Adaptive internet of things and machine learning techniques for managing the complexity of intelligent systems big data

2021 ◽  
pp. 1-1
Author(s):  
Ahmed A. Elngar
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Internet Of Things ◽  
Intelligent Systems ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-3 ◽  
Author(s):  
David Gil ◽  
Magnus Johnsson ◽  
Higinio Mora ◽  
Julian Szymanski
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Internet Of Things ◽  
Machine Learning Techniques ◽  
Learning Techniques

Machine Learning for Internet of Things

2019 ◽  
pp. 57-83 ◽  
Author(s):  
Ramgopal Kashyap
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Internet Of Things ◽  
Information Quality ◽  
Physical World ◽  
Machine Learning Techniques ◽  
Iot Applications ◽  
Learning Techniques ◽  
The Way

Fast advancements in equipment, programming, and correspondence advances have permitted the rise of internet-associated tangible gadgets that give perception and information estimation from the physical world. It is assessed that the aggregate number of internet-associated gadgets being utilized will be in the vicinity of 25 and 50 billion. As the numbers develop and advances turn out to be more develop, the volume of information distributed will increment. Web-associated gadgets innovation, alluded to as internet of things (IoT), keeps on broadening the present internet by giving network and cooperation between the physical and digital universes. Notwithstanding expanded volume, the IoT produces big data described by speed as far as time and area reliance, with an assortment of numerous modalities and changing information quality. Keen handling and investigation of this big data is the way to creating shrewd IoT applications. This chapter evaluates the distinctive machine learning techniques that deal with the difficulties in IoT information.

Machine Learning for Internet of Things

2021 ◽  
pp. 976-1002
Author(s):  
Ramgopal Kashyap
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Internet Of Things ◽  
Information Quality ◽  
Physical World ◽  
Machine Learning Techniques ◽  
Iot Applications ◽  
Learning Techniques ◽  
The Way

Fast advancements in equipment, programming, and correspondence advances have permitted the rise of internet-associated tangible gadgets that give perception and information estimation from the physical world. It is assessed that the aggregate number of internet-associated gadgets being utilized will be in the vicinity of 25 and 50 billion. As the numbers develop and advances turn out to be more develop, the volume of information distributed will increment. Web-associated gadgets innovation, alluded to as internet of things (IoT), keeps on broadening the present internet by giving network and cooperation between the physical and digital universes. Notwithstanding expanded volume, the IoT produces big data described by speed as far as time and area reliance, with an assortment of numerous modalities and changing information quality. Keen handling and investigation of this big data is the way to creating shrewd IoT applications. This chapter evaluates the distinctive machine learning techniques that deal with the difficulties in IoT information.

scholarly journals Estimating the CAP greening effect by machine learning techniques: A big data ex post analysis

2021 ◽  
Vol 119 ◽  
pp. 44-53
Author(s):  
Danilo Bertoni ◽  
Giacomo Aletti ◽  
Daniele Cavicchioli ◽  
Alessandra Micheletti ◽  
Roberto Pretolani
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Machine Learning Techniques ◽  
Ex Post ◽  
Learning Techniques ◽  
Ex Post Analysis

scholarly journals Leveraging Artificial Intelligence and Big Data to Optimize COVID-19 Clinical Public Health and Vaccination Roll-Out Strategies in Africa

2021 ◽  
Vol 18 (15) ◽  
pp. 7890
Author(s):  
Bruce Mellado ◽  
Jianhong Wu ◽  
Jude Dzevela Kong ◽  
Nicola Luigi Bragazzi ◽  
Ali Asgary ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Developed Countries ◽  
Machine Learning Techniques ◽  
Multiple Data ◽  
Learning Techniques ◽  
Unequal Access ◽  
Roll Out

COVID-19 is imposing massive health, social and economic costs. While many developed countries have started vaccinating, most African nations are waiting for vaccine stocks to be allocated and are using clinical public health (CPH) strategies to control the pandemic. The emergence of variants of concern (VOC), unequal access to the vaccine supply and locally specific logistical and vaccine delivery parameters, add complexity to national CPH strategies and amplify the urgent need for effective CPH policies. Big data and artificial intelligence machine learning techniques and collaborations can be instrumental in an accurate, timely, locally nuanced analysis of multiple data sources to inform CPH decision-making, vaccination strategies and their staged roll-out. The Africa-Canada Artificial Intelligence and Data Innovation Consortium (ACADIC) has been established to develop and employ machine learning techniques to design CPH strategies in Africa, which requires ongoing collaboration, testing and development to maximize the equity and effectiveness of COVID-19-related CPH interventions.

scholarly journals Predictors of outpatients’ no-show: big data analytics using apache spark

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tahani Daghistani ◽  
Huda AlGhamdi ◽  
Riyad Alshammari ◽  
Raed H. AlHazme
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Negative Impact ◽  
Big Data Analytics ◽  
Quality Of Healthcare ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Healthcare Organizations ◽  
Data Framework ◽  
Learning Techniques

AbstractOutpatients who fail to attend their appointments have a negative impact on the healthcare outcome. Thus, healthcare organizations facing new opportunities, one of them is to improve the quality of healthcare. The main challenges is predictive analysis using techniques capable of handle the huge data generated. We propose a big data framework for identifying subject outpatients’ no-show via feature engineering and machine learning (MLlib) in the Spark platform. This study evaluates the performance of five machine learning techniques, using the (2,011,813‬) outpatients’ visits data. Conducting several experiments and using different validation methods, the Gradient Boosting (GB) performed best, resulting in an increase of accuracy and ROC to 79% and 81%, respectively. In addition, we showed that exploring and evaluating the performance of the machine learning models using various evaluation methods is critical as the accuracy of prediction can significantly differ. The aim of this paper is exploring factors that affect no-show rate and can be used to formulate predictions using big data machine learning techniques.

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