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Smart Healthcare System Based on Cloud-Internet of Things and Deep Learning

Benzhen Guo, Yanli Ma, Jingjing Yang, Zhihui Wang, Enas Abdulhay
2021 Journal of Healthcare Engineering  
Smart phones are adopted as gateway devices to achieve data standardization and preprocess to generate health gray-scale map uploaded to the cloud server.  ...  The cloud server realizes the business logic processing and uses the deep learning model to carry out the gray-scale map calculation of health parameters.  ...  cloud server through smart phones.  ... 
doi:10.1155/2021/4109102 pmid:34257851 pmcid:PMC8260290 fatcat:wt7jjifgurcw5ozivn7e4e2v2a

Machine Learning at the Network Edge: A Survey [article]

M.G. Sarwar Murshed, Christopher Murphy, Daqing Hou, Nazar Khan, Ganesh Ananthanarayanan, Faraz Hussain
2021 arXiv   pre-print
Deploying machine learning systems on such edge computing devices alleviates the above issues by allowing computations to be performed close to the data sources.  ...  A typical solution involves offloading data to external computing systems (such as cloud servers) for further processing but this worsens latency, leads to increased communication costs, and adds to privacy  ...  The DNN receives input from end-devices and produces final output in the cloud. That is, the DNN is distributed over the entire end-edge-cloud architecture.  ... 
arXiv:1908.00080v4 fatcat:mw4lwwvzf5gupjr6pgdgnabeuu

Edge Network Optimization Based on AI Techniques: A Survey

Mitra Pooyandeh, Insoo Sohn
2021 Electronics  
The goal of the network edge is to move computation from cloud servers to the edge of the network near the IoT devices.  ...  AI is becoming a key component in many edge devices, including cars, drones, robots, and smart IoT devices. This paper describes the role of AI in a network edge.  ...  Though cloud-based distributed smart surveillance systems have the ability to aggregate and analyze video information, managing them presents a major challenge.  ... 
doi:10.3390/electronics10222830 fatcat:kddhl7usq5aj5g3mr623lb24hm

Dynamic Resource Allocation and Memory Management Using Machine Learning for Cloud Environments

Dipak Raghunath Patil
2020 International Journal of Advanced Trends in Computer Science and Engineering  
On each smart phone, a Femto Cloud processing power service is enabled to measure device computational resources and sharing power with the other portable devices, and energy details.  ...  Incorporating MEC near smart phones with the aid of an increasing system capacity and low latency will elevate data analytics.  ... 
doi:10.30534/ijatcse/2020/255942020 fatcat:jnsh7k4lhnfvdgocprgcsw6mde

Machine Learning Systems for Intelligent Services in the IoT: A Survey [article]

Wiebke Toussaint, Aaron Yi Ding
2020 arXiv   pre-print
It covers the latest developments (up to 2020) on scaling and distributing ML across cloud, edge, and IoT devices.  ...  With a multi-layered framework to classify and illuminate system design choices, this survey exposes fundamental concerns of developing and deploying ML systems in the rising cloud-edge-device continuum  ...  Deep learning applications for mobile phones and wearables have been driving the development of more efficient and lightweight machine learning approaches, as on-device memory and processing power, energy  ... 
arXiv:2006.04950v3 fatcat:xrjcioqkrrhpvgmwmutiajgfbe

Table of Contents

2018 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC)  
Algorithms in Cloud Computing 783-788 Software Defined Networking: From Theory to Practice 789-794 The Review of the Commercial Quantum Key Distribution System Comparison and Evaluation of Real Time Reservation  ...  4G LTE Mobile Devices 770-775 795-799 Optimal Target Set Selection via Opinion Dynamics 806-811 Secure Multimedia Communication over Mobile Ad-hoc Networks 812-817 838-841  ... 
doi:10.1109/pdgc.2018.8745765 fatcat:hgy7virczjhcrosc2susu6smzq

Federated Learning for Internet of Things: A Comprehensive Survey [article]

Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, H. Vincent Poor
2021 arXiv   pre-print
Federated Learning (FL) has emerged as a distributed collaborative AI approach that can enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices without the need  ...  The important lessons learned from this review of the FL-IoT services and applications are also highlighted.  ...  The feasibility of FL has been tested in Gboard on Android phones where each phone runs a query suggestion model based on local on-device information and collaborates with a cloud server.  ... 
arXiv:2104.07914v1 fatcat:b5wsrfcbynel7jqdxpfw4ftwh4

A Review of Machine Learning and IoT in Smart Transportation

Fotios Zantalis, Grigorios Koulouras, Sotiris Karabetsos, Dionisis Kandris
2019 Future Internet  
From the reviewed articles it becomes profound that there is a possible lack of ML coverage for the Smart Lighting Systems and Smart Parking applications.  ...  As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application.  ...  Smart phones, embedded systems, wireless sensors, and almost every electronic device are connected to a local network or the internet, leading to the era of the Internet of Things (IoT).  ... 
doi:10.3390/fi11040094 fatcat:6xneyx7ynrgn7p2yl5efy76cee

Quantifying Uncertainty in Internet of Medical Things and Big-Data Services Using Intelligence and Deep Learning

Fadi Al-Turjman, Hadi Zahmatkesh, Leonardo Mostarda
2019 IEEE Access  
Accordingly, we aim to find a way to calculate and predict the price per big data service over the cloud using AI and deep learning.  ...  The use of AI techniques can be applied in multilevel to provide a kind of deep learning to further improve the performance of the system under study.  ...  This in turn causes enhancements in the reliability of services. Nowadays, we are all connected with our computers, smart-phones, and many other objects and devices that can send and receive data.  ... 
doi:10.1109/access.2019.2931637 fatcat:bzlaipmsmjanlk267eq3vvsl6a

Communication-Efficient Edge AI: Algorithms and Systems [article]

Yuanming Shi, Kai Yang, Tao Jiang, Jun Zhang, Khaled B. Letaief
2020 arXiv   pre-print
AI at the edge requires close cooperation among edge devices, such as smart phones and smart vehicles, and edge servers at the wireless access points and base stations, which however result in heavy communication  ...  This is driven by the explosive growth of data, advances in machine learning (especially deep learning), and easy access to vastly powerful computing resources.  ...  Zhi Ding from the University of California at Davis for insightful and constructive comments to improve the presentation of this work.  ... 
arXiv:2002.09668v1 fatcat:nhasdzb7t5dt5brs2r7ocdzrnm

Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions

Leila Ismail, Rajkumar Buyya
2022 Sensors  
The recent upsurge of smart cities' applications and their building blocks in terms of the Internet of Things (IoT), Artificial Intelligence (AI), federated and distributed learning, big data analytics  ...  the network generation systems from 1G to AI-enabled 6G and its employed self-learning models.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s22155750 pmid:35957307 pmcid:PMC9371016 fatcat:kecdmc72b5cejkkkcm7fmtv54u

Convergence of Recommender Systems and Edge Computing: A Comprehensive Survey

Chuan Sun, Hui Li, Xiuhua Li, Junhao Wen, Qingyu Xiong, Wei Zhou
2020 IEEE Access  
The proliferation of smart phones, smart wearable devices and other Internet of Thing (IoT) devices has gradually driven many novel emerging services which are latency-sensitive and computation-intensive  ...  The conventional recommender systems based on cloud computing are incapable of digging the information of user demands.  ...  With the proliferation of smart phones, smart wearable devices and other Internet of Thing (IoT) devices, as well as the tremendous growth of Internet of Vehicles (IoV), the number of IoT devices will  ... 
doi:10.1109/access.2020.2978896 fatcat:s6nzfjnconfxzm7twy5zmyl5gq

Papers by Title

2019 2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW)  
: A Case Study to Improve Crosswalk Safety in Taipei Smart Home Personalizing Services Based on Adaptive Cloud IOT Devices With Bigdata and Deep Learning Technique Smartphone Mobile-Learning Application  ...  based on Behavioral Studies Distributed Energy Harvesting Management Algorithm in Internet of Things Devices Early Warning System for Smart Street Lights ECG Classification Based on Unfixed-Length Segmentation  ... 
doi:10.1109/icce-tw46550.2019.8991721 fatcat:62376ymadzge3g5xomicr5tesq

An Overview on Analyzing Deep Learning and Transfer Learning Approaches for Health Monitoring

Yiting Wang, Shah Nazir, Muhammad Shafiq, Jude Hemanth
2021 Computational and Mathematical Methods in Medicine  
Healthcare systems are having large-scale infrastructure of electronic devices, medical information systems, wearable and smart devices, medical records, and handheld devices.  ...  A detailed report of the existing literature in terms of deep learning and transfer learning is the dire need and facilitating of modern healthcare.  ...  Acknowledgments This work was sponsored in part by the Natural Science Foundation of Hunan Province (2020JJ4121).  ... 
doi:10.1155/2021/5552743 fatcat:zglmxp2x4ff6xadqkuqzjb4fca

AN EFFICIENT FOG ENABLED HEALTHCARE MAINTENANCE SYSTEM USING INTERNET OF THINGS WITH DEEP LEARNING STRATEGIES

G. S. Gunanidhi, R. Krishnaveni
2021 Information Technology in Industry  
With the help of deep learning procedures, the health records are clearly prioritized and maintained into the server end for monitoring.  ...  Internet of Things (IoT) is the ruling term now-a-days, in which it attracts several smart gadgets and application due to its robust nature and support.  ...  Healthcare Scheme with Fog Interface The fog computing interface is adapted with the proposed deep learning principle in terms of reducing the data storage over the remote cloud server and providing the  ... 
doi:10.17762/itii.v9i1.184 fatcat:mugly6pzvfforkry56xssijkji
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