A novel three-tier Internet of Things architecture with machine learning algorithm for early detection of heart diseases

2018 ◽  
Vol 65 ◽  
pp. 222-235 ◽  
Author(s):  
Priyan Malarvizhi Kumar ◽  
Usha Devi Gandhi
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Early Detection ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Machine Learning Algorithm

scholarly journals Machine Learning for Internet of Things

2019 ◽  
Vol 8 (07) ◽  
pp. 24680-24782
Author(s):  
Manisha Bagri ◽  
Neha Aggarwal
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
Weather Prediction ◽  
Support Vector ◽  
The Internet ◽  
Machine Learning Algorithm ◽  
Learning Patterns ◽  
New Development ◽  
Share Data

By 2020 around 25-50 billion devices are likely to be connected to the internet. Due to this new development, it gives rise to something called Internet of Things (IoT). The interconnected devices can generate and share data over a network. Machine Learning plays a key role in IoT to handle the vast amount of data. It gives IoT and devices a brain to think, which is often called as intelligence. The data can be feed to machines for learning patterns, based on training the machines can identify to predict for the future. This paper gives a brief explanation of IoT. This paper gives a crisp explanation of machine learning algorithm and its types. However, Support Vector Machine (SVM) is explained in details along with its merits and demerits. An algorithm is also proposed for weather prediction using SVM for IoT.

scholarly journals Proximity Environmental Feature Based Tree Health Assessment Scheme Using Internet of Things and Machine Learning Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3115 ◽  
Author(s):  
Yang Wei ◽  
Hao Wang ◽  
Kim Fung Tsang ◽  
Yucheng Liu ◽  
Chung Kit Wu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Health Assessment ◽  
Sensor Data ◽  
Machine Learning Algorithm ◽  
Tree Health ◽  
Environmental Feature ◽  
Feature Based

Improperly grown trees may cause huge hazards to the environment and to humans, through e.g., climate change, soil erosion, etc. A proximity environmental feature-based tree health assessment (PTA) scheme is proposed to prevent these hazards by providing guidance for early warning methods of potential poor tree health. In PTA development, tree health is defined and evaluated based on proximity environmental features (PEFs). The PEF takes into consideration the seven surrounding ambient features that strongly impact tree health. The PEFs were measured by the deployed smart sensors surrounding trees. A database composed of tree health and relative PEFs was established for further analysis. An adaptive data identifying (ADI) algorithm is applied to exclude the influence of interference factors in the database. Finally, the radial basis function (RBF) neural network (NN), a machine leaning algorithm, has been identified as the appropriate tool with which to correlate tree health and PEFs to establish the PTA algorithm. One of the salient features of PTA is that the algorithm can evaluate, and thus monitor, tree health remotely and automatically from smart sensor data by taking advantage of the well-established internet of things (IoT) network and machine learning algorithm.

scholarly journals The Abnormal Detection for Network Traffic of Power IoT Based on Device Portrait

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jiaxuan Fei ◽  
Qigui Yao ◽  
Mingliang Chen ◽  
Xiangqun Wang ◽  
Jie Fan
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Network Traffic ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Traffic Data ◽  
Network Environment ◽  
Security Measures ◽  
Privacy Leakage ◽  
Traffic Characteristics

The construction of power Internet of things is an important development direction for power grid enterprises. Although power Internet of things is a kind of network, it is denser than the ordinary Internet of things points and more complex equipment types, so it has higher requirements for network security protection. At the same time, due to the special information perception and transmission mode in the Internet of things, the information transmitted in the network is easy to be stolen and resold, and traditional security measures can no longer meet the security protection requirements of the new Internet of things devices. To solve the privacy leakage and security attack caused by the illegal intrusion in the network, this paper proposes to construct a device portrait for terminal devices in the power Internet of things and detect abnormal traffic in the network based on device portrait. By collecting traffic data in the network environment, various network traffic characteristics are extracted, and abnormal traffic is analyzed and identified by the machine learning algorithm. By collecting the traffic data in the network environment, the features are extracted from the physical layer, network layer, and application layer of the message, and the device portrait is generated by a machine learning algorithm. According to the established attack mode, the corresponding traffic characteristics are analyzed, and the detection of abnormal traffic is achieved by comparing the attack traffic characteristics with the device portrait. The experimental results show that the accuracy of this method is more than 90%.

scholarly journals Heart Disease Prediction Using Machine Learning Algorithm

2019 ◽  
Vol 8 (10) ◽  
pp. 2603-2606
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Decision Tree ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Tree Classifier

Heart disease is a common problem which can be very severe in old ages and also in people not having a healthy lifestyle. With regular check-up and diagnosis in addition to maintaining a decent eating habit can prevent it to some extent. In this paper we have tried to implement the most sought after and important machine learning algorithm to predict the heart disease in a patient. The decision tree classifier is implemented based on the symptoms which are specifically the attributes required for the purpose of prediction. Using the decision tree algorithm, we will be able to identify those attributes which are the best one that will lead us to a better prediction of the datasets. The decision tree algorithm works in a way where it tries to solve the problem by the help of tree representation. Here each internal node of the tree represents an attribute, and each leaf node corresponds to a class label. The support vector machine algorithm helps us to classify the datasets on the basis of kernel and it also groups the dataset using hyperplane. The main objective of this project is to try and reduce the number of occurrences of the heart diseases in patients

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