Chittaranjan Ray

Alakananda Mitra, David H. Fleisher, Vangimalla Reddy et al.

High-dimensional biological data often exhibit a severe mismatch between feature dimensionality and sample size, making reliable classification difficult in extremely small-data regimes. In these settings, kernel methods can lose discriminative power when latent compression fails to preserve class-separating structure. We study this problem in fine-grained plant phenomics and propose a hybrid workflow that compresses 1280-dimensional deep image embeddings into a 64-dimensional PCA space and then restructures them into an 11-dimensional supervised latent space using Linear Discriminant Analysis (LDA), followed by GPU-accelerated Quantum Kernel Alignment (QKA) on NVIDIA L40S hardware. Empirically, supervised latent restructuring substantially improves the geometric separability of the compressed representation, increasing the Silhouette coefficient from 0.003 in the raw embedding space and -0.006 in PCA-64 to 0.197 in the supervised LDA-11 space. However, downstream classical evaluation reveals a clear compression trade-off: Linear SVM and XGBoost improve in the restructured latent space, whereas RBF-SVM and Random Forest degrade under the same 11-dimensional bottleneck. Under a constrained optimization budget, QKA in this regime remains challenging, indicating that latent geometry alone is not sufficient for strong trainable quantum performance. These findings position representation geometry as a central design variable in small-data quantum learning and expose the practical difficulty of recovering nonlinear discriminative structure from aggressively compressed biological representations.

Alakananda Mitra, Sahila Beegum, David Fleisher et al.

The cotton industry in the United States is committed to sustainable production practices that minimize water, land, and energy use while improving soil health and cotton output. Climate-smart agricultural technologies are being developed to boost yields while decreasing operating expenses. Crop yield prediction, on the other hand, is difficult because of the complex and nonlinear impacts of cultivar, soil type, management, pest and disease, climate, and weather patterns on crops. To solve this issue, we employ machine learning (ML) to forecast production while considering climate change, soil diversity, cultivar, and inorganic nitrogen levels. From the 1980s to the 1990s, field data were gathered across the southern cotton belt of the United States. To capture the most current effects of climate change over the previous six years, a second data source was produced using the process-based crop model, GOSSYM. We concentrated our efforts on three distinct areas inside each of the three southern states: Texas, Mississippi, and Georgia. To simplify the amount of computations, accumulated heat units (AHU) for each set of experimental data were employed as an analogy to use time-series weather data. The Random Forest Regressor yielded a 97.75% accuracy rate, with a root mean square error of 55.05 kg/ha and an R2 of around 0.98. These findings demonstrate how an ML technique may be developed and applied as a reliable and easy-to-use model to support the cotton climate-smart initiative.