1. Early detection of neurological diseases using machine learning and deep learning techniques: A review
2. A predictive method for emotional sentiment analysis by deep learning from EEG of brainwave data
3. Machine learning and deep learning models for early-stage detection of Alzheimer's disease and its proliferation in human brain
4. Recurrent neural network model for identifying epilepsy based neurological auditory disorder
5. Recurrent neural network model for identifying neurological auditory disorder
6. Dementia diagnosis with EEG using machine learning
7. Computational methods for translational brain-behavior analysis
8. Clinical applications of deep learning in neurology and its enhancements with future directions
9. Ensemble sparse intelligent mining techniques for cognitive disease
10. Cognitive therapy for brain diseases using deep learning models
11. Cognitive therapy for brain diseases using artificial intelligence models
12. Clinical applications of deep learning in neurology and its enhancements with future predictions
13. An intelligent diagnostic approach for epileptic seizure detection and classification using machine learning
14. Neural signaling and communication using machine learning
15. Classification of neurodegenerative disorders using machine learning techniques
16. New trends in deep learning for neuroimaging analysis and disease prediction
17. Prevention and diagnosis of neurodegenerative diseases using machine learning models
18. Artificial intelligence-based early detection of neurological disease using noninvasive method based on speech analysis
19. An insight into applications of deep learning in neuroimaging
20. Incremental variance learning-based ensemble classification model for neurological disorders
21. Early detection of Parkinsons disease using adaptive machine learning techniques: A review
22. Convolutional neural network model for identifying neurological visual disorder
1. Early detection of neurological diseases using machine learning and deep learning techniques: A review
2. A predictive method for emotional sentiment analysis by deep learning from EEG of brainwave data
3. Machine learning and deep learning models for early-stage detection of Alzheimer's disease and its proliferation in human brain
4. Recurrent neural network model for identifying epilepsy based neurological auditory disorder
5. Recurrent neural network model for identifying neurological auditory disorder
6. Dementia diagnosis with EEG using machine learning
7. Computational methods for translational brain-behavior analysis
8. Clinical applications of deep learning in neurology and its enhancements with future directions
9. Ensemble sparse intelligent mining techniques for cognitive disease
10. Cognitive therapy for brain diseases using deep learning models
11. Cognitive therapy for brain diseases using artificial intelligence models
12. Clinical applications of deep learning in neurology and its enhancements with future predictions
13. An intelligent diagnostic approach for epileptic seizure detection and classification using machine learning
14. Neural signaling and communication using machine learning
15. Classification of neurodegenerative disorders using machine learning techniques
16. New trends in deep learning for neuroimaging analysis and disease prediction
17. Prevention and diagnosis of neurodegenerative diseases using machine learning models
18. Artificial intelligence-based early detection of neurological disease using noninvasive method based on speech analysis
19. An insight into applications of deep learning in neuroimaging
20. Incremental variance learning-based ensemble classification model for neurological disorders
21. Early detection of Parkinsons disease using adaptive machine learning techniques: A review
22. Convolutional neural network model for identifying neurological visual disorder
1. Early detection of neurological diseases using machine learning
and deep learning techniques: A review
2. A predictive method for emotional sentiment analysis by deep
learning from EEG of brainwave data
3. Machine learning and deep learning models for early-stage
detection of Alzheimer's disease and its proliferation in human
brain
4. Recurrent neural network model for identifying epilepsy based
neurological auditory disorder
5. Recurrent neural network model for identifying neurological
auditory disorder
6. Dementia diagnosis with EEG using machine learning
7. Computational methods for translational brain-behavior
analysis
8. Clinical applications of deep learning in neurology and its
enhancements with future directions
9. Ensemble sparse intelligent mining techniques for cognitive
disease
10. Cognitive therapy for brain diseases using deep learning
models
11. Cognitive therapy for brain diseases using artificial
intelligence models
12. Clinical applications of deep learning in neurology and its
enhancements with future predictions
13. An intelligent diagnostic approach for epileptic seizure
detection and classification using machine learning
14. Neural signaling and communication using machine learning
15. Classification of neurodegenerative disorders using machine
learning techniques
16. New trends in deep learning for neuroimaging analysis and
disease prediction
17. Prevention and diagnosis of neurodegenerative diseases using
machine learning models
18. Artificial intelligence-based early detection of neurological
disease using noninvasive method based on speech analysis
19. An insight into applications of deep learning in
neuroimaging
20. Incremental variance learning-based ensemble classification
model for neurological disorders
21. Early detection of Parkinsons disease using adaptive machine
learning techniques: A review
22. Convolutional neural network model for identifying neurological
visual disorder
Dr. Ajith Abraham is a Pro Vice-Chancellor at Bennette University.
He is the director of Machine Intelligence Research Labs (MIR
Labs), Australia. MIR Labs are a not-for-profit scientific network
for innovation and research excellence connecting industry and
academia. His research focuses on real world problems in the fields
of machine intelligence, cyber-physical systems, Internet of
things, network security, sensor networks, Web intelligence, Web
services, and data mining. He is the Chair of the IEEE Systems Man
and Cybernetics Society Technical Committee on Soft Computing. He
is editor-in-chief of Engineering Applications of Artificial
Intelligence (EAAI) and serves on the editorial board of several
international journals. He received his PhD in Computer Science
from Monash University, Melbourne, Australia.
Sujata Dash holds the position of Professor at the Information
Technology School of Engineering and Technology, Nagaland
University, Dimapur Campus, Nagaland, India, bringing more than
three decades of dedicated service in teaching and mentoring
students. She has been honoured with the prestigious Titular
Fellowship from the Association of Commonwealth Universities,
United Kingdom. As a testament to her global contributions, she
served as a visiting professor in the Computer Science Department
at the University of Manitoba, Canada. With a prolific academic
record, she has authored over 200 technical papers published in
esteemed international journals, and conference proceedings, and
edited book chapters by reputed publishers Serving as a reviewer
and Associate Editor for approximately 15 international
journals.
Dr. Subhendu Kumar Pani received his Ph.D. from Utkal University,
Odisha, India in the year 2013. He is working as a professor at
Krupajal Engineering College under BPUT, Odisha, India. He has more
than 20 years of teaching and research experience His research
interests include Data mining, Big Data Analysis, web data
analytics, Fuzzy Decision Making and Computational Intelligence. He
is the recipient of 5 researcher awards. In addition to research,
he has guided two PhD students and 31 M. Tech students. He has
published 150 International Journal papers (100 Scopus index). His
professional activities include roles as Book Series Editor (CRC
Press, Apple Academic Press, Wiley-Scrivener), Associate Editor,
Editorial board member and/or reviewer of various International
Journals. He is an Associate with no. of the conference societies.
He has more than 250 international publications, 5 authored books,
25 edited and upcoming books; 40 book chapters into his account. He
is a fellow in SSARSC and a life member in IE, ISTE, ISCA, and
OBA.OMS, SMIACSIT, SMUACEE, CSI.
LAURA GARCÍA-HERNÁNDEZ received the M.Sc. degree in computer
science from the Universitat Oberta de Catalunya, Spain, in 2007,
and the European Ph.D. degree in Engineering from the University of
Córdoba, Spain, and also from the Institut Français de Mécanique
Avancée, Clermont-Ferrand, France, in 2011. She has been an Invited
Professor during a semester in the Institut Français de Mécanique
Avancée, Clermont-Ferrand. She is currently an Associate Professor
in the Area of Project Engineering at the University of Córdoba,
Spain. Her primary areas of research are engineering design
optimization, intelligent systems, machine learning, user adaptive
systems, interactive evolutionary computation, project management,
risk prevention in automatic systems, and educational technology.
In these fields, she has authored or co-authored more than 70
international research publications. She has given several invited
talks in different countries. She has realized several postdoctoral
internships in different countries with a total duration of more
than two years. She received the prestigious National Government
Research Grant ‘‘José Castillejo’’ for supporting their post-doc
research during six months in the University of Algarve, Portugal.
She has been an Investigator Principal in two Spanish research
projects and has also been an Investigator Collaborator in some
research contracts and projects. She is an Expert Member of ISO/TC
184/SC working team and the National Standards Institute of Spain
(UNE). Moreover, she is a member of the Spanish Association of
Engineering Projects (IPMA Spain). Considering her research, she
received the Young Researcher Award granted by the Spanish
Association of Engineering Projects (IPMA), Spain, in 2015.
Additionally, she received two times the General Council of
Official Colleges Award at prestigious International Conference on
Project Management and Engineering both 2017 and 2018 editions. She
is the Co-Editor-in-Chief of the Journal of Information Assurance
and Security. Also, she is an Associate Editor in the following ISI
Journals: Applied Soft Computing, Complex & Intelligent Systems,
and Journal of Intelligent Manufacturing.
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