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
7201441575
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
AAE-2673-2019

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NO POVERTY1
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0
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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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1
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AFFORDABLE AND CLEAN ENERGY7
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DECENT WORK AND ECONOMIC GROWTH8
DECENT WORK AND ECONOMIC GROWTH
0
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INDUSTRY, INNOVATION AND INFRASTRUCTURE9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
8
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REDUCED INEQUALITIES10
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0
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SUSTAINABLE CITIES AND COMMUNITIES11
SUSTAINABLE CITIES AND COMMUNITIES
4
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RESPONSIBLE CONSUMPTION AND PRODUCTION12
RESPONSIBLE CONSUMPTION AND PRODUCTION
4
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CLIMATE ACTION13
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4
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LIFE BELOW WATER14
LIFE BELOW WATER
4
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LIFE ON LAND15
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0
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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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Scholarly Output

197

Articles

103

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163/271

Supervised MSc Theses

13

Supervised PhD Theses

8

WoS Citation Count

2079

Scopus Citation Count

3045

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WoS Citations per Publication

10.55

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15.46

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42

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21

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Author: Guzel, Mehmet SerdarSubject: deep learning

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    Article
    Citation - WoS: 5
    Citation - Scopus: 8
    A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification
    (Mdpi, 2024) Kadhim, Yezi Ali; Guzel, Mehmet Serdar; Mishra, Alok
    Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep learning techniques, which were based on a convolutional neural network (CNN) or autoencoder, to extract features and combine them with the next step of the meta-heuristic algorithm in order to select optimal features using the particle swarm optimization (PSO) algorithm. This combination sought to reduce the dimensionality of the datasets while maintaining the original performance of the data. This is considered an innovative method and ensures highly accurate classification results across various medical datasets. Several classifiers were employed to predict the diseases. The COVID-19 dataset found that the highest accuracy was 99.76% using the combination of CNN-PSO-SVM. In comparison, the brain tumor dataset obtained 99.51% accuracy, the highest accuracy derived using the combination method of autoencoder-PSO-KNN.