Evidence map›Paper›PMID 34960274›Full record

ArticleSensors (Basel, Switzerland)2021

A Decision Support System for Face Sketch Synthesis Using Deep Learning and Artificial Intelligence.

Irfan Azhar, Muhammad Sharif, Mudassar Raza, Muhammad Attique Khan, Hwan-Seung Yong

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

5 authors.

Irfan AzharDepartment of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan.ORCID 0000-0003-1996-3029
Muhammad SharifDepartment of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan.ORCID 0000-0002-1355-2168
Mudassar RazaDepartment of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47040, Pakistan.
Muhammad Attique KhanDepartment of Computer Science, HITEC University Taxila, Taxila 47080, Pakistan.ORCID 0000-0002-6347-4890
Hwan-Seung YongDepartment of Computer Science & Engineering, Ewha Womans University, Seoul 03760, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The recent development in the area of IoT technologies is likely to be implemented extensively in the next decade. There is a great increase in the crime rate, and the handling officers are responsible for dealing with a broad range of cyber and Internet issues during investigation. IoT technologies are helpful in the identification of suspects, and few technologies are available that use IoT and deep learning together for face sketch synthesis. Convolutional neural networks (CNNs) and other constructs of deep learning have become major tools in recent approaches. A new-found architecture of the neural network is anticipated in this work. It is called Spiral-Net, which is a modified version of U-Net fto perform face sketch synthesis (the phase is known as the compiler network C here). Spiral-Net performs in combination with a pre-trained Vgg-19 network called the feature extractor F. It first identifies the top

Indexed as

Artificial IntelligenceDeep LearningAlgorithmsFaceNeural Networks, Computerconvolutional neural networkface recognitionNLDAOpenBRsketch synthesissmart citiesSpiral-NetU-NetVgg-19 net

Identifiers

PMID34960274
PMCPMC8708226

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.