Toward In-Network Deep Machine Learning for Identifying Mobile Applications and Enabling Application Specific Network Slicing

Network slicing allows operators to sell customized slices to various tenants at different prices. To provide better-performing and cost-efficient services, network slicing is looking to intelligent resource management approaches to be aligned to users’ activities per slice. In this article, we propose a radio access network (RAN) slicing design methodology for quality of service (QoS) provisioning, for differentiated services in a 5G network. A performance model is constructed for each service using machine learning (ML)-based approaches, optimized using interference coordination approaches, and used to facilitate service level agreement (SLA) mapping to the radio resource. The optimal bandwidth allocation is dynamically adjusted based on instantaneous network load conditions. We investigate the application of machine learning in solving the radio resource slicing problem and demonstrate the advantage of machine learning through extensive simulations. A case study is presented to demonstrate the effectiveness of the proposed radio resource slicing approach.

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The Challenges of Telemedicine in Rheumatology

Frontiers in Medicine ◽

10.3389/fmed.2021.746219 ◽

2021 ◽

Vol 8 ◽

Author(s):

Yujie Song ◽

Laurène Bernard ◽

Christian Jorgensen ◽

Gilles Dusfour ◽

Yves-Marie Pers

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Mobile Devices ◽

Rheumatic Diseases ◽

Mobile Applications ◽

Data Security ◽

Patient Monitoring ◽

Health Interventions ◽

The Past ◽

Legal Problems

During the past 20 years, the development of telemedicine has accelerated due to the rapid advancement and implementation of more sophisticated connected technologies. In rheumatology, e-health interventions in the diagnosis, monitoring and mentoring of rheumatic diseases are applied in different forms: teleconsultation and telecommunications, mobile applications, mobile devices, digital therapy, and artificial intelligence or machine learning. Telemedicine offers several advantages, in particular by facilitating access to healthcare and providing personalized and continuous patient monitoring. However, some limitations remain to be solved, such as data security, legal problems, reimbursement method, accessibility, as well as the application of recommendations in the development of the tools.

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An approach to assessment of dynamic software variability in mobile applications development

Bulletin of V.N. Karazin Kharkiv National University, series «Mathematical modeling. Information technology. Automated control systems» ◽

10.26565/2304-6201-2018-40-02 ◽

2018 ◽

Keyword(s):

Machine Learning ◽

Programming Language ◽

Mobile Applications ◽

Product Line ◽

Number Of Children ◽

Java Programming ◽

Complexity Metrics ◽

Programming Tools ◽

Dynamic Software ◽

Code Complexity

The article describes the approach to the assessment of code reuse in Dynamic Product Line lines (DSPL). Some existing mechanisms to realize software variability in DSPL, such as machine learning, adaptive configurations based on Java programming tools which allow developing DSPL, especially in mobile applications domain, have been reviewed. During the development, some methods for the implementation of the variability specific to the selected programming language have been tested. For each of these mechanisms, such as Weighted Methods per Class, Response for a Class, Depth of Inheritance Tree, Coupling Between Objects, Number of Children, the code complexity metrics have been calculated. Based on these results the code reusability extent can be estimated for each of given variation mechanisms.

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Development of Mobile Application for Faculty Time out Information

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.d1421.029420 ◽

2020 ◽

Vol 9 (4) ◽

pp. 168-174

Keyword(s):

Machine Learning ◽

Internet Of Things ◽

Software Development ◽

Mobile Applications ◽

Mobile Application ◽

Low Level ◽

Time Out ◽

The World ◽

Faculty Time ◽

High Level

In the trend of mobile applications, the world is surfing through many applications for various personal and professional purposes. In every domain including the cutting-edge technology such as Machine learning, IoT (Internet of Things), representing the data to the user in a proper and understanding manner is very important. This is where mobile applications come to use. Mobile applications can be used to resolve many communication issues especially when communication is between low level to high level and vice versa. This application is made to serve as one of the best ways of communication between faculty and students especially when the faculty is not available in the cabin and the student is willing to meet the faculty at the same time. The mobile application uses Dart Language with Flutter UI Software Development

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Application of Machine Learning Classification to Detect Fraudulent E‑wallet Deposit Notification SMSes

The African Journal of Information and Communication ◽

10.23962/10539/29195 ◽

2020 ◽

pp. 1-13

Author(s):

Fillemon S. Enkono ◽

Nalina Suresh

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Mobile Applications ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Svm Classifier ◽

Machine Learning Classification

Fraudulent e-wallet deposit notification SMSes designed to steal money and goods from m-banking users have become pervasive in Namibia. Motivated by an observed lack of mobile applications to protect users from such deceptions, this study evaluated the ability of machine learning to detect the fraudulent e-wallet deposit notification SMSes. The naïve Bayes (NB) and support vector machine (SVM) classifiers were trained to classify both ham (desired) SMSes and scam (fraudulent) e-wallet deposit notification SMSes. The performances of the two classifier models were then evaluated. The results revealed that the SVM classifier model could detect the fraudulent SMSes more efficiently than the NB classifier.

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Teaching students to develop GIS applications

Management of Education ◽

10.25726/w0687-1762-2053-o ◽

2021 ◽

pp. 249-257

Author(s):

Наталия Дмитриевна Хрулёва

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Mobile Applications ◽

Spatial Representation ◽

Information Technologies ◽

New Technologies ◽

Spatial Information ◽

Application Development ◽

Mobile Gis ◽

Gis Applications

Мобильные ГИС-приложения становятся все более сложными, как решаемые с их помощью задачи. Обычное ГИС-приложение должно включать такие элементы, как искусственный интеллект, распознавание образов или машинное обучение, реляционные или нереляционные базы данных, пространственное представление и рассуждения. Такие компании, как Google и Apple, разрабатывают новые технологии, связанные с разработкой мобильных приложений. Например, Apple представила в 2019 году на WWDC2019 и WWDC2020 новую технологию под названием SwiftUI, которая направлена на сложности разработки мобильного приложения и позволяющая интегрировать такие технологии, как Mapkit, для представления пространственной информации. В данной работе представлены исследования преимуществ использования SwiftUI для интеграции Mapkit в качестве основы пространственного представления для облегчения разработки мобильных ГИС-приложений. Информационные технологии имеют большое разнообразие применений в различных областях науки. Например, искусственный интеллект и машинное обучение - это технологии, которые начинают широко использоваться в мобильных приложениях. Целью данной работы является исследования способов разработки мобильных приложений, которые могут выполнять представление и вычисления информации в соответствии с требованиями. Mobile GIS applications are becoming more and more complex, as the tasks they solve are. A typical GIS application should include elements such as artificial intelligence, pattern recognition or machine learning, relational or non-relational databases, spatial representation and reasoning. Companies such as Google and Apple are developing new technologies related to the development of mobile applications. For example, Apple introduced a new technology called SwiftUI at WWDC2019 and WWDC2020 in 2019, which aims to reduce the complexity of mobile application development and allows integrating technologies such as Mapkit to represent spatial information. This paper presents studies of the advantages of using SwiftUI to integrate Mapkit as a basis for spatial representation to facilitate the development of mobile GIS applications. Information technologies have a wide variety of applications in various fields of science. For example, artificial intelligence and machine learning are technologies that are beginning to be widely used in mobile applications. The purpose of this work is to investigate ways to develop mobile applications that can perform the presentation and calculation of information in accordance with the requirements.

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