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
A., Mishra
Alok M.
M., Alok
M.,Alok
Mishra, Alok
Mishra,A.
A.,Mıshra
A.,Mishra
Alok, Mıshra
A., Mıshra
Mıshra,A.
Job Title
Profesor Doktor
Email Address
alok.mishra@atilim.edu.tr
Main Affiliation
Software Engineering
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Website
ORCID ID
0000-0003-1275-2050
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
AAE-2673-2019

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9
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QUALITY EDUCATION4
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6
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1
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CLEAN WATER AND SANITATION6
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AFFORDABLE AND CLEAN ENERGY7
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INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
8
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SUSTAINABLE CITIES AND COMMUNITIES11
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4
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RESPONSIBLE CONSUMPTION AND PRODUCTION
4
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4
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LIFE BELOW WATER
4
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LIFE ON LAND15
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PEACE, JUSTICE AND STRONG INSTITUTIONS16
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10
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PARTNERSHIPS FOR THE GOALS17
PARTNERSHIPS FOR THE GOALS
2
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197

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103

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161/259

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13

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8

WoS Citation Count

2079

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3045

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10.55

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21

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Author: Akbulut, AkhanSubject: deep learning

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    Article
    Citation - WoS: 6
    Deep Learning-Based Defect Prediction for Mobile Applications
    (Mdpi, 2022) Jorayeva, Manzura; Akbulut, Akhan; Catal, Cagatay; Mishra, Alok
    Smartphones have enabled the widespread use of mobile applications. However, there are unrecognized defects of mobile applications that can affect businesses due to a negative user experience. To avoid this, the defects of applications should be detected and removed before release. This study aims to develop a defect prediction model for mobile applications. We performed cross-project and within-project experiments and also used deep learning algorithms, such as convolutional neural networks (CNN) and long short term memory (LSTM) to develop a defect prediction model for Android-based applications. Based on our within-project experimental results, the CNN-based model provides the best performance for mobile application defect prediction with a 0.933 average area under ROC curve (AUC) value. For cross-project mobile application defect prediction, there is still room for improvement when deep learning algorithms are preferred.