Published Paper Details:

According to the World Health Organization (WHO), the corona virus (COVID-19) pandemic is putting even the best healthcare systems across the world under tremendous pressure. The early detection of this type of virus will help in relieving the pressure of the healthcare systems. Chest X-rays has been playing a crucial role in the diagnosis of diseases like Pneumonia. As COVID-19 is a type of influenza, it is possible to diagnose using this imaging technique. With rapid development in the area of Machine Learning (ML), there had been intelligent systems to classify between Pneumonia and Normal patients. This work proposes the machine learning-based logistic regression classification algorithm. Bioinformatics and genomic signal processing use computational techniques to solve various biological problems. They aim to study the information allied with genetic materials such as the Deoxyribonucleic Acid (DNA), the Ribonucleic acid (RNA), and the proteins. Fast and precise identification of the protein coding regions in DNA sequence is one of the most important tasks in analysis. Existing Digital Signal Processing (DSP) methods provide less accurate and computationally complex solution with greater background noise. Hence, improvements in accuracy, computational complexity, and reduction in background noise are essential in identification of the protein-coding regions in the DNA sequences. In this work, a new DSP based method is introduced to detect the protein coding regions in DNA sequences. Here, the DNA sequences are converted into numeric sequences using electron ion interaction potential (EIIP) representation. In this work, we use DNA sequence of the coronavirus. The test is conducted using the databases available in the National Centre for Biotechnology Information (NCBI) site.