Ambreen A. Khan, Alina Arshad, R. Ellahi and Sadiq M. Sait
This paper aims to deal with the heat transmission of Sutterby fluid-containing gyrotactic microorganism by incorporating non-Darcy resistance law. The mathematical modeling is…
Abstract
Purpose
This paper aims to deal with the heat transmission of Sutterby fluid-containing gyrotactic microorganism by incorporating non-Darcy resistance law. The mathematical modeling is based on nanoparticle concentration, energy, momentum and motile microorganism equations.
Design/methodology/approach
The governing nonlinear coupled equations are first rendered into nonlinear ordinary equations using appropriate transformation and are then solved analytically by using the optimal homotopy.
Findings
Graphical illustration of results depict the behavior of flow involved physical parameters on temperature, gyrotactic microorganism, concentration and velocity. Additionally, local Nusselt number and skin friction coefficient are computed numerically and validated through comparison with existing literature as a special case of proposed model. It is found that the temperature profile decreases by increasing values of Brownian-motion parameter and Prandtl number. An increase in thermophoresis parameter and Schmidt number results in decrease in concentration of nanoparticles. Bioconvection Peclet number corresponds to decreasing behavior of nondimensional gyrotactic microorganism field is observed. Finally, a comparison with the existing literature is made, and an excellent agreement is seen.
Originality/value
To the best of the authors’ knowledge, this study is reported for the first time.
Details
Keywords
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.
Details
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