Dr. Anchit Bijalwan’s research papers on machine learning for medical diagnosis get published in Q1 and Q2 journals
May 06, 2024
16:34:24
As an expert in machine learning research and eager to apply this technology in Medicine, Dr. Anchit Bijalwan, a Lecturer in the School of Computing & Innovative Technologies at BUV, has conducted 2 invaluable research papers, which involve in-depth analysis and provides readers with profound insights into machine learning in medicine, contributing to the future development of treatments for lung cancer and skin cancer.
Nowadays, the use of machine learning in healthcare for data analysis and support in diagnosis, treatment, and health management is widespread. This combination of artificial intelligence and medicine is bringing significant benefits to humanity.
Dr. Bijalwan is a senior lecturer at the School of Computing at British University Vietnam (BUV). His research areas include privacy and security, network forensics, and machine learning. His recent research involves utilising machine learning for lung disease, specifically COPD, and skin cancer diagnosis.
Dr. Anchit Bijalwan, a senior lecturer at the School of Computing at British University Vietnam (BUV)
His significant contributions to both Medicine and machine learning have been published in three journal papers within the first two months of this year:
A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimisation, published in Scientific Reports (a Q1 journal), proposes a novel hybrid model for skin cancer detection by integrating Hybrid U-Net and Improved MobileNet-V3 features. The study not only presents a high-performing model but also offers valuable insights for future research in automated skin cancer detection, suggesting potential therapeutic impacts. Learn more about this research here.
The investigation titled A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review, published on SpringerLink (a Q2 journal), emphasizes the complexity of identifying pulmonary conditions like COVID-19, pneumonia, and COPD, stressing the crucial role of advanced machine learning (ML) and imaging diagnostics. The study investigates important lung disorders, analyzes pertinent data sets, and comprehensively assesses machine learning techniques. Learn more about this research here.
“As we embrace machine learning in analyzing and predicting diseases, robust data security measures and transparent algorithms are essential to build trust and ensure the technology benefits everyone equally. It offers exciting possibilities in disease analysis and prediction, ethical considerations, data privacy concerns, and potential biases in algorithms must be addressed to ensure responsible and equitable application,” shared Dr. Bijalwan.
Being an expert in cloud computing, he also published a high-impact paper on Scientific Reports, titled Enhancing System Performance in Cloud Computing through Hybrid Cloud Load Balancing and Host Utilization Prediction Method Using Deep Learning and Optimization Techniques, which underscores the significance of load balancing in cloud computing to improve overall system efficiency. You can read more about this research here.
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