Gaussian Map to Improve Firefly Algorithm Performance

Rabbi, Fazle; Ayaz, Muhammad; Dayupay, Johnry; Oyebode, Oluwadare Joshua; GIDO, NATHANIEL G.; Adhikari, Nirmal; Tabuena, Almighty C.; Ajibade, Samuel-Soma M.; Bassey, Mbiatke Anthony

doi:10.1109/icsgrc55096.2022.9845171

Cited by 14 publications

(7 citation statements)

References 15 publications

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“…According to previous studies, social networking in higher education has its own set of difficulties and hurdles. College students in the United Kingdom were studied for their use of social networking [22]. The study had 76 questionnaire participants and 14 interview participants.…”

Section: Literature Reviewmentioning

confidence: 99%

Technological Acceptance Model for Social Media Networking in e-Learning in Higher Educational Institutes

Ajibade¹,

Zaïdi²

2023

IJIET

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Twitter and Facebook are popular among college educators. The use of social media in schools of higher learning has also been the subject of study. The use of social media has opened up new avenues of contact, collaboration, and participation between students and teachers. Accepting students and educators who make use of technological tools to do so requires insight into the factors that shape their propensity to do so. Using the Technology Acceptance Model (TAM) framework, which highlights perceived ease of use, perceived usefulness, and behavioral intention to use new technologies, this paper investigates the extent to which Nigerians are adopting social networking media for e-learning. Quantitative studies made use of surveys. Teachers and students from four different Nigerian schools participated in this survey. The suggested model factors were predicted using structural equation modeling (SEM). Intentions to utilize social media for e-learning by students and faculty at Nigerian institutions were shown to be impacted by these factors: perceived ease of use and perceived utility. The research is limited in that it does not offer any insight into interactive factors such interaction with research group members and peers, interaction with supervisors or lecturers, engagement, or active collaborative learning.

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Section: Literature Reviewmentioning

confidence: 99%

Technological Acceptance Model for Social Media Networking in e-Learning in Higher Educational Institutes

Ajibade¹,

Zaïdi²

2023

IJIET

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“…The accessibility of funding is critical to the growth and development of any field of study or research endeavour [ 58 , 59 ]. Funding avails researchers of the platform for providing much-needed resources such as infrastructure, and equipment, along with monies for salaries, and other forms of remuneration needed for successfully carrying out research [ [60] , [61] , [62] ].…”

Section: Top Researching Countriesmentioning

confidence: 99%

A research landscape bibliometric analysis on climate change for last decades: Evidence from applications of machine learning

Ajibade,

Zaidi,

Bekun

et al. 2023

Heliyon

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“…On the flip side, the cutting-edge FHIR standard for patient clinical information presents plenty of new opportunities for visualizing, analyzing, and automating various types of healthcare data. With each passing day, fresh use cases for FHIR data analytics are building in the healthcare industry, such as real-time alerts for patient satisfaction, identifying patterns in patients’ medical records across datasets, real-time visibility into patient readmission rates, cost savings while upholding top-notch care quality, and countless more [ 17 , 18 , 19 , 20 ]. However, analyzing and implementing these use cases can prove challenging owing to the young stage and practicality of FHIR.…”

Section: Introductionmentioning

confidence: 99%

Transforming Healthcare Analytics with FHIR: A Framework for Standardizing and Analyzing Clinical Data

et al. 2023

Self Cite

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In this study, we discussed our contribution to building a data analytic framework that supports clinical statistics and analysis by leveraging a scalable standards-based data model named Fast Healthcare Interoperability Resource (FHIR). We developed an intelligent algorithm that is used to facilitate the clinical data analytics process on FHIR-based data. We designed several workflows for patient clinical data used in two hospital information systems, namely patient registration and laboratory information systems. These workflows exploit various FHIR Application programming interface (APIs) to facilitate patient-centered and cohort-based interactive analyses. We developed an FHIR database implementation that utilizes FHIR APIs and a range of operations to facilitate descriptive data analytics (DDA) and patient cohort selection. A prototype user interface for DDA was developed with support for visualizing healthcare data analysis results in various forms. Healthcare professionals and researchers would use the developed framework to perform analytics on clinical data used in healthcare settings. Our experimental results demonstrate the proposed framework’s ability to generate various analytics from clinical data represented in the FHIR resources.

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Gaussian Map to Improve Firefly Algorithm Performance

Cited by 14 publications

References 15 publications

Technological Acceptance Model for Social Media Networking in e-Learning in Higher Educational Institutes

Technological Acceptance Model for Social Media Networking in e-Learning in Higher Educational Institutes

A research landscape bibliometric analysis on climate change for last decades: Evidence from applications of machine learning

Transforming Healthcare Analytics with FHIR: A Framework for Standardizing and Analyzing Clinical Data

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