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Mangayarkarasi Ramaiah

Bio: Mangayarkarasi Ramaiah is an academic researcher from VIT University. The author has contributed to research in topics: Curvature & Polygon. The author has an hindex of 3, co-authored 8 publications receiving 28 citations.

Papers
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Journal ArticleDOI
07 Feb 2021
TL;DR: A novel intrusion detection system to detect malicious attacks targeted at a smart environment using a correlation tool and a random forest method to detect the predominant independent variables for improvising neural‐based attack classifier.
Abstract: Internet usage became increasingly ubiquitous. The concern regarding security and privacy has become essential for Internet users. As the usage of the Internet increases the number of cybe...

29 citations

Journal ArticleDOI
TL;DR: The ontology based disease information system is being build and semantic based rules are designed to respond to the corresponding user query, mainly focusing on improving the query results and also supports ease of use to the user.

11 citations

Journal ArticleDOI
TL;DR: An algorithm for polygonal approximation based on local integral deviation based onLocal integral deviation shows that the proposed procedure produces polygon from a digital curve approximating high as well as low curvature regions with almost equal precision.
Abstract: An algorithm for polygonal approximation based on local integral deviation is presented. The algorithm is tested on various shapes with varying number of dominant points. A comparative study of the proposed procedure with other iterative methods shows that the proposed procedure produces polygon from a digital curve approximating high as well as low curvature regions with almost equal precision.

7 citations

Proceedings ArticleDOI
01 Mar 2019
TL;DR: This paper summarizes the predicted accuracy, precision and F-score of various machine learning algorithms and compares them to find the best suited algorithm to predict the impact of the liver diseases.
Abstract: Researchers all across the world have been working extensively for developing system in health care domain. Many people are struggling to clear their doubts about health issues by doctors or other medical personnel to confirm or clarify their diagnosis. The health management system is an end user support and online consultation system. The system is fed with counts of various pigments and chemicals present in an individual’s body which are necessary for determining liver health. Four algorithms have been implemented for this. This paper summarizes the predicted accuracy, precision and F-score of various machine learning algorithms and compares them to find the best suited algorithm to predict the impact of the liver diseases.

3 citations

Journal ArticleDOI
TL;DR: The proposed technique iteratively deletes points whose deviation is minimal from the line segment joining its neighbours to produce the polygon by preserving the significant vertices such as sharp turning, with less approximation error.
Abstract: This paper presents a technique that uses distance to a point as a metric to measure the collinearity to delete quasi linear points on a digital curve. The technique described here iteratively deletes points whose deviation is minimal from the line segment joining its neighbours. The results of the proposed technique are compared with recent iterative point elimination techniques. The comparative results show that the proposed technique produces the polygon by preserving the significant vertices such as sharp turning, with less approximation error.

3 citations


Cited by
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Journal ArticleDOI
TL;DR: NLP, used in conjunction with NLP dictionaries and machine learning, predicted risk ratings on the HCR-20, START, and DASA, based on EHR content, and the most accurate prediction was attained on the DasA dataset using the sentiment dictionary and the LMT and SVM algorithms.

39 citations

Journal ArticleDOI
TL;DR: In this paper , the authors identify a lacuna in open databases that undermine collective endeavours to better manage this set of risks, and they posit that the lack of available data on cyber risk poses a serious problem for stakeholders seeking to tackle this issue.
Abstract: Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020, indicating an increase of more than 50% since 2018. With the average cyber insurance claim rising from USD 145,000 in 2019 to USD 359,000 in 2020, there is a growing necessity for better cyber information sources, standardised databases, mandatory reporting and public awareness. This research analyses the extant academic and industry literature on cybersecurity and cyber risk management with a particular focus on data availability. From a preliminary search resulting in 5219 cyber peer-reviewed studies, the application of the systematic methodology resulted in 79 unique datasets. We posit that the lack of available data on cyber risk poses a serious problem for stakeholders seeking to tackle this issue. In particular, we identify a lacuna in open databases that undermine collective endeavours to better manage this set of risks. The resulting data evaluation and categorisation will support cybersecurity researchers and the insurance industry in their efforts to comprehend, metricise and manage cyber risks.The online version contains supplementary material available at 10.1057/s41288-022-00266-6.

36 citations

Journal ArticleDOI
17 Feb 2022
TL;DR: In this article , the authors identify a lacuna in open databases that undermine collective endeavours to better manage this set of risks, and they posit that the lack of available data on cyber risk poses a serious problem for stakeholders seeking to tackle this issue.
Abstract: Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020, indicating an increase of more than 50% since 2018. With the average cyber insurance claim rising from USD 145,000 in 2019 to USD 359,000 in 2020, there is a growing necessity for better cyber information sources, standardised databases, mandatory reporting and public awareness. This research analyses the extant academic and industry literature on cybersecurity and cyber risk management with a particular focus on data availability. From a preliminary search resulting in 5219 cyber peer-reviewed studies, the application of the systematic methodology resulted in 79 unique datasets. We posit that the lack of available data on cyber risk poses a serious problem for stakeholders seeking to tackle this issue. In particular, we identify a lacuna in open databases that undermine collective endeavours to better manage this set of risks. The resulting data evaluation and categorisation will support cybersecurity researchers and the insurance industry in their efforts to comprehend, metricise and manage cyber risks.The online version contains supplementary material available at 10.1057/s41288-022-00266-6.

23 citations

Journal ArticleDOI
TL;DR: This systematic review investigated the role of formal ontologies in information systems development, i.e., how these graphs-based structures can be beneficial during the analysis and design of the information systems.
Abstract: Computational ontologies are machine-processable structures which represent particular domains of interest. They integrate knowledge which can be used by humans or machines for decision making and problem solving. The main aim of this systematic review is to investigate the role of formal ontologies in information systems development, i.e., how these graphs-based structures can be beneficial during the analysis and design of the information systems. Specific online databases were used to identify studies focused on the interconnections between ontologies and systems engineering. One-hundred eighty-seven studies were found during the first phase of the investigation. Twenty-seven studies were examined after the elimination of duplicate and irrelevant documents. Mind mapping was substantially helpful in organising the basic ideas and in identifying five thematic groups that show the main roles of formal ontologies in information systems development. Formal ontologies are mainly used in the interoperability of information systems, human resource management, domain knowledge representation, the involvement of semantics in unified modelling language (UML)-based modelling, and the management of programming code and documentation. We explain the main ideas in the reviewed studies and suggest possible extensions to this research.

18 citations

Journal ArticleDOI
01 Jan 2021
TL;DR: An extensive review of the progress of applying Artificial Intelligence in forecasting and detecting liver diseases and then summarizes related limitations of the studies followed by future research is provided.
Abstract: There has been a rapid growth in the use of automatic decision-making systems and tools in the medical domain. By using the concepts of big data, deep learning, and machine learning, these systems extract useful information from large medical datasets and help physicians in making accurate and timely decisions regarding predictions and diagnosis of diseases. In this regard, this study provides an extensive review of the progress of applying Artificial Intelligence in forecasting and detecting liver diseases and then summarizes related limitations of the studies followed by future research.

15 citations