scholarly journals Analysis of DNA Sequence Classification Using CNN and Hybrid Models

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hemalatha Gunasekaran ◽  
K. Ramalakshmi ◽  
A. Rex Macedo Arokiaraj ◽  
S. Deepa Kanmani ◽  
Chandran Venkatesan ◽  
...  
Keyword(s):  
Dna Sequence ◽  
Selection Process ◽  
High Accuracy ◽  
Machine Learning Techniques ◽  
Biomedical Data ◽  
Sequence Classification ◽  
Learning Techniques ◽  
Testing Data ◽  
Bidirectional Lstm

In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine learning techniques have used to complete this task in recent years successfully. Identification and classification of viruses are essential to avoid an outbreak like COVID-19. Regardless, the feature selection process remains the most challenging aspect of the issue. The most commonly used representations worsen the case of high dimensionality, and sequences lack explicit features. It also helps in detecting the effect of viruses and drug design. In recent days, deep learning (DL) models can automatically extract the features from the input. In this work, we employed CNN, CNN-LSTM, and CNN-Bidirectional LSTM architectures using Label and K -mer encoding for DNA sequence classification. The models are evaluated on different classification metrics. From the experimental results, the CNN and CNN-Bidirectional LSTM with K -mer encoding offers high accuracy with 93.16% and 93.13%, respectively, on testing data.

Hybrid System based on Rough Sets and Genetic Algorithms for Medical Data Classifications

2013 ◽  
Vol 3 (4) ◽  
pp. 31-46 ◽  
Author(s):  
Hanaa Ismail Elshazly ◽  
Ahmad Taher Azar ◽  
Aboul Ella Hassanien ◽  
Abeer Mohamed Elkorany
Keyword(s):  
Genetic Algorithm ◽  
Hybrid System ◽  
Selection Process ◽  
Decision Rules ◽  
Medical Data ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Biomedical Data ◽  
Hybrid Techniques ◽  
Learning Techniques

Computational intelligence provides the biomedical domain by a significant support. The application of machine learning techniques in medical applications have been evolved from the physician needs. Screening, medical images, pattern classification, prognosis are some examples of health care support systems. Typically medical data has its own characteristics such as huge size and features, continuous and real attributes that refer to patients' investigations. Therefore, discretization and feature selection process are considered a key issue in improving the extracted knowledge from patients' investigations records. In this paper, a hybrid system that integrates Rough Set (RS) and Genetic Algorithm (GA) is presented for the efficient classification of medical data sets of different sizes and dimensionalities. Genetic Algorithm is applied with the aim of reducing the dimension of medical datasets and RS decision rules were used for efficient classification. Furthermore, the proposed system applies the Entropy Gain Information (EI) for discretization process. Four biomedical data sets are tested by the proposed system (EI-GA-RS), and the highest score was obtained through three different datasets. Other different hybrid techniques shared the proposed technique the highest accuracy but the proposed system preserves its place as one of the highest results systems four three different sets. EI as discretization technique also is a common part for the best results in the mentioned datasets while RS as an evaluator realized the best results in three different data sets.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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