MIRASS: Medical Informatics Research Activity Support System Using Information Mashup Network

Kiah, Miss Laiha Mat; Zaidan, B. B.; Zaidan, A. A.; Nabi, Mohamed S.; Ibraheem, Rabiu

doi:10.1007/s10916-014-0037-x

Cited by 31 publications

(5 citation statements)

References 20 publications

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“…The priority of healthcare services is based on the emergency status of patients. The identification of innovative research contributions for the provision of effective and efficient health care systems is an important and challenging task [39][40][41][42][43][44][45][46][47][48]. Various studies are conducted in bioinformatics to improve the prioritization process and provide a solution for the scalability problems in health care services [38,49].…”

Section: Healthcare Scalability Importance and Challengesmentioning

confidence: 99%

Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

et al. 2020

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Deep Learning (DL) algorithms enabled computational models consist of multiple processing layers that represent data with multiple levels of abstraction. In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. Consequently, deep learning has dramatically changed and improved the means of recognition, prediction, and diagnosis effectively in numerous areas of healthcare such as pathology, brain tumor, lung cancer, abdomen, cardiac, and retina. Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification, prediction, evaluation.). A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study. A coherent taxonomy of research landscape from the literature has also been mapped, and the major aspects of this emerging field have been discussed and analyzed. A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area.

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Section: Healthcare Scalability Importance and Challengesmentioning

confidence: 99%

Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

et al. 2020

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“…Systems developed to support research activity include, Papits [12] for sharing information about "know-who" in a university laboratory, MIRASS [13] for improving usability of scientific search engines for medical informatics through data mashup, and systems for managing repositories of resources accumulated through research activities in a laboratory [14] [15].…”

Section: Research Activity Conciergementioning

confidence: 99%

Task Execution Support in Research Activity using RAC System

Ohira

Morita

Nagao

2017

Annals of Computer Science and Information Systems

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Abstract-Research activities carried out daily in research laboratories, include conducting investigations, giving seminars on progress, performing experiments, analyzing results, and writing papers, but all of these research activities involve steady, methodical work that does not produce immediate, visible results. For this reason, a mechanism to maintain motivation when research is not going well or to help students get on track with research when they have just been assigned to a laboratory could be useful. Students that have just begun their research may not yet understand how to proceed. We previously developed a research activity concierge (RAC) system, which is a platform to encompass general research activities, and applied gamification to this system to keep user motivation high. However, even with the RAC, non-research-savvy students have difficulty handling challenges and executing tasks. In this research, we focused on discussions in seminars and introduced a mechanism to support task execution in students' research activities by implementing automatic extraction of task statements into the RAC.

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“…Systems developed to support research activity include, Papits [11] for sharing information about ``know-who'' in a university laboratory, MIRASS [12] for improving usability of scientific search engines for medical informatics through data mashup, and systems for managing repositories of resources accumulated through research activities in a laboratory [13,14].…”

Section: Research Activity Conciergementioning

confidence: 99%

Gamifying research activity support system

Ohira

Sugiura

Nagao

2016

Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality

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Various research activities are carried out every day in research laboratories, including conducting investigations, giving seminars on progress, performing experiments, analyzing results, and writing papers, but all of these activities involve steady, methodical work that does not produce immediate, visible results. For this reason, a mechanism to maintain motivation when research is not going well or to help students get on track with research when they have just been assigned to a laboratory could be useful. Students that have just begun their research may not yet understand how to proceed. In this research, we have developed a research activity concierge (RAC) system, which is a platform to encompass general research activities, and applied gamification to this system to keep motivation high. Furthermore, we investigated the effect of the RAC system on the action setting and motivation and analyzed operational logs to improve usability. CCS Concepts • Human-centered computing ➝ Collaborative and social computing ➝ Collaborative and social computing theory, concepts and paradigms • Human-centered computing ➝ Ubiquitous and mobile computing ➝ Ubiquitous and mobile computing systems and tools.

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MIRASS: Medical Informatics Research Activity Support System Using Information Mashup Network

Cited by 31 publications

References 20 publications

Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

Task Execution Support in Research Activity using RAC System

Gamifying research activity support system

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