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Rongjun Xie

Bio: Rongjun Xie is an academic researcher from RMIT University. The author has contributed to research in topics: Extreme learning machine & Knowledge extraction. The author has an hindex of 3, co-authored 3 publications receiving 21 citations.

Papers
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Journal ArticleDOI
TL;DR: This paper proposes a Collaborative Extreme Learning Machine (CELM) with a Confidence Interval (CI), which is an enhanced version of the traditional Extreme learning machine (ELM), and improves the prediction accuracy by considering where plausible predictions lie.

14 citations

Journal ArticleDOI
TL;DR: The extensive experimental analysis demonstrates that the proposed p2p learning model is efficient in learning and sharing for patient diagnosis and shows the potential impact under different network topologies, network sizes and the number of learning peers.

10 citations

Journal ArticleDOI
TL;DR: The proposed model ideally can save 20% volume of data in the collection and can reduce 75% waiting time of data with the highest priority before predicting, which helps to provide diagnostic decisions in a proper time according to patients' urgency.

9 citations


Cited by
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Journal ArticleDOI
TL;DR: This study aims to explore the usage of support vector machine (SVM) in the prediction of dementia and validate its performance through statistical analysis and provides evidence that better performance values for dementia prediction are achieved by low gamma and high regularized values.

143 citations

Journal ArticleDOI
TL;DR: The findings indicate that, despite the recency of the subject, research in H4.0 has been conducted in an interdisciplinary way with a diversified set of applications and functionalities.
Abstract: This paper aims at examining the trends, challenges and theoretical gaps in the implementation of Healthcare 4.0 (H4.0) based on a scoping review of the literature. For that, we searched journal ar...

88 citations

Journal ArticleDOI
TL;DR: This paper aims to summarize and categorize existing benefits/challenges on incorporating blockchain in healthcare domain, and provide a framework that will facilitate new research activities and establish the state of evidence with in-depth assessment.
Abstract: Healthcare is a data-intensive domain, once a considerable volume of data is daily to monitoring patients, managing clinical research, producing medical records, and processing medical insurance claims. While the focus of applications of blockchain in practice has been to build distributed ledgers involving virtual tokens, the impetus of this emerging technology has now extended to the medical domain. With the increased popularity, it is crucial to study how this technology accompanied with a system for smart contracts can support and challenge the healthcare domain for all interrelated actors (patients, physicians, insurance companies, regulators) and involved assets (e.g. patients' data, physician's data, equipment's and drug's supply chain, etc.). The contributions of this paper are the following: (i) report the results of a systematic literature review conducted to identify, extract, evaluate and synthesize the studies on the symbiosis of blockchain in healthcare; (ii) summarize and categorize existing benefits/challenges on incorporating blockchain in healthcare domain; (iii) provide a framework that will facilitate new research activities; and (iv) establish the state of evidence with in-depth assessment.

68 citations

Journal ArticleDOI
TL;DR: A literature review of state-of-the-art machine learning algorithms for disaster and pandemic management and how these algorithms can be combined with other technologies to address disaster andPandemic management is provided.
Abstract: This article provides a literature review of state-of-the-art machine learning (ML) algorithms for disaster and pandemic management. Most nations are concerned about disasters and pandemics, which, in general, are highly unlikely events. To date, various technologies, such as IoT, object sensing, UAV, 5G, and cellular networks, smartphone-based system, and satellite-based systems have been used for disaster and pandemic management. ML algorithms can handle multidimensional, large volumes of data that occur naturally in environments related to disaster and pandemic management and are particularly well suited for important related tasks, such as recognition and classification. ML algorithms are useful for predicting disasters and assisting in disaster management tasks, such as determining crowd evacuation routes, analyzing social media posts, and handling the post-disaster situation. ML algorithms also find great application in pandemic management scenarios, such as predicting pandemics, monitoring pandemic spread, disease diagnosis, etc. This article first presents a tutorial on ML algorithms. It then presents a detailed review of several ML algorithms and how we can combine these algorithms with other technologies to address disaster and pandemic management. It also discusses various challenges, open issues and, directions for future research.

54 citations

Journal ArticleDOI
14 Feb 2019
TL;DR: The hybrid model has a tremendous predictive capacity in the prediction of people with breast cancer maintaining acceptable levels of accuracy with good ability to act on false positives and false negatives, assisting the scientific milieu with its forecasts with the significant characteristic of interpretability of breast cancer.
Abstract: Research on predictions of breast cancer grows in the scientific community, providing data on studies in patient surveys. Predictive models link areas of medicine and artificial intelligence to collect data and improve disease assessments that affect a large part of the population, such as breast cancer. In this work, we used a hybrid artificial intelligence model based on concepts of neural networks and fuzzy systems to assist in the identification of people with breast cancer through fuzzy rules. The hybrid model can manipulate the data collected in medical examinations and identify patterns between healthy people and people with breast cancer with an acceptable level of accuracy. These intelligent techniques allow the creation of expert systems based on logical rules of the IF/THEN type. To demonstrate the feasibility of applying fuzzy neural networks, binary pattern classification tests were performed where the dimensions of the problem are used for a model, and the answers identify whether or not the patient has cancer. In the tests, experiments were replicated with several characteristics collected in the examinations done by medical specialists. The results of the tests, compared to other models commonly used for this purpose in the literature, confirm that the hybrid model has a tremendous predictive capacity in the prediction of people with breast cancer maintaining acceptable levels of accuracy with good ability to act on false positives and false negatives, assisting the scientific milieu with its forecasts with the significant characteristic of interpretability of breast cancer. In addition to coherent predictions, the fuzzy neural network enables the construction of systems in high level programming languages to build support systems for physicians’ actions during the initial stages of treatment of the disease with the fuzzy rules found, allowing the construction of systems that replicate the knowledge of medical specialists, disseminating it to other professionals.

51 citations