Practical machine learning based on cloud computing resources

Agavanakis, Kyriakos; Καρπέτας, Γεώργιος; Taylor, Michael; Pappa, Evangelia; Michail, C.; Filos, John; Trachana, Varvara; Papathanasiou, Ioanna V.

doi:10.1063/1.5117023

Cited by 12 publications

(6 citation statements)

References 24 publications

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“…The key research tool is the closed structured questionnaire of Kaptein, (2003), which was distributed electronically to a random sample of employees in Audit and Advisory Firms and also follows the work of Agavanakis et al (2019). The primary field research resulted in 613 responses of employees of different levels of management.…”

Section: Methodsmentioning

confidence: 99%

Detecting fraud and corruption in audit and advisory firms: A socio-demographic investigation

Pappa Evangelia,

Panagiotis Spanos,

John Filos

et al. 2023

Int. J. Sci. Res. Arch.

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The aim of the research is to identify in what extent the employees’ socio-demographic determinants affect the detection of fraud and corruption incidents in workplace environment. The methodology was used for data collection is primary source through random sampling. A total of 613 valid questionnaires based on employees in Greek audit and advisory firms were answered. Key non-parametric tests as Friedman test and Spearman’s correlation were used. It was found that all fraud and corruption variables are positively correlated and, in most cases, founded statistically significant. Furthermore, the top mean rank among fraud incidents was the personal use of internet at work and among the corruption incidents was nepotism. Considering the socio-demographic characteristics, it is observed that most aged and experienced employees, irrespectively hierarchical position, or gender; tend to have no significant differences about their perceptions of previous fraud and corruption incidents. The research contributes towards enhancing the fraud and corruption detection offering substantial implications for fraud examiners and researchers.

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Section: Methodsmentioning

confidence: 99%

Detecting fraud and corruption in audit and advisory firms: A socio-demographic investigation

Pappa Evangelia,

Panagiotis Spanos,

John Filos

et al. 2023

Int. J. Sci. Res. Arch.

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“…Although the bulk of articles was published by scientific institutes in developed nations, the constraints of conducting ML research in underprivileged locations are quickly diminishing. On the one hand, ML requires less advanced on-site technology than it once did, and many ML operations can be conveniently executed via cloud services [ 10 ]. On the other hand, more public medical datasets are made available every day, which can enable ML research in locations that lack access to extensive institutional data [ 11 ].…”

Section: Synthesismentioning

confidence: 99%

Machine Learning in Cardiovascular Imaging: A Scoping Review of Published Literature

et al. 2022

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Purpose of Review In this study, we planned and carried out a scoping review of the literature to learn how machine learning (ML) has been investigated in cardiovascular imaging (CVI). Recent Findings During our search, we found numerous studies that developed or utilized existing ML models for segmentation, classification, object detection, generation, and regression applications involving cardiovascular imaging data. We first quantitatively investigated the different aspects of study characteristics, data handling, model development, and performance evaluation in all studies that were included in our review. We then supplemented these findings with a qualitative synthesis to highlight the common themes in the studied literature and provided recommendations to pave the way for upcoming research. Summary ML is a subfield of artificial intelligence (AI) that enables computers to learn human-like decision-making from data. Due to its novel applications, ML is gaining more and more attention from researchers in the healthcare industry. Cardiovascular imaging is an active area of research in medical imaging with lots of room for incorporating new technologies, like ML. Supplementary Information The online version contains supplementary material available at 10.1007/s40134-022-00407-8.

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“…Existing approaches to primary allocation and subsequent resource reallocation in cloud computing systems can be divided into the following three types: manual resource assignment, resource scheduling by the cloud computing manager, and resource scheduling in a virtualization environment [5,8].…”

Section: Analysis Of Recent Research and Publicationsmentioning

confidence: 99%

“…For example, a consumer may install and run any software, which may include operating systems, platform and application software [2]. The cloud computing system [3][4][5] includes a wide range of computing resources: servers, storage systems, network devices. Resources can be of the same type or heterogeneous in performance, instruction set, ratio of the number of processor cores to the amount of RAM, etc.…”

Section: Introductionmentioning

confidence: 99%

Development of the Methods for Resource Reallocation in Cloud Computing Systems

Davydov

Hrebeniuk

2020

Innovative Technologies and Scientific Solutions for Industries

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The subject matter of the article is development of the models and methods of load and resource balancing and reallocation in cloud computing systems based on the infrastructure as a service model. The goal of the work is to increase the efficiency of available resources usage in cloud computing systems (such as RAM, disk space, CPU, network) by developing the model for adaptive management of resource reallocation. This will allow new virtual machines to be launched with minimal performance degradation for already running applications. The following tasks were solved in the article: development of an complex approach to manage resource reallocation in cloud systems, including decomposition of the cloud computing system into zones (based on the defining features of the resources provided in each zone), initial resource allocation (based on the hierarchy analysis method) and resources reallocation within cloud computing system (based on the developed method); development of a method for computing resources reallocation in cloud computing systems; evaluation of the effectiveness of the developed method. To solve the set tasks, approaches and methods of dynamic load balancing were used, as well as methods of theoretical research, which are based on the scientific provisions of the theory of artificial intelligence, static, functional and system analysis. The following results were obtainedon the basis of existing load balancing methods in cloud computing systems analysis, the main features of existing resource allocation methods were identified, their advantages and disadvantages were given. On the basis of the conducted analytical study, the necessity of improving the existing methods of resource reallocation has been proved. A method and an algorithm for computing resources reallocation within cloud computing systems have been developed. This make it possible to reduce the values of the coefficient of computing resources uneven usage while minimizing the cost of moving them. The results obtained have been confirmed by experiments carried out using software for creating private infrastructure cloud services and cloud storages. Conclusions: the improvement of the reallocation and load balancing method in cloud computing systems has increased the ability of these systems to launch new virtual machines with a minimum decrease in the performance of already running applications.

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Practical machine learning based on cloud computing resources

Cited by 12 publications

References 24 publications

Detecting fraud and corruption in audit and advisory firms: A socio-demographic investigation

Detecting fraud and corruption in audit and advisory firms: A socio-demographic investigation

Machine Learning in Cardiovascular Imaging: A Scoping Review of Published Literature

Development of the Methods for Resource Reallocation in Cloud Computing Systems

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