A machine learning approach-based power theft detection using GRF optimization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
A. Prakash ◽  
A. Shyam Joseph ◽  
R. Shanmugasundaram ◽  
C.S. Ravichandran
Keyword(s):  
Machine Learning ◽  
Transmission Lines ◽  
High Speed ◽  
Design Methodology ◽  
Learning Approach ◽  
Smart Meter ◽  
Financial Loss ◽  
Content Type ◽  
Garra Rufa ◽  
Machine Learning Approach

Purpose This paper aims to propose a machine learning approach-based power theft detection using Garra Rufa Fish (GRF) optimization. Here, the analyzing of power theft is an important part to reduce the financial loss and protect the electricity from fraudulent users. Design/methodology/approach In this section, a new method is implemented to reduce the power theft in transmission lines and utility grids. The detection of power theft using smart meter with reliable manner can be achieved by the help of GRF algorithm. Findings The loss of power due to non-technical loss is small by using this proposed algorithm. It provides some benefits like increased predicting capacity, less complexity, high speed and high reliable output. The result is analyzed using MATLAB/Simulink platform. The result is compared with an existing method. According to the comparison result, the proposed method provides the good performance than existing method. Originality/value The proposed method gives good results of comparison than those of the other techniques and has an ability to overcome the associated problems.

Detection and evaluation of heating load of building by machine learning

Sensor Review ◽  
2018 ◽  
Vol 38 (1) ◽  
pp. 99-101 ◽  
Author(s):  
Khaled Mohamed Himair Swhli ◽  
Srdjan Jovic ◽  
Nebojša Arsic ◽  
Petar Spalevic
Keyword(s):  
Machine Learning ◽  
Design Methodology ◽  
Diffuse Radiation ◽  
Dew Point ◽  
Learning Approach ◽  
Nonlinear Phenomenon ◽  
Content Type ◽  
Machine Learning Approach ◽  
Heating Load ◽  
Temperature Radiation

Purpose This paper aims to explore detection of heating load of building by machine learning. Detection of heating load of building is very important in design of buildings due to efficient energy consumption. Design/methodology/approach In this study, detection of heating load of building based on effects of dry-bulb temperature, dew-point temperature, radiation, diffuse radiation and wind speed was analyzed. Machine learning approach was implemented for such a purpose. Findings The obtained results could be useful for future planning of heating load of buildings. Because the heating load of building is a very nonlinear phenomenon, it is suitable to use machine learning approach to avoid the nonlinearity of the system. Originality/value The obtained results could be used effectively in detection of heating load of buildings.

scholarly journals ELECTRE tree: a machine learning approach to infer ELECTRE Tri-B parameters

2021 ◽  
Vol 55 (4) ◽  
pp. 586-608
Author(s):  
Gabriela Montenegro Montenegro de Barros ◽  
Valdecy Pereira ◽  
Marcos Costa Roboredo
Keyword(s):  
Machine Learning ◽  
Design Methodology ◽  
Majority Vote ◽  
Content Type ◽  
Ensemble Technique ◽  
Machine Learning Approach ◽  
Electre Tri ◽  
Criteria Weights ◽  
Second Procedure ◽  
Decision Boundaries

PurposeThis paper presents an algorithm that can elicitate (infer) all or any combination of elimination and choice expressing reality (ELECTRE) Tri-B parameters. For example, a decision maker can maintain the values for indifference, preference and veto thresholds, and the study’s algorithm can find the criteria weights, reference profiles and the lambda cutting level. The study’s approach is inspired by a machine learning ensemble technique, the random forest, and for that, the authors named the study’s approach as ELECTRE tree algorithm.Design/methodology/approachFirst, the authors generate a set of ELECTRE Tri-B models, where each model solves a random sample of criteria and alternates. Each sample is made with replacement, having at least two criteria and between 10% and 25% of alternates. Each model has its parameters optimized by a genetic algorithm (GA) that can use an ordered cluster or an assignment example as a reference to the optimization. Finally, after the optimization phase, two procedures can be performed; the first one will merge all models, finding in this way the elicitated parameters and in the second procedure, each alternate is classified (voted) by each separated model, and the majority vote decides the final class.FindingsThe authors have noted that concerning the voting procedure, nonlinear decision boundaries are generated and they can be suitable in analyzing problems of the same nature. In contrast, the merged model generates linear decision boundaries.Originality/valueThe elicitation of ELECTRE Tri-B parameters is made by an ensemble technique that is composed of a set of multicriteria models that are engaged in generating robust solutions.

Applying machine learning approach to predict students’ performance in higher educational institutions

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammed Nasiru Yakubu ◽  
A. Mohammed Abubakar
Keyword(s):  
Machine Learning ◽  
Academic Success ◽  
Secondary Data ◽  
Learning Approach ◽  
Content Type ◽  
Tertiary Institutions ◽  
Knowledge Based ◽  
Knowledge Based Economy ◽  
Machine Learning Approach ◽  
Nigerian University

Purpose Academic success and failure are relevant lifelines for economic success in the knowledge-based economy. The purpose of this paper is to predict the propensity of students’ academic performance using early detection indicators (i.e. age, gender, high school exam scores, region, CGPA) to allow for timely and efficient remediation. Design/methodology/approach A machine learning approach was used to develop a model based on secondary data obtained from students’ information system in a Nigerian university. Findings Results revealed that age is not a predictor for academic success (high CGPA); female students are 1.2 times more likely to have high CGPA compared to their male counterparts; students with high JAMB scores are more likely to achieve academic success, high CGPA and vice versa; students from affluent and developed regions are more likely to achieve academic success, high CGPA and vice versa; and students in Years 3 and 4 are more likely to achieve academic success, high CGPA. Originality/value This predictive model serves as a classifier and useful strategy to mitigate failure, promote success and better manage resources in tertiary institutions.

scholarly journals A Machine Learning Approach To Identify Hydrogenosomal Proteins in Trichomonas vaginalis

2011 ◽  
Vol 11 (2) ◽  
pp. 217-228 ◽  
Author(s):  
David Burstein ◽  
Sven B. Gould ◽  
Verena Zimorski ◽  
Thorsten Kloesges ◽  
Fuat Kiosse ◽  
...  
Keyword(s):  
Machine Learning ◽  
Trichomonas Vaginalis ◽  
Transmitted Disease ◽  
Protozoan Parasite ◽  
Immunofluorescence Assay ◽  
Learning Approach ◽  
Content Type ◽  
A Genome ◽  
Machine Learning Approach ◽  
Targeting Signal

ABSTRACT The protozoan parasite Trichomonas vaginalis is the causative agent of trichomoniasis, the most widespread nonviral sexually transmitted disease in humans. It possesses hydrogenosomes—anaerobic mitochondria that generate H 2 , CO 2 , and acetate from pyruvate while converting ADP to ATP via substrate-level phosphorylation. T. vaginalis hydrogenosomes lack a genome and translation machinery; hence, they import all their proteins from the cytosol. To date, however, only 30 imported proteins have been shown to localize to the organelle. A total of 226 nuclear-encoded proteins inferred from the genome sequence harbor a characteristic short N-terminal presequence, reminiscent of mitochondrial targeting peptides, which is thought to mediate hydrogenosomal targeting. Recent studies suggest, however, that the presequences might be less important than previously thought. We sought to identify new hydrogenosomal proteins within the 59,672 annotated open reading frames (ORFs) of T. vaginalis , independent of the N-terminal targeting signal, using a machine learning approach. Our training set included 57 gene and protein features determined for all 30 known hydrogenosomal proteins and 576 nonhydrogenosomal proteins. Several classifiers were trained on this set to yield an import score for all proteins encoded by T. vaginalis ORFs, predicting the likelihood of hydrogenosomal localization. The machine learning results were tested through immunofluorescence assay and immunodetection in isolated cell fractions of 14 protein predictions using hemagglutinin constructs expressed under the homologous SCSα promoter in transiently transformed T. vaginalis cells. Localization of 6 of the 10 top predicted hydrogenosome-localized proteins was confirmed, and two of these were found to lack an obvious N-terminal targeting signal.

scholarly journals Edge Detection and Machine Learning Approach to Identify Flow Structures on Schlieren and Shadowgraph Images

2020 ◽  
pp. paper15-1-paper15-14
Author(s):  
Irina Znamenskaya ◽  
Igor Doroshchenko ◽  
Daria Tatarenkova
Keyword(s):  
Machine Learning ◽  
Shock Wave ◽  
Shock Waves ◽  
Edge Detection ◽  
High Speed ◽  
Gas Flow ◽  
Flow Structures ◽  
Learning Approach ◽  
Wave Detection ◽  
Machine Learning Approach

Schlieren, shadowgraph and other types of refraction-based techniques have been often used to study gas flow structures. They can capture strong density gradients, such as shock waves. Shock wave detection is a very important task in analyzing unsteady gas flows. High-speed imaging systems, including high-speed cameras, are widely used to record large arrays of shadowgraph images. To process large datasets of the high-speed shadowgraph images and automatically detect shock waves, convective plumes and other gas flow structures, two computer software systems based on the edge detection and machine learning with convolutional neural networks (CNN) were developed. The edge-detection software utilizes image filtering, noise removing, background image subtraction in the frequency domain and edge detection based on the Canny algorithm. The machine learning software is based on CNN. We developed two neural networks working together. The first one classifies the image dataset and finds images with shock waves. The other CNN solves the regression task and defines shock wave position (single number) based on image pixels tensor (3-D array of numbers) for each image. The supervised learning code based on example input-output pairs was developed to train models. It was shown, that the machine learning approach gives better results in shock wave detection accuracy, especially for low-quality images with a strong noise level. Software system for automated shadowgraph images processing and x-t curves of the shock wave and convective plume movement plotting was developed.

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