RESUMEN
A Cloud Access Security Broker (CASB) is a security enforcement point or cloud-based software that is placed between cloud service users and cloud applications of cloud computing (CC) which is used to run the dimensionality, heterogeneity, and ambiguity correlated with cloud services. They permit the organization to amplify the reach of their security approaches past their claim framework to third-party computer programs and storage. In contrast to other systematic literature reviews (SLR), this one is directed at the client setting. To identify and evaluate methods to understand CASB, the SLR discusses the literature, citing a comprehension of the state-of-the-art and innovative characterization to describe. An SLR was performed to compile CASB related experiments and analyze how CASBs are designed and formed. These studies are then analyzed from different contexts, like motivation, usefulness, building approach, and decision method. The SLR has discussed the contrasts present between the studies and implementations, with planning accomplishments conducted with combinations of market-based courses of action, simulation tools, middleware's, etc. Search words with the keywords, which were extracted from the Research Questions (RQs), were utilized to recognize the essential consideration from the journal papers, conference papers, workshops, and symposiums. This SLR has distinguished 20 particular studies distributed from 2011 to 2021. Chosen studies were evaluated concurring to the defined RQs for their eminence and scope to particular CASB in this way recognizing a few gaps within the literature. Unlike other studies, this one concentrates on the customer's viewpoint. The survey uses a systematic analysis of the literature to discover and classify techniques for realizing CASB, resulting in a comprehensive grasp of the state-of-the-art and a novel taxonomy to describe CASBs. To assemble studies relating to CASB and investigate how CASB are engineered, a systematic literature review was done. These investigations are then evaluated from a variety of angles, including motivation, functionality, engineering approach, and methodology. Engineering efforts were directed at a combination of "market-based solutions", "middlewares", "toolkits", "algorithms", "semantic frameworks", and "conceptual frameworks", according to the study, which noted disparities in the studies' implementations. For further understanding, the different independent parameters influencing the CASB are studied using PCA (Principal Component Analysis). The outcome of their analysis was the identification of five parameters influencing the PCA analysis. The experimental results were used as input for Research Surface Methodology (RSM) to obtain an empirical model. For this, five-level coding was employed for developing the model and considered three dependent parameters and four center values. For more understanding of these independent variables' influence, on the CASB study, RSM analysis was employed. It was observed from the CCD (Central Composite Design) model that the actual values show significant influence with R2 = 0.90. This wide investigation reveals that CASB is still in a formative state. Even though vital advancement has been carried out in this zone, obvious challenges stay to be tended to, which have been highlighted in this paper.
RESUMEN
The COVID-19, Coronavirus Disease 2019, emerged as a hazardous disease that led to many causalities across the world. Early detection of COVID-19 in patients and proper treatment along with awareness can help to contain COVID-19. Proposed Fuzzy Cloud-Based (FCB) COVID-19 Diagnosis Assistant aims to identify the patients as confirmed, suspects, or suspicious of COVID-19. It categorized the patients into four categories as mild, moderate, severe, or critical. As patients register themselves online on the FCB COVID-19 DA in real-time, it creates the database for the same. This database helps to improve diagnostic accuracy as it contains the latest updates from real-world cases data. A team of doctors, experts, consultants are integrated with the FCB COVID-19 DA for better consultation and prevention. The ultimate aim of this proposed theory of FCB COVID-19 DA is to take control of COVID-19 pandemic and de-accelerate its rate of transmission among the society.
RESUMEN
Wireless sensor networks (WSNs) are becoming very common in numerous manufacturing industries; especially where it is difficult to connect a sensor to a sink. This is an evolving issue for researchers attempting to contribute to the proliferation of WSNs. Monitoring a WSN depends on the type of collective data the sensor nodes have acquired. It is necessary to quantify the performance of these networks with the help of network reliability measures to ensure the stable operation of WSNs. Reliability plays a key role in the efficacy of any large-scale application of WSNs. The communication reliability in a wireless sensor network is an influential parameter for enhancing network performance for secure, desirable, and successful communication. The reliability of WSNs must incorporate the design variables, coverage, lifetime, and connectivity into consideration; however, connectivity is the most important factor, especially in a harsh environment on a large scale. The proposed algorithm is a one-step approach, which starts with the recognition of a specific spanning tree only. It utilizes all other disjoint spanning trees, which are generated directly in a simple manner and consume less computation time and memory. A binary decision illustration is presented for the enumeration of K-coverage communication reliability. In this paper, the issue of computing minimum spanning trees was addressed and it is a pertinent method for further evaluating reliability for WSNs. This paper inspects the reliability of WSNs and proposes a method for evaluating the flow-oriented reliability of WSNs. Further, a modified approach for the sum-of-disjoint products to determine the reliability of WSN from the enumerated minimal spanning trees is proposed. The proposed algorithm when implemented for different sizes of WSNs demonstrates its applicability to WSNs of various scales. The proposed methodology is less complex and more efficient in terms of reliability.
RESUMEN
Distribution system is the means of revenue for electric utility. It needs to be restored at the earliest if any feeder or complete system is tripped out due to fault or any other cause. Further, uncertainty of the loads, result in variations in the distribution network's parameters. Thus, an intelligent algorithm incorporating hybrid fuzzy-grey relation, which can take into account the uncertainties and compare the sequences is discussed to analyse and restore the distribution system. The simulation studies are carried out to show the utility of the method by ranking the restoration plans for a typical distribution system. This algorithm also meets the smart grid requirements in terms of an automated restoration plan for the partial/full blackout of network.
RESUMEN
Computer based pattern recognition is a process that involves several sub-processes, including pre-processing, feature extraction, feature selection, and classification. Feature extraction is the estimation of certain attributes of the target patterns. Selection of the right set of features is the most crucial and complex part of building a pattern recognition system. In this work we have combined multiple features extracted using seven different approaches. The novelty of this approach is to achieve better accuracy and reduced computational time for recognition of handwritten characters using Genetic Algorithm which optimizes the number of features along with a simple and adaptive Multi Layer Perceptron classifier. Experiments have been performed using standard database of CEDAR (Centre of Excellence for Document Analysis and Recognition) for English alphabet. The experimental results obtained on this database demonstrate the effectiveness of this system.
RESUMEN
Today, most of the organizations trust on their age old legacy applications, to support their business-critical systems. However, there are several critical concerns, as maintainability and scalability issues, associated with the legacy system. In this background, cloud services offer a more agile and cost effective platform, to support business applications and IT infrastructure. As the adoption of cloud services has been increasing recently and so has been the academic research in cloud migration. However, there is a genuine need of secondary study to further strengthen this research. The primary objective of this paper is to scientifically and systematically identify, categorize and compare the existing research work in the area of legacy to cloud migration. The paper has also endeavored to consolidate the research on Security issues, which is prime factor hindering the adoption of cloud through classifying the studies on secure cloud migration. SLR (Systematic Literature Review) of thirty selected papers, published from 2009 to 2014 was conducted to properly understand the nuances of the security framework. To categorize the selected studies, authors have proposed a conceptual model for cloud migration which has resulted in a resource base of existing solutions for cloud migration. This study concludes that cloud migration research is in seminal stage but simultaneously it is also evolving and maturing, with increasing participation from academics and industry alike. The paper also identifies the need for a secure migration model, which can fortify organization's trust into cloud migration and facilitate necessary tool support to automate the migration process.
