N
N. Arunkumar
Researcher at Shanmugha Arts, Science, Technology & Research Academy
Publications - 61
Citations - 4785
N. Arunkumar is an academic researcher from Shanmugha Arts, Science, Technology & Research Academy. The author has contributed to research in topics: Feature (computer vision) & Support vector machine. The author has an hindex of 32, co-authored 57 publications receiving 3279 citations.
Papers
More filters
Journal ArticleDOI
Secure Medical Data Transmission Model for IoT-Based Healthcare Systems
Mohamed Elhoseny,Gustavo Ramirez-Gonzalez,Osama Abu-Elnasr,Shihab A. Shawkat,N. Arunkumar,Ahmed Farouk +5 more
TL;DR: The proposed hybrid security model for securing the diagnostic text data in medical images proved its ability to hide the confidential patient’s data into a transmitted cover image with high imperceptibility, capacity, and minimal deterioration in the received stego-image.
Journal ArticleDOI
Enabling technologies for fog computing in healthcare IoT systems
Ammar Awad Mutlag,Mohd Khanapi Abd Ghani,N. Arunkumar,Mazin Abed Mohammed,Mazin Abed Mohammed,Othman Mohd +5 more
TL;DR: A systematic literature review of the technologies for fog computing in the healthcare IoT systems field and analyzing the previous is presented, providing motivation, limitations faced by researchers, and suggestions proposed to analysts for improving this essential research field.
Journal ArticleDOI
A deep learning approach for Parkinson’s disease diagnosis from EEG signals
Shu Lih Oh,Yuki Hagiwara,U. Raghavendra,Rajamanickam Yuvaraj,N. Arunkumar,Murugappan Murugappan,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya +8 more
TL;DR: An automated detection system for Parkinson’s disease employing the convolutional neural network (CNN) employing the thirteen-layer CNN architecture which can overcome the need for the conventional feature representation stages is proposed.
Journal ArticleDOI
Optimal deep learning model for classification of lung cancer on CT images
TL;DR: An innovative automated diagnosis classification method for Computed Tomography images of lungs with the assistance of Optimal Deep Neural Network (ODNN) and Linear Discriminate Analysis (LDA) is presented.
Journal ArticleDOI
Hybrid optimization with cryptography encryption for medical image security in Internet of Things
TL;DR: This paper investigated the security of medical images in IoT by utilizing an innovative cryptographic model with optimization strategies, and identified a diverse encryption algorithm with its optimization methods with the most extreme peak signal-to-noise ratio values.