Institutul de Matematică şi Informatică "Vladimir Andrunachievici"
Universitatea de Stat din MoldovaRO EN
Residual Neural Network in Genomics
Authors: Sara Sabba, Meroua Smara, Mehdi Benhacine, Loubna Terra, Zine Eddine Terra
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Abstract
Residual neural network (ResNet) is a Deep Learning model introduced by He et al. in 2015 to enhance traditional convolutional neural networks proposed to solve computer vision problems. It uses skip connections over some layer blocks to avoid vanishing gradient problem. Currently, many researches are focused to test and prove the efficiency of the ResNet on different domains such as genomics. In fact, the study of human genomes provides important information on the detection of diseases and their best treatments. Therefore, most of the scientists opted for bioinformatics solutions to get results in a reasonable time. In this paper, our interest is to show the effectiveness of the ResNet model on genomics. For that, we propose two new ResNet models to enhance the results of two genomic problems previously resolved by CNN models. The obtained results are very promising and they proved the performance of our ResNet models compared to the CNN models.Sara Sabba
Department of Software Technologies and Information Systems,
Laboratory of Data Science and Artificial Intelligence(LISIA),
Abdelhamid Mahri University, Constantine 2, Algeria.
E-mail: Meroua Smara, Mehdi Benhacine, Loubna Terra, Zine Eddine Terra
Faculty of New Technologies of Information and Communication
Department of Software Technologies and Information Systems,
Abdelhamid Mahri University, Constantine 2, Algeria.
E-mail:
E-mail:
E-mail:
E-mail:
Department of Software Technologies and Information Systems,
Laboratory of Data Science and Artificial Intelligence(LISIA),
Abdelhamid Mahri University, Constantine 2, Algeria.
E-mail: Meroua Smara, Mehdi Benhacine, Loubna Terra, Zine Eddine Terra
Faculty of New Technologies of Information and Communication
Department of Software Technologies and Information Systems,
Abdelhamid Mahri University, Constantine 2, Algeria.
E-mail:
E-mail:
E-mail:
E-mail:
DOI
https://doi.org/10.56415/csjm.v30.17Fulltext
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Contents
- Foreword
- An Intelligent Detection of Malicious Intrusions in IoT Based on Machine Learning and Deep Learning Techniques
- Residual Neural Network in Genomics
- Privacy and Reader-first Authentication in Vaudenay's RFID Model with Temporary State Disclosure
- Kurtosis-Based Feature Selection Method using Symmetric Uncertainty to Predict the Air Quality Index
- Wiener Index of Some Brooms
- Graph-based decision making for varying complexity multicriteria problems
