Automatic Detection of Congestive Heart Failure Based on a Hybrid Deep Learning Algorithm in the Internet of Medical Things

Big data is a massive and diverse form of unstructured data, which needs proper analysis and management. It is another great technological revolution after the Internet, the Internet of Things, and cloud computing. This paper firstly studies the related concepts and basic theories as the origin of research. Secondly, it analyzes in depth the problems and challenges faced by Chinese government management under the impact of big data. Again, we explore the opportunities that big data brings to government management in terms of management efficiency, administrative capacity, and public services and believe that governments should seize opportunities to make changes. Brainlike computing attempts to simulate the structure and information processing process of biological neural network. This paper firstly analyzes the development status of e-government at home and abroad, studies the service-oriented architecture (SOA) and web services technology, deeply studies the e-government and SOA theory, and discusses this based on the development status of e-government in a certain region. Then, the deep learning algorithm is used to construct the monitoring platform to monitor the government behavior in real time, and the deep learning algorithm is used to conduct in-depth mining to analyze the government's intention behavior.

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An Integrated Deep Learning Algorithm for Detecting Lung Nodules with Low-dose CT and Its Application in 6G-enabled Internet of Medical Things

IEEE Internet of Things Journal ◽

10.1109/jiot.2020.3023436 ◽

2020 ◽

pp. 1-1

Author(s):

Wei Wang ◽

Fang Liu ◽

Xiaohui Zhi ◽

Tong Zhang ◽

Chuanchao Huang

Keyword(s):

Deep Learning ◽

Low Dose ◽

Learning Algorithm ◽

Lung Nodules ◽

Low Dose Ct ◽

Deep Learning Algorithm ◽

Internet Of Medical Things

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Automatic Detection of Diabetic Retinopathy in Retinal Fundus Photographs Based on Deep Learning Algorithm

Translational Vision Science & Technology ◽

10.1167/tvst.8.6.4 ◽

2019 ◽

Vol 8 (6) ◽

pp. 4 ◽

Cited By ~ 17

Author(s):

Feng Li ◽

Zheng Liu ◽

Hua Chen ◽

Minshan Jiang ◽

Xuedian Zhang ◽

...

Keyword(s):

Diabetic Retinopathy ◽

Deep Learning ◽

Learning Algorithm ◽

Automatic Detection ◽

Deep Learning Algorithm ◽

Fundus Photographs ◽

Retinal Fundus

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Intrusion Detection in IoT Networks Using Deep Learning Algorithm

Information ◽

10.3390/info11050279 ◽

2020 ◽

Vol 11 (5) ◽

pp. 279 ◽

Cited By ~ 1

Author(s):

Bambang Susilo ◽

Riri Fitri Sari

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Learning Strategies ◽

Learning Algorithm ◽

Human Life ◽

Denial Of Service ◽

The Internet ◽

Dos Attacks ◽

Deep Learning Algorithm ◽

Iot Devices

The internet has become an inseparable part of human life, and the number of devices connected to the internet is increasing sharply. In particular, Internet of Things (IoT) devices have become a part of everyday human life. However, some challenges are increasing, and their solutions are not well defined. More and more challenges related to technology security concerning the IoT are arising. Many methods have been developed to secure IoT networks, but many more can still be developed. One proposed way to improve IoT security is to use machine learning. This research discusses several machine-learning and deep-learning strategies, as well as standard datasets for improving the security performance of the IoT. We developed an algorithm for detecting denial-of-service (DoS) attacks using a deep-learning algorithm. This research used the Python programming language with packages such as scikit-learn, Tensorflow, and Seaborn. We found that a deep-learning model could increase accuracy so that the mitigation of attacks that occur on an IoT network is as effective as possible.

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