We present a framework thatfeatures a two-stage approach, where the first stage employs YOLO for crater detection andlocalisation. In the second stage, our framework features crater classification using CNN, ResNet andYOLO. Therefore, we detect and identify different types of craters and present a summary report withremote sensing data for a selected region. We consider selected regions for craters and identificationfrom Mars and the Moon based on remote sensing data. Our results indicate that YOLO demonstratesthe most balanced crater detection performance, while ResNet excels in identifying large craters withhigh precision. However, ResNet reported poor performance for large and medium craters for bothMars and the Moon, while CNN achieved the best performance for small craters on Mars

Ma, Y., Guo, J.,Yu, Z. Chandra, R.

Ma, Y., Guo, J., Yu, Z. et al. Deep learning framework for crater detection and identification on the Moon and Mars. npj Space Explor. 2, 19 (2026)

In this study, we present a machine learning-based framework that utilises remote sensing data to analyse land use and land cover changes from 2013 to 2024 in Nadi, Fiji. We used Landsat 8 satellite imagery for the study region and created a training dataset with labels for supervised machine learning. We use Google Earth Engine and unsupervised machine learning via K-means clustering to generate the land cover map. We utilise a framework that uses convolutional neural networks (CNNs) and compares with conventional machine learning models to classify the land cover types of the selected regions. We present a visualisation of change detection, highlighting urban area changes over time to monitor map changes. Our results indicate that the CNN model performs similarly to other machine learning models (0.96 F1-score) in terms of classification performance, but better captures the development of urban areas as verified by qualitative analysis. Our study ascertains that Nadi has experienced a rapid urbanisation process, and the expansion extended outward, taking over the sugar farms.

Yadvendra Gurjar, Ruoni Wen, Ehsan Farahbakhsh, Rohitash Chandra

Advances in Space Research, Volume 77, Issue 9, 1 May 2026, Pages 8521-8537

In this study, we provide an automated assessment of the translation quality of Google Translate with human experts using sentiment and semantic analysis. In order to demonstrate our framework, we select the classic early twentieth-century novel ’The True Story of Ah Q’ with selected Mandarin Chinese to English translations. We use Google Translate to translate the given text into English and then conduct a chapter-wise sentiment analysis and semantic analysis to compare the extracted sentiments across the different translations. Our results indicate that the precision of Google Translate differs both in terms of semantic and sentiment analysis when compared to human expert translations. We find that Google Translate is unable to translate some of the specific words or phrases in Chinese, such as Chinese traditional idiomatic expressions. The mistranslations may be due to a lack of contextual significance and historical knowledge of China.

Xuechun Wang, Rodney Beard, Rohitash Chandra

Natural Language Processing Journal, Volume 13, December 2025, 100188

Used LLMs for sentiment and abuse analysis of Hollywood movie dialogues. Analyzed 1,000+ movie subtitles to track trends in emotion and abuse

Chandra, R., & Ren, G.

Machine Learning with Applications, Volume 22, December 2025, 100749

We present a machine learning framework to map the spatial distribution of minerals on Mars. Our framework utilises the Self-Organising Map and k-means clustering to identify clusters of spectral signatures of minerals.

Lovelock, Tejay, and Rohitash Chandra

Remote Sensing 17 (21) (2025) 3578 https://doi.org/10.3390/rs17213578

We present a computational framework for analyzing anti-Hindu sentiment (Hinduphobia) during the COVID-19 period, introducing an abuse detection and sentiment analysis approach for longitudinal analysis on X.

Singh, A., & Chandra, R.

IEEE Access, vol. 13, pp. 175309-175335, 2025, https://doi.org/10.1109/ACCESS.2025.3617514

Hinduism seeks to provide insight into the nature of the universe and is not antithetical to science. Rohitash Chandra explains why he sees value in bringing together science and spirituality in the quest for knowledge. – “I envision a world where we are fearless in bringing science and religion (spirituality) together. Hinduism is built on the philosophical foundations of the search for the truth and shares this vision with modern science. However, the focus of Hinduism has largely been on investigating the nature of consciousness. Several universities in the West have a Centre for Consciousness. This is just the beginning of the singularity in the quest for knowledge, where science and spirituality merge. We need to embrace science but also embrace humanities and spirituality, and ensure that our current and future generations are not a target of scientism that forces one to view science as a dogmatic religion.”

R. Chandra

Nature Human Behaviour, World View (2024)