Comparison of Machine Learning Techniques for the Detection of the Electricity Theft

2017 ◽  
Author(s):  
Guodong Gu ◽  
Qingsu He ◽  
Bo Wang ◽  
Bo Dai
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Electricity Theft ◽  
Learning Techniques

Detection of Electricity Theft by Applying Deep Learning Techniques in Electrical Distribution Network: A Comprehensive Review

2021 ◽  
Vol 14 ◽  
Author(s):  
Rakhi Yadav ◽  
Yogendra Kumar
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Power Plants ◽  
Distribution Networks ◽  
Research Area ◽  
Machine Learning Techniques ◽  
Electricity Theft ◽  
Learning Techniques ◽  
Electrical Distribution Network ◽  
The Impact

Introduction: Non-Technical Losses (NTL) occur up to 40 % of the total electric transmission and distribution power. Hence, across the world, the power system is facing many challenges. The occurrence of such large amounts of losses cannot be ignored. These losses have severe impacts on distribution utilities. The performance of electric distribution networks adversely affects due to these losses. The reduction of these NTL consequently reduces the requirement of new power plants to fulfil the demand-supply gap. Hence, NTL is an emerging research area for electrical engineers. This paper has covered various deep learning and machine learning models used to detect non-technical losses. Discussion: There is a lack of research in this field so far. The existing literature only shows the detection of non-technical losses using a machine and deep learning. This paper also provides the causes of NTL followed by an impact on economies, a variation of NTL in different countries. Further, we have provided a comparative analysis based on several essential parameters. We have also discussed various simulation tools. Moreover, several challenges occur during machine and deep learning-based detection of NTL, and its possible solutions are also discussed. Conclusion: In the present paper, we have reviewed the impact of NTLs on economies, potential revenue losses, and electricity provider's profit. Further, it provides a detailed review of deep learning and machine learning techniques used to detect the NTL. This survey has also discussed challenges in machine learning-based detection of NTL, followed by their possible solutions. In addition, this paper also provides details about various tools and simulation environments used to detect the NTL. We are confident that this comprehensive survey will help the researchers to research this thrust area.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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