arXiv subjects

Md Hasibur Rahman

Publications and source records attributed to Md Hasibur Rahman.

3 recordsLinked to original sources

arXiv2026

From greenhouse climate to individual leaves: an organ-resolved model of lettuce growth

Greenhouse climate management aims to improve crop production while limiting energy use. This requires knowing how a crop will respond before conditions are changed. A crop digital twin can support this decision only if it represents how plant physiology and structure develop together. A unified framework was developed to simulate lettuce growth from the physiology of individual leaves. Each leaf received the conditions at its position in the canopy and contributed carbon through photosynthesis. Part of this carbon was used for maintenance and the remainder supported growth, distributed among leaves by their age, size and local environment. The predicted leaf mass, area and age generated an evolving three-dimensional plant in NVIDIA Isaac Sim. Ray tracing calculated the radiation intercepted by each leaf and returned it to photosynthesis, so structure and growth influenced each other over time. Against greenhouse measurements, the relative root mean square error was 9.5% for total dry weight and 9.2%, 12.7% and 13.1% for leaf number, canopy diameter and largest-leaf area, respectively. A 30% decrease in incident radiation reduced final dry weight by 10.4%, while the same increase raised it by 6.9%, and adding 200 ppm carbon dioxide raised it by 46.1%. Within a simulated 40-plant block, interior plants accumulated 8.6% less dry weight than border plants with identical initial states, and the leaf-specific tipburn index rose in the enclosed leaves over the period in which tipburn appeared on the greenhouse plants. Resolving individual leaves therefore explains how local exposure changes plant growth within the greenhouse. The framework provides the forward plant model needed for a bidirectional digital twin, where observations of the physical plant can update predictions and support greenhouse climate decisions.

cs.CV↗

arXiv2026

A Usable and Secure Bengali CAPTCHA

Text-based CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) have traditionally been a simple, affordable, lightweight, yet very effective security mechanism to distinguish human users from automated bots on the web, serving as a preventive measure against many cyberattacks. However, the dependence on the English script creates usability issues for non-native speakers, limiting accessibility for regional communities where English is not widely understood. In this work, we have proposed and implemented a text CAPTCHA mechanism with 6 variants on the Bengali language, designed specifically for native Bengali-speaking users, which is the first of its kind to the best of our knowledge. Our proposed Bengali CAPTCHA exhibits robust security against automated OCR-based attacks, limited to only 0-20% average character recognition rate across 6,000 challenges (1,000 per variant approx.). Furthermore, our design demonstrates high human usability, evaluated with 110 participants, achieving success rates of 56.25% to 90.29% and average response times of 6.69 to 9.9 seconds across all six variants, thereby standing out among text-based CAPTCHA benchmarks.

cs.CR↗

arXiv2025

Hybrid Tokenization Strategy for DNA Language Model using Byte Pair Encoding and K-MER Methods

This paper presents a novel hybrid tokenization strategy that enhances the performance of DNA Language Models (DLMs) by combining 6-mer tokenization with Byte Pair Encoding (BPE-600). Traditional k-mer tokenization is effective at capturing local DNA sequence structures but often faces challenges, including uneven token distribution and a limited understanding of global sequence context. To address these limitations, we propose merging unique 6mer tokens with optimally selected BPE tokens generated through 600 BPE cycles. This hybrid approach ensures a balanced and context-aware vocabulary, enabling the model to capture both short and long patterns within DNA sequences simultaneously. A foundational DLM trained on this hybrid vocabulary was evaluated using next-k-mer prediction as a fine-tuning task, demonstrating significantly improved performance. The model achieved prediction accuracies of 10.78% for 3-mers, 10.1% for 4-mers, and 4.12% for 5-mers, outperforming state-of-the-art models such as NT, DNABERT2, and GROVER. These results highlight the ability of the hybrid tokenization strategy to preserve both the local sequence structure and global contextual information in DNA modeling. This work underscores the importance of advanced tokenization methods in genomic language modeling and lays a robust foundation for future applications in downstream DNA sequence analysis and biological research.

cs.CL↗