Computational Intelligence in Data Mining

Behera, Himansu Sekhar; Mohapatra, Durga Himansu Sekhar Behera Prasad

doi:10.1007/978-981-10-3874-7

Cited by 3 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The research type facet led to the categorization of the articles considered in the study based on the research approaches used. The categorization followed the format proposed and explained by Wieringa et al (2006) and Roul and Sahoo (2017). The research types include validation, evaluation, solution proposal, philosophical, opinion, discussion, study protocols, and experience studies as shown in Table 2.…”

Section: Designing Of a Classification Schemementioning

confidence: 99%

Applying advanced technologies to improve clinical trials: a systematic mapping study

Ngayua

Agyei-Boahene

2020

Scientometrics

View full text Add to dashboard Cite

The increasing demand for new therapies and other clinical interventions has made researchers conduct many clinical trials. The high level of evidence generated by clinical trials makes them the main approach to evaluating new clinical interventions. The increasing amounts of data to be considered in the planning and conducting of clinical trials has led to higher costs and increased timelines of clinical trials, with low productivity. Advanced technologies including artificial intelligence, machine learning, deep learning, and the internet of things offer an opportunity to improve the efficiency and productivity of clinical trials at various stages. Although researchers have done some tangible work regarding the application of advanced technologies in clinical trials, the studies are yet to be mapped to give a general picture of the current state of research. This systematic mapping study was conducted to identify and analyze studies published on the role of advanced technologies in clinical trials. A search restricted to the period between 2010 and 2020 yielded a total of 443 articles. The analysis revealed a trend of increasing research interests in the area over the years. Recruitment and eligibility aspects were the main focus of the studies. The main research types were validation and evaluation studies. Most studies contributed methods and theories, hence there exists a gap for architecture, process, and metric contributions. In the future, more empirical studies are expected given the increasing interest to implement the AI, ML, DL, and IoT in clinical trials.

show abstract

Section: Designing Of a Classification Schemementioning

confidence: 99%

Applying advanced technologies to improve clinical trials: a systematic mapping study

Ngayua

Agyei-Boahene

2020

Scientometrics

View full text Add to dashboard Cite

show abstract

Comparative analysis of task level heuristic scheduling algorithms in cloud computing

Jadoon¹,

Asghar

2022

J Supercomput

View full text Add to dashboard Cite

ENDURUNS: An Integrated and Flexible Approach for Seabed Survey Through Autonomous Mobile Vehicles

Marini

Gjeci²,

Govindaraj

et al. 2020

JMSE

View full text Add to dashboard Cite

The oceans cover more than two-thirds of the planet, representing the vastest part of natural resources. Nevertheless, only a fraction of the ocean depths has been explored. Within this context, this article presents the H2020 ENDURUNS project that describes a novel scientific and technological approach for prolonged underwater autonomous operations of seabed survey activities, either in the deep ocean or in coastal areas. The proposed approach combines a hybrid Autonomous Underwater Vehicle capable of moving using either thrusters or as a sea glider, combined with an Unmanned Surface Vehicle equipped with satellite communication facilities for interaction with a land station. Both vehicles are equipped with energy packs that combine hydrogen fuel cells and Li-ion batteries to provide extended duration of the survey operations. The Unmanned Surface Vehicle employs photovoltaic panels to increase the autonomy of the vehicle. Since these missions generate a large amount of data, both vehicles are equipped with onboard Central Processing units capable of executing data analysis and compression algorithms for the semantic classification and transmission of the acquired data.

show abstract

Computational Intelligence in Data Mining

Cited by 3 publications

References 2 publications

Applying advanced technologies to improve clinical trials: a systematic mapping study

Applying advanced technologies to improve clinical trials: a systematic mapping study

Comparative analysis of task level heuristic scheduling algorithms in cloud computing

ENDURUNS: An Integrated and Flexible Approach for Seabed Survey Through Autonomous Mobile Vehicles

Contact Info

Product

Resources

About