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.

scholarly journals Iterative machine learning applied to annotation of text datasets

2021 ◽  
Author(s):  
Thiago Abdo ◽  
Fabiano Silva
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Computational Cost ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Learning Techniques ◽  
The Creation ◽  
The Impact ◽  
High Computational Cost

The purpose of this paper is to analyze the use of different machine learning approaches and algorithms to be integrated as an automated assistance on a tool to aid the creation of new annotated datasets. We evaluate how they scale in an environment without dedicated machine learning hardware. In particular, we study the impact over a dataset with few examples and one that is being constructed. We experiment using deep learning algorithms (Bert) and classical learning algorithms with a lower computational cost (W2V and Glove combined with RF and SVM). Our experiments show that deep learning algorithms have a performance advantage over classical techniques. However, deep learning algorithms have a high computational cost, making them inadequate to an environment with reduced hardware resources. Simulations using Active and Iterative machine learning techniques to assist the creation of new datasets are conducted. For these simulations, we use the classical learning algorithms because of their computational cost. The knowledge gathered with our experimental evaluation aims to support the creation of a tool for building new text datasets.

scholarly journals Prediction of Covid-19 Daily Case in Indonesia Using Long Short Term Memory Method

Teknika ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 62-67
Author(s):  
Faisal Dharma Adhinata ◽  
Diovianto Putra Rakhmadani
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Short Term Memory ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Short Term ◽  
Term Memory ◽  
Learning Techniques ◽  
Long Short Term Memory ◽  
The Impact

The impact of this pandemic affects various sectors in Indonesia, especially in the economic sector, due to the large-scale social restrictions policy to suppress this case's growth. The details of the growth of Covid-19 in Indonesia are still fluctuating and cannot be fully understood. Recently it has been developed by researchers related to the prediction of Covid-19 cases in various countries. One of them is using a machine learning technique approach to predict cases of daily increase Covid-19. However, the use of machine learning techniques results in the MSE error value in the thousands. This high number indicates that the prediction data using the model is still a high error rate compared to the actual data. In this study, we propose a deep learning approach using the Long Short Term Memory (LSTM) method to build a prediction model for the daily increase cases of Covid-19. This study's LSTM model architecture uses the LSTM layer, Dropout layer, Dense, and Linear Activation Function. Based on various hyperparameter experiments, using the number of neurons 10, batch size 32, and epochs 50, the MSE values were 0.0308, RMSE 0.1758, and MAE 0.13. These results prove that the deep learning approach produces a smaller error value than machine learning techniques, even closer to zero.

scholarly journals Trust in Intrusion Detection Systems: An Investigation of Performance Analysis for Machine Learning and Deep Learning Models

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Basim Mahbooba ◽  
Radhya Sahal ◽  
Wael Alosaimi ◽  
Martin Serrano
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Learning Technologies ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Network Intrusion ◽  
Learning Techniques ◽  
The Impact

To design and develop AI-based cybersecurity systems (e.g., intrusion detection system (IDS)), users can justifiably trust, one needs to evaluate the impact of trust using machine learning and deep learning technologies. To guide the design and implementation of trusted AI-based systems in IDS, this paper provides a comparison among machine learning and deep learning models to investigate the trust impact based on the accuracy of the trusted AI-based systems regarding the malicious data in IDs. The four machine learning techniques are decision tree (DT), K nearest neighbour (KNN), random forest (RF), and naïve Bayes (NB). The four deep learning techniques are LSTM (one and two layers) and GRU (one and two layers). Two datasets are used to classify the IDS attack type, including wireless sensor network detection system (WSN-DS) and KDD Cup network intrusion dataset. A detailed comparison of the eight techniques’ performance using all features and selected features is made by measuring the accuracy, precision, recall, and F1-score. Considering the findings related to the data, methodology, and expert accountability, interpretability for AI-based solutions also becomes demanded to enhance trust in the IDS.

scholarly journals Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning

Vibration ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 341-356
Author(s):  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Machine Learning Techniques ◽  
Rolling Stock ◽  
Raw Data ◽  
Learning Techniques ◽  
Combined Defects

Various techniques have been developed to detect railway defects. One of the popular techniques is machine learning. This unprecedented study applies deep learning, which is a branch of machine learning techniques, to detect and evaluate the severity of rail combined defects. The combined defects in the study are settlement and dipped joint. Features used to detect and evaluate the severity of combined defects are axle box accelerations simulated using a verified rolling stock dynamic behavior simulation called D-Track. A total of 1650 simulations are run to generate numerical data. Deep learning techniques used in the study are deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Simulated data are used in two ways: simplified data and raw data. Simplified data are used to develop the DNN model, while raw data are used to develop the CNN and RNN model. For simplified data, features are extracted from raw data, which are the weight of rolling stock, the speed of rolling stock, and three peak and bottom accelerations from two wheels of rolling stock. In total, there are 14 features used as simplified data for developing the DNN model. For raw data, time-domain accelerations are used directly to develop the CNN and RNN models without processing and data extraction. Hyperparameter tuning is performed to ensure that the performance of each model is optimized. Grid search is used for performing hyperparameter tuning. To detect the combined defects, the study proposes two approaches. The first approach uses one model to detect settlement and dipped joint, and the second approach uses two models to detect settlement and dipped joint separately. The results show that the CNN models of both approaches provide the same accuracy of 99%, so one model is good enough to detect settlement and dipped joint. To evaluate the severity of the combined defects, the study applies classification and regression concepts. Classification is used to evaluate the severity by categorizing defects into light, medium, and severe classes, and regression is used to estimate the size of defects. From the study, the CNN model is suitable for evaluating dipped joint severity with an accuracy of 84% and mean absolute error (MAE) of 1.25 mm, and the RNN model is suitable for evaluating settlement severity with an accuracy of 99% and mean absolute error (MAE) of 1.58 mm.

scholarly journals Sentiment Analysis using various Machine Learning and Deep Learning Techniques

2021 ◽  
pp. 385-394
Author(s):  
V Umarani ◽  
A Julian ◽  
J Deepa
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Analysis Process ◽  
Learning Techniques

Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application.

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