analysis and processing of textual information was analyzed; methods of textual information processing and Data Mining algorithms are analyzed.

show abstract

Investigation of Forecasting Methods of the State of Complex IT-Projects with the Use of Deep Learning Neural Networks

Cited by 15 publications

References 6 publications

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

Artificial Intelligence Enabled Project Management: A Systematic Literature Review

Get Real Get Better: A Framework for Developing Agile Program Management in the U.S. Navy Supported by the Application of Advanced Data Analytics and AI

Optimization of Analysis and Minimization of Informationlosses in Text Mining

Contact Info

Product

Resources

About