Tushar Verma
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Tushar Verma has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Cebajel Tanan
Department of Biosciences and Bioengineering, Indian Institute of Technology Dharwad, Dharwad 580011, India
1 shared publication - Dimple Saikia
Department of Biosciences and Bioengineering, Indian Institute of Technology Dharwad, Dharwad 580011, India
1 shared publication - Prajwal Avati
Department of Biosciences and Bioengineering, Indian Institute of Technology Dharwad, Dharwad 580011, India
1 shared publication - Rishikesh Panday
Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee 247667, India
1 shared publication - Ritam Dadhara
Department of Biosciences and Bioengineering, Indian Institute of Technology Dharwad, Dharwad 580011, India
1 shared publication - Surya Pratap Singh
Indian Institute of Technology Dharwad
1 shared publication
Latest Publications
- Open Access
Combating Antimicrobial Resistance: Spectroscopy Meets Machine Learning
One of the major health challenges that humans have been facing for the last few decades is antimicrobial resistance (AMR), where antibiotics stop responding to infections and, thereby, take a considerable amount of time to cure them while increasing mortality rates. There are various steps that have been taken by researchers and organizations to identify, cure, and prevent this urgent issue. Here, in this review, we have tried to illustrate how machine learning has been used with different spectroscopic analytical platforms—Raman spectroscopy (RS), Fourier-transform infrared spectroscopy (FTIR), and nuclear magnetic resonance (NMR)—to further accelerate the understanding and early detection of AMR. The combination of ML algorithms with advanced spectroscopic techniques delivers faster and deeper insights into the different mechanisms of bacterial resistance, delivering novel solutions.
Open AccessCombating Antimicrobial Resistance: Spectroscopy Meets Machine Learning
One of the major health challenges that humans have been facing for the last few decades is antimicrobial resistance (AMR), where antibiotics stop responding to infections and, thereby, take a considerable amount of time to cure them while increasing mortality rates. There are various steps that have been taken by researchers and organizations to identify, cure, and prevent this urgent issue. Here, in this review, we have tried to illustrate how machine learning has been used with different spectroscopic analytical platforms—Raman spectroscopy (RS), Fourier-transform infrared spectroscopy (FTIR), and nuclear magnetic resonance (NMR)—to further accelerate the understanding and early detection of AMR. The combination of ML algorithms with advanced spectroscopic techniques delivers faster and deeper insights into the different mechanisms of bacterial resistance, delivering novel solutions.