Mıshra, Alok

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Profile URL
https://hdl.handle.net/20.500.14411/6902
Name Variants
Mishra, A.
Mishra, A
Mishra A.
Alok, Mishra
Mıshra, Alok
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Alok M.
M., Alok
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Mishra, Alok
Mishra,A.
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A.,Mishra
Alok, Mıshra
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Mıshra,A.
Job Title
Profesor Doktor
Email Address
alok.mishra@atilim.edu.tr
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Software Engineering
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Website
ORCID ID
0000-0003-1275-2050
Scopus Author ID
7201441575
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Google Scholar ID
WoS Researcher ID
AAE-2673-2019

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Documents

170

Citations

2558

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Scholarly Output

197

Articles

103

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163/271

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13

Supervised PhD Theses

8

WoS Citation Count

2079

Scopus Citation Count

3045

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0

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0

WoS Citations per Publication

10.55

Scopus Citations per Publication

15.46

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42

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21

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Sensors7
TEM Journal7
Computers in Human Behavior4
Applied Sciences4
Electronics Information and Planning4
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Author: Guzel, Mehmet SerdarSubject: machine learning

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    Article
    Citation - WoS: 19
    Citation - Scopus: 24
    A Novel Hybrid Machine Learning Based System To Classify Shoulder Implant Manufacturers
    (Mdpi, 2022) Sivari, Esra; Guzel, Mehmet Serdar; Bostanci, Erkan; Mishra, Alok
    It is necessary to know the manufacturer and model of a previously implanted shoulder prosthesis before performing Total Shoulder Arthroplasty operations, which may need to be performed repeatedly in accordance with the need for repair or replacement. In cases where the patient's previous records cannot be found, where the records are not clear, or the surgery was conducted abroad, the specialist should identify the implant manufacturer and model during preoperative X-ray controls. In this study, an auxiliary expert system is proposed for classifying manufacturers of shoulder implants on the basis of X-ray images that is automated, objective, and based on hybrid machine learning models. In the proposed system, ten different hybrid models consisting of a combination of deep learning and machine learning algorithms were created and statistically tested. According to the experimental results, an accuracy of 95.07% was achieved using the DenseNet201 + Logistic Regression model, one of the proposed hybrid machine learning models (p < 0.05). The proposed hybrid machine learning algorithms achieve the goal of low cost and high performance compared to other studies in the literature. The results lead the authors to believe that the proposed system could be used in hospitals as an automatic and objective system for assisting orthopedists in the rapid and effective determination of shoulder implant types before performing revision surgery.