Intelligent Patient Monitoring for Arrhythmia and Congestive Failure Patients Using Internet of Things and Convolutional Neural Network

2019 ◽  
Author(s):  
Kaouter Karboub ◽  
Mohamed Tabaa ◽  
Sofiene Dellagi ◽  
Abbas Dandache ◽  
Fouad Moutaouakkil
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Convolutional Neural Network ◽  
Patient Monitoring ◽  
Congestive Failure

scholarly journals A Reconfigurable Streaming Deep Convolutional Neural Network Accelerator for Internet of Things

2018 ◽  
Vol 65 (1) ◽  
pp. 198-208 ◽  
Author(s):  
Li Du ◽  
Yuan Du ◽  
Yilei Li ◽  
Junjie Su ◽  
Yen-Cheng Kuan ◽  
...  
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network

scholarly journals Intelligent Recognition of Medical Motion Image Combining Convolutional Neural Network With Internet of Things

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 145462-145476 ◽  
Author(s):  
Yucheng Zhou ◽  
Zhixian Gao
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Convolutional Neural Network ◽  
Intelligent Recognition

scholarly journals An Integrative Framework for Online Prognostic and Health Management Using Internet of Things and Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2338 ◽  
Author(s):  
Yuanju Qu ◽  
Xinguo Ming ◽  
Siqi Qiu ◽  
Maokuan Zheng ◽  
Zengtao Hou
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Internet Of Things ◽  
Convolutional Neural Network ◽  
Health Management ◽  
Added Value ◽  
Time Data ◽  
Integrative Framework ◽  
Operational Level ◽  
Prognostic And Health Management

With the development of the internet of things (IoTs), big data, smart sensing technology, and cloud technology, the industry has entered a new stage of revolution. Traditional manufacturing enterprises are transforming into service-oriented manufacturing based on prognostic and health management (PHM). However, there is a lack of a systematic and comprehensive framework of PHM to create more added value. In this paper, the authors proposed an integrative framework to systematically solve the problem from three levels: Strategic level of PHM to create added value, tactical level of PHM to make the implementation route, and operational level of PHM in a detailed application. At the strategic level, the authors provided the innovative business model to create added value through the big data. Moreover, to monitor the equipment status, the health index (HI) based on a condition-based maintenance (CBM) method was proposed. At the tactical level, the authors provided the implementation route in application integration, analysis service, and visual management to satisfy the different stakeholders’ functional requirements through a convolutional neural network (CNN). At the operational level, the authors constructed a self-sensing network based on anti-inference and self-organizing Zigbee to capture the real-time data from the equipment group. Finally, the authors verified the feasibility of the framework in a real case from China.

scholarly journals Design and Implementation of Multilayer Security for ATM Machines

2021 ◽  
Vol 10 (2) ◽  
pp. 39-43
Author(s):  
Anjalin Joy ◽  
◽  
Caren Babu ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Face Recognition ◽  
Internet Of Things ◽  
Convolutional Neural Network ◽  
Fingerprint Identification ◽  
Design And Implementation ◽  
Automated Teller Machine

Automated teller machine (ATM) nowadays are a favourite spot for attackers as they are available everywhere and are much easier to rob. Generally, ATM attacks can be either physical ATM attacks or ATM-related fraud attacks. In this paper the idea of an ATM system with multilayer security is proposed with the help of internet of things (IoT), fingerprint identification and face recognition to increase the security of ATM. The physical ATM counter attacks can be identified by using specific sensors to detect changes in vibration and temperature in the ATM counter. To prevent ATM related fraud attacks the proposed system has additional security features like fingerprint identification and face recognition along with ATM number verification. The convolutional neural network (CNN) and machine learning based face recognition is used in this work which is quite reliable. Failures in any of the above steps cancel the transactions and so the proposed system provides multi layer security which makes it impossible for the attackers to break the ATM security. The proposed system will help to increase the security of the ATM and provide safe and secure ATM transactions.

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