Evidence map›Paper›PMID 35684668›Full record

ArticleSensors (Basel, Switzerland)2022

Sudeep Tanwar, Aparna Kumari, Darshan Vekaria, Maria Simona Raboaca, Fayez Alqahtani, Amr Tolba, Bogdan-Constantin Neagu, Ravi Sharma

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
2.4field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 26 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 6 institutions in 3 countries.

Sudeep TanwarDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India.ORCID 0000-0002-1776-4651
Aparna KumariInstitute of Computer Technology, Ganpat University, Ahmedabad 384012, India.ORCID 0000-0001-5991-6193
Darshan VekariaDepartment of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, India.
Maria Simona RaboacaNational Research and Development Institute for Cryogenic and Isotopic Technologies-ICSI Rm. Valcea, Uz-Inei Street, No. 4, Raureni, P.O. Box 7, 240050 Rm. Valcea, Romania.ORCID 0000-0002-7277-4377
Fayez AlqahtaniSoftware Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia.ORCID 0000-0001-8972-5953
Amr TolbaComputer Science Department, Community College, King Saud University, Riyadh 11437, Saudi Arabia.ORCID 0000-0003-3439-6413
Bogdan-Constantin NeaguDepartment of Power Engineering, "Gheorghe Asachi" Technical University of Iasi, 700050 Iasi, Romania.ORCID 0000-0002-8413-0317
Ravi SharmaCentre for Inter-Disciplinary Research and Innovation, University of Petroleum and Energy Studies, Dehradun 248007, India.
King Saud University · SANirma University · INGanpat University · INGheorghe Asachi Technical University of Iași · RONational Institute for Research and Development of Isotopic and Molecular Technologies · ROUniversity of Petroleum and Energy Studies · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Integrating information and communication technology (ICT) and energy grid infrastructures introduces smart grids (SG) to simplify energy generation, transmission, and distribution. The ICT is embedded in selected parts of the grid network, which partially deploys SG and raises various issues such as energy losses, either technical or non-technical (i.e., energy theft). Therefore, energy theft detection plays a crucial role in reducing the energy generation burden on the SG and meeting the consumer demand for energy. Motivated by these facts, in this paper, we propose a deep learning (DL)-based energy theft detection scheme, referred to as

Indexed as

Deep LearningComputer Communication NetworksPhysical PhenomenaSupport Vector MachineTheftdeep learningdemand response managementenergy consumption predictionenergy theftLSTMsmart grid

Identifiers

PMID35684668
PMCPMC9185229
OpenAlexW4281685206

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.