Deep Learning for Medical Applications with Unique Data

Dr. Aditya Khamparia has expertise in teaching, entrepreneurship, and research and development of 11 years. He is presently working as Assistant Professor in Babasaheb Bhimrao Ambedkar University, Satellite Centre, Amethi, India. He received his Ph.D. degree from Lovely Professional University, Punjab, India in May 2018. He has completed his M. Tech. from VIT University, Vellore, Tamil Nadu, India and B. Tech. from RGPV, Bhopal, Madhya Pradesh, India. He has completed his PDF from UNIFOR, Brazil. He has published around 105 research papers along with book chapters including more than 25 papers in SCI indexed Journals with cumulative impact factor of above 100 to his credit. Additionally, he has authored and edited eleven books. Furthermore, he has served the research field as a Keynote Speaker/Session Chair/Reviewer/TPC member/Guest Editor and many more positions in various conferences and journals. His research interest include machine learning, deep learning for biomedical health informatics, educational technologies, and computer vision. Dr. Utku Kose is an Associate Professor at Süleyman Demirel University, Turkey. He received his PhD from Selcuk University, Turkey, in the field of computer engineering. He has more than 100 publications to his credit, including Deep Learning for Medical Decision Support Systems, Springer; Artificial Intelligence Applications in Distance Education, IGI Global; Smart Applications with Advanced Machine Learning and Human-Centered Problem Design, Springer; Artificial Intelligence for Data-Driven Medical Diagnosis, DeGruyter; Computational Intelligence in Software Modeling, DeGruyter; Data Science for Covid-19, Volumes 1 and 2, Elsevier/Academic Press; and Deep Learning for Medical Applications with Unique Data, Elsevier/Academic Press, among others. Dr. Kose is a Series Editor of the Biomedical and Robotics Healthcare series from Taylor & Francis/CRC Press. His research interests include artificial intelligence, machine ethics, artificial intelligence safety, optimization, chaos theory, distance education, e-learning, computer education, and computer science. Ashish Khanna is Professor in Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, New Delhi, India, and is also a Visiting Professor at the University of Valladolid, Spain. His research interests include distributed systems, MANET, FANET, VANET, Internet of Things, and machine learning.Dr. Valentina Emilia Balas is Full Professor in the Department of Automatics and Applied Software, Faculty of Engineering, Aurel Vlaicu University of Arad, Romania. She holds a PhD in Applied Electronics and Telecommunications from the Polytechnic University of Timisoara. Her research interests include intelligent systems, fuzzy systems, soft computing, smart sensors, information fusion, modeling, and simulation.Dr. Balas serves as Director of the Intelligent Systems Research Center and Director of International Relations, Programs and Projects at Aurel Vlaicu University of Arad. She is Editor-in-Chief of the International Journal of Advanced Intelligence Paradigms and International Journal of Computational Systems Engineering, and serves on the editorial boards of several scientific journals. She has led and contributed to numerous national and international research initiatives, including the European Union-funded BioCell-NanoART project on bio-inspired cellular nano-architectures. She is active in several professional societies and technical committees, including IEEE, the European Society for Fuzzy Logic and Technology, and the Society for Industrial and Applied Mathematics.

• 1. A deep learning approach for the prediction of heart attacks based on data analysis2. A comparative study on fully convolutional networks—FCN-8, FCN-16, and FCN-32: A case of brain tumor3. Deep learning applications for disease diagnosis4. An artificial intelligent cognitive approach for classification and recognition of white blood cells employing deep learning for medical applications5. Deep learning on medical image analysis on COVID-19 x-ray dataset using an X-Net architecture6. Early prediction of heart disease using a deep learning approach7. Machine learning and deep learning algorithms in disease prediction: Future trends for the healthcare system8. Automatic detection of white matter hyperintensities via mask region-based convolutional neural networks using magnetic resonance images9. Diagnosing glaucoma with optic disk segmenting and deep learning from color retinal fundus images10. An artificial intelligence framework to ensure a trade-off between sanitary and economic perspectives during the COVID-19 pandemic11. Prediction of COVID-19 using machine learning techniques