The re-kindled fascination in machine learning (ML), observed over the last few decades, has also percolated into natural sciences and engineering. ML algorithms are now used in …
In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine learning techniques have used to …
Ransomware, a malware designed to encrypt data for ransom payments, is a potential threat to fog layer nodes as such nodes typically contain considerably amount of sensitive data …
Machine learning algorithms are becoming very efficient in intrusion detection systems with their real time response and adaptive learning process. A robust machine learning model …
A Poernomo, DK Kang - Neural networks, 2018 - Elsevier
Training a deep neural network with a large number of parameters often leads to overfitting problem. Recently, Dropout has been introduced as a simple, yet effective regularization …
Motivation During the last decade, improvements in high-throughput sequencing have generated a wealth of genomic data. Functionally interpreting these sequences and finding …
A Lopez-Rincon, A Tonda, L Mendoza-Maldonado… - Scientific reports, 2021 - nature.com
In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural …
Abstract Motivation Transposable elements (TEs) classification is an essential step to decode their roles in genome evolution. With a large number of genomes from non-model …
Since the rapid onset of the COVID‐19 or SARS‐CoV‐2 pandemic in the world in 2019, extensive studies have been conducted to unveil the behavior and emission pattern of the …