Evidence map›Paper›PMID 30203246›Full record

ArticleJournal of medical systems2018

Developing Charcot-Marie-Tooth Disease Recognition System Using Bacterial Foraging Optimization Algorithm Based Spiking Neural Network.

Abdulaziz Abdullah Al-Kheraif, Mohamed Hashem, Mohammed Sayed S Al Esawy

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Article in Journal of medical systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

3 authors.

Abdulaziz Abdullah Al-KheraifDental Biomaterials Research Chair, Dental Health Department, College of Applied Medical Sciences, King Saud University, P.O Box 10219, Riyadh, 11433, Saudi Arabia. aalkhuraif@ksu.edu.sa.
Mohamed HashemDental Biomaterials Research Chair, Dental Health Department, College of Applied Medical Sciences, King Saud University, P.O Box 10219, Riyadh, 11433, Saudi Arabia.
Mohammed Sayed S Al EsawyCollege of Engineering, King Saud University, Riyadh, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the developing technology Charcot-Marie-Tooth (CMT) disease is one of the teeth diseases which are occurred due to the genetic reason. The CMT disease affects the muscle tissue which reduces the progressive growth of the muscle. So, the CMT disease needs to be recognized carefully for eliminating the risk factors in the early stage. At the time of this process, the system handles the difficulties while performing feature extraction and classification part. So, the teeth images are processed by applying the normalization method which eliminates the salt and pepper noise from data. From that, modified group delay function along with Cepstral coefficient features are extracted with effective manner. After that Bacterial Foraging Optimization Algorithm based features are selected. Then the selected features are examined by applying the Bacterial Foraging Optimization Algorithm based spiking neural network which successfully recognizes the CMT disease. At that point the productivity of the framework is assessed with the assistance of exploratory outcomes.

Indexed as

AlgorithmsCharcot-Marie-Tooth DiseaseHumansBacterial foraging optimization algorithm based spiking neural networkCharcot–Marie–tooth (CMT) diseaseModified group delay functionNormalization process

Identifiers

PMID30203246

What OpenQuestion holds

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Registered trials

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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.