Risk prediction using natural language processing of electronic mental health records in an inpatient forensic psychiatry setting

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. 

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Cyber risk and cybersecurity: a systematic review of data availability

/pdf/cyber-risk-and-cybersecurity-a-systematic-review-of-data-1k47k809.pdf

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.

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Formal Ontologies in Information Systems Development: A Systematic Review

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.

Machine Learning in liver disease diagnosis: Current progress and future opportunities

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...

An intrusion detection system using optimized deep neural network architecture

Ontology Based Disease Information System

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.

Polygonal approximation of digital planar curve using local integral deviation

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.

Analytical Comparison of Machine Learning Techniques for Liver Dataset

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.

Mangayarkarasi Ramaiah

Papers

An intrusion detection system using optimized deep neural network architecture

Ontology Based Disease Information System

Polygonal approximation of digital planar curve using local integral deviation

Analytical Comparison of Machine Learning Techniques for Liver Dataset

An iterative point elimination technique to retain significant vertices on digital planar curves