scholarly journals Machine Learning and LPWAN Based Internet of Things Applications in Healthcare Sector during COVID-19 Pandemic

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1615
Author(s):  
Zeeshan Ali Khan ◽  
Ubaid Abbasi ◽  
Sung Won Kim
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
K Nearest Neighbors ◽  
Iot Applications ◽  
Processing Resources ◽  
Multiple Services ◽  
Applications Of Machine Learning

Low power wide area networks (LPWAN) are comprised of small devices having restricted processing resources and limited energy budget. These devices are connected with each other using communication protocols. Considering their available resources, these devices can be used in a number of different Internet of Things (IoT) applications. Another interesting paradigm is machine learning, which can also be integrated with LPWAN technology to embed intelligence into these IoT applications. These machine learning-based applications combine intelligence with LPWAN and prove to be a useful tool. One such IoT application is in the medical field, where they can be used to provide multiple services. In the scenario of the COVID-19 pandemic, the importance of LPWAN-based medical services has gained particular attention. This article describes various COVID-19-related healthcare services, using the the applications of machine learning and LPWAN in improving the medical domain during the current COVID-19 pandemic. We validate our idea with the help of a case study that describes a way to reduce the spread of any pandemic using LPWAN technology and machine learning. The case study compares k-Nearest Neighbors (KNN) and trust-based algorithms for mitigating the flow of virus spread. The simulation results show the effectiveness of KNN for curtailing the COVID-19 spread.

Wiki-Health

2014 ◽  
pp. 59-77 ◽  
Author(s):  
Yang Li ◽  
Chao Wu ◽  
Li Guo ◽  
Chun-Hsiang Lee ◽  
Yike Guo
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Well Being ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Intelligent Data Analysis ◽  
Potential Barriers ◽  
Context Specific ◽  
Modern Technologies

Quickly evolving modern technologies such as cloud computing, Internet of things, and intelligent data analysis have created great opportunities for better living. The authors visualize the role these technological innovations will play in the healthcare sector as they spearhead a shift in focus from offering better healthcare services only to people with problems to helping everyone achieve a healthier lifestyle. In this chapter, the authors first discuss the existing and potential barriers followed by an in-depth demonstration of a service platform named Wiki-Health that takes advantage of cloud computing and Internet of things for personal well-being data management. It is a social platform, which is designed and implemented for data-driven and context-specific discovery of citizen communities in the areas of health, fitness, and well-being. At the end of the chapter, the authors analyse a case study to illustrate how the Wiki-Health platform can be used to serve a real world personal health training application.

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 Applying Machine Learning for Healthcare: A Case Study on Cervical Pain Assessment with Motion Capture

2020 ◽  
Vol 10 (17) ◽  
pp. 5942 ◽  
Author(s):  
Juan de la Torre ◽  
Javier Marin ◽  
Sergio Ilarri ◽  
Jose J. Marin
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Gradient Boosting ◽  
Support Vector ◽  
Cervical Pain ◽  
K Nearest Neighbors ◽  
Network Algorithms ◽  
Vector Machines ◽  
Real Scenario

Given the exponential availability of data in health centers and the massive sensorization that is expected, there is an increasing need to manage and analyze these data in an effective way. For this purpose, data mining (DM) and machine learning (ML) techniques would be helpful. However, due to the specific characteristics of the field of healthcare, a suitable DM and ML methodology adapted to these particularities is required. The applied methodology must structure the different stages needed for data-driven healthcare, from the acquisition of raw data to decision-making by clinicians, considering the specific requirements of this field. In this paper, we focus on a case study of cervical assessment, where the goal is to predict the potential presence of cervical pain in patients affected with whiplash diseases, which is important for example in insurance-related investigations. By analyzing in detail this case study in a real scenario, we show how taking care of those particularities enables the generation of reliable predictive models in the field of healthcare. Using a database of 302 samples, we have generated several predictive models, including logistic regression, support vector machines, k-nearest neighbors, gradient boosting, decision trees, random forest, and neural network algorithms. The results show that it is possible to reliably predict the presence of cervical pain (accuracy, precision, and recall above 90%). We expect that the procedure proposed to apply ML techniques in the field of healthcare will help technologists, researchers, and clinicians to create more objective systems that provide support to objectify the diagnosis, improve test treatment efficacy, and save resources.

scholarly journals Miniaturized Printed Inverted-F Antenna for Internet of Things: A Design on PCB with a Meandering Line and Shorting Strip

2018 ◽  
Vol 2018 ◽  
pp. 1-5 ◽  
Author(s):  
Cheuk Yin Cheung ◽  
Joseph S. M. Yuen ◽  
Steve W. Y. Mung
Keyword(s):  
Internet Of Things ◽  
Printed Circuit Board ◽  
Low Cost ◽  
Circuit Board ◽  
Ism Band ◽  
Printed Circuit ◽  
Iot Applications ◽  
Low Profile ◽  
Measurement Results

This paper focuses on a printed inverted-F antenna (PIFA) with meandering line and meandering shorting strip under 2.4 GHz industrial, scientific, and medical (ISM) band for Internet of things (IoT) applications. Bluetooth Low Energy (BLE) technology is one of potential platforms and technologies for IoT applications under ISM band. Printed circuit board (PCB) antenna commonly used in commercial and medical applications because of its small size, low profile, and low cost compared to low temperature cofired ceramic (LTCC) technology. The proposed structure of PIFA is implemented on PCB to gain all these advantages. Replacing conventional PCB line in PIFA by the meandering line and meandering shorting strip improves the efficiency of the PIFA as well as the bandwidth. As a case study, design and measurement results of the proposed PIFA are presented.

scholarly journals ‘Ai Theory of Justice’: Using Rawlsian Approaches to Legislate Better on Machine Learning in Government

Amicus Curiae ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 338-360
Author(s):  
Jamie Grace ◽  
Roxanne Bamford
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
John Rawls ◽  
Data Analytics ◽  
The Public ◽  
Theory Of Justice ◽  
Big Data Technologies ◽  
Applications Of Machine Learning

Policymaking is increasingly being informed by ‘big data’ technologies of analytics, machine learning and artificial intelligence (AI). John Rawls used particular principles of reasoning in his 1971 book, A Theory of Justice, which might help explore known problems of data bias, unfairness, accountability and privacy, in relation to applications of machine learning and AI in government. This paper will investigate how the current assortment of UK governmental policy and regulatory developments around AI in the public sector could be said to meet, or not meet, these Rawlsian principles, and what we might do better by incorporating them when we respond legislatively to this ongoing challenge. This paper uses a case study of data analytics and machine-learning regulation as the central means of this exploration of Rawlsian thinking in relation to the redevelopment of algorithmic governance.

scholarly journals A Review on Identifying Suitable Machine Learning Approach for Internet of Things Applications

2021 ◽  
Vol 3 (3) ◽  
pp. 128-145
Author(s):  
R. Valanarasu
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Smart Transportation ◽  
Iot Applications ◽  
Enhance Efficiency ◽  
The Internet Of Things

Recently, IoT is referred as a descriptive term for the idea that everything in the world should be connected to the internet. Healthcare and social goods, industrial automation, and energy are just a few of the areas where the Internet of Things applications are widely used. Applications are becoming smarter and linked devices are enabling their exploitation in every element of the Internet of Things [IoT]. Machine Learning (ML) methods are used to improve an application's intelligence and capabilities by analysing the large amounts of data. ML and IoT have been used for smart transportation, which has gained the increasing research interest. This research covers a range of Internet of Things (IoT) applications that use suitable machine learning techniques to enhance efficiency and reliability in the intelligent automation sector. Furthermore, this research article examines and identifies various applications such as energy, high-quality sensors associated, and G-map associated appropriate applications for IoT. In addition to that, the proposed research work includes comparisons and tabulations of several different machine learning algorithms for IoT applications.

scholarly journals Robust randomized optimization with k nearest neighbors

2019 ◽  
Vol 17 (05) ◽  
pp. 819-836 ◽  
Author(s):  
Henry W. J. Reeve ◽  
Ata Kabán
Keyword(s):  
Machine Learning ◽  
Metric Space ◽  
Nearest Neighbor ◽  
Broad Class ◽  
Simple Algorithm ◽  
Nearest Neighbors ◽  
Optimal Rate ◽  
K Nearest Neighbors ◽  
Noisy Function ◽  
Applications Of Machine Learning

Modern applications of machine learning typically require the tuning of a multitude of hyperparameters. With this motivation in mind, we consider the problem of optimization given a set of noisy function evaluations. We focus on robust optimization in which the goal is to find a point in the input space such that the function remains high when perturbed by an adversary within a given radius. Here we identify the minimax optimal rate for this problem, which turns out to be of order [Formula: see text], where [Formula: see text] is the sample size and [Formula: see text] quantifies the smoothness of the function for a broad class of problems, including situations where the metric space is unbounded. The optimal rate is achieved (up to logarithmic factors) by a conceptually simple algorithm based on [Formula: see text]-nearest neighbor regression.

scholarly journals Security Assurance Modelling of Security Incident in Healthcare using the Generic Security Template (GST)

2020 ◽  
Author(s):  
Ying He ◽  
Cunjin Luo
Keyword(s):  
Healthcare Services ◽  
Healthcare Organization ◽  
Lessons Learned ◽  
Security Requirements ◽  
Healthcare Sector ◽  
Healthcare Organizations ◽  
Security Incident ◽  
Security Assurance ◽  
Security Incidents

Abstract Background: The recent industry reports show that the number of security incidents in healthcare sector is still increasing, especially the high severity incident, such as data leakage incident and ransomware, which can lead to significant impact on healthcare services. It is imperative for the organizations to learn lessons from those incidents. Traditional ways to disseminate lessons learned are based on text approach, the linear format of which can obscure relationships among concepts and discourage readers from integrating information across ideas. Graphical diagrams can serve this purpose, as it can communicate both individual elements of information and relationships between them. Methods: The Generic Security Template (GST) has been proposed to support the exchange of lessons learned from security incidents. It utilises graphical notations to communicate both individual elements of information and relationships between them. This paper conducts a case study by adopting the GST to capture and structure the incident information of a data leakage incident in a UK healthcare organization in order to facilitate incident exchange. Results: The results show that, the GST was able to visualise and depict the key elements, including lessons learned, the associated security requirements and organizational contextual information identified from the selected data leakage incident case study from NHS. GST provides a unified way to communicate incident information. Conclusions: This research has significance for the healthcare organizations to improve their incident learning practices. It fosters an environment where different stakeholders can speak the same language while exchanging the lessons learned from the security incidents. Future work will consider apply the GST to analyse other complex security incidents such as the advanced persistent threats (APTs) in healthcare organizations and extend the use of the GST in other industries. Keywords: Security Assurance Modelling, Generic Security Template (GST), Security Incident, Healthcare Organization.

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