The purpose of this paper is to look at the importance of investment feasibility analysis.
Abstract
Purpose
The purpose of this paper is to look at the importance of investment feasibility analysis.
Design/methodology/approach
The paper addresses the question of whether a planned course of action is likely to achieve individual or enterprise objectives, given the available resources and specific constraints. Key issues are the legal, physical, and financial feasibility of a project proposal. Feasibility typically addresses the issue of the most appropriate use for a particular site, the most appropriate site for a predetermined use, or the most appropriate outlet for investment funds.
Findings
Even though changes in the economy have increased risk or lowered returns, the investment market continues to devise innovative and attractive investment strategies and the determined investor keeps searching for profitable projects.
Originality/value
The paper gives investment principles that should be taken into consideration and will be of interest to those in a similar field.
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Keywords
Abstract
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Keywords
Catalin Popescu, Gabriela Oprea, Daniela Steluţa Uţă, Augustin Mitu and Alina Gabriela Brezoi
The European Union (EU) is providing a wide range of instruments to its members in implementing a green, resilient economy. These instruments are not designed only for governments…
Abstract
The European Union (EU) is providing a wide range of instruments to its members in implementing a green, resilient economy. These instruments are not designed only for governments and state representatives but also for small businesses and entrepreneurs. The ability of those two-targeted audiences to understand and adopt these instruments, as well as their way to react and profit from the EU-stated drives, determines one’s country capacity to absorb European funding and create economic growth. The present chapter proposes a presentation of the new European model for economic growth and of the advantages proposed with the European Green Deal, the European proposal to the world for a resilient, adaptable, and environmentally friendly economy.
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Anum Paracha and Junaid Arshad
Advances in machine learning (ML) have made significant contributions to the development of intelligent and autonomous systems leading to concerns about resilience of such systems…
Abstract
Purpose
Advances in machine learning (ML) have made significant contributions to the development of intelligent and autonomous systems leading to concerns about resilience of such systems against cyberattacks. This paper aims to report findings from a quantitative analysis of literature within ML security to assess current research trends in ML security.
Design/methodology/approach
The study focuses on statistical analysis of literature published between 2000 and 2023, providing quantitative research contributions targeting authors, countries and interdisciplinary studies of organizations. This paper reports existing surveys and a comparison of publications of attacks on ML and its in-demand security. Furthermore, an in-depth study of keywords, citations and collaboration is presented to facilitate deeper analysis of this literature.
Findings
Trends identified between 2021 and 2022 highlight an increase in focus on adversarial ML – 40\% more publications compared to 2020–2022 with more than 90\% publications in journals. This paper has also identified trends with respect to citations, keywords analysis, annual publications, co-author citations and geographical collaboration highlighting China and the USA as the countries with highest publications count and Biggio B. as the researcher with collaborative strength of 143 co-authors which highlight significant pollination of ideas and knowledge. Keyword analysis highlighted deep learning and computer vision as the most common domains for adversarial attacks due to the potential to perturb images whilst being challenging to identify issues in deep learning because of complex architecture.
Originality/value
The study presented in this paper identifies research trends, author contributions and open research challenges that can facilitate further research in this domain.
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Keywords
- Adversarial machine learning
- Cyber threats
- Privacy preservation
- Secure machine learning
- Bibliometrics
- Quantitative analysis
- Analytical study
- Adversarial attack vectors
- Poisoning machine learning
- Evasion attacks
- Test-time attacks
- Differential privacy
- Data sanitization
- Adversarial re-training
- Data perturbation