Application of cognitive computing in healthcare, cybersecurity, big data and IoT: A literature review

Sreedevi, A.; Harshitha, T. Nitya; Sugumaran, Vijayan; Shankar, Prem

doi:10.1016/j.ipm.2022.102888

Cited by 95 publications

(25 citation statements)

References 67 publications

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“…In this case study seven patient were contributed for the prediction of seizure disease using support vector machine 8 36 Integrated features With rapid improvement and ever-increasing data in healthcare domain make it more complex to retrieve data regarding specific research oriented and disease diagnosing problem. To address this critical problem Tian 36 presented the concept of integrated features to integrate CT scan images in big data domain for efficient retrieval and disease diagnosing purposes 9 194 Functional features This systematic review work has outlined the use of cognitive computing and functional features reported in healthcare, cybersecurity, and big data …”

Section: Resultsmentioning

confidence: 99%

Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing

Khan

Nazir

2022

Sci Rep

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Big data has revolutionized the world by providing tremendous opportunities for a variety of applications. It contains a gigantic amount of data, especially a plethora of data types that has been significantly useful in diverse research domains. In healthcare domain, the researchers use computational devices to extract enriched relevant information from this data and develop smart applications to solve real-life problems in a timely fashion. Electronic health (eHealth) and mobile health (mHealth) facilities alongwith the availability of new computational models have enabled the doctors and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. Digital transformation of healthcare systems by using of information system, medical technology, handheld and smart wearable devices has posed many challenges to researchers and caretakers in the form of storage, minimizing treatment cost, and processing time (to extract enriched information, and minimize error rates to make optimum decisions). In this research work, the existing literature is analysed and assessed, to identify gaps that result in affecting the overall performance of the available healthcare applications. Also, it aims to suggest enhanced solutions to address these gaps. In this comprehensive systematic research work, the existing literature reported during 2011 to 2021, is thoroughly analysed for identifying the efforts made to facilitate the doctors and practitioners for diagnosing diseases using healthcare big data analytics. A set of rresearch questions are formulated to analyse the relevant articles for identifying the key features and optimum management solutions, and laterally use these analyses to achieve effective outcomes. The results of this systematic mapping conclude that despite of hard efforts made in the domains of healthcare big data analytics, the newer hybrid machine learning based systems and cloud computing-based models should be adapted to reduce treatment cost, simulation time and achieve improved quality of care. This systematic mapping will also result in enhancing the capabilities of doctors, practitioners, researchers, and policymakers to use this study as evidence for future research.

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Section: Resultsmentioning

confidence: 99%

Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing

Khan

Nazir

2022

Sci Rep

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“…e McKinsey Global Institute, in its article, "Big Data: the next frontier for innovation, competition and productivity," defines big data as data that are beyond the ability of traditional database software tools to collect, store, manage and analyze [13]. "According to "Steve" (IBM's "digital gatekeeper"), the era of networked big data is characterized by the full integration of the triple world of people, machines and things, unprecedented growth in the amount of data, and the sheer complexity of data patterns-the hallmarks of the era of networked big data [14]. McAfee, the world's largest professional security technology company, views big data as an intelligent activity aimed at turning big data insights into business advantage and a prerequisite for analytics [15].…”

Section: Related Workmentioning

confidence: 99%

Optimal Management of Computer Network Security in the Era of Big Data

Min-feng

2022

Journal of Computer Networks and Communications

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As the “new oil of the future,” big data is becoming the leading industry of the new economy, the core asset of the country and enterprises, the “new blue ocean” to be pursued, and the national strategy to be developed by all countries. The development of big data and its related technology supports and promotes a new round of technological innovation, making a new generation of information security technology reform and innovation, bringing opportunities and challenges to optimize, and consolidating national information security. In the era of big data, what kind of challenges and impacts will information security face? and is it crucial to explore the response strategies? At present, China has risen to become the world’s largest number of Internet users and the largest number of people using smartphones, but because China’s information security is the initial stage, involving information security, especially national information security laws and regulations are not much, the national social supervision and monitoring mechanisms are not much, the application level of science and technology content is relatively backward, the core technology has a patent technology not much, resulting in the flood of network data nowadays. Therefore, the underground illegal “data industry chain” activities are rampant. Therefore, this paper proposes a security-aware model based on the combination of distributed data analysis technology and data features. The model uses data features to dynamically generate a library of situational anomalies, effectively solving the problem of analyzing and processing rapidly and dynamically generated data streams, increasing the detection rate to more than 98%, effectively reducing the possibility of false detection, and having good results on large-scale datasets.

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“…These reviews are mainly based on studies in a particular domain to explore issues of ML modeling. This explorative study sometimes assesses existing literature related to big data or applies data analytics to healthcare (Belle et al, 2015;Sreedevi et al, 2022). Within the solution design space in clinical care, we aim to recognize a broader methodological scope and anticipation for possible utilization of design science research (DSR) 1 for guiding machine learning-based solution model design.…”

Section: Introductionmentioning

confidence: 99%

Extending artificial intelligence research in the clinical domain: a theoretical perspective

2022

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Academic research to the utilization of artificial intelligence (AI) has been proliferated over the past few years. While AI and its subsets are continuously evolving in the fields of marketing, social media and finance, its application in the daily practice of clinical care is insufficiently explored. In this systematic review, we aim to landscape various application areas of clinical care in terms of the utilization of machine learning to improve patient care. Through designing a specific smart literature review approach, we give a new insight into existing literature identified with AI technologies in the clinical domain. Our review approach focuses on strategies, algorithms, applications, results, qualities, and implications using the Latent Dirichlet Allocation topic modeling. A total of 305 unique articles were reviewed, with 115 articles selected using Latent Dirichlet Allocation topic modeling, meeting our inclusion criteria. The primary result of this approach incorporates a proposition for future research direction, abilities, and influence of AI technologies and displays the areas of disease management in clinics. This research concludes with disease administrative ramifications, limitations, and directions for future research.

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Application of cognitive computing in healthcare, cybersecurity, big data and IoT: A literature review

Cited by 95 publications

References 67 publications

Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing

Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing

Optimal Management of Computer Network Security in the Era of Big Data

Extending artificial intelligence research in the clinical domain: a theoretical perspective

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