Face-It-Up: a scientific app for face processing using mobile devices and machine learning APIs

2018 ◽  
Author(s):  
Emilio Barcelos ◽  
Oge Marques ◽  
Jhanon James
Keyword(s):  
Machine Learning ◽  
Mobile Devices ◽  
Face Processing

Crowdsourcing Recognized Image Objects In Mobile Devices Through Machine Learning

2021 ◽  
Author(s):  
Athanasios Giannikis ◽  
Efthimios Alepis ◽  
Maria Virvou
Keyword(s):  
Machine Learning ◽  
Mobile Devices ◽  
Image Objects

A Novel Resource Management Framework for Fog Computing by Using Machine Learning Algorithm

2020 ◽  
pp. 42-52
Author(s):  
Shanthi Thangam Manukumar ◽  
Vijayalakshmi Muthuswamy
Keyword(s):  
Machine Learning ◽  
Resource Management ◽  
Mobile Devices ◽  
Service Providers ◽  
Learning Algorithm ◽  
Fog Computing ◽  
Mobile Network ◽  
Resource Provisioning ◽  
Management Framework ◽  
Iot Devices

With the development of edge devices and mobile devices, the authenticated fast access for the networks is necessary and important. To make the edge and mobile devices smart, fast, and for the better quality of service (QoS), fog computing is an efficient way. Fog computing is providing the way for resource provisioning, service providers, high response time, and the best solution for mobile network traffic. In this chapter, the proposed method is for handling the fog resource management using efficient offloading mechanism. Offloading is done based on machine learning prediction technology and also by using the KNN algorithm to identify the nearest fog nodes to offload. The proposed method minimizes the energy consumption, latency and improves the QoS for edge devices, IoT devices, and mobile devices.

scholarly journals Linear SVM-Based Android Malware Detection for Reliable IoT Services

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Hyo-Sik Ham ◽  
Hwan-Hee Kim ◽  
Myung-Sup Kim ◽  
Mi-Jung Choi
Keyword(s):  
Machine Learning ◽  
Mobile Devices ◽  
Malware Detection ◽  
Information Leakage ◽  
Support Vector ◽  
Android Malware ◽  
Machine Learning Classifiers ◽  
Android Malware Detection ◽  
Learning Classifiers ◽  
Linear Svm

Current many Internet of Things (IoT) services are monitored and controlled through smartphone applications. By combining IoT with smartphones, many convenient IoT services have been provided to users. However, there are adverse underlying effects in such services including invasion of privacy and information leakage. In most cases, mobile devices have become cluttered with important personal user information as various services and contents are provided through them. Accordingly, attackers are expanding the scope of their attacks beyond the existing PC and Internet environment into mobile devices. In this paper, we apply a linear support vector machine (SVM) to detect Android malware and compare the malware detection performance of SVM with that of other machine learning classifiers. Through experimental validation, we show that the SVM outperforms other machine learning classifiers.

scholarly journals The Challenges of Telemedicine in Rheumatology

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.

scholarly journals Mushroom Diagnosis Assistance System Based on Machine Learning by Using Mobile Devices

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Intisar Shadeed Al-Mejibli ◽  
Dhafar Hamed Abd
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Mobile Devices ◽  
Mobile Application ◽  
Assistance System ◽  
Wild Mushrooms ◽  
Safety Issues ◽  
Novel Approach ◽  
Determination Process

Picking the wild mushrooms from the wild and forests for food purpose or for fun has become a public issue within the last years because many types of mushrooms are poisonous. Proper determination of mushrooms is one of the key safety issues in picking activities of it, which is widely spread, in countries. This contribution proposes a novel approach to support determination of the mushrooms through using a proposed system with mobile devices.  Part of the proposed system is a mobile application that easily used by a user - mushroom picker. Hence, the mushroom type determination process can be performed at any location based on specific attributes of it. The mushroom type determination application runs on Android devices that are widely spread and inexpensive enough to enable wide exploitation by users. This paper developed Mushroom Diagnosis Assistance System (MDAS) that can be used on a mobile phone. Two classifiers are used which are Naive Bays and Decision Tree to classify the mushroom types.  The proposed approach selects the most effective of the already known mushroom attributes, and then specify the mushroom type. The use of specific features in mushroom determination process achieved very accurate results.

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