scholarly journals IOT Framework for Heart Diseases Prediction Using Machine Learning

2021 ◽  
Vol 10 (3) ◽  
pp. 2036-2041
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Heart Diseases ◽  
Healthcare Services ◽  
Patient Data ◽  
Classification Algorithms ◽  
Machine Learning Classification ◽  
Abnormal Form

latest evolution in the domain of internet of things and technology of sensors can be carrier for online healthcare services. The modern rectification in IOT sector enhance the employed rate in online healthcare control. So according to in this paper we proposed a framework for heart diseases prediction with the help of IOT and machine learning. Basically IOT sensors attached with patient body and that sensors transfers the patient data into dataset form. Then help with the machine learning classification algorithms predict the data for patient having anormal or abnormal form of data

scholarly journals Machine Learning and LPWAN Based Internet of Things Applications in Healthcare Sector during COVID-19 Pandemic

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1615
Author(s):  
Zeeshan Ali Khan ◽  
Ubaid Abbasi ◽  
Sung Won Kim
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
K Nearest Neighbors ◽  
Iot Applications ◽  
Processing Resources ◽  
Multiple Services ◽  
Applications Of Machine Learning

Low power wide area networks (LPWAN) are comprised of small devices having restricted processing resources and limited energy budget. These devices are connected with each other using communication protocols. Considering their available resources, these devices can be used in a number of different Internet of Things (IoT) applications. Another interesting paradigm is machine learning, which can also be integrated with LPWAN technology to embed intelligence into these IoT applications. These machine learning-based applications combine intelligence with LPWAN and prove to be a useful tool. One such IoT application is in the medical field, where they can be used to provide multiple services. In the scenario of the COVID-19 pandemic, the importance of LPWAN-based medical services has gained particular attention. This article describes various COVID-19-related healthcare services, using the the applications of machine learning and LPWAN in improving the medical domain during the current COVID-19 pandemic. We validate our idea with the help of a case study that describes a way to reduce the spread of any pandemic using LPWAN technology and machine learning. The case study compares k-Nearest Neighbors (KNN) and trust-based algorithms for mitigating the flow of virus spread. The simulation results show the effectiveness of KNN for curtailing the COVID-19 spread.

scholarly journals Machine learning classification of new asteroid families members

2020 ◽  
Author(s):  
Valerio Carruba
Keyword(s):  
Machine Learning ◽  
Family Members ◽  
Classification Algorithms ◽  
New Members ◽  
Machine Learning Classification ◽  
New Family ◽  
Robotic Telescope ◽  
Traditional Approaches ◽  
Asteroid Families

<p>Asteroid families are groups of asteroids that are the product of collisions or of the rotational fission of a parent object.  These groups are mainly identified in proper elements or frequencies domains.   Because of robotic telescope surveys, the number of known asteroids has increased from about 10,000 in the early 90's to more than 750,000 nowadays. Traditional approaches for identifying new members of asteroid families, like the hierarchical clustering method (HCM), may   struggle to keep up with the growing rate of new discoveries. Here we used machine learning classification algorithms to identify new family members based on the orbital distribution in proper (a,e,sin(i)) of previously known family constituents. We compared the outcome of nine classification algorithms from stand alone and ensemble approaches.  The Extremely Randomized Trees (ExtraTree) method had the highest precision, enabling to  retrieve up to 97% of family members identified with standard HCM.</p>

scholarly journals Wastewater Plant Reliability Prediction Using the Machine Learning Classification Algorithms

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1518
Author(s):  
Lazar Z. Velimirović ◽  
Radmila Janković ◽  
Jelena D. Velimirović ◽  
Aleksandar Janjić
Keyword(s):  
Machine Learning ◽  
Wastewater Treatment ◽  
Water Levels ◽  
Reliability Prediction ◽  
Classification Algorithms ◽  
Water Pump ◽  
Pump Failure ◽  
Machine Learning Classification ◽  
Capacity Current ◽  
Prediction Systems

One way to optimize wastewater treatment system infrastructure, its operations, monitoring, maintenance and management is through development of smart forecasting, monitoring and failure prediction systems using machine learning modeling. The aim of this paper was to develop a model that was able to predict a water pump failure based on the asymmetrical type of data obtained from sensors such as water levels, capacity, current and flow values. Several machine learning classification algorithms were used for predicting water pump failure. Using the classification algorithms, it was possible to make predictions of future values with a simple input of current values, as well as predicting probabilities of each sample belonging to each class. In order to build a prediction model, an asymmetrical type dataset containing the aforementioned variables was used.

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