The Problems of Design and Application of Switching Neural Networks in Creation of Artificial Intelligence

Sheptunov, Sergey A.; Sukhanova, Nataliya

doi:10.1109/itqmis51053.2020.9322887

Cited by 3 publications

(10 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This set of eligible breakdowns was assessed for academic and scientific relevance and quality. Academic Documents, Conference Paper (43); Article (29); Review (3); Letter (1); and retracted (1).…”

Section: Methodsmentioning

confidence: 99%

“…The concept of AI was introduced following the creation of the notion of digital computing machine in an attempt to ascertain whether a machine is able to 'think' [1], or if the machine can carry out humans' tasks [13]. AI is vast domain of Information and Computer Technologies (ICT in further text), which aims at designing systems that can operate autonomously, analogous to the individuals' decision making process [14].In terms of AI, a machine may learn from experience through processing immeasurable quantity of data, while distinguishing patterns in it, as in the case of Siri [15] and image recognition [16], technologies based on Machine Learning that is a subtheme of AI, de-fined as intelligent systems with the capacity to think and learn [1].…”

Section: Literature Trends: Ai and Systems Securitymentioning

confidence: 99%

“…The assumption that the human brain may be deemed quite comparable to computers in some ways, offers the spontaneous basis for Artificial Intelligence (AI in further text), which is supported by psychology through the idea of humans and animals operating like machines that process information by devices of associative memory [1]. Nowadays, researchers are working on the possibilities of AI to cope with varying issues of systems security across diverse sectors.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The impact of Artificial Intelligence on Data System Security: A Literature Review

Rosário¹,

Raimundo²

2021

Preprint

View full text Add to dashboard Cite

Diverse forms of artificial intelligence (AI in further text) are at the forefront of triggering digital security innovations, based on the threats that are arising in this post COVID world. On the one hand, companies are experiencing difficulty in dealing with security challenges with regard to a variety of issues ranging from system openness, decision making, quality control and web domain, just to mention a few. On the other hand, in the last decade, research has focused on security capabilities based on tools such as platform complacency, intelligent trees, modeling methods and outage management systems, in an effort to understanding the interplay between AI and those issues. The dependence on the emergence of AI in running industries and shaping the education, transports and health sectors is now well known in literature. AI is increasingly employed in managing data security across economic sectors. Thus, a literature review of AI and system secu-rity within the current digital society is opportune. This paper aims at identifying research trends in the field through a Systematic Bibliometric Literature Review (LRSB) of research on AI and system security. The review entails 77 articles published in Scopus® database, presenting up-to-date knowledge on the topic. The LRSB results were synthesized across current research subthemes. Findings are presented. The originality of the paper relies on its LRSB method, together with extant review of articles that have not been categorized so far. Implications for future re-search are suggested.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Literature Trends: Ai and Systems Securitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The impact of Artificial Intelligence on Data System Security: A Literature Review

Rosário¹,

Raimundo²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The assumption that the human brain may be deemed quite comparable to computers in some ways offers the spontaneous basis for artificial intelligence (AI), which is supported by psychology through the idea of humans and animals operating like machines that process information by devices of associative memory [1]. Nowadays, researchers are working on the possibilities of AI to cope with varying issues of systems security across diverse sectors.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Artificial Intelligence on Data System Security: A Literature Review

Raimundo

Rosário

2021

Sensors

View full text Add to dashboard Cite

Diverse forms of artificial intelligence (AI) are at the forefront of triggering digital security innovations based on the threats that are arising in this post-COVID world. On the one hand, companies are experiencing difficulty in dealing with security challenges with regard to a variety of issues ranging from system openness, decision making, quality control, and web domain, to mention a few. On the other hand, in the last decade, research has focused on security capabilities based on tools such as platform complacency, intelligent trees, modeling methods, and outage management systems in an effort to understand the interplay between AI and those issues. the dependence on the emergence of AI in running industries and shaping the education, transports, and health sectors is now well known in the literature. AI is increasingly employed in managing data security across economic sectors. Thus, a literature review of AI and system security within the current digital society is opportune. This paper aims at identifying research trends in the field through a systematic bibliometric literature review (LRSB) of research on AI and system security. the review entails 77 articles published in the Scopus® database, presenting up-to-date knowledge on the topic. the LRSB results were synthesized across current research subthemes. Findings are presented. the originality of the paper relies on its LRSB method, together with an extant review of articles that have not been categorized so far. Implications for future research are suggested.

show abstract

“…Comparison of models on the problem of segmentation of brain damage zones in rodents showed the advantage of the RatLesNetv2 neural network. Nevertheless, to solve each specific problem it is necessary to develop a unique tool in the form of a specialized NN, which can be subsequently improved or replaced by a new, even more efficient solution [20]. An important condition of any specialized development is the need to adapt it to specific regional standards and for it to work with the equipment and data formats used by the organization.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of neural network structure for the analysis of complex structured ocular fundus images

Tatarkanov¹,

Alexandrov²,

Glashev³

2021

J Appl Eng Science

View full text Add to dashboard Cite

This paper proposes an algorithm for synthesizing a neural network (NN) structure to analyze complex structured, low entropy, ocular fundus images, characterized by iterative tuning of the adaptive model’s solver modules. This algorithm will assist in synthesizing models of NNs that meet the predetermined characteristics of the classification quality. The relevance of automating the process of ocular diagnostics of fundus pathologies is due to the need to develop domestic medical decision-making systems. Because of using the developed algorithm, the NN structure is synthesized, which will include two solver modules, and is intended to classify the dual-alternative information. Automated hybrid NN structures for intelligent segmentation of complex structured, low entropy, retinal images should provide increased efficiency of ocular diagnostics of fundus pathologies, reduce the burden on specialists, and decrease the negative impact of the human factor in diagnosis.

show abstract

The Problems of Design and Application of Switching Neural Networks in Creation of Artificial Intelligence

Cited by 3 publications

References 10 publications

The impact of Artificial Intelligence on Data System Security: A Literature Review

The impact of Artificial Intelligence on Data System Security: A Literature Review

The Impact of Artificial Intelligence on Data System Security: A Literature Review

Synthesis of neural network structure for the analysis of complex structured ocular fundus images

Contact Info

Product

Resources

About