scholarly journals Outlier detection in WSN by entropy based machine learning approach

2020 ◽  
Vol 20 (3) ◽  
pp. 1435
Author(s):  
Manmohan Singh Yadav ◽  
Shish Ahamad
Keyword(s):  
Machine Learning ◽  
Outlier Detection ◽  
Sensor Data ◽  
Learning Approach ◽  
Random Subspace ◽  
Environmental Disasters ◽  
Machine Learning Approach ◽  
The World ◽  
Prediction Capability

<p>Environmental disasters like flooding, earthquake etc. causes catastrophic effects all over the world. WSN based techniques have become popular in susceptibility modelling of such disaster due to their greater strength and efficiency in the prediction of such threats. This paper demonstrates the machine learning-based approach to predict outlier in sensor data with bagging, boosting, random subspace, SVM and KNN based frameworks for outlier prediction using a WSN data. First of all database is pre processed with 14 sensor motes with presence of outlier due to intrusion. Subsequently segmented database is created from sensor pairs. Finally, the data entropy is calculated and used as a feature to determine the presence of outlier used different approach. Results show that the KNN model has the highest prediction capability for outlier assessment.</p>

P 120 A machine learning approach to detecting sleep and sleep disorders in acceleration sensor data

2017 ◽  
Vol 128 (10) ◽  
pp. e388
Author(s):  
R. Leenings ◽  
C. Glatz ◽  
A. Heidbreder ◽  
M. Boentert ◽  
G. Pipa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Disorders ◽  
Sensor Data ◽  
Learning Approach ◽  
Acceleration Sensor ◽  
Machine Learning Approach

scholarly journals Rain Prediction Using Rule-Based Machine Learning Approach

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Muchamad Taufiq Anwar ◽  
Saptono Nugrohadi ◽  
Vita Tantriyati ◽  
Vikky Aprelia Windarni
Keyword(s):  
Machine Learning ◽  
Natural Disasters ◽  
Human Life ◽  
Meteorological Data ◽  
Training Model ◽  
Weather Data ◽  
Learning Approach ◽  
Rule Based ◽  
Machine Learning Approach ◽  
The World

Rain prediction is an important topic that continues to gain attention throughout the world. The rain has a big impact on various aspects of human life both socially and economically, for example in agriculture, health, transportation, etc. Rain also affects natural disasters such as landslides and floods. The various impact of rain on human life prompts us to build a model to understand and predict rain to provide early warning in various fields/needs such as agriculture, transportation, etc. This research aims to build a rain prediction model using a rule-based Machine Learning approach by utilizing historical meteorological data. The experiment using the J48 method resulted in up to 77.8% accuracy in the training model and gave accurate prediction results of 86% when tested against actual weather data in 2020.

A Comprehensive Review on Online News Popularity Prediction using Machine Learning Approach

2019 ◽  
Vol 5 (1) ◽  
pp. 7
Author(s):  
Priyanka Rathord ◽  
Dr. Anurag Jain ◽  
Chetan Agrawal
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Online News ◽  
Machine Learning Techniques ◽  
News Article ◽  
Learning Approach ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
The World ◽  
Popularity Prediction

With the help of Internet, the online news can be instantly spread around the world. Most of peoples now have the habit of reading and sharing news online, for instance, using social media like Twitter and Facebook. Typically, the news popularity can be indicated by the number of reads, likes or shares. For the online news stake holders such as content providers or advertisers, it’s very valuable if the popularity of the news articles can be accurately predicted prior to the publication. Thus, it is interesting and meaningful to use the machine learning techniques to predict the popularity of online news articles. Various works have been done in prediction of online news popularity. Popularity of news depends upon various features like sharing of online news on social media, comments of visitors for news, likes for news articles etc. It is necessary to know what makes one online news article more popular than another article. Unpopular articles need to get optimize for further popularity. In this paper, different methodologies are analyzed which predict the popularity of online news articles. These methodologies are compared, their parameters are considered and improvements are suggested. The proposed methodology describes online news popularity predicting system.

scholarly journals Wearable Sensor Data to Track Subject-Specific Movement Patterns Related to Clinical Outcomes Using a Machine Learning Approach

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2828 ◽  
Author(s):  
Dylan Kobsar ◽  
Reed Ferber
Keyword(s):  
Machine Learning ◽  
Clinical Outcomes ◽  
Movement Patterns ◽  
Movement Pattern ◽  
Human Movement ◽  
Sensor Data ◽  
Learning Approach ◽  
Wearable Sensor ◽  
Machine Learning Approach ◽  
Subject Specific

Wearable sensors can provide detailed information on human movement but the clinical impact of this information remains limited. We propose a machine learning approach, using wearable sensor data, to identify subject-specific changes in gait patterns related to improvements in clinical outcomes. Eight patients with knee osteoarthritis (OA) completed two gait trials before and one following an exercise intervention. Wearable sensor data (e.g., 3-dimensional (3D) linear accelerations) were collected from a sensor located near the lower back, lateral thigh and lateral shank during level treadmill walking at a preferred speed. Wearable sensor data from the 2 pre-intervention gait trials were used to define each individual’s typical movement pattern using a one-class support vector machine (OCSVM). The percentage of strides defined as outliers, based on the pre-intervention gait data and the OCSVM, were used to define the overall change in an individual’s movement pattern. The correlation between the change in movement patterns following the intervention (i.e., percentage of outliers) and improvement in self-reported clinical outcomes (e.g., pain and function) was assessed using a Spearman rank correlation. The number of outliers observed post-intervention exhibited a large association (ρ = 0.78) with improvements in self-reported clinical outcomes. These findings demonstrate a proof-of-concept and a novel methodological approach for integrating machine learning and wearable sensor data. This approach provides an objective and evidence-informed way to understand clinically important changes in human movement patterns in response to exercise therapy.

Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging

2015 ◽  
Vol 20 (12) ◽  
pp. 121305 ◽  
Author(s):  
Weizhi Li ◽  
Weirong Mo ◽  
Xu Zhang ◽  
John J. Squiers ◽  
Yang Lu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diagnostic Imaging ◽  
Outlier Detection ◽  
Learning Approach ◽  
Machine Learning Approach

A machine-learning approach for damage detection in aircraft structures using self-powered sensor data

2017 ◽  
Author(s):  
Hadi Salehi ◽  
Saptarshi Das ◽  
Shantanu Chakrabartty ◽  
Subir Biswas ◽  
Rigoberto Burgueño
Keyword(s):  
Machine Learning ◽  
Damage Detection ◽  
Sensor Data ◽  
Learning Approach ◽  
Aircraft Structures ◽  
Machine Learning Approach ◽  
Self Powered

scholarly journals Machine learning approach for confirmation of COVID-19 cases: positive, negative, death and release

2020 ◽  
Vol 2 (3) ◽  
pp. 172-177
Author(s):  
Shawni Dutta ◽  
◽  
Samir Kumar Bandyopadhyay ◽  
Keyword(s):  
Infectious Disease ◽  
Machine Learning ◽  
Original Data ◽  
World Health ◽  
Learning Approach ◽  
Human Beings ◽  
Verification Method ◽  
Number Of Patients ◽  
Machine Learning Approach ◽  
The World

Introduction: Corona Virus Infectious Disease (COVID-19) is the infectious disease. The COVID-19 disease came to earth in early 2019. It is expanding exponentially throughout the world and affected an enormous number of human beings starting from the last month. The World Health Organization (WHO) on March 11, 2020 declared COVID-19 was characterized as “Pandemic”. This paper proposed approach for confirmation of COVID-19 cases after the diagnosis of doctors. The objective of this study uses machine learning method to evaluate how much predicted results are close to original data related to Confirmed-Negative-Released-Death cases of COVID-19. Materials and methods: For this purpose, a verification method is proposed in this paper that uses the concept of Deep-learning Neural Network. In this framework, Long shrt-term memory (LSTM) and Gated Recurrent Unit (GRU) are also assimilated finally for training the dataset. The prediction results are tally with the results predicted by clinical doctors. Results: The results are obtained from the proposed method with accuracy 87 % for the “confirmed Cases”, 67.8 % for “Negative Cases”, 62% for “Deceased Case” and 40.5 % for “Released Case”. Another important parameter i.e. RMSE shows 30.15% for Confirmed Case, 49.4 % for Negative Cases, 4.16 % for Deceased Case and 13.72 % for Released Case. Conclusions: The outbreak of Coronavirus has the nature of exponential growth and so it is difficult to control with limited clinical persons for handling a huge number of patients within a reasonable time. So it is necessary to build an automated model, based on machine learning approach, for corrective measure after the decision of clinical doctors.

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