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JCR Impact Factor: 0.825
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Current issue: Aug 2022
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PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering in 2021 is 2.5, the same as for 2020 but better than all our previous results.

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

2021-Jun-06
SCOPUS published the CiteScore for 2020, computed by using an improved methodology, counting the citations received in 2017-2020 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering in 2020 is 2.5, better than all our previous results.

2021-Apr-15
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  3/2020 - 7

 HIGHLY CITED PAPER 

Diagnosis of Alzheimer's Disease from Brain Magnetic Resonance Imaging Images using Deep Learning Algorithms

SUGANTHE, R. C. See more information about SUGANTHE, R. C. on SCOPUS See more information about SUGANTHE, R. C. on IEEExplore See more information about SUGANTHE, R. C. on Web of Science, LATHA, R. S. See more information about  LATHA, R. S. on SCOPUS See more information about  LATHA, R. S. on SCOPUS See more information about LATHA, R. S. on Web of Science, GEETHA, M. See more information about  GEETHA, M. on SCOPUS See more information about  GEETHA, M. on SCOPUS See more information about GEETHA, M. on Web of Science, SREEKANTH, G. R. See more information about SREEKANTH, G. R. on SCOPUS See more information about SREEKANTH, G. R. on SCOPUS See more information about SREEKANTH, G. R. on Web of Science
 
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Download PDF pdficon (1,411 KB) | Citation | Downloads: 826 | Views: 1,432

Author keywords
artificial intelligence, artificial neural network, image classification, machine learning, medical diagnosis

References keywords
disease(23), alzheimer(22), neural(7), prediction(6), learning(6), networks(5), diagnosis(5), deep(5), convolutional(5), techniques(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2020-08-31
Volume 20, Issue 3, Year 2020, On page(s): 57 - 64
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.03007
Web of Science Accession Number: 000564453800007
SCOPUS ID: 85090360650

Abstract
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Alzheimer's disease is one amongst the progressive disorder that cruelly affects the brain cells. It causes the death of nerve cells and tissue loss in brain. It usually tends to start slowly and aggravates overtime. The symptoms of Alzheimer's disease vary from person to person depending on the severity of the unhealthiness. It exhibits behavioral symptoms such as communication impairments, memory loss, taking a longer time to complete usual activities, and change in attitude and behavior. If the problem worsens over time, then it cannot be cured. Hence it should be identified at the earlier stage itself and treat the patient to lead a normal life on their own. Deep learning algorithms exhibit marvelous performance over conventional machine learning algorithms in identifying the complex patterns in the large volumes of high-dimensional medical imaging data. Hence, recently significant attention has been paid to apply deep learning for medical diagnosis. In this research, Deep Convolution Neural Network (DCNN) and VGG-16 inspired CNN (VCNN) models have been built to classify the different stages of Alzheimer's Disease from the Magnetic Resonance Imaging(MRI) images. Experiments are carried out on an ADNI dataset and the results obtained show that the proposed models achieved excellent accuracy.


References | Cited By

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Cited-By SCOPUS

SCOPUS® Times Cited: 10
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Cited-By CrossRef

[1] Machine learning approach for detection of keratoconus, Shanthi, S, Nirmaladevi, K, Pyingkodi, M, Dharanesh, K, Gowthaman, T, Harsavardan, B, IOP Conference Series: Materials Science and Engineering, ISSN 1757-8981, Issue 1, Volume 1055, 2021.
Digital Object Identifier: 10.1088/1757-899X/1055/1/012112
[CrossRef]

[2] A Deep Learning Framework for Football Match Prediction, Nivetha, S.K., Geetha, M., Suganthe, R.C., Prabakaran, R Manoj, Madhuvanan, S, Sameer, A Mohamed, 2022 International Conference on Computer Communication and Informatics (ICCCI), ISBN 978-1-6654-8035-2, 2022.
Digital Object Identifier: 10.1109/ICCCI54379.2022.9740760
[CrossRef]

[3] Personality Prediction for Online Interview, Nivetha, S.K., Geetha, M., Latha, R.S., Sneha, K., Sobika, S., Yamuna, C., 2022 International Conference on Computer Communication and Informatics (ICCCI), ISBN 978-1-6654-8035-2, 2022.
Digital Object Identifier: 10.1109/ICCCI54379.2022.9740980
[CrossRef]

[4] A Hybrid Deep Learning Based Character Identification Model Using CNN, LSTM, And CTC To Recognize Handwritten English Characters And Numerals, Geetha, M., Suganthe, R.C., Nivetha, S.K., Hariprasath, S., Gowtham, S., Deepak, C.S., 2022 International Conference on Computer Communication and Informatics (ICCCI), ISBN 978-1-6654-8035-2, 2022.
Digital Object Identifier: 10.1109/ICCCI54379.2022.9740746
[CrossRef]

[5] Performance of CNN Architectures in the Detection of Covid-19 Disease, Renukadevi, N.T., Saraswathi, K., Vigneshwaran, N., Pradeep, V., Arulsanthosh, T.B., 2021 Innovations in Power and Advanced Computing Technologies (i-PACT), ISBN 978-1-6654-2691-6, 2021.
Digital Object Identifier: 10.1109/i-PACT52855.2021.9696954
[CrossRef]

[6] A Time-Series Based Yield Forecasting Model Using Stacked Lstm To Predict The Yield Of Paddy In Cauvery Delta Zone In Tamilnadu, Geetha, M., Suganthe, R.C., Nivetha, S.K., Anju, R., Anuradha, R., Haripriya, J., 2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), ISBN 978-1-6654-3647-2, 2022.
Digital Object Identifier: 10.1109/ICEEICT53079.2022.9768441
[CrossRef]

[7] Automatic Detection of Tea Leaf Diseases using Deep Convolution Neural Network, Latha, R.S., Sreekanth, G.R., Suganthe, R.C., Rajadevi, R., Karthikeyan, S., Kanivel, S., Inbaraj, B., 2021 International Conference on Computer Communication and Informatics (ICCCI), ISBN 978-1-7281-5875-4, 2021.
Digital Object Identifier: 10.1109/ICCCI50826.2021.9402225
[CrossRef]

[8] Automatic Fruit Detection System using Multilayer Deep Convolution Neural Network, Latha, R.S., Sreekanth, G.R., Suganthe, R.C., Geetha, M., Swathi, N., Vaishnavi, S., Sonasri, P., 2021 International Conference on Computer Communication and Informatics (ICCCI), ISBN 978-1-7281-5875-4, 2021.
Digital Object Identifier: 10.1109/ICCCI50826.2021.9402513
[CrossRef]

[9] Convolutional Neural Network Based Multi Class Classification Model for Brain Tumor Diagnosis, Suganthe, R.C., Latha, R.S., Geetha, M., Abirami, S.P., Anaya, P., Ganga Sri, A., 2022 International Conference on Computer Communication and Informatics (ICCCI), ISBN 978-1-6654-8035-2, 2022.
Digital Object Identifier: 10.1109/ICCCI54379.2022.9740805
[CrossRef]

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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